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IBM Informix TimeSeries Data User's Guide

1. FOR READ ONLY ALL a See Milecsocze Pee Required item with choice DISTINCT Only one item must be oe UNIQUE i present UNIQUE E EEE esas a Optional items with choice FOR UPDATE FOR UPDATE are shown below the main FOR READ ONLY line one of which you might specify The values below the main NEXT se NEXT ensen oe a cucetcsccceiescoasces Tos line are optional one of PRIOR which you might specify If p PRIOR PREVIOUS you do not specify an item PREVIOUS the value above the line is used by default f AA Optional items Several items l y are allowed a comma must Sietucckcscceacsstsce precede each repetition index_name index_name table_name table_name Table Reference Bisi gt gt Table Reference gt lt Reference to a syntax segment Table Reference L view _ table synonym Table Reference view table synonym Syntax segment How to read a command line syntax diagram Command line syntax diagrams use similar elements to those of other syntax diagrams Some of the elements are listed in the table in Syntax Diagrams Creating a no conversion job gt gt onpladm create job job mp t d device D database
2. IBM Informix TimeSeries Data User s Guide Union function The Union function performs a union of multiple time series either over the entire length of each time series or over a clipped portion of each time series Syntax Union ts TimeSeries returns TimeSeries Union set_ts set TimeSeries returns TimeSeries Union begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 ts TimeSeries returns TimeSeries Union begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 set_ts set TimeSeries returns TimeSeries ts The time series that form the union Union can take from two to eight time series arguments set_ts A set of time series begin_stamp The begin point of the clip end_stamp The end point of the clip Description The second and fourth forms of the function perform a union of a set of time series The resulting time series has one DATETIME YEAR TO FRACTION 5 column followed by each column in each time series in order When using the second or fourth form it is important to ensure that the order of the time series in the set is deterministic so that the elements remain in the correct order Since the type of the resulting time series is different from that of the input time series the result of the union must be cast Union can be thought of as an outer join on the time stamp In a union the resulting time series has a calenda
3. The data is saved to disk after every KB of data is loaded The data is attempted to be inserted 10 times before returning a failure You can change these values and set other load job properties such as logging load errors by editing load properties in the plug in You can reuse the definitions that you created in other load jobs After you create a load job you can run it from the command line with the command line loader application The maximum number of load jobs you can run simultaneously is limited by the capabilities of your computer The TimeSeries plug in includes cheat sheets that provide detailed instructions for creating load jobs and loading data Related concepts for loading time series data on Related tasks Installing the IBM Informix TimeSeries Plug in for Data Studio on page 1 25 Creating a load job to load data from a file Use the IBM Informix TimeSeries Plug in for Data Studio to create a load job that loads time based data from a file into existing time series instances Loading data from a file has the following additional requirements e The data can be formatted as a single delimited double delimited fixed width or LSE formatted data stream for example one or more files or URLs e The data must be ASCII characters e The pipe symbol is interpreted as a delimiter e Field delimiters can be any ASCII character or regular expression representable in Java e The maximu
4. TimeSeries data type on page 2 6 TimeSeries data type technical overview on page 1 5 Related reference L CREATE ROW TYPE statement SQL Syntax DATETIME data type SQL Reference Create the database table After you create the TimeSeries subtype use the CREATE TABLE statement to create a table with a column of that subtype You have the following options and restrictions when you create a table with at TimeSeries column e You can create the table in a dbspace that uses non default page size e You cannot use delimited identifiers for table or column names e If you plan to replicate time series data with Enterprise Replication the primary key column must not be an opaque data type e If you plan to write a loader program the name of the table and the name of the TimeSeries column must not contain uppercase letters e You can fragment the table When you fragment the table and enable PDQ certain queries can run faster TimeSeries routines that select time series data can run in parallel The table can be fragmented by any method Queries on a fragmented virtual table that is based on the table can run in parallel The table must be fragmented by expression e You can include other options of the CREATE TABLE statement The basic syntax for creating a table with a TimeSeries subtype column is CREATE TABLE table_name coll any_data_type col2 any_data_type coln TimeSeries subtype_nam
5. To query on the stock_data column you must use time series functions For example the following query uses the Apply function to obtain the closing price select stock_id Apply final stock_data TimeSeries one_real from daily_stocks In this query one_real is a row type that is created to hold the results of the query and is created with this statement create row type one_real timestamp datetime year to fraction 5 result real To obtain price and volume information within a specific time range use a query that has the following format select stock_id Clip stock_data t1 t2 from daily_stocks Create the virtual table The following statement uses the TSCreateVirtualTab procedure to create a virtual table called daily_stocks_no_ts based on daily_stocks execute procedure TSCreateVirtualTab daily_ stocks _no_ts daily_stocks 4 8 IBM Informix TimeSeries Data User s Guide Because the statement does not specify the NewTimeSeries parameter daily_stocks_no_ts does not allow inserts of elements that do not have a corresponding time series in daily_stocks Also the statement omits the TSVTMode parameter so TSVTMode assumes its default value of 0 Therefore if you insert data into daily_stocks_no_ts the database server uses PutElemNoDups to add an element to the underlying time series in daily_stocks The following table illustrates the virtual table daily_stocks_no_ts Table 4 5 The daily_stock
6. For a complete list of what s new in this release go to http pic dhe ibm com infocenter informix v121 topic com ibm po doc new_features_ce htm Table 1 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC4 Overview Reference Enhancements to the time series Java class library When you write a Java application with the time series Java class library now you can define time series objects with the new builder classes Previously you defined time series objects with string representations of SQL statements Builder classes reduce the possibility of errors and improve usability The methods in the Java class library run faster than in previous releases The time series Java class library has the following enhancements for creating time series objects e You can now determine whether the definitions of two calendars or calendar patterns are the same e You can create calendar patterns and calendars with new IfmxCalendarPattern Builder and IfmxCalendar Builder classes e You can create and manage containers with the new TimeSeriesContainer and TimeSeriesContainer Builder classes e You can create TimeSeries row types with the new TimeSeriesRowType and TimeSeriesRowType Builder classes e You can create a simpler custom type map that uses a PatternClassMap instead of individual entries for each data type with the new TimeSeriesTypeMap and TimeSeriesTypeMap Builder classes The IfmxT
7. GetFirstElementStamp function on page 7 53 GetLastElementStamp function on page 7 57 Faster queries through virtual tables Queries on time series virtual tables now run faster because qualifiers to a WHERE clause that contain multiple column constant or expression parameters are processed through the virtual table interface instead of through SQL processing Example code conventions Examples of SQL code occur throughout this publication Except as noted the code is not specific to any single IBM Informix application development tool If only SQL statements are listed in the example they are not delimited by semicolons For instance you might see the code in the following example CONNECT TO stores_demo DELETE FROM customer WHERE customer_num 121 COMMIT WORK DISCONNECT CURRENT To use this SQL code for a specific product you must apply the syntax rules for that product For example if you are using an SQL API you must use EXEC SQL Introduction xvii at the start of each statement and a semicolon or other appropriate delimiter at the end of the statement If you are using DB Access you must delimit multiple statements with semicolons Tip Ellipsis points in a code example indicate that more code would be added in a full application but it is not necessary to show it to describe the concept that is being discussed For detailed directions on using SQL statements for a particular appl
8. P1 elements for 2012 01 01 P2 elements for 2012 01 02 P3 elements for 2012 01 03 P4 elements for 2012 01 04 P5 elements for 2012 01 05 P6 elements for 2012 01 06 Partition P7 moved into the active window Example 3 Increase the active window size The following example increases the size of the active window to 10 execute function TSContainerManage readings container set active window size to 10 The following message describes the result Chapter 7 Time series SQL routines 7 103 Set active window size succeeded 0 partitions moved Although the active window size is larger partitions in the dormant window are not moved back into the active window Example 4 Destroy partitions The following example destroys partitions in the dormant window execute function TSContainerManage readings container destroy dormant partitions before 2012 01 08 The following message describes the result destroy succeeded 6 partitions destroyed The partitions are now distributed between the active and the dormant windows in the following way e Partitions that are in the active window P7 elements for 2012 01 07 P8 elements for 2012 01 08 P9 elements for 2012 01 09 P10 elements for 2012 01 10 e No partitions in the dormant window Although some of the active partitions are before 2012 01 08 they are not destroyed Example 5 Change the destroy behavior The follow
9. _ gt pproject table n S server T target Setting the Run Mode Introduction X X XX Notes 1 See page Z 1 This diagram has a segment that is named Setting the Run Mode which according to the diagram footnote is on page Z 1 If this was an actual cross reference you would find this segment on the first page of Appendix Z Instead this segment is shown in the following segment diagram Notice that the diagram uses segment start and end components Setting the run mode eS TTT oT ot p a To see how to construct a command correctly start at the upper left of the main diagram Follow the diagram to the right including the elements that you want The elements in this diagram are case sensitive because they illustrate utility syntax Other types of syntax such as SQL are not case sensitive The Creating a No Conversion Job diagram illustrates the following steps 1 Include onpladm create job and then the name of the job 2 Optionally include p and then the name of the project 3 Include the following required elements e n e d and the name of the device e D and the name of the database e t and the name of the table 4 Optionally you can include one or more of the following elements and repeat them an arbitrary number of times e S and the server name e T and the target server name e The run mode To set the run mode
10. daily_stocks calendar daycal origin 2011 01 03 00 00 00 00000 put_elemtreduced_log Related concepts The display of data in virtual tables on page 4 3 Insert data through virtual tables on page 4 4 Related tasks Creating a time series virtual table on page 4 5 Loading data from a file into a virtual table on page 3 34 4 7 Chapter 4 Virtual tables for time series data Example of creating a virtual table This example shows how to create a virtual table on a table that contains time series data and the difference between querying the base table and the virtual table To improve clarity these examples use values t1 through t6 to indicate DATETIME values rather than showing complete DATETIME strings Query the base table The base table daily_stocks was created with the following statements create row type stock_bar timestamp datetime year to fraction 5 high real low real final real vol real create table daily stocks stock_id int stock_name lvarchar stock_data TimeSeries stock_bar The daily_stocks base table contains the following data Table 4 4 The daily_stocks base table stock_id stock_name stock_data 900 AAO1 t1 7 25 6 75 7 1000000 t2 7 5 6 875 7 125 1500000 901 IBM t1 97 94 25 95 2000000 t2 97 95 5 96 3000000 905 FNM t1 49 25 47 75 48 2500000 t2 48 75 48 48 25 3000000
11. e day The partitions contain data from one day The container uses the ts_1day calendar e week The partitions contain data from one week The container uses the ts_lweek calendar e month Default The partitions contain data from one calendar month The container uses the ts_Imonth calendar e year The partitions container data from one calendar year The container uses the ts_lyear calendar active_windowsize Optional The maximum number of partitions in the active window e 0 Default No size limit e Positive integer The maximum number of partitions in the active window dormant_windowsize Optional The maximum number of partitions in the dormant window e 0 Default No size limit e Positive integer The maximum number of partitions in the dormant window Start with a value that is equal to or greater than the size of the active window window_spaces Optional The dbspaces in which partitions are stored e NULL Default Partitions are created in the dbspace that is specified by the dbspace_name argument e A comma separated list of dbspace names Partitions are created in the listed dbspaces in round robin order The list cannot include temporary dbspaces or sbspaces window_control Optional A flag that indicates how many partitions are destroyed and whether Chapter 7 Time series SQL routines 7 95 7 96 active partitions can be destroyed when the number of partitions that must be detached is
12. elem_insert 128 You can quickly insert elements directly into containers given the following constraints e The base table has a primary key e The time series instances exist and are stored in containers e Base table columns are not being updated Cannot be combined with the settings 16 32 or 64 Cannot be combined with the NewTimeSeries parameter reduced_log 256 Reduces how many log records are generated when you insert elements into containers By default every element that you insert generates two log records one for the inserted element and one for the page header update If this flag is set page header updates are logged per transaction instead of per element The INSERT statements must be run within a transaction without other types of SQL statements The elements that are inserted are not visible by dirty reads until after the transaction commits scan_hidden 512 Hidden elements are displayed in the virtual table See Null and hidden elements on page 4 23 scan_discreet 1024 When you select data from a virtual table by timestamps only rows whose timestamps are exactly equal to the timestamps specified in the query are returned Update columns in the base table When you create a virtual table with the TSCreateVirtualTab procedure you can update the data in the base table from the virtual table The following table describes how to control updating columns in the base tabl
13. Parallel database queries are enabled the PDQPRIORITY environment variable is set to a value other than OFF and the MAX_PDQPRIORITY configuration parameter is set to a value other than 0 or 1 The routine is referenced in the WHERE clause of a SELECT statement Routines that are referenced in the Projection clause are not run in parallel The routine must select data from a table that contains time series data or a time series system table Routines that insert update or delete data do not run in parallel Chapter 7 Time series SQL routines 7 7 e The routine cannot call another routine that cannot run in parallel If a routine that can run in parallel calls another routine that cannot run in parallel neither routine runs in parallel e The routine cannot include a collection data type as an argument or return value For example if the Intersect function includes a parameter that has a SET data type the function is not run in parallel The routines in the following list can run in parallel Related reference L MAX_PDQPRIORITY configuration parameter Administrator s Reference PDOPRIORITY environment variable SOL Reference AggregateBy function on page 7 11 on page 7 15 page 7 2 ApplyCalendar function on page 7 24 AgoregateRange function as ApplyBinaryTsOp function on ApplyUnaryTsOp function on page 7 26 Asin function on page 7 2 Atan function o
14. The following example returns the start dates of the calendar patterns for each calendar in the CalendarTable table select c_name CalPattStartDate c_name from CalendarTable Related reference CalStartDate function on page 6 5 The ts_cal_pattstartdate function on page 9 9 Collapse function The Collapse function collapses the specified calendar pattern into destination units which must have a larger interval unit than the interval unit of the specified calendar pattern Syntax Collapse cal_patt CalendarPattern interval 1varchar returns CalendarPattern cal_patt The calendar pattern to be collapsed interval The destination time interval minute hour day week month or year Description If any part of a destination unit is on the whole unit is considered on Returns The collapsed calendar pattern Example The following statements convert an hourly calendar pattern into a daily calendar pattern select from CalendarPatterns where cp_name workweek_hour cp_name workweek_hour cp_pattern 32 of f 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour select Collapse cp_pattern day from CalendarPatterns where cp_name workweek_hour expression 1 off 5 on 1 off day Related reference Expand function on page 5 4 Chapter 5 Calendar pattern routines 5 3 Expand function The Expand function expands the specified calendar pattern into the
15. e 0 Default No dormant partitions are destroyed e A positive integer The maximum number of partitions that can be destroyed in an operation If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count value the operation fails Usage For all containers you can change the container pool and the extent sizes of the partitions For rolling window containers you can also change the window sizes and attach detach or destroy partitions Returns A message that describes the result of the command Examples The following examples are based on a rolling window container named readings_container that has a day interval an active window size of 5 and a dormant window size of 10 The time series elements use a 15 minute calendar with the range from 2012 01 01 00 00 00 00000 to 2012 01 10 23 45 00 00000 The partitions are distributed between the active and the dormant windows in the following way e Partitions that are in the active window P6 elements for 2012 01 06 P7 elements for 2012 01 07 P8 elements for 2012 01 08 P9 elements for 2012 01 09 P10 elements for 2012 01 10 e Partitions that are in the dormant window P1 elements for 2012 01 01 P2 elements for 2012 01 02 P3 elements for 2012 01 03 P4 elements for 2012 01 04 P5 elements for 2012 01 05 Example 1 Detach partitions The following example moves partitions from the acti
16. pages slots total 2029 241907 2169 1 row s retrieved The containers have only 140 pages of available space Example Monitor a group of containers Suppose that you have containers that have the following names e active_cntl e active_cnt2 e historical_cnt1 The following statement returns the information for the two containers that have names that begin with active EXECUTE FUNCTION TSContainerUsage activex pages slots total Chapter 7 Time series SQL routines 7 115 202 4197 169 1 row s retrieved Related concepts Monitor containers on page 3 18 Related reference TSContainerTotalPages function on page 7 112 TSContainerTotalUsed function on page 7 113 TSContainerPctUsed function on page 7 105 TSContainerNElems function on page 7 104 Time series storage on page 1 14 TSCreate function The TSCreate function creates an empty regular time series or a regular time series populated with the given set of data The new time series can also have user defined metadata attached to it Syntax TSCreate cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name varchar returns TimeSeries with handlesnulls TSCreate cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar set_rows set returns TimeSeries with ha
17. 1 7 1 12 1 13 1 14 1 19 1 19 1 20 1 23 1 23 1 24 gt 1 25 1 25 1 26 1 26 1 26 1 27 1 28 2 1 2 1 2 4 2 8 2 8 2 8 4 29 2 10 2 11 2 12 2 12 3 2 Creating the data load file Loading the time series data Accessing time series data through a virtual table Example Create and load a hertz time series Example Create and load a compressed time series Example Create and load a time series with JSON data Defining a calendar Predefined calendars Create a time series column Creating a TimeSeries subtype Create the database table Creating containers Rules for rolling window containers Monitor containers Manage container pools Create a time series Creating a time series with inetidida Time series input function Create a time series with the output of a function Load data into an existing time series IBM Informix TimeSeries Plug in for Data Studio Writing a loader program Loading JSON data Loading data from a file into a virtual table Load data with the BulkLoad function Load small amounts of data with SQL functions Delete time series data Manage packed data Chapter 4 Virtual tables for time series data Performance of queries on virtual tables The structure of virtual tables The display of data in virtual tables Insert data through virtual tables Creating a time series virtual table TSCreat
18. Description If there is no element at the specified timepoint no elements are deleted and no error is raised The API equivalent of DelElem is ts_del_elem Hidden time stamps cannot be deleted Returns A time series with one element deleted set activity_data DelElem activity_data 2011 01 05 12 58 09 23456 datetime year to fraction 5 where stock_id 600 Related concepts Delete time series data on page 3 37 Related reference Time series routines that run in parallel on page 7 7 DelClip function on page 7 4 DelTrim function on page 7 4 GetElem function on page 7 5 HideElem function on page 7 6 page 7 7 function on page 9 22 ella mj E oIa 3 3 EIS z oie O g 3 O g 5 a o Oo RIRAN ILLS N z ao Q D D D 3 cain S UpdElem function on page 7 15 z pa he oO oO 3 A function on page 9 22 DelRange function 7 44 The DelRange function deletes all elements in the specified time range in the specified time series instance including the delimiting timepoints The DelRange function is similar to the DelTrim function except that the DelRange function deletes elements and reclaims space from any part of a regular time series Syntax DelRange ts TimeSeries begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries
19. Informix Product Family Informix Version 12 10 IBM Informix TimeSeries Data User s Guide all Informix Product Family Informix Version 12 10 IBM Informix TimeSeries Data User s Guide all Note Before using this information and the product it supports read the information in Notices on page D 1 This edition replaces SC27 4535 02 This document contains proprietary information of IBM It is provided under a license agreement and is protected by copyright law The information contained in this publication does not include any product warranties and any statements provided in this manual should not be interpreted as such When you send information to IBM you grant IBM a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligation to you Copyright IBM Corporation 2006 2014 US Government Users Restricted Rights Use duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp Contents Introduction About this publication Types of users Assumptions about your is amp ale What s new in TimeSeries data for Informix Version 12 10 Example code conventions Additional documentation Compliance with industry siendatas Syntax diagrams How to read a cotrmiand line syntax diagram Keywords and punctuation Identifiers and names How to provide documentation feedback Chapter 1 Inform
20. The key3 column is compressed by the linear swing door compression type with a maximum deviation of 0 10 The key4 column is compressed by the linear boxcar compression type with a maximum deviation of 0 10 The key5 column is compressed by the quantization linear swing door compression type with a compression size of 2 bytes a maximum deviation of 0 15 a lower bound of 100 and an upper bound of 100000 The key6 column is compressed by quantization linear boxcar with a compression size of 2 bytes a maximum deviation of 0 25 a lower bound of 100 and an upper bound of 100000 Example Create a hertz time series The following statement creates a time series that stores 50 records per second in each element BEGIN INSERT INTO tstable VALUES 1 TSCreateIrr ts_1sec 2014 01 01 00 00 00 00000 0 50 0 container1 COMMIT Related concepts Hertz time series on page 1 page 1 10 e 5 ss E lg 2 z2 O afs 10 o Jo n la b a la 5 oO B Fj 5 oO ie oO 2 20 g gt w Creating a time series with metadata on page 3 23 S A Ola JE zaja oja Pjg co ee 32 m aS ola oO wo O 5 4 page 3 22 GetMetaData function on page 7 59 GetMetaTypeName function on page 7 5 Z page 7 116 The ts_create_with_metadata function on page 9 1 E WN Q oO o D oO a G D ie ize m O 5 N function on page 9 1 UpdMetaData funct
21. begin_stamp The beginning of the range It must be greater than or equal to the calendar origin num_stamps The number of time stamps to return Description This function is useful if you must print out the time stamps of a series of regular time series elements If the range is known getting an array of all of the time stamps is more efficient than using ts_time on each element The caller is responsible for freeing the result of this function Returns A list of time stamps Related reference CalIndex function on page 6 2 CalRange function on page 6 3 CalStamp function on page 6 cal function on page 9 9 FI a R OIGO 813 4 le alz ENEA A _cal_ function on page 9 10 The ts_cal_stamp function R ck wn cr 3 2 function on page 9 51 The ts_cal_stamp function The ts_cal_stamp function returns the time stamp at a given number of calendar intervals before or after a given time stamp The returned time stamp is located in allocated memory so the caller should free it using mi_free ts_cal_stamp MI_CONNECTION conn mi_string xcal_name mi_datetime tstamp mi_integer offset Chapter 9 Time series API routines 9 11 conn A valid DataBlade API connection cal_name The name of the calendar tstamp The input time stamp offset The number of calendar intervals before or after the input time stamp Use a negative number to indicate an offset befo
22. context_ptr gt col_data 6 0 IBM Informix TimeSeries Data User s Guide context_ptr gt carryover context_ptr gt priority NULL void mi_save_set_destroy context_ptr gt save_set context_ptr gt save_set NULL void mi_close context_ptr gt conn mi_free context_ptr gt calendar_name context_ptr gt calendar_name NULL mi_free context_ptr gt container_name context_ptr gt container_name NULL context_ptr gt conn NULL End of close context kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name update_series purpose Update all the time series back into the table returns notes kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void update_series loader_context_t context_ptr mi_integer i Q register struct sec_entry_s entry_ptr NULL struct sec_entry_s next_entry_ptr NULL MI_STATEMENT statement NULL char buf 256 mi_integer rc 0 MI_DATUM values 2 0 0 mi_integer lengths 2 1 sizeof mi_integer static const mi_integer nulls 2 0 0 static const mi_string const types 2 timeseries day_info integer mi_unsigned_integer yield_count 0 sprintf buf update s set series where Secid mi_get_varlen context_ptr gt table_name mi_get_vardata context_ptr gt table_name statement mi_prepare context_ptr
23. on page 1 8 Example Create and load a hertz time series on page 3 5 Example Create and load a compressed time series on Faster queries by running time series routines in parallel Time series SQL routines that you include in the WHERE clause of SELECT statements return results faster when they run in parallel If you fragment the table that contains the time series data and enable PDQ time series SQL routines run in parallel Time series routines that run in parallel on page 7 7 Introduction Xiii Table 2 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC3 continued Overview Reference Control the destroy behavior for rolling window containers You can limit the number of partitions of a rolling window container that can be destroyed in an operation You control how many partitions are destroyed and whether active partitions can be destroyed when the number of partitions that must be detached is greater than the size of the dormant window When you create a rolling window container set the destroy_count parameter to a positive integer and the window_control parameter to 2 or 3 in the TSCreateContainer function You can change the destroy behavior of an existing rolling window container by including the weontrol parameter in the TSContainerManage function TSContainerCreate procedure on page 7 93 TSContainerManage function on page 7 99 Monitor group
24. 14 0000000000 2011 01 01 04 30 00 00000 27 14 0000000000 2011 01 01 04 45 00 00000 27 15 0000000000 2011 01 01 05 00 00 00000 28 11 0000000000 Related reference GetElem function on page 7 52 GetNthElem function on page 7 62 Chapter 7 Time series SQL routines 7 89 TSRowNumToList function on page 7 146 TSAddPrevious function The TSAddPrevious function sums all the values it is called with and returns the current sum every time it is called The current argument is not included in the sum Syntax TSAddPrevious current_value smallfloat returns smallfloat TSAddPrevious current_value double precision returns double precision current_value The current value Description Use the TSAddPrevious function within an AggregateBy or Apply function The TSAddPrevious function can take parameters that are columns of a time series Use the same parameter format as the AggregateBy or Apply function accepts Returns The sum of all previous values returned by this function Example The following example uses the TSAddPrevious function to calculate the summation of the average dollars into or out of a market or equity select Apply TSAddPrevious vol final low high final 0001 high low high low final 3 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 08 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries one_real
25. Appendix B The TSIncLoad procedure example B 13 day_elem_ptr ts_make_elem_with_buf sec_entry_ptr gt tsdesc context_ptr gt col_data context_ptr gt col_is_null NULL day_elem ptr return 1 End of read_line kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkxk name TSIncLoad purpose UDR for incremental loading of timeseries from a file kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk void TSIncLoad mi_lvarchar table_name the table that holds the time series mi_lvarchar file_name The name of the file containing the data It must be accessible on the server machine The following parameters are only used to create new time series mi_lvarchar calendar_name mi_datetime origin mi_integer threshold mi_boolean regular mi_lvarchar container_name mi_integer nelems MI_FPARAM fParamPtr FILE_BUF buf 0 ts_tselem day_elem NULL ts_tsdesc tsdesc NULL ts_timeseries ts NULL mi_datetime tstamp 0 loader_context_t context 0 mi_unsigned_integer yield_count 0 sec_entry_t sec_entry_ptr NULL int null_line 0 init_context table_name calendar_name origin threshold regular container_name nelems amp context open_buf file_name amp buf while read_line amp context amp buf amp tsdesc amp day_elem amp null_line amp tstamp
26. PreparedStatement pStmt conn prepareStatement sq1Cmd ResultSet rSet pStmt executeQuery com informix timeseries IfmxTimeSeries ts rSet next ts IfmxTimeSeries rSet getObject 1 In this example rSet is a valid java sql ResultSet object After running the SELECT statement the getObject method is used to put the time series data into the variable ts an IfmxTimeSeries object The TimeSeries type is at column 1 in the result set The example assumes that an entry has been made into the conn object type map for the TimeSeries column ts Because the IfmxTimeSeries class implements the JDBC ResultSet interface you can treat an IfmxTimeSeries object as if it is an ordinary result set For example you can use the next method to iterate through the elements of the time series as shown in the following example ts beforeFirst while ts next java sql Timestamp tStamp ts getTimestamp 1 int coll ts getInt 2 int col2 ts getInt 3 The example shows that you use the beforeFirst method to position the time series cursor before the beginning of the time series and then the next method to iterate through the elements While looping through the elements the program uses the getTimestamp method to extract the time stamp into the variable tStamp and the getInt method to extract the first column of data into coll and the second column into col2 The columns of a time series element are numbered starting with t
27. Table created INSERT INTO sm VALUES met0 origin 2011 07 11 00 00 00 00000 calendar sm_15min container sm0 threshold 0 regular 1 0 2 1 3 0 4 2 5 3 6 9 7 3 8 0 9 0 123 0 NULL 0 NULL 0 400 3 1 row s inserted Example Count elements when a column is null The following statement counts the number of elements where the energy column has a null value SELECT CountIf readings energy IS NULL FROM sm expression IBM Informix TimeSeries Data User s Guide 2 1 row s retrieved Two elements contain null values for the energy column Example Count elements that match a value in one of two columns The following statement counts the number of elements where either the value of the energy column is equal to 1 or the value of the ind column is equal to 0 SELECT CountIf readings energy 1 or ind 0 FROM sm expression 5 1 row s retrieved Five elements meet the criteria Example Count elements in a specific time range The following statement counts the number of elements where the value of the energy column is greater than or equal to 5 from 2011 07 11 01 00 00 00000 until the end of the time series SELECT CountIf readings energy gt 5 2011 07 11 01 00 00 00000 datetime year to fraction 5 FROM sm expression 6 1 row s retrieved Six elements meet the criteria Example Count elements greater than a value The following statemen
28. The equivalent SQL function is HideElem Returns The modified time series If there is no element at the given time stamp an error is raised Related reference The ts_elem function on page 9 22 The TS_ ELEM HIDDEN macro on page 9 23 The TS _ ELEM NULL macro on page 9 24 The ts_reveal_elem function on page 9 50 The ts_previous_valid function on page 9 45 The ts_index function The ts_index function converts from a time stamp to an index offset for a regular time series Syntax mi_integer ts_index ts_tsdesc tsdesc mi_datetime tstamp tsdesc The time series descriptor returned by ts_open tstamp The time stamp to convert Description Consider a time series that starts on Monday January 1 and keeps track of weekdays Calling ts_index with a time stamp argument that corresponds to Monday January 1 would return 0 a time stamp argument corresponding to Tuesday January 2 would return 1 a time stamp argument corresponding to Monday January 8 would return 5 and so on The equivalent SQL function is GetIndex Returns An offset into the time series If the time stamp falls before the time series origin or if it is not a valid point in the calendar 1 is returned otherwise the return value is always a positive integer Chapter 9 Time series API routines 9 35 Related reference The ts_nth_elem function on page 9 43 The ts_ins_elem function
29. The input format for the Calendar data type is a quoted text string p gt startdate start_date 7 gt _pattstart pattern_date _pattstart pattern_date 1 pattern Calendar pattern i gt lt patina poeenn none m Notes 1 See CalendarPattern data type on page 2 1 Table 2 2 Calendar data type parameter values Value Data type Description start_date DATETIME YEAR TO Defines when the calendar starts FRACTION 5 If you do not specify a start date the calendar pattern start date is used The calendar start date does not affect the origin of the time series The origin of the time series specifies the earliest date for elements in the time series The origin can be before the calendar start date IBM Informix TimeSeries Data User s Guide Table 2 2 Calendar data type parameter values continued Value Data type Description pattern_date DATETIME YEAR TO Defines when the calendar pattern starts FRACTION 5 If both the calendar start date and the pattern start date are included the pattern start date must be the same as or later than the calendar start data by a number of intervals that is less than or equal to the number of interval lengths in the pattern length If you do not specify a calendar pattern start date the calendar start date is used pattern_name VARCHAR Name of calendar pattern to use from CalendarPatterns
30. a r WA 2 JE cya e Ole o 5 3 4 E function on page 9 46 The ts_put_elem_no_dups function on page 9 47 The ts_elem function on page 9 22 _get_ts function on page 9 3 aa a ie ale olg pri oO o fer 8 Pee w CO E function on page 9 3 The ts_make_elem_with_buf function on page 9 39 P 5 x lt xs function on page 9 41 The ts_put_nth_elem function 9 48 The ts_put_nth_elem function puts new elements into an existing regular time series at a specified offset Syntax ts_timeseries ts_put_nth_elem ts_tsdesc tsdesc ts_tselem tselem mi_integer N tsdesc The time series to be updated tselem The element to add any time stamp in the element is ignored Z The offset indicating where the element to add should be placed Offsets start at 0 IBM Informix TimeSeries Data User s Guide Returns The original time series with the element added If the time series is irregular an error is raised Related reference The ts_index function on page 9 35 The ts_elem function on page 9 22 The ts_get_ts function on page 9 33 The ts_make_elem function on page 9 38 The ts_put_ts function The ts_put_ts function updates a destination time series with the elements from the source time series Syntax ts_timeseries ts_put_ts ts_tsdesc src_tsdesc ts_tsd
31. a 1 for an example of ts_close Related reference The ts_open function on page 9 44 The ts_closest_elem function The ts_closest_elem function returns the first element or column s of an element that is non null and closest to the given time stamp Syntax ts_tselem ts_closest_elem ts_tsdesc tdesc mi_datetime tstamp mi_string cmp mi_string xcol_list mi_integer flags mi_integer isNull mi_integer off tdesc A time series descriptor returned by ts_open tstamp The time stamp to start searching from cmp A comparison operator Valid values for cmp are lt lt gt and gt col_list To search for an element with a particular set of columns non null specify a list of column names separated by a vertical bar An error is raised if any of the column names do not exist in the time series sub rowtype To search for a non null element set col_list to NULL flags Determines whether hidden elements should be returned Valid values for the flags parameter are defined in tseries h They are e TS_CLOSEST_NO_FLAGS no special flags e TS_CLOSEST_RETNULLS_FLAGS return hidden elements isNull The isNull parameter must not be NULL On return it is set with the null indicator bits found in tseries h These are e 0 element is not hidden and is allocated e TS_NULL_NOTALLOCED element has not been written to e TS _NULL_HIDDEN element is hidden off If the time series is regular the offset
32. as described next result The resulting date Description Valid values for the interval parameter can be found in tseries h They are e TS_SECOND e TS_MINUTE IBM Informix TimeSeries Data User s Guide e TS_HOUR e TS_DAY e TS_WEEK e TS_MONTH e TS_YEAR If the result parameter is NULL then a result mi_datetime structure is allocated and returned otherwise the return value is the given result parameter Returns The time stamp at the specified number of intervals before the start date Related reference The ts_tstamp_difference function on page 9 51 The ts_tstamp_plus function The ts_tstamp_plus function The ts_tstamp_plus function returns a time stamp at a specified number of intervals after a starting date you specify Syntax mi_datetime ts_tstamp_plus mi_datetime xstartdate mi_integer cnt mi_integer interval mi_datetime result startdate The date to start from cnt The number of intervals to add to the start date interval The interval as described next result The resulting date Description Valid values for the interval parameter can be found in tseries h They are e TS_SECOND e TS_MINUTE e TS_HOUR e TS_DAY e TS_WEEK e TS_MONTH e TS_YEAR If the result parameter is NULL then a result mi_datetime structure is allocated and returned otherwise the return value is the given result parameter Chapter 9 Time series API routines 9 53 Returns The
33. code AA t 2011 01 18 00 00 00 00000 high 69 75000000000 low 68 75000000000 final 69 62500000000 vol 281 2000100000 code AA t 2011 01 19 00 00 00 00000 high 69 62500000000 low 69 12500000000 final 69 62500000000 vol 96 69999700000 3 row s retrieved The following query shows that the inst_vt514 table does contain the hidden element and the null element select from inst_vt514 where code AA and t between datetime 2011 01 14 year to day and datetime 2011 01 19 year to day order by t code AA t 2011 01 14 00 00 00 00000 high 69 25000000000 low 68 37500000000 final 68 62500000000 vol 462 0000000000 code AA t 2011 01 17 00 00 00 00000 high low final vol code AA t 2011 01 18 00 00 00 00000 high 69 75000000000 low 68 75000000000 final 69 62500000000 vol 281 2000100000 code AA t 2011 01 19 00 00 00 00000 high 69 62500000000 low 69 12500000000 final 69 62500000000 vol 96 6999970000 4 row s retrieved Related concepts The display of data in virtual tables on page 4 3 Insert data through virtual tables on page 4 4 Related reference Chapter 4 Virtual tables for time series data 4 25 Example of creating a fragmented virtual table on page 4 10 PutElemNoDups function on page 7 79 PutElem function on page 7 77 Example of creating a virtual table on page 4 8 Drop a virtual table You use the DROP statement to destroy a vir
34. continued Overview Reference Write a custom program to load time series data You can use time series SQL routines to write a custom program that loads time series data into the Informix database You can load data in parallel in a highly efficient manner by controlling what data is loaded into which containers You can include a custom loader program in your application Writing a loader program on page 3 31 Enhancements to the Informix TimeSeries Plug in for Data Studio When you use the Informix TimeSeries Plug in for Data Studio you can load time series data into an Informix database directly from another database You do not have to export the data into a file When you create a table definition specify a connection to a database and a query to return the data that you want to load You can preview the returned data to validate the query You can also set other properties of the load job within the plug in Return the timestamp of the first or last time series element You can return the timestamp of the first or last element in a time series by running the GetFirstElementStamp function or the GetLastElementStamp function You can choose whether the element can be null or must contain data For example you can return the first element that has data to determine the number of null elements between the origin and the first element that has data Create a load job to load data from a database on page
35. continued Task type Description Perform an arithmetic Add two time series together Plus function on page 7 77 operation on one or two time series Subtract one time series from another Minus function on page 7 74 Multiply one time series by another Times function on page 7 86 Divide one time series by another Divide function on page 7 46 Raise the first argument to the power of the second Pow function on page 7 77 Get the absolute value Abs function on page 7 11 Exponentiate the time series Exp function on page 7 48 Get the natural logarithm of a time series Logn function on page 7 74 Get the modulus or remainder of a division of one time series by another function on page 7 75 Negate a time series Negate function on page 7 75 Return the argument and the argument is bound to the unary operator Positive function on page 7 77 Round the time series to the nearest whole number Round function on page 7 84 Get the square root of the time series Sqrt function on page 7 85 Get the cosine of the time series Get the sine of the time series Get the tangent of the time series Get the arc cosine of the time series Get the arc sine of the time series Get the arc tangent of the time series Get the arc tangent for two time series Atan2 function on page 7 27 Apply an arithmetic Apply a binary function to a pair of time series or to a time series
36. dbspaces The following table compares the different strategies Chapter 1 Informix TimeSeries solution 1 15 Container1 dbs0 Table 1 7 Comparison of container strategies Strategy The table is fragmented among multiple dbspaces and containers are created automatically Data distribution method The time series data is distributed among the same dbspaces as the table fragments Inserting data The data is stored in containers in round robin order Deleting data by date range You run the TSContainerPurge function to delete data by date range in all containers You create multiple containers in multiple dbspaces You decide how to distribute the data You can distribute the time series data by primary key values You can decide on a specific set of primary key values for each container You specify the appropriate container name for the primary key values when you insert data You run the TSContainerPurge function to delete data by date range in all containers You create a rolling window container The time series data is distributed by date interval Each date interval is stored in its own partition Partitions are stored in multiple dbspaces You specify the rolling window container name when you insert data The container controls in which dbspace the data is stored You configure an automatic purge policy to delete data by date range or yo
37. falies on page 7 9 lues on page 7 9 The default is 0 Chapter 7 Time series SQL routines 7 77 7 78 Description If the time stamp is NULL the data is appended to the time series for regular time series or an error is raised for irregular time series For regular time series if there is data at the given timepoint it is updated with the new data otherwise the new data is inserted For irregular time series if there is no data at the given timepoint the new data is inserted If there is data at the given timepoint the following algorithm is used to determine where to place the data 1 Round the time stamp up to the next second 2 Search backwards for the first element less than the new time stamp 3 Insert the new data at this time stamp plus 10 microseconds The row type passed in must match the subtype of the time series Hidden elements cannot be updated The API equivalent of PutElem is ts_put_elem Returns A modified time series that includes the new values Example The following example appends an element to a time series update daily_stocks set stock_data PutElem stock_data row NULL datetime year to fraction 5 2 3 3 4 5 6 67 stock_bar where stock_name IBM The following example updates a time series update activity_stocks set activity_data PutElem activity_data row 2011 08 25 09 06 00 00000 6 25 1000 1 007 2 1 stock_trade where stock_id 600 Rel
38. following interval names Second e Minute e Hour Day e Week e Month e Year For irregular time series and hertz time series the interval is significant only in defining on and off periods The interval does not determine the number of elements For hertz time series the effective interval for recording data is the value of the hertz parameter when the time series is created num_intervals A positive integer that represents the number of interval units Interval units are either valid intervals for time series data if followed by on or invalid intervals for time series data if followed by off The maximum number of interval units either on or off in a calendar pattern is 2035 Internal calculations take longer to perform if you use a long calendar pattern For compressed time series the off keyword is not allowed Usage The information inside the braces is the pattern specification The pattern specification has one or more elements that consist of n the number of interval units and either on or off to signify valid or invalid intervals Elements are separated by commas The information after the braces is the interval size The calendar pattern length is how many intervals before the calendar pattern starts over after all timepoints in the pattern specification are exhausted the pattern is repeated For this reason a weekly calendar pattern with daily intervals must contain exactly seven intervals and a da
39. function on page 9 11 The following functions are used only with regular time series ts_current_offset ts_index ts_nth_elem ts_put_last_elem ts_put_nth_elem ts_time Some of the API routines are much the same as SQL routines The mapping is shown in the following table API routine SQL routine ts_cal_index CalIndex ts_cal_range CalRange 9 6 IBM Informix TimeSeries Data User s Guide API routine ts_cal_stamp ts_create ts_create_with_metadata SQL routine CalStamp TSCreate TSCreatelIrr TSCreate TSCreatelIrr ts_del_elem DelElem ts_elem GetElem ts_first_elem GetFirstElem ts_get_calname GetCalendarName ts_get_containername GetContainerName ts_get_metadata GetMetaData ts_get_origin GetOrigin ts_hide_elem HideElem ts_index GetIndex ts_ins_elem InsElem ts_last_elem GetLastElem ts_nelems GetNelems ts_next_valid GetNextValid ts_nth_elem GetNthElem ts_previous_valid GetPrevious Valid ts_put_elem PutElem ts_put_elem_no_dups PutElemNoDups ts_put_ts PutTimeSeries ts_reveal_elem RevealElem ts_time GetStamp ts_update_metadata UpdMetaData ts_upd_elem UpdElem The ts_begin_scan function The ts_begin_scan function begins a scan of elements in a time series Syntax ts_tscan ts_begin_scan ts_tsdesc mi_integer flags mi_datetime begin_stamp mi_datetime end_stamp tsdesc Returned by ts_open flags Determines how
40. gt hash_size 256 void mi_query_finish context_ptr gt conn context_ptr gt hash mi_zalloc context_ptr gt hash_size sizeof context_ptr gt hash context_ptr gt col_data 1 MI_DATUM mi_new_var 1 carryover context_ptr gt col_data 6 MI_DATUM mi_new_var 1 priority if NULL context_ptr gt hash NULL context_ptr gt col_data 1 NULL context_ptr gt col_data 6 mi_db_error_raise NULL MI_EXCEPTION Not enough memory context_ptr gt carryover mi_get_vardata mi_lvarchar context_ptr gt col_data 1 context_ptr gt col_data 4 MI_DATUM amp context_ptr gt price context_ptr gt col_data 5 MI_DATUM amp context_ptr gt yield context_ptr gt priority mi_get_vardata mi_lvarchar context_ptr gt col_data 6 context_ptr gt save_set mi_save_set_create context_ptr gt conn End of init_context kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name close_context purpose Close the context structure Free up all allocated memory kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void close_context loader_context_t context_ptr mi_free context_ptr gt hash context_ptr gt hash NULL context_ptr gt hash_size 0 mi_var_free mi_lvarchar context_ptr gt col_data 1 mi_var_free mi_lvarchar context_ptr gt col_data 6 context_ptr gt col_data 1
41. hidden elements are returned 4 Indicates that the scan runs with the TS_SCAN_SKIP_HIDDEN flag set hidden elements are not returned For example consider an input time series that has a daily calendar with no off days it has data from weekdays and weekends If you aggregate this data by a business day calendar 5 days on 2 days off starting on a Monday a flags argument of 0 causes weekend data to be aggregated with the next Monday s data and a flags argument of 1 causes weekend data to be aggregated with the previous Friday s data For another example consider a quarterly calendar that is defined as startdate 2010 1 1 00 00 00 00000 pattstart 2010 1 1 00 00 00 00000 pattern 1 on 2 off month Chapter 7 Time series SQL routines 7 13 7 14 If you aggregate this calendar with either a flags argument of 0 or no flags argument all input points up to but not including 2010 2 1 00 00 00 00000 are aggregated into the first output element All points from 2010 2 1 00 00 00 00000 up to but not including 2010 5 1 00 00 00 00000 are aggregated into the second output element and so on If the flags argument is 1 all input points up to but not including 2010 4 1 00 00 00 00000 are aggregated into the first output element All points from 2010 4 1 00 00 00 00000 up to but not including 2010 7 1 00 00 00 00000 are aggregated into the second output element and so on The AggregateBy clause might look like this Aggreg
42. k Description This function runs over a fixed number of elements not over a fixed length of time therefore it might not be appropriate for irregular time series The first num_values 1 outputs result from shorter windows the first output is derived from the first input time the second output is derived from the first two IBM Informix TimeSeries Data User s Guide input times and so on Null elements in the input also result in shortened windows Returns A DOUBLE PRECISION running median of the last k values Example This statement produces a time series from the running median over a 10 element window of the column high of stock_data You can refer to the columns of a time series as colname or colnumber for example high or 1 select stock_name Apply TSRunningMed high 10 stock_data TimeSeries stock_bar TimeSeries one_real from daily_stocks Related reference TSRunningSum function The TSRunningSum function computes a running sum over SMALLFLOAT or DOUBLE PRECISION values Syntax TSRunningSum value smallfloat num_values integer returns smallfloat TSRunningSum value double precision num_values integer returns double precision value The input value to include in the running sum num_values The number of values to include in the running sum k Description A running sum is the sum of the last k values where k is supplied by the user If a value is NULL the previous value is
43. lt 100 FROM tj expression 1 1 row s retrieved Related reference Time series routines that run in parallel on page 7 7 DelClip function 7 42 The DelClip function deletes all elements in the specified time range including the delimiting timepoints for the specified time series instance The DelClip function differs from the DelTrim function in its handling of deletions from the end of a regular time series DelTrim shortens the time series and reclaims space whereas DelClip replaces elements with null values Syntax DelClip ts TimeSeries begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries ts The time series to act on begin_stamp The begin point of the range IBM Informix TimeSeries Data User s Guide end_stamp The end point of the range flags Valid values for the flags argument are described in The flags argument falues on page 73 lues on page 7 9 The default value is 0 Description You can use DelClip to delete hidden elements from a time series instance If the begin or end point of the range falls before the origin of the time series or after the last element in the time series an error is raised When DelClip operates on a regular time series instance it replaces elements with null elements it never changes the number of elements in a regular time series Returns A time series with all eleme
44. mi_integer hertz ts_timeseries ts_create MI_CONNECTION mi_string mi_datetime mi_integer mi_integer MI_TYPEID mi_integer mi_string mi_string conn calname conn calname xorigin threshold flags typeid nelem container compression A valid DataBlade API connection The name of the calendar origin The time series origin threshold flags typeid nelems The time series threshold If the time series stores this number or more elements it is stored in a container If the time series holds fewer than this number it is stored directly in the row that contains it threshold must be greater than or equal to 0 and less than 256 0 Regular time series 1 Irregular time series 3 Hertz time series 5 Compressed time series The ID of the new type for the time series to be created The initial number of elements to create space for in the time series This space is reclaimed if not used after the time series is written into the database 9 17 Chapter 9 Time series API routines container The container for holding the time series Can be NULL if the time series can fit in a row or is not going to be assigned to a table hertz Optional An integer 1 255 that specifies the number of records per second Implicitly creates an irregular time series The value of the threshold parameter must be 0 The value of the flags parameter must be 3 compression Optional A string tha
45. on page 7 6 HideRange function on page 7 68 Intersect function on page 7 71 IsRegular function on page 7 73 Lag function on page 7 73 oO N N a Logn function on page 7 N Minus function on page 7 Mod function on page 7 7 Negate function on page 7 7 gI O1 NullCleanup function on page 7 7 N Plus function on page 7 7 Positive function on page 7 7 Pow function on page 7 7 Q RevealElem function on page 7 8 RevealRange function on page 7 83 Round function on page 7 8 Sin function on page 7 85 Sqrt function on page 7 8 Tan function on page 7 8 Times function on pag TSToXML function on page 7 154 Union function on page 7 15 WithinC and WithinR functions on page 7 161 5 o RSI The flags argument values The time series SQL functions that insert data have a flags argument to determine how elements are inserted The value of the flags argument is the sum of the flag values that you want to use Table 7 2 The values of the flags argument Value Meaning Restrictions TSOPEN_RDWRITE 0 Default Indicates that the time series can be read and written to Chapter 7 Time series SQL routines 7 9 Table 7 2 The values of the flags argument continued Flag Value Meaning Restrictions TSOPEN_READ_HIDDEN 1 Indicates t
46. returns a null value Chapter 1 Informix TimeSeries solution 1 7 1 8 Hertz data and compressed numeric data are stored in irregular time series because multiple records are packed into each element However hertz and compressed time series require regularly spaced data and behave differently from standard irregular time series Related reference Create a time series on page 3 22 Packed time series A packed time series stores records for multiple timepoints in each element to reduce storage space You can create a packed time series if you have hertz data or you have numeric data that you want to compress Both hertz and compressed numeric data must be recorded at regular intervals Packed elements save approximately 4 bytes per record as compared to a regular time series not including the savings for compressing the data For a hertz time series each time series element is packed with records for one second Hertz data is recorded at a regular subsecond frequency For example an electrical grid might have phasor measurement units that measure electrical waves at 50 hertz For a compressed time series each time series element is packed with compressed records until the size of the element approaches 4 KB All of the columns in the TimeSeries subtype must be numeric You define compression separately for each column except the first timestamp column which is compressed by default For example a weather station might me
47. strlen token n c returns if is_null token context_ptr gt carryover token 0 context_ptr gt col_is_null 1 MI_FALSE spread integer pricing _bmk_id integer for i 2 i lt 4 i c get_token buf_ptr token token_buf_len if STREAM_EOF c amp amp strlen token n c return if is_null token context_ptr gt col_data i MI_DATUM atoi token context_ptr gt col_is_nul l i MI_FALSE price float yield float for i 4 i lt 6 i c get_token buf_ptr token token_buf_len if STREAM_EOF c amp amp strlen token n c return if is_null token double context_ptr gt col_data i atof token context_ptr gt col_is_null i MI_FALSE priority char 1 c get_token buf_ptr token token_buf_len if STREAM EOF c n c amp amp 0 strlen token returns if is_null token context_ptr gt priority token 0 context_ptr gt col_is_null 6 MI_FALSE End of read_day_ data B 12 IBM Informix TimeSeries Data User s Guide kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name read_line purpose Read a line from the file fetch the time series descriptor corresponding to the Secid create a time series element for the line and convert the date into an mi_datetime structu
48. 00 pattern 1 off 5 on 1 off day select OrOp cl c_calendar c2 c_calendar from CalendarTable cl CalendarTable c2 where cl c_name daycal and c2 c_name hourcal The query returns the following result expression startdate 2011 01 01 00 00 00 pattstart 2011 01 02 00 00 00 pattern 24 off 120 on 24 off hour Related reference OrOp function on page 5 5 AndOp function on page 6 1 IBM Informix TimeSeries Data User s Guide Chapter 7 Time series SQL routines Time series SQL routines create instances of a particular time series type and then add data to or change data in the time series type SQL routines are also provided to examine analyze manipulate and aggregate the data within a time series The several data types and tables used throughout the examples in this chapter are listed in the following table Type Table Description stock_bar Type containing timestamp DATETIME high low final and vol columns daily_stocks Table containing stock_id stock_name and stock_data columns stock_trade Type containing timestamp DATETIME price vol trade broker buyer and seller columns activity_stocks Table containing stock_id and activity_data columns For more information about these data types and tables see Creating a TimeSeries subtype on page 3 13 and Create the database table on page 3 14 The schema for these examples is in the INFORMIXDIR TimeSeries version examples
49. 00000 calendar smartmeter regular threshold Q container TSContainerPoolRoundRobin readings 33070 13 00 100 00 9 98e 34 19347 4 00 100 00 1 007e 35 17782 18 00 100 00 9 83e 34 During the running of the INSERT statement the TSContainerPoolRoundRobin function runs with the following values TSContainerPoolRoundRobin smartmeters rawreadings smartmeter_row 0 readings The TSContainerPoolRoundRobin function sorts the container names alphabetically returns the container name ctn_sm0 to the INSERT statement and the data is stored in the ctn_sm0 container The TSContainerPoolRoundRobin function specifies to store the data from the next INSERT statement in the container named ctn_sm1 and the data from the third INSERT statement in the container named ctn_sm2 For the fourth INSERT statement the TSContainerPoolRoundRobin function returns to the beginning of the container list and specifies to store the data in the container named ctn_sm0 and so on Related reference TSContainerCreate procedure on page 7 93 TSContainerPoolRoundRobin function on page 7 107 TSContainerSetPool procedure on page 7 111 User defined container pool policy You can create a policy for inserting data into containers within a container pool The user defined container policy you create must have one of the following function signatures Syntax PolicyName table_name lvarchar column_name lvarchar subtype
50. 1 0 239 0 239 OK 1 0 086 0 086 The GROUP BY and ORDER BY clauses order the results by state The explain output for this query shows that the query ran in parallel Estimated Cost 63 Estimated of Rows Returned 1 Maximum Threads 5 IBM Informix TimeSeries Data User s Guide Temporary Files Required For Order By Group By 1 informix 1 SEQUENTIAL SCAN 2 informix v VTI SCAN Parallel fragments ALL VTI Filters informix greaterthan informix v tstamp datetime 2011 02 07 23 30 00 00000 year to fraction 5 Filters informix v loc_esi_id informix 1 loc_esi_id NESTED LOOP JOIN 3 informix c INDEX PATH 1 Index Keys customer_num Parallel fragments ALL Lower Index Filter informix customer_num informix c customer_num NESTED LOOP JOIN Related concepts The TSVTMode parameter on page 4 16 L dbaccessdemo command Create demonstration databases DB Access Guide Related reference ACTER FRAGMENT statement GOL Syntax BET POOPRIORITY statement COL Syntax BET ISOLATION statement GOL Syntax gt BET EXPLAIN statement GOL Syntax TSCreateExpressionVirtualTab procedure The TSCreateExpressionVirtualTab procedure creates a virtual table that is based on the results of an expression that was performed on a table that contains a TimeSeries column The resulting virtual table is read only Syntax TSCreateExpressionVirtual Tab VirtualTableName varchar BaseTableName lvarchar expre
51. 1 2014 01 01 00 00 00 00000 v1 1 5 v2 20 5 1 2014 01 01 00 15 00 00000 v1 2 0 v2 17 4 1 2014 01 01 00 30 00 00000 v1 1 9 v2 20 2 1 2014 01 01 00 45 00 00000 v1 1 8 v2 19 7 4 row s retrieved 13 Insert a row through the virtual table You must explicitly cast the JSON data to the JSON data type and then cast the data to the BSON data type INSERT INTO virt_tstable_j values 1 2014 01 01 01 00 00 00000 vl 2 1 v2 20 1 json bson Related concepts JSON time series on page 1 12 Related tasks Creating a TimeSeries subtype on page 3 13 Writing a loader program on page 3 31 Loading JSON data on page 3 33 Related reference Create the database table on page 3 14 Time series input function on page 3 24 TSCreateVirtualTab procedure on page 4 5 L CREATE EXTERNAL TABLE Statement SQL Syntax Defining a calendar 3 12 A time series definition must include a calendar A calendar includes a calendar pattern which can be defined separately or within the calendar definition You can create a calendar or choose a predefined calendar To create a calendar 1 Optional Create a named calendar pattern by inserting a row into the CalendarPatterns table by using the format of the CalendarPattern data type A named calendar pattern is useful if you plan to use the same calendar pattern in multiple calendars N Create a calendar by inserting
52. 153 TSRowToList function on page 7 147 TSColINumToList function 7 92 The TSColNumToList function takes a TimeSeries column and returns a list collection of rows containing the values of one of the columns in the elements of the time series Null elements are not added to the list Syntax TSColNumToList ts TimeSeries colnum integer returns list S The time series to act on colnum The column to return Description The column is specified by its number column numbering starts at 1 with the first column following the time stamp column Because this aggregate function can return rows of any type the return value must be explicitly cast at runtime IBM Informix TimeSeries Data User s Guide Returns A list collection of rows Example This query returns a list of all values in the column high select from table select TSColNumToList stock_data 1 list real not null from daily_stocks Related reference TSColINameToList function on page 7 91 Transpose function on page 7 86 TSRowNameToList function on page 7 145 TSRowNumToList function on page 7 146 TSSetToList function on page 7 153 TSRowToList function on page 7 147 TSContainerCreate procedure The TSContainerCreate procedure creates a container to store the time series data for the specified TimeSeries subtype You can create a container in one dbspace a container that spans multi
53. 20 element window Again the first 19 output elements are exceptions because they result from windows of fewer than 20 elements The first is NULL because correlation is undefined for just one element select Apply TSRunningCor 0 high 1 high 20 dsl stock_data TimeSeries stock_bar dsl stock_data TimeSeries stock_bar TimeSeries one_real from daily_stocks dsl daily stocks ds2 where dsl stock_name IBM and ds2 stock_name AA1 Tip When a start date is supplied to the Apply function the first num_values 1 output elements are still formed from incomplete windows The Apply function never looks at data before the specified start date Related reference Apply function on page 7 18 TSRunningAve function on page 7 147 ingMed function F A TSRunningSum function on page 7 151 TSRunning Var function on page 7 152 TSRunningMed function 7 150 The TSRunningMed function computes the median of a time series over a running window This function is useful only when used within the Apply function Syntax TSRunningMed value double precision num_values integer returns double precision TSRunningMed value real num_values integer returns double precision value The first input value to use to calculate the running median Typically the name of a DOUBLE FLOAT or REAL column in your time series num_values The number of values to include in the running median
54. 9 36 The ts_put_elem_no_dups function on page 9 47 The ts_upd_elem function on page 9 5 TS The TS_ELEM HIDDEN macro The TS ELEM HIDDEN macro determines whether the STATUS indicator returned by ts_elem ts_nth_elem ts_first_elem and similar functions is set because the associated element was hidden Syntax TS_ELEM_HIDDEN mi_integer STATUS STATUS The mi_integer argument previously passed to ts_elem ts_nth_elem ts_first_elem or a similar function Description This macro returns a nonzero value if the associated element is hidden This macro is often used in concert with TS ELEM NULL Returns A nonzero value if the element associated with the STATUS argument was previously hidden by the ts_hide_elem function Related reference Chapter 9 Time series API routines 9 23 The ts_elem function on page 9 22 The TS_ELEM_NULL macro The ts_first_elem function on page 9 25 The ts_hide_elem function on page 9 34 The ts_last_elem function on page 9 37 The ts_next function on page 9 41 The ts_next_valid function on page 9 4 2 The ts_nth_elem function on page 9 43 The ts_previous_valid function on page 9 45 The TS _ ELEM NULL macro The TS_ELEM_NULL macro determines whether the STATUS indicator returned by ts_elem ts_nth_elem ts_first_elem or a similar function is NULL because
55. BSON column If the BSON field name is the same as another column in the TimeSeries data type you must precede the field name with the column name and a dot bson_column_name bson_field_name Chapter 7 Time series SQL routines 7 39 7 40 op An operator Can be lt lt gt or gt Must be surrounded by quotation marks value The value that is used in the comparison Can be either a number a string or NULL Sting values must be surrounded by quotation marks Usage Use the CountIf function to determine how many elements fit criteria that are based on the values of the columns within the TimeSeries subtype For example you can apply criteria on multiple columns or determine whether a column has any null values You can select a time range or query the entire time series Returns An integer that represents the number of elements that fit the criteria Examples The examples are based on the following time series INSERT INTO CalendarTable c_name c_calendar VALUES sm_15min startdate 2011 07 11 00 00 00 00000 pattstart 2011 07 11 00 00 00 00000 pattern 1 on 14 off minute 1 row s inserted EXECUTE PROCEDURE TSContainerCreate sm0 tsspaceO sm_row 0 0 Routine executed CREATE ROW TYPE sm_row t datetime year to fraction 5 energy smallint ind smallint Row type created CREATE TABLE sm meter_id varchar 255 primary key readings TimeSeries sm_row IN tsspace
56. For example the dormant window contains partitions for January February and March You specify to destroy partitions before March The February and January partitions are destroyed You can change the size of the dormant window If you make it smaller the action that is taken depends on the window_control parameter The behavior is the same as when you add partitions e You can change the type and number of partitions that can be destroyed Related concepts Delete time series data on page 3 37 Related reference Time series storage on page 1 14 TSContainerCreate procedure on page 7 9 TSContainerManage function on page 7 99 Monitor containers You can view information about time series containers such as the properties of containers how large containers are and how full containers are If you monitor the containers over time you can predict how quickly containers fill and how much data fits into each container Use the IBM OpenAdmin Tool OAT for Informix to monitor containers Alternatively query system tables and run time series SQL routines To view information about the properties of containers query the TSContainerTable table For rolling window containers view information about partitions in the TSContainerUsageActiveWindowVTI and TSContainerUsageDormantWindowVTI tables IBM Informix TimeSeries Data User s Guide To view specific information about how full a container is run one of
57. HideElem ts TimeSeries multiset_tstamps multiset datetime year to fraction 5 not null flags integer default 0 returns TimeSeries ts The source time series tstamp The time stamp to be made invisible multiset_tstamps The multiset of time stamps to be made invisible flags Valid values for the tk argument are described in The flags argument alues on page 7 9 The default is 0 Description After an element is hidden reading that element returns NULL and writing it results in an error message It is however possible to use ts_begin_scan to read hidden elements Chapter 7 Time series SQL routines 7 67 select HideElem stock_data 2011 01 03 00 00 00 00000 from daily_stocks where stock_name IBM Related concepts Calendar data type on page 2 4 Related reference N Time series routines that run in parallel on page 7 CalendarPattern data type on page 2 N DelClip function on page 7 4 page 7 4 DelTrim function on page 7 4 g m D B ER c 5 Q fay 5 5 Ajj Go RevealElem function on page 7 83 The ts_begin_scan function on page 9 7 The ts_hide_elem function on page 9 34 The ts_reveal_elem function on page 9 50 O T Q oO F oO ma c 3 O Ey 5 HideRange function 7 68 Syntax HideRange ts TimeSeries start datetime year to fraction 5 end datetime year to fraction 5 flags
58. MI_CONNECTION conn mi_integer rc Chapter 7 Time series SQL routines 7 21 nulls 0 MI_TRUE nulls 1 MI_FALSE conn mi_open NULL NULL NULL if rc mi_value row 1 MI_DATUM amp high amp len MI_ERROR mi_db_error_raise conn MI_EXCEPTION ts_test_float_sql corrupted argument row if rc MI_NULL_VALUE goto retisnull if rc mi_value row 2 MI_DATUM amp low amp len MI_ERROR mi_db_error_raise conn MI_EXCEPTION ts_test_float_sql corrupted argument row if rc MI_NULL_VALUE goto retisnull r xhigh low values 1 void amp r rowdesc mi_row_desc_create mi_typestring_to_id conn one_real result mi_row_create conn rowdesc MI_DATUM values nulls mi_close conn return result retisnull mi_fp_setreturnisnull fp 0 MI_TRUE return MI_ROW NULL end of applyfunc c Then create the following SQL function create function HighLowDiff arg stock_bar returns one_real external name tmp applyfunc bld high_low_diff language C select stock_name Apply HighLowDiff stock_data TimeSeries one_real from daily_stocks The following query is equivalent to the previous query but it does not have the performance advantages of using a function as the expression to evaluate select stock_name Apply high low stock_data TimeSeries one_real from daily_stocks Related reference AgeregateBy function on pa
59. MI_EXCEPTION buf if 0 buf_ptr gt buf_len return STREAM EOF Increment buf_ptr gt line_no until we have started on the next a not when the newline character is seen i buf_ptr gt flags amp LDBUF_LAST_CHAR_EOL een enon eile amp LDBUF_LAST_CHAR_EOL c buf_ptr gt data buf_ptr gt buf_index 4 if n c buf_ptr gt flags LDBUF_LAST_CHAR_EOL return c End of get_char kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name close_buf purpose Close a file attached to a buffer notes kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void close_buf FILE_BUF buf_ptr mi_file_close buf_ptr gt fd buf_ptr gt fd MI_ERROR buf_ptr gt buf_index 0 buf_ptr gt buf_len 0 buf_ptr gt file_name NULL Appendix B The TSIncLoad procedure example B 7 B 8 End of close buf kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name get_token purpose Get the next token from an input stream returns The token in a buffer and the next character after the buffer notes Assumes that the tokens are separated by white space kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkxk static mi_integer get_token FILE_BUF buf_ptr mi_string token size_t token_buf_len register mi_integer c
60. Only one of the syntax elements that share the same dotted decimal number can specify a symbol For example if you hear the lines 2 FILE 2 1 KEEP and 2 1 DELETE you know that KEEP is the default option for the FILE keyword In this example if you include the FILE keyword but do not specify an option default option KEEP is applied A default option also applies to the next higher dotted decimal number In this example if the FILE keyword is omitted default FILE KEEP is used However if you hear the lines 2 FILE 2 1 2 1 1 KEEP and 2 1 1 DELETE the default option KEEP only applies to the next higher dotted decimal number 2 1 which does not have an associated keyword and does not apply to 2 FILE Nothing is used if the keyword FILE is omitted Specifies a syntax element that can be repeated zero or more times A dotted decimal number followed by the symbol indicates that this syntax element can be used zero or more times that is it is optional and can be IBM Informix TimeSeries Data User s Guide repeated For example if you hear the line 5 1 data area you know that you can include more than one data area or you can include none If you hear the lines 3 3 HOST and 3 STATE you know that you can include HOST STATE both together or nothing Notes 1 If a dotted decimal number has an asterisk next to it and there is only one item with that dotted decimal number you can repeat that same item more
61. Returns The element if any preceding the given time stamp The element returned must not be freed by the caller It is overwritten after two calls to fetch an element using this tsdesc time series descriptor For irregular time series if no valid element precedes the given time stamp NULL is returned NULL is also returned if the given time stamp is less than or equal to the origin of the time series Related reference The ts_hide_elem function on page 9 34 The ts_next function on page 9 41 The ts_put_elem function 9 46 The ts_put_elem function puts new elements into an existing time series Syntax ts_timeseries ts_put_elem ts_tsdesc tsdesc ts_tselem tselem mi_datetime tstamp tsdesc A descriptor of the time series to be modified returned by ts_open tselem The element to add tstamp The time stamp at which to put the element The time stamp column of tselem is ignored Description If the time stamp is NULL the data is appended to the time series for regular time series or an error is raised for irregular time series For regular time series if there is data at the given timepoint it is updated with the new data otherwise the new data is inserted For irregular time series if there is no data at the given timepoint the new data is inserted If there is data at the given timepoint then the following algorithm is used to determine where to place the
62. The following query returns the time stamp of the beginning of a time series select GetStamp stock_data 0 from daily_stocks where stock_name IBM Related reference Time series routines that run in parallel on page 7 7 ClipGetCount function on page 7 37 GetIndex function on page 7 55 GetNelems function on page 7 60 CalIndex function on page 6 2 CalRange function on page 6 3 N GetElem function on page 7 5 GetNthElem function on page 7 62 The ts_time function on page 9 51 7 66 IBM Informix TimeSeries Data User s Guide GetThreshold function The GetThreshold function returns the threshold associated with the specified time series Syntax GetThreshold ts TimeSeries returns integer ts The source time series Description The equivalent API function is ts_get_threshold Returns The threshold of the supplied time series Example The following query returns the threshold of the specified time series select GetThreshold stock_data from daily_stocks Related reference Time series routines that run in parallel on page 7 7 The ts_get_threshold function on page 9 33 HideElem function The HideElem function marks an element or a set of elements at a given time stamp as invisible Syntax HideElem ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns TimeSeries
63. The time stamp column of tselem is ignored Description If the time stamp is NULL the data is appended to the time series for regular time series or an error is raised for irregular time series If there is data at the given timepoint it is updated with the new data otherwise the new data is inserted The element passed in must match the subtype of the time series Hidden elements cannot be updated The equivalent SQL function is PutElemNoDups Returns The original time series with the element added Related reference PutElemNoDups function on page 7 79 Chapter 9 Time series API routines 9 47 The ts_put_elem function on page 9 46 The ts_elem function on page 9 22 The ts_get_ts function on page 9 33 The ts_ins_elem function on page 9 36 The ts_make_elem function on page 9 38 The ts_next function on page 9 41 The ts_put_last_elem function The ts_upd_elem function on page 9 54 The ts_put_last_elem function The ts_put_last_elem function puts new elements at the end of an existing regular time series Syntax ts_timeseries ts_put_last_elem ts_tsdesc tsdesc ts_tselem tselem tsdesc The time series to be updated tselem The element to add any time stamp in the element is ignored Returns The original time series with the element added If the time series is irregular an error is raised S 7 f o a
64. The ts_ins_elem function puts an element into an existing time series at a given timepoint Syntax ts_timeseries ts_ins_elem ts_tsdesc tsdesc ts_tselem tselem mi_datetime tstamp tsdesc A descriptor of the time series to be modified returned by ts_open tselem The element to add tstamp The timepoint at which to add the element The time stamp column of the tselem is ignored Description The equivalent SQL function is InsElem Returns The original time series with the new element added If the time stamp is not a valid timepoint in the time series an error is raised If there is already an element at the given time stamp an error is raised Related reference InsElem function on page 7 69 The ts_del_elem function on page 9 22 The ts_elem function on page 9 22 The ts_free procedure on page 9 26 The ts_make_elem function on page 9 38 The ts_make_elem_with_buf function on page 9 39 The ts_put_elem function on page 9 46 The ts_upd_elem function on page 9 54 The ts_put_elem_no_dups function on page 9 4 7 The TS_IS_INCONTAINER macro 9 36 The TS_IS INCONTAINER macro determines whether the time series data is stored in a container Syntax TS_IS_INCONTAINER ts_timeseries ts ts A pointer to a time series IBM Informix TimeSeries Data User s Guide Returns This function returns nonzero if the
65. a container For rolling window containers the TSContainerNElems function returns the sum of the number of elements in the specified set of partitions Returns The number of elements Example The following statement returns the number of elements stored in the container named mult_container EXECUTE FUNCTION TSContainerNElems mult_container elements 26 1 row s retrieved The following statement returns the number of elements stored in all containers EXECUTE FUNCTION TSContainerNE1ems NULL elements 241907 1 row s retrieved Related concepts Monitor containers on page 3 18 Related reference TSContainerUsage function on page 7 114 TSContainerTotalPages function on page 7 112 TSContainerTotalUsed function on page 7 113 TSContainerPctUsed function Time series storage on page 1 14 TSContainerPctUsed function The TSContainerPctUsed function returns the percentage of space that is used in the specified container or in all containers Chapter 7 Time series SQL routines 7 105 Syntax TSContainerPctUsed container_name varchar 128 1 TSContainerPctUsed container_name varchar 128 1 rw_flag integer default 0 container_name Specifies which container to return information about Must be an existing container name You can include wildcard characters from the MATCHES operator The function returns information for all containers that have na
66. a Java applet The database server is connected to the JDBC driver which is connected to the applet The applet is also connected to a browser which is connected to a web server that communicates with the database Web server HTTP servlet subclass es Request __ JDBC driver Response Figure 8 3 Runtime architecture for a Java servlet A request from an application goes through a web server an HTTP servlet subclass and the JDBC driver to the database The database sends responses back along the same path Summary of time series classes The Java class library contains classes that you use to complete all necessary tasks for creating and managing time series You can create time series objects with the Builder classes that are provided for each class The following table lists the Java class that you need for each of the tasks in creating and managing a time series Table 8 1 Time series tasks and the corresponding Java classes Tasks Java classes Create query and manage a custom type TimeSeriesTypeMap map for time series data types TimeSeriesTypeMap Builder Create query and manage calendar patterns IfmxCalendarPattern IfmxCalendarPattern Builder IBM Informix TimeSeries Data User s Guide Table 8 1 Time series tasks and the corresponding Java classes continued Tasks Java classes Create query and manage calendars I fmxCalendar IfmxCalendar Builder Create query an
67. a container run the TSContainerCreate procedure For Enterprise Replication you must create containers with the same names on every replication server before you start replication To control whether multiple sessions write to a container at one time run the TSContainerLock procedure To delete a container run the TSContainerDestroy procedure Related concepts Planning for replication of time series data on page 1 23 Related reference TSContainerPurge function on page 7 108 Planning for data storage on page 1 20 Chapter 3 Create and manage a time series through SQL 3 15 3 16 TSContainerLock procedure on page 7 99 Rules for rolling window containers Rolling window containers store data in partitions by date intervals and can automatically delete old data The active window and the dormant window have a set of behaviors that affect how the data is handled and how you can interact with the data To create a rolling window container run the TSContainerCreate function Specify the size of a partition Specify maximum sizes for the active and dormant windows Enable the automatic deletion of old partitions Specify multiple dbspaces in which to store partitions To change the properties or the layout of rolling window containers run the TSContainerManage function You can change the window sizes attach or detach partitions destroy partitions change whether partitions are destroyed a
68. a registered trademark of The Open Group in the United States and other countries Other company product or service names may be trademarks or service marks of others IBM Informix TimeSeries Data User s Guide Index A Abs function 7 11 Absolute value determining 7 11 Accessibility C 1 dotted decimal format of syntax diagrams C 1 keyboard C 1 shortcut keys C 1 syntax diagrams reading in a screen reader C 1 Acos function 7 11 Adding previous values to current 7 90 Adding two time series 7 77 AgegregateBy function 7 11 7 15 Aggregating time series values 7 11 ALTER TYPE statement 1 26 AndOp function 5 1 6 1 Applets 8 1 Apply function 7 18 virtual tables 4 8 ApplyBinaryTsOp function 7 23 ApplyCalendar function 7 24 Applying a calendar to a time series 7 24 Applying an expression to a time series 7 18 ApplyOpToTsSet function 7 25 ApplyUnaryTsOp function 7 26 Arc cosine determining 7 11 Arc sine determining 7 27 Arc tangent determining 7 27 Arithmetic functions binary 7 27 unary 7 156 Asin function 7 27 Atan function 7 27 Atan2 function 7 27 autopool container pool 3 19 Average computing running 7 148 BaseTableName parameter 4 5 beforeFirst method 8 4 Binary arithmetic functions Atan2 7 27 description of 7 27 Divide 7 46 Minus 7 74 Mod 7 75 Plus 7 77 Pow 7 77 Times 7 86 BulkLoad function 3 35 7 30 C Calendar 1 4 Calendar data type 2 4 Calendar pattern 1 4 Calendar pattern routines 5 1 Calendar pa
69. a row into the CalendarTable table by using the format of the Calendar data type Include either the name of an existing calendar pattern or a calendar pattern definition To use a predefined calendar specify one when you create a time series with the TSCreate or TSCreatelrr function You can change a predefined calendar to meet your needs by updating the row for the calendar in the CalendarTable table Related concepts Calendar data type on page 2 4 CalendarPatterns table on page 2 8 IBM Informix TimeSeries Data User s Guide CalendarTable table on page 2 8 Related reference CalendarPattern data type on page 2 1 Predefined calendars You can use predefined calendars when you create a time series Predefined calendars are stored in rows in the CalendarTable table You can change a predefined calendar by updating the row for the calendar in the CalendarTable table If you upgrade from a previous release of the Informix TimeSeries solution or the IBM Informix TimeSeries DataBlade Module and an existing calendar is defined with the same name as one of the predefined calendars the existing calendar is not replaced by the predefined calendar The following table contains the properties of predefined calendars Table 3 5 Predefined calendars Calendar name Interval duration Start date and time ts_1lmin Once a minute 2011 01 01 00 00 00 00000 ts_15min Once every 15 minutes 201
70. a time series getting 9 14 numbering with Java 8 4 TimeSeries type 3 13 type information getting for 9 15 Command line loader application 3 29 3 30 Comparing two time stamps 9 21 Comparing two values 7 91 compliance with standards xviii Compressed data 1 10 creating time series 7 118 deleting 3 37 inserting 3 37 Compression specifying 3 24 Container pool default 3 19 round robin order 3 20 user defined policy 3 20 Container pools 1 15 creating 3 19 user defined policy 3 21 Containers 1 15 creating 3 15 7 93 dbspaces 1 15 destroying 7 98 determining implicitly 3 27 instance ID of a time series in a getting 7 71 managing 7 99 monitor 3 18 moving 3 19 multiple writers 7 99 name of getting 7 51 9 29 name setting 7 84 rolling window 1 15 3 16 specifying 3 24 system table for 2 9 2 10 2 11 2 12 time series determining if itis ina 9 36 TSContainerNElems 7 104 TSContainerPctUsed 7 106 TSContainerTotalPages 7 112 TSContainerTotalUsed 7 113 TSContainerUsage 7 114 Converting element to a row 9 24 row to element 9 50 time series data to tabular form 7 87 Copying one time series into another 9 49 time series 9 16 Cos function 7 38 Cosine determining 7 38 Countlf function 7 38 Counting elements returned by an expression 7 38 CREATE ROW TYPE statement 3 13 CREATE TABLE statement 3 14 Creating irregular time series 7 118 regular time series 7 116 table for time series 3 14 time series 3 22 3 27 9 17 9 18 time ser
71. a virtual table or by writing a loader program When you query a time series that contains JSON documents the results are automatically cast from BSON to JSON However when you select data from a virtual table on a JSON time series you must cast the BSON column to JSON to view the documents You can aggregate individual fields within JSON documents by specifying the field name in the aggregate operation You cannot aggregate an entire BSON column When you run the TSRollup AggregateBy or AggregateRange function you must cast the results to a TimeSeries row type that has the appropriate types of columns for the results of the aggregate operation Related tasks Loading ISON data on page 3 33 Calendar Every time series is associated with a calendar A calendar defines a set of valid times for elements in a time series Each calendar has a calendar pattern of time periods during which data is allowed or prohibited Data can be recorded during on periods but cannot be recorded during off periods The calendar pattern is based on a time interval for example second minute hour day or month A start date specifies when the pattern starts Suppose you want to collect data once a day Monday through Friday The following table illustrates when data collection is allowed or on and prohibited or off The pattern has an interval of a day a start date on a Sunday and specifies one day off five days on and one day off
72. actual value and the compressed value The range of values is 0 through the largest value of the data type of the associated column The value 0 indicates that no deviation is allowed See Compressed numeric time series on page 1 10 metadata Optional The user defined metadata to be put into the time series multiset_rows Optional A multiset of rows to populate the time series The type of these rows must be the same as the subtype of the time series Description If the TSCreatelrr function is called with the metadata parameter then metadata is saved in the time series If you include the compression parameter each time series element is packed with compressed records until the size of the element approaches 4 KB or the transaction is committed You must run the TSCreateIrr function within an explicit transaction If you include the hertz parameter each time series element is packed with the number of records that are specified by the hertz parameter An element is saved to disk after a record for the last subsecond boundary is inserted or the transaction is IBM Informix TimeSeries Data User s Guide committed You must run the TSCreatelrr function within an explicit transaction Returns An irregular hertz or compressed time series Example Create an empty time series The following example creates an empty irregular time series select TSCreateIrr daycal 2011 01 05 00 00 00 00000 20 0 NULL set_d
73. amp sec_entry_ptr yield_count B 14 IBM Informix TimeSeries Data User s Guide Periodically once every 64 input lines check for interrupts and yield the processor to other threads if 0 yield_count amp 0x3f if mi_interrupt_check mi_db_error_raise NULL MI_EXCEPTION Load aborted mi_yield if null_line continue ts ts_put_elem_no_dups tsdesc day_elem amp tstamp if sec_entry_ptr gt in_row amp amp TS_IS_INCONTAINER ts sec_entry_ptr gt in_row 0 increment_instances_created amp context if NULL day_elem ts_free_elem tsdesc day_elem close_buf amp buf update_series amp context close_context amp context End of TSIncLoad Appendix B The TSIncLoad procedure example B 15 B 16 IBM Informix TimeSeries Data User s Guide Appendix C Accessibility IBM strives to provide products with usable access for everyone regardless of age or ability Accessibility features for IBM Informix products Accessibility features help a user who has a physical disability such as restricted mobility or limited vision to use information technology products successfully Accessibility features The following list includes the major accessibility features in IBM Informix products These features support e Keyboard only operation e Interfaces that are commonly used by screen readers e The attachment of alternative input and outpu
74. and p2 cp_name fourday_day expression 1 off 5 on 1 off day The following OrOp statement returns the union of one hourly and one daily calendar pattern select Or0p pl cp_pattern p2 cp_pattern from CalendarPatterns pl CalendarPatterns p2 where pl cp_name workweek_day and p2 cp_name workweek_hour expression 24 off 120 on 24 off hour Related reference OrOp function on page 6 5 IBM Informix TimeSeries Data User s Guide Chapter 6 Calendar routines You can use calendar routines to manipulate calendars Calendar routines can perform the following types of operations e Create the intersection of calendars e Create the union of calendars e Return information about the calendar Calendar routines can be useful when comparing time series that are based on different calendars For example to compare peak time business usage of a computer network across multiple countries requires accounting for different sets of public holidays in each country An efficient way to handle this is to define a calendar for each country and then create the calendar intersections to perform business day comparisons Calendar routines can be run in SQL statements or sent from an application using the DataBlade API function mi_exec Related concepts Calendar on page 1 13 Calendar data type on page 2 4 AndOp function The AndOp function returns the intersection of the two calendars Syntax And
75. argument is NULL a new time series element is returned Related reference The ts_elem_to_row function on page 9 24 The ts_free_elem procedure on page 9 26 The ts_ins_elem function on page 9 36 The ts_make_elem function on page 9 38 The ts_put_last_elem function on page 9 48 The ts_upd_elem function on page 9 54 The ts_make_stamp function 9 40 The ts_make_stamp function constructs a time stamp from the year month day hour minute second and microsecond values and puts them into the mi_datetime pointed to by the dt argument Syntax mi_datetime ts_make_stamp MI_CONNECTION conn mi_datetime dt mi_integer year mi_integer month mi_integer day mi_integer hour mi_integer minute mi_integer second mi_integer ms conn A valid DataBlade API connection dt The time stamp to fill in The caller should supply the buffer year The year to put into the returned mi_datetime month The month to put into the returned mi_datetime day The day to put into the returned mi_datetime hour The hour to put into the returned mi_datetime minute The minute to put into the returned mi_datetime second The second to put into the returned mi_datetime ms The microsecond to put into the returned mi_datetime Returns A pointer to the same mi_datetime structure that was passed in Related reference IBM Informix TimeSeries Data User s Guide The ts_nelems
76. columns from the base table If the TSVTMode parameter includes the value 2 null time series elements are displayed as null values in the virtual table Hidden elements also show as null values If the TSVTMode parameter does not include the value 2 null time series elements do not show in the virtual table If the TSVTMode parameter includes the value 512 hidden time series elements are displayed in the virtual table otherwise they do not The following statements create four virtual tables that are all based on the same base table named inst which contains the TimeSeries column named bars Each of the tables uses a different value for the TSVTMode parameter The inst_vt0 table does not show null or hidden elements The inst_vt2 table shows null elements The inst_vt512 table shows hidden elements The inst_vt514 table shows null and hidden elements execute procedure TSCreateVirtualTab inst_vt0 inst 0 execute procedure TSCreateVirtualTab inst_vt2 inst 2 execute procedure TSCreateVirtualTab inst_vt512 inst 512 execute procedure TSCreateVirtualTab inst_vt514 inst 514 The following statement hides one element by using the HideElem function update inst set bars HideElem bars datetime 2011 01 18 year to day where code AA 1 row s updated Chapter 4 Virtual tables for time series data 4 23 4 24 The following query shows that the inst_vt0 table does not contain the hidde
77. corresponding column is by reference or by value The values in the values array must not be freed by the caller Related reference The ts_datetime_cmp function on page 9 21 The ts_col_cnt function on page 9 14 The ts_get_calname function The ts_get_calname function returns the name of the calendar associated with the given time series Syntax mi_string ts_get_calname ts_timeseries ts ts The source time series Chapter 9 Time series API routines 9 27 Description The equivalent SQL function is GetCalendarName Returns The name of the calendar This value must be freed by the caller with mi_free The ts_get_col_by_name function The ts_get_col_by_name function pulls out the individual piece of data from an element in the column with the given name Syntax MI_DATUM ts_get_col_by_name ts_tsdesc tsdesc ts_tselem tselem mi_string colname mi_boolean isNull mi_integer off tsdesc A pointer returned by ts_open tselem An element to get column data from colname The name of the column in the element isNull A pointer to a null indicator off For a regular time series off is the offset of the element in the time series For an irregular time series off is ignored Returns The data in the specified column If the type of the column is by reference a pointer is returned If the type is by value the data itself is returned The caller cannot free this value On re
78. description of the compatibility requirements Description These functions operate in an analogous fashion to the arithmetic functions that have been overloaded to operate on time series See the description of these functions in Binary arithmetic functions on page 7 27 for more information For example Plus ts1 ts2 is equivalent to ApplyBinaryTsOp Plus ts1 ts2 Returns A time series of the same type as the first time series argument which can result in a loss of precision The return type can be explicitly cast to a compatible time series type with more precision to avoid this problem See Binary arithmetic functions on page 7 27 nctions on page 7 27 for more information Chapter 7 Time series SQL routines 7 23 Example The following example uses ApplyBinaryTSOp to implement the Plus function create row type simple series stock_id int data TimeSeries one_real create table daily_high of type simple series insert into daily_high select stock_id Apply high NULL datetime year to fraction 5 NULL datetime year to fraction 5 stock_data TimeSeries one_real from daily_stocks create table daily_low of type simple series insert into daily_low select stock_id Apply 0 low NULL datetime year to fraction 5 NULL datetime year to fraction 5 stock_data TimeSeries one_real from daily_stocks create table daily_avg of type simple series insert into daily_avg select 1 stock_id A
79. destination units which must have a smaller interval unit than the interval unit of the specified calendar pattern Syntax Expand cal_patt CalendarPattern interval lvarchar returns CalendarPattern cal_patt The calendar pattern to expand ee The destination time interval second minute hour day week or month Description When a month is expanded it is assumed to have 30 days Returns The expanded calendar pattern Example The following statements convert a daily calendar pattern into an hourly calendar pattern select from CalendarPatterns where cp_name workweek_day cp_name workweek_day cp_pattern 1 off 5 on 1 off day select Expand cp_pattern hour from CalendarPatterns where cp_name workweek_day expression 24 off 120 on 24 off hour Related reference Collapse function on page 5 3 NotOp function 5 4 The NotOp function turns all on intervals off and all off intervals on in the specified calendar pattern Syntax NotOp cal_patt CalendarPattern returns CalendarPattern cal_patt The calendar pattern to convert Returns The inverted calendar pattern IBM Informix TimeSeries Data User s Guide Example The following statement converts the workweek_day calendar select from CalendarPatterns where cp_name workweek_day cp_name workweek_day cp_pattern 1 off 5 on 1 off day select NotOp cp_pattern from CalendarPatterns where cp_name workw
80. directory Related concepts for accessing time series data on Copyright IBM Corp 2006 2014 7 1 Time series SQL routines sorted by task Time series SQL routines are sorted into logical areas that are based on the type of task Table 7 1 Time series SQL routines by task type Task type Description Get information from a Get the origin GetOrigin function on page 7 64 time series Get the interval GetInterval function on page 7 55 Get the calendar Get the calendar name Get the container name Get user defined metadata Get the metadata type GetMetaTypeName function on page 7 55 Determine whether a time series is regular Get the instance ID if the time series is stored in a container Determine whether a time series contains packed data GetPacked function on page Get the frequency of hertz data GetHertz function on page 7 54 Get the compression type of compressed data GetCompression function on page 7 50 Convert between atime Return the offset for the specified timestamp GetIndex function on page 7 55 stamp and an offset Return the time stamp for the specified offset GetStamp function on page 7 66 Count the number of Return the number of elements GetNelems function on page 7 60 elements Get the number of elements between two time stamps ClipGetCount function on page 7 37 Get the number of elements that match the criteri
81. ending for a time series 9 25 X 6 IBM Informix TimeSeries Data User s Guide Screen reader reading syntax diagrams C 1 SDS and time series 1 24 SELECT DISTINCT statement 1 26 Servlets 8 1 session_number tre file 4 26 SetContainerName function 7 84 SetOrigin function 7 85 Shortcut keys keyboard C 1 Sin function 7 85 Sine determining 7 85 Software requirements 1 25 SQL statements ALTER TYPE 1 26 CREATE ROW TYPE 3 13 CREATE TABLE 3 14 INSERT 3 24 restrictions for time series 1 26 SELECT DISTINCT 1 26 UPDATE 3 35 virtual tables 4 1 Sqrt function 7 86 Square root determining 7 86 standards xviii Start date calendar of 2 4 calendar pattern of 2 4 Storage time series data 1 20 Storage for time series 1 15 Subtracting one time series from another 7 74 Sum running 7 151 Syntax diagrams reading in a screen reader C 1 System tables CalendarPatterns 2 8 CalendarTable 2 8 TSContainerTable 2 9 TSContainerUsageActiveWindowVTI 2 11 TSContainerUsageDormantWindowVTI_ 2 12 TSContainerWindowTable 2 10 TSInstanceTable 2 12 T Table 2 10 2 11 2 12 Table 2 9 Tables virtual 4 1 4 8 Tabular form converting time series data to 7 87 Tan function 7 86 Tangent determining 7 86 Threshold for containers specifying 3 24 time series examples directory 1 27 1 28 Time series 1 19 accessing 1 24 calendar pattern routines 5 1 calendar routines 6 1 compressed 1 10 concepts 1 4 creating 3 1 data types 2 8 decisions 1 19 deleting d
82. energy reading between 2011 0 01 and 2011 01 02 SELECT FROM smartmeters_vti_agg_max_min WHERE t gt 2011 01 01 00 00 00 00000 datetime year to fraction 5 AND t lt 2011 01 02 23 59 59 99999 datetime year to fraction 5 meter_id t valuel value2 met00000 2011 01 01 00 00 00 00000 37 00000000000 9 000000000000 met00000 2011 01 02 00 00 00 00000 34 00000000000 8 000000000000 met00001 2011 01 01 00 00 00 00000 36 00000000000 9 000000000000 met00001 2011 01 02 00 00 00 00000 36 00000000000 10 00000000000 met00002 2011 01 01 00 00 00 00000 34 00000000000 9 000000000000 met00002 2011 01 02 00 00 00 00000 36 00000000000 10 00000000000 6 row s retrieved Example 2 Find the daily maximum of a running average The following statement creates a virtual table that is named smartmeters_vti_daily_max that contains the daily maximum of the running average of the energy readings EXECUTE PROCEDURE TSCreateExpressionVirtual Tab smartmeters_vti_daily_max smartmeters AggregateBy max value smartmeter_daily Apply TSRunningAvg energy 4 ts_begin_time ts_end_time ts_col_name TimeSeries onereal_row 0 onereal_row 0 readings The ts_col_name parameter is replaced by the column name that is specified by the TSCreateExpression VirtualTab procedure in this case readings The ts_begin_time and ts_end_time parameters are replaced when the virtual table is queried Chapter 4 Virtual tables
83. file as a CLOB data type a ROW data type that is compatible with the TimeSeries data type or an SQL query to extract the data from the source database e The data must be compatible with Informix data types A loader program creates a loader session A loader session loads data into a specific TimeSeries column You must use separate loader sessions for every TimeSeries column Opening a loader session takes time Leave a session open instead of repeatedly opening and closing the session Within a loader session you can open multiple database sessions so that you can load data in parallel To write a loader program that uses one database session 1 Initialize a global context and open a database session by running the TSL_Init function 2 Copy data from a file or input stream into the database server by running the TSL_Put TSL_PutRow or TLS_PutSQL function You run this function many times while you load data 3 Save data to disk by running the TSL_FlushAll or TSL_Commit function You can view information about the last flush operation by running the TSL_FlushInfo function 4 If necessary change the logging mode by running the TSL_SetLogMode function 5 Monitor the progress of loaded and saved data by running the TSL_GetLogMessage function 6 Close the database session by running the TSL_SessionClose function 7 Remove the global context and shut down the loader by running the TSL_Shutdown procedure To write a l
84. flags integer default 0 returns row cH S The source time series z The offset or position of an entry in the time series This value cannot be less than 0 IBM Informix TimeSeries Data User s Guide flags Valid values for the flags argument are described in The flags argument falues on page 7 5 lues on page 7 9 The default is 0 Description For irregular time series the GetNthElem function returns the Nth element that is found For regular time series the Nth element is also the Nth interval from the beginning of the time series The API equivalent of this function is ts_nth_elem Returns A row value for the requested offset including all the time series data at that timepoint and the time stamp of the entry in the time series calendar The type of the row is the same as the time series subtype If the offset is greater than the offset of the last element in the time series NULL is returned Example The following query returns the last element in a time series select GetNthElem stock_data GetNelems stock_data 1 from daily_stocks where stock_name IBM The following query returns the element in a time series at a certain time stamp this could also be done with GetElem select GetNthElem stock_data GetIndex stock_data 2011 01 04 00 00 00 00000 from daily_stocks where stock_name IBM Related reference Time series routines that run in parallel on page 7 7 Clip funct
85. flushes data for all containers to disk in a single transaction Syntax TSL_FlushA11 handle lvarchar writeflag integer DEFAULT 1 returns integer handle The table and column name combination that is returned by the TSL_Attach or the TSL_Init function writeflag Optional An integer that represents whether the duplicate elements are allowed for irregular time series and whether logging is reduced You must supply a value for the duplicate element behavior 1 or 5 and can optionally add the value for reduced logging 256 1 Default Duplicate elements are allowed 5 Duplicate elements replace existing elements 1 256 257 Duplicate elements are allowed Logging is reduced See the description of the TSOPEN REDUCED_LOG flag in The flags 5 256 261 Duplicate elements replace existing elements Logging is reduced Usage Use the TSL_FlushAll function to write time series data to disk as part of a loader program You must run the TSL_FlushAll function in a transaction in the context of a loader session that was initialized by the TSL_Init function You run the TSL_FlushAll function to save data that is loaded by the TSL_Put TSL_PutSQL or TSL_PutRow function If you specify that duplicate elements are allowed for irregular time series with the writeflag argument value of 1 the default or 257 1 256 the TSL_FlushAll function inserts data in the same way as the PutElem function You can specify that du
86. follow the Setting the Run Mode segment diagram to include f optionally include d p or a and then optionally include 1 or u 5 Follow the diagram to the terminator Keywords and punctuation Keywords are words that are reserved for statements and all commands except system level commands A keyword in a syntax diagram is shown in uppercase letters When you use a keyword in a command you can write it in uppercase or lowercase letters but you must spell the keyword exactly as it appears in the syntax diagram You must also use any punctuation in your statements and commands exactly as shown in the syntax diagrams IBM Informix TimeSeries Data User s Guide Identifiers and names Variables serve as placeholders for identifiers and names in the syntax diagrams and examples You can replace a variable with an arbitrary name identifier or literal depending on the context Variables are also used to represent complex syntax elements that are expanded in other syntax diagrams A variable in a syntax diagram an example or text is shown in lowercase italic The following syntax diagram uses variables to illustrate the general form of a simple SELECT statement gt gt SELECT column_name FROM table_name gt lt When you write a SELECT statement of this form you replace the variables column_name and table_name with the name of a specific column and table How to provide documentation feedback You are enco
87. following example finds the union of two time series and returns data only for time stamps between 2011 01 03 and 2011 01 05 select Union 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 05 00 00 00 00000 datetime year to fraction 5 sl stock_data s2 stock_data TimeSeries stock_bar_union from daily stocks sl daily_stocks s2 where sl stock_name IBM and s2 stock_name HWP Related reference Time series routines that run in parallel on page 7 7 Apply function on page 7 18 Intersect function on page 7 71 IBM Informix TimeSeries Data User s Guide UpdElem function The UpdElem function updates an existing element in a time series Syntax UpdElem ts TimeSeries row_value row flags integer default 0 returns TimeSeries ts The time series to update row_value The new row data flags Valid values for the flags argument are described in The flags argument falies on page 7 9 lues on page 7 9 The default is 0 Description The element must be a row type of the correct type for the time series beginning with a time stamp If there is no element in the time series with the given time stamp an error is raised Hidden elements cannot be updated The API equivalent of UpdElem is ts_upd_elem Returns A new time series containing the updated element Example The following example updates a single element in an irregular time series update act
88. following statement saves the data to disk with reduced logging and replaces existing elements that have the same timestamps as new elements BEGIN WORK EXECUTE FUNCTION TSL_FlushAl1 ts_data raw_reads 261 COMMIT WORK Related tasks Writing a loader program on page 3 31 Related reference TSL_Flushinfo function The TSL_FlushInfo function returns information about the last flush operation that saved data to disk Syntax TSL_FlushInfo handle lvarchar returns TSL_FlushInfo_r handle The table and column name combination that was included in a TSL_Flush TSL_FlushAll or TSL_Commit function Usage Use the TSL_FlushInfo function to view information about the last time that time series data for the specified table and column name was saved to disk as part of a loader program You can flush data to disk with the TSL_Flush TSL_FlushAll or TSL_Commit function You must run the TSL_FlushInfo function in the context of Chapter 7 Time series SQL routines 7 129 a loader session that was initialized by the TSL_Init function Returns A row type that has the following format TSL_FlushInfo_r containers integer elements integer duplicates integer instance_ids integer commits integer rollbacks integer exceptions integer errors integer J containers The number of containers that were affected by the flush elements The number of elements that were affected by the flush duplicates The numb
89. for time series data 4 15 The following query shows the maximum daily average energy readings for two days SELECT FROM smartmeters_vti_daily_max WHERE t gt 2011 01 01 00 00 00 00000 datetime year to fraction 5 AND t lt 2011 01 02 23 59 59 99999 datetime year to fraction 5 meter_id t value met00000 2011 01 01 00 00 00 00000 30 25000000000 met00000 2011 01 02 00 00 00 00000 29 50000000000 met00001 2011 01 01 00 00 00 00000 29 75000000000 met00001 2011 01 02 00 00 00 00000 31 00000000000 met00002 2011 01 01 00 00 00 00000 31 25000000000 met00002 2011 01 02 00 00 00 00000 28 75000000000 6 row s retrieved Related concepts Related tasks Creating a time series virtual table on page 4 5 The TSVTMode parameter 4 16 The TSVTMode parameter configures the behavior and display of the virtual table for time series data You use the TSVTMode parameter with the TSCreateVirtualTab procedure to control e How data is updated in the base table when you insert data into the virtual table e Whether NULL time series elements are displayed in a virtual table e Whether to fragment the virtual table so that queries can be run on the virtual table in parallel e Whether updates to existing rows in the base table require accurate values for columns that are not part of the primary key e Whether existing values in columns other than the TimeSeries column or the primary key columns can be updated e Whether N
90. four irregular time series instances UNLOAD TO irregular_purge unl SELECT GetContainerName readings InstanceId readings 2011 10 01 23 00 00 00000 varchar 25 FROM sm WHERE meter_id IN met12 met2 met3 met62 The resulting control file includes the ending timestamp instead of the element index number for example sm4 4 2011 10 01 23 45 00 00000 sm4 6 2011 10 01 23 45 00 00000 sm5 5 2011 10 01 23 45 00 00000 sm5 11 2011 10 01 23 45 00 00000 Related concepts Delete time series data on page 3 37 Related tasks Related reference GetIndex function on page 7 55 GetContainerName function on page 7 51 IBM Informix TimeSeries Data User s Guide Instanceld function on page 7 71 TSContainerSetPool procedure The TSContainerSetPool procedure moves the specified container into the specified container pool Syntax TSContainerSetPool container_name varchar 128 1 pool_name varchar 128 1 default null TSContainerSetPool container_name varchar 128 1 container_name The name of the container to move pool_name The name of the container pool in which to move the container Description You can use the TSContainerSetPool procedure to move a container into a container pool move a container from one container pool to another or remove a container from a container pool Containers that created automatically are in the container pool named autopool by default I
91. function on page 7 60 Getinterval function The GetInterval function returns the interval used by a time series Syntax GetInterval ts TimeSeries returns lIvarchar ts The source time series Chapter 7 Time series SQL routines 7 55 Description The calendars used by time series values can record intervals of one second minute hour day week month or year The underlying interval of the calendar describes how often a time series records data Returns An LVARCHAR string that describes the time series interval Example The following query finds all stocks that are not traded on a daily basis select stock_name from daily_stocks where GetInterval stock_data lt gt day Related reference Time series routines that run in parallel on page 7 GetCalendar function on page 7 48 GetClosestElem function on page 7 49 CalendarPattern data type on page 2 1 GetOrigin function on page 7 64 TSCreate function on page 7 116 GetLastElem function 7 56 The GetLastElem function returns the final entry stored in a time series Syntax GetLastElem ts TimeSeries flags integer default 0 returns row S The source time series flags Valid values for the flags argument are described in falues on page 7 9 lues on page 7 9 The default is 0 Description The API equivalent of this function is ts_last_elem Returns A row type value contain
92. function ClipCount function ClipGetCount function Cos function Countlf function DelClip function DelElem function DelRange function DelTrim function Divide function ElemIsHidden function ElemIsNull function Exp function FindHidden funcion GetCalendar function GetCalendarName function GetClosestElem function GetCompression function GetContainerName function GetElem function GetFirstElem function GetFirstElementStamp function GetHertz function GetIndex function GetInterval function GetLastElem function a GetLastElementStamp function GetLastNonNull function GetLastValid function GetMetaData function GetMetaTypeName function GetNelems function GetNextNonNull function GetNextValid function GetNthElem function GetOrigin function GetPacked function GetPreviousValid function GetStamp function GetThreshold function HideElem function HideRange function InsElem function Contents Vv InsSet function Instanceld function Intersect function IsRegular function Lag function Logn function Minus function Mod function Negate function NullCleanup function Plus function Positive function Pow function PutElem function PutElemNoDups function PutNthElem function PutSet function PutTimeSeries function RevealElem function RevealRange function Round function Los SetContainerName function SetOrigin function Sin functio
93. get_char buf_ptr register mi_integer i 0 while STREAM EOF c amp amp isspace c c get_char buf_ptr for STREAM EOF c amp amp isspace c c get_char buf_ptr if i gt token_buf_len 1 char err_buf 128 sprintf err_buf Word is too long on line d buf_ptr gt line_no mi_db_error_raise NULL MI_EXCEPTION err_buf token it c token i 0 return c End of get_token kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name increment_instances_created purpose Increment the instances_created field and update statistics when it crosses a threshold If the statistics for the time series instance table were never updated then the server would not use the index on the instance table and time series opens would be very slow returns nothing notes kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void increment_instances_created loader_context_t context_ptr context_ptr gt instances_createdt if 50 context_ptr gt instances_ created returns IBM Informix TimeSeries Data User s Guide void mi_exec context_ptr gt conn update statistics high for table tsinstancetable id MI_QUERY_BINARY End of increment_instances_created kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name get_sec_e
94. gt conn buf NULL if NULL statement mi_db_error_raise NULL MI_EXCEPTION mi_prepare failed Look at all the entries in the hash table for i context_ptr gt hash_size 1 0 lt i i for entry_ptr context_ptr gt hash i NULL entry_ptr entry_ptr next_entry_ptr if NULL entry_ptr gt tsdesc yield_count if 0 yield_count amp 0x3f if mi_interrupt_check mi_db_error_raise NULL MI_EXCEPTION Load aborted mi_yield Appendix B The TSIncLoad procedure example B 5 values 0 ts_get_ts entry_ptr gt tsdesc values 1 MI_DATUM entry_ptr gt sec_id lengths 0 mi_get_varlen ts_get_ts entry_ptr gt tsdesc if mi_exec_prepared_statement statement MI_BINARY l 2 values lengths int nulls char types 0 NULL l MI_OK mi_db_error_raise NULL MI_EXCEPTION mi_exec_prepared_statement update failed ts_close entry_ptr gt tsdesc next_entry_ptr entry_ptr gt next mi_free entry_ptr context_ptr gt hash i NULL End of update series kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name open_buf purpose Open a file for reading and attach it to a buffer kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void open_buf mi_lvarchar file_name FILE_BUF buf_ptr mi_string file_name_str mi_lvarchar_to_string file_na
95. implement it Consider the size of your active and dormant windows and how you insert data For example the active and dormant windows each have a maximum size of three intervals If you insert data that has a timestamp of seven intervals later than the most recent original data all of your original data is destroyed The following list of rules describe the behavior of the active window and dormant windows if you set both the window sizes to positive numbers The examples that are provided assume that the active window has a monthly interval has a maximum size of 6 partitions and contains partitions for January February and March The dormant window has a maximum size of 3 partitions Rules for querying inserting and modifying data e You cannot query insert update or delete data from a partition in a dormant window IBM Informix TimeSeries Data User s Guide e You can query insert update and delete the data in the partitions that are in the active window For example you can insert update or delete an element for February 28 e If you add data with timestamps that are after the latest partition new partitions are created automatically in the active window For example you insert an element for March 2 A new partition is created for March e You can add data that is earlier than the oldest partition if both of the following conditions are met The data does not have timestamps that are before the origin of the rol
96. in parallel on page 7 7 Chapter 7 Time series SQL routines 7 155 Unary arithmetic functions 7 156 The standard unary functions Abs Acos Asin Atan Cos Exp Logn Negate Positive Round Sin Sqrt and Tan are extended to operate on time series Syntax Function ts TimeSeries returns TimeSeries S The time series to act on Description The resulting time series has the same regularity calendar and sequence of time stamps as the input time series It is derived by applying the function to each element of the input time series If there is a variant of the function that operates directly on the input element type then that variant is applied to each element Otherwise the function is applied to each non time stamp column of the input time series Returns The same type of time series as the input unless it is cast then it returns the type of time series to which it is cast Example The following query converts the daily stock price and volume data into log space create table log_stock stock_id int data TimeSeries stock_bar insert into log stock select stock_id Logn stock_data from daily_stocks Related reference Abs function on page 7 11 Acos function on page 7 1 ApplyUnaryTsOp function on page 7 26 Asin function on page 7 27 Atan function on page 7 2 N Binary arithmetic functions on page 7 2 oo oo ol
97. insert time series data that contains JSON documents with the time series input function as an INSERT statement the database server automatically casts the JSON documents to BSON When you create a virtual table that is based on a time series that contains JSON documents you can insert rows through the virtual table You must explicitly cast the JSON data to the JSON data type and then cast the data to the BSON data pe When you query a virtual table you must cast the contents of the BSON column to JSON to view the data To write a loader program to load JSON data into a time series 1 Do the prerequisite tasks that are necessary for a loader program including creating a TimeSeries subtype creating a table creating a container and instantiating a time series 2 Create an external table and load time series data from a file or a pipe For example use the following format for loading from a file Chapter 3 Create and manage a time series through SQL 3 33 3 34 CREATE EXTERNAL TABLE ext_table_name primary_key_col data_type tstamp_col DATETIME YEAR TO FRACTION 5 json_col JSON USING FORMAT DELIMITED DATAFILES DISK path filename s The ext_table_name is the name that you give to the external table The primary_key_col is the name of the primary key column of the time series table The primary key can consist of multiple columns The tstamp_col is the name of the timestamp column The data_type
98. integer default 0 returns TimeSeries S The time series to act on start The starting time stamp Q nd The ending time stamp flags Valid values for the flags parameter are described in The flags argument alues on page 7 9 The default is 0 Description After an element is hidden reading that element returns NULL and writing it results in an error message It is however possible to use ts_begin_scan to read hidden elements as described in The ts_begin_scan function on page 9 7 IBM Informix TimeSeries Data User s Guide If the time stamp is not a valid timepoint in the time series an error is raised Returns The modified time series Related reference Time series routines that run in parallel on page 7 7 HideElem function on page 7 6 RevealRange function on page 7 83 InsElem function N The InsElem function inserts an element into a time series Syntax InsElem ts TimeSeries row_value row flags integer default 0 returns TimeSeries ts The time series to act on row_value The row type value to be added to the time series flags Valid values for the flags argument are described in falues on page 7 5 lues on page 7 9 The default is 0 Description The element must be a row type of the correct type for the time series beginning with a valid time stamp If there is already an element with that time stamp in the time series the insertion i
99. is the data type of the primary key column The json_col is the name of the column that contains JSON documents The path is the directory for the data file The filename is the name of the data file o Write a loader program that loads the time series data from the external table Run the TSL_PutSQL function with a SELECT statement that returns data from the external table and casts the JSON column to BSON For example use the following format to run the TSL_PutSQL function EXECUTE FUNCTION TSL_PutSQL ts_table_name ts_col SELECT primary_key_col tstamp_col Json_col bson FROM ext_table_name You cannot load JSON data with the TSL_Put or TSL_PutRowfunctions Related concepts JSON time series on page 1 12 Related tasks Example Create and load a time series with JSON data on page 3 10 Writing a loader program on page 3 31 Related reference L CREATE EXTERNAL TABLE Statement SQL Syntax Time series input function on page 3 24 Loading data from a file into a virtual table Data that you insert into a virtual table is written to the underlying base table Therefore you can use the virtual table to load your data that is in a relational format in a file into a TimeSeries column Often it is easier to format your raw data to load a virtual table than to load a TimeSeries column directly especially if you must perform incremental loading You can load data from a virtual table that was created
100. is used timestamp The format of the timestamp is specified by the timestamp_format argument of the TSL_Init function The default format of the timestamp is YYYY mm dd HH MM SS year month day hour minute seconds values Values for the columns in the TimeSeries subtype Separate multiple values with pipe characters and end the list of values with a pipe character Each element must be on a separate line Each line must end with a newline character n If necessary enable newline characters in quoted strings by setting the ALLOW_NEWLINE configuration parameter or running the IFXK_ALLOW_NEWLINE procedure Usage Use the TSL_Put function to load time series data as part of a loader program You must run the TSL_Put function in the context of a loader session that was initialized by the TSL_Init function You can run the TSL_Put function multiple times in the same session The data is stored in the database server until you run the TSL_Flush function to write the data to disk Returns e An integer that indicates the number of records that were inserted e An exception if no records were inserted Examples These examples run in the context of an initialized loader session Example Load an element The following statement loads one element into a table named tsdata that has a primary key column named pkcol EXECUTE FUNCTION TSL_Put tsdata pkcol MX230001 2011 01 01 00 00 00 23 4 56 7 Example 2 Load a file The foll
101. lvarchar irregular integer user_data lvarchar returns lIvarchar Chapter 3 Create and manage a time series through SQL 3 21 PolicyName table_name lvarchar column_name varchar subtype lvarchar irregular integer returns lIvarchar PolicyName The name of the user defined function table_name The table into which the time series data is being inserted column_name The name of the time series column into which data is being inserted subtype The name of the TimeSeries subtype irregular Whether the time series is regular 0 or irregular 1 user_data Optional argument for the name of the container pool Description Write a container pool policy function to select containers in which to insert time series data For example the TSContainerPoolRoundRobin function inserts data into containers in a round robin order You can write a policy function to insert data into the container with the most free space or by using other criteria You can either specify the name of the container pool with the user_data argument or include code for choosing the appropriate container pool in the policy function The container pool must exist before you can insert data into it and at least one container within the container pool must be configured for the same TimeSeries subtype as used by the data being inserted Include the policy function in the container argument of an INSERT statement The policy function returns container names
102. mi_string_to_lvarchar timeseries DAY_INFO_TYPE_NAME typeid mi_typename_to_id context_ptr gt conn typename mi_var_free typename if NULL typeid mi_db_error_raise NULL MI_EXCEPTION Type timeseries DAY_INFO_TYPE_NAME not defined context_ptr gt ts_typeid typeid context_ptr gt table_name table_name context_ptr gt calendar_name mi_lvarchar_to_string calendar_name Appendix B The TSIncLoad procedure example B 3 B 4 context_ptr gt origin origin context_ptr gt threshold threshold context_ptr gt regular regular context_ptr gt container_name mi_lvarchar_to_string container_name context_ptr gt nelems nelems Use the size count of the table as the hash table size sprintf buf select count from s table_name_len mi_get_vardata table _name if MI_OK mi_exec context_ptr gt conn buf MI_QUERY_BINARY mi_db_error_raise NULL MI_EXCEPTION mi_exec failed if MI_ROWS mi_get_result context_ptr gt conn sprintf buf Could not get size of s table table_name_len mi_get_vardata table_name mi_db_error_raise NULL MI_EXCEPTION buf if NULL row mi_next_row context_ptr gt conn amp err mi_db_error_raise NULL MI_EXCEPTION mi_next_row failed if MI_NORMAL_VALUE mi_value row 0 amp retbuf amp retlen dectoint mi_decimal retbuf amp context_ptr gt hash_size context_ptr
103. of January 2011 The second argument is a SUM operator that sums the daily electricity usage by postal code The resulting table contains a row for each postal code Each row has a time series that contains the sum of the electricity that is used by customers who live in that postal code for each day in January 2011 Example Sum of electricity usage for one hour The following example adds all the electricity usage values for the specified time range of one hour in the ts_data table in the stores_demo database for the customers that have a postal code of 94063 SELECT TSRollup raw_reads SUM value start 2010 11 10 11 45 00 end 2010 11 10 12 45 00 FROM ts_data customer customer_ts data WHERE customer zipcode 94063 AND customer_ts_data customer_num customer customer_num AND customer_ts_data loc_esi_id ts_data loc_esi_id Example Maximum value of a field in a BSON column This example is based on the following row type and time series definition The TimeSeries row type contains an INTEGER column that is named v1 and the BSON column contains a field that is also named v1 CREATE ROW TYPE rb timestamp datetime year to fraction 5 data bson v1 int INSERT INTO tj VALUES 1 origin 2011 01 01 00 00 00 00000 calendar ts_15min container kontainer threshold 0 regular v1 99 20 IBM Informix TimeSeries Data User s Guide The following statement creates a TimeSeries data type to hold the results of the aggr
104. of the container for the given time series An empty string is returned if the time series is not located in a container Example The following example gets the name of the container holding the stock with ID 600 Chapter 7 Time series SQL routines 7 51 select GetContainerName activity_data from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel on page 7 7 The ts_get_containername function on page 9 29 GetElem function 7 52 The GetElem function extracts the element for the given time stamp Syntax GetElem ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns row ts The source time series tstamp The time stamp of the entry flags Valid values for the flags argument are described in fralues on page 7 9 lues on page 7 9 The default is 0 Description If the time stamp is for a time that is not part of the calendar or if it falls before the origin of the given time series NULL is returned In some cases GetLastValid GetNextValid or GetPrevious Valid might be more appropriate For a regular time series the data extracted is associated with the time period containing the time stamp For example if the time series is set to hourly 8 00 a m to 5 00 p m the time stamp 3 15 p m would return 3 00 p m and the data associated with that time The API equivalent of this function is ts_elem Ret
105. of the returned element will be returned in the off parameter if off is not NULL Chapter 9 Time series API routines 9 13 Description The search algorithm that ts_closest_elem uses is as follows e If cmp is any of lt or gt the search starts at tstamp e If cmp is lt the search starts at the first element before tstamp e If cmp is gt the search starts at the first element after tstamp The tstamp and cmp parameters are used to determine where to start the search The search continues in the direction indicated by cmp until an element is found that qualifies If no element qualifies then the return value is NULL Important For irregular time series values in an irregular element persist until the next element This means that any of the previous equals operations on an irregular time series will look for lt first If cmp is gt and the lt operations fails the operation then looks forward for the next element otherwise NULL is returned Returns An element that meets the criteria described The ts_col_cnt function The ts_col_cnt function returns the number of columns contained in each element of a time series Syntax mi_integer ts_col_cnt ts_tsdesc tsdesc tsdesc A time series descriptor returned by ts_open Returns The number of columns Related reference The ts_get_all_cols procedure on page 9 27 The ts_col_id function 9 14 The ts_col_id functio
106. on page 7 7 i Apply function on page 7 1 Clip function on page 7 31 ClipCount function on page 7 3 GetIndex function on page 7 5 9 GetNelems function on page 7 6 GetNthElem function on page 7 62 GetStamp function on page 7 66 The ts_nelems function on page 9 41 The Cos function returns the cosine of its argument It is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Exp Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Countlif function The CountIf function counts the number of elements that match the criteria of a simple arithmetic expression Syntax CountIf ts TimeSeries expr lvarchar begin_stamp datetime year to fraction 5 default null end_stamp datetime year to fraction 5 default null returns integer CountIf ts TimeSeries col lvarchar op lvarchar value lvarchar begin_stamp datetime year to fraction 5 default null end_stamp datetime year to fraction 5 default null returns integer 7 38 IBM Informix TimeSeries Data User s Guide CountIf ts col op value TimeSeries lvarchar lvarchar decimal begin_stamp datetime year to fraction 5 default null end_stamp datetime year to fraction 5 default null returns integer Syntax of e
107. pattern how long a timepoint lasts When timepoints are valid The times when elements can Define a calendar pattern be accepted Chapter 1 Informix TimeSeries solution 1 19 1 20 Table 1 8 Properties of a time series continued Time series property Description How to define Data in the time series The time stamp and the Create a TimeSeries data other data that is collected type for each time stamp Time series table The table that contains the Create a table with a TimeSeries data type TimeSeries column column Location Where the time series data is Create one or more stored containers Origin The earliest timestamp of Create a time series any element Regularity Whether the timepoints are Create a regular or an evenly spaced or arbitrarily _ irregular time series spaced Metadata Optional information Create a time series with included with the time series metadata that can be retrieved by routines Hertz The data is recorded at a Create an irregular time regularly spaced subsecond series with a hertz value frequency Compression Optional compression of Create an irregular time time series data that is only series with compression numeric definitions Related concepts L Create time series through the wire listener SON compatibility Related reference Chapter 3 Create and manage a time series through SQL on page 3 1 Ch
108. row The row must be freed by the caller using the mi_row_free procedure Related reference The ts_free_elem procedure on page 9 26 The ts_make_elem function on page 9 38 The ts_make_elem_with_buf function on page 9 39 The ts_row_to_elem function on page 9 50 The ts_end_scan procedure The ts_end_scan procedure ends a scan of a time series It releases resources acquired by ts_begin_scan Upon return no more elements can be retrieved using the given ts_tscan pointer Syntax void ts_end_scan ts_tscan scan scan The scan to be ended Example See the ts_interp function Appendix A The Interp function example on page A 1 for an example of ts_end_scan Related reference The ts_begin_scan function on page 9 7 The ts_first_elem function The ts_first_elem function returns the first element in the time series Syntax ts_tselem ts_first_elem ts_tsdesc tsdesc mi_integer STATUS tsdesc The time series descriptor returned by ts_open STATUS A pointer to an mi_integer value See The ts_hide_elem function on age 9 34 for an explanation of the STATUS argument Chapter 9 Time series API routines 9 25 Description If the time series is regular the first element is always the origin of the time series If the time series is irregular the first element is the one with the earliest time stamp The value must not be freed by the ca
109. running the following functions e DelClip e Delltem e DelRange e DelTrim You cannot delete individual records from an element Regardless of the time range you specify whole elements are deleted You cannot delete elements from a compressed time series To delete compressed data you must delete the time series instance For example you can delete a row in the table that contains packed data Select packed data You can run any time series function that selects data on packed data You do not need to know that the data is packed When packed data is returned by a time series function every packed record appears as an individual element The following example shows a returned value from a hertz time series SELECT GetElem ts_2k 2014 01 01 00 00 02 50000 FROM tstable50 expression ROW 2014 01 01 00 00 02 50000 2 50 5050 Similarly when you create a virtual table that is based on packed data each packed record is a row in the virtual table The following example shows four values in a virtual table that are from the same element in a hertz time series SELECT FROM vt_tstable_2k WHERE tstamp lt 2014 01 01 00 00 00 08000 id 50 tstamp 2014 01 01 00 00 00 00000 tssmallint 2 tsint 50 tsbigint 5050 id 50 tstamp 2014 01 01 00 00 00 02000 tssmallint 2 tsint 50 tsbigint 5050 id 50 tstamp 2014 01 01 00 00 00 04000 tssmallint 2 3 38 IBM Informix TimeSeries Data User s Guide tsint 50 tsbigint 5050
110. s Guide TSSetTraceLevel function The TSSetTraceLevel function sets the trace level of a trace class Syntax TSSetTraceLevel traceLevelSpec lvarchar returns integer traceLevelSpec A character string specifying the trace level for a specific trace class The format is TS_VTI_DEBUG traceLevel Description TSSetTraceLevel sets the trace level of a trace class The trace level determines what information is recorded for a given trace class The trace class for virtual table information is TS_VTI_DEBUG The level to enable tracing for the TS_VTI_DEBUG trace class is 1001 You must set the tracing level to 1001 or greater to enable tracing By default the trace level is below 1001 TSSetTraceLevel calls the mi_set_trace_level DataBlade API function For more information about mi_set_trace_level see the IBM Informix DataBlade API Programmer s Guide Returns Returns 0 on success 1 on failure Example The following example turns tracing on execute function TSSetTraceLevel TS_VTI_DEBUG 1001 Chapter 4 Virtual tables for time series data 4 27 4 28 IBM Informix TimeSeries Data User s Guide Chapter 5 Calendar pattern routines You can use calendar pattern routines to manipulate calendar patterns Calendar pattern routines can perform the following types of operations e Create the intersection of calendar patterns e Create the union of calendar patterns e Alter a calendar pattern Calendar and calendar patt
111. specifies which row in the table to update Related concepts for loading time series data on Related reference BulkLoad function on page 7 30 Data file formats for BulkLoad Two data formats are supported for the file loaded by BulkLoad e Using type constructors e Using tabs Each line of the client file must have all the data for one element The type constructor format follows the row type convention comma separated columns surrounded by parentheses and preceded by the ROW type constructor The first two lines of a typical file look like this row 2011 01 03 00 00 00 00000 1 1 2 2 row 2011 01 04 00 00 00 00000 10 1 20 2 If you include collections in a column within the row data type use a type constructor SET MULTISET or LIST and curly braces surrounding the collection values A row including a set of rows has this format row timestamp set row value value row value value value The tab format separates the values by tabs It is only recommended for single level rows that do not contain collections or row data types The first two lines of a typical file in this format look like this Chapter 3 Create and manage a time series through SQL 3 35 3 36 2011 01 03 00 00 00 00000 1 1 ug 2011 01 04 00 00 00 00000 10 1 20 2 The spaces between entries represent a tab In both formats NULL indicates a null entry The first file format is also produced when you use the onload utility Thi
112. stamp The type of the row is the same as the time series subtype If the time stamp is less than or equal to the time series origin NULL is returned Example The following query gets the first element that precedes time stamp 2011 01 05 in a regular time series select GetPreviousValid stock_data 2011 01 05 00 00 00 00000 from daily_stocks where stock_name IBM Chapter 7 Time series SQL routines 7 65 The following query gets the first element that precedes time stamp 2011 01 05 in an irregular time series select GetPreviousValid activity_data 2011 01 05 00 00 00 00000 from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel on page 7 7 GetElem function on page 7 52 GetLastElem function on page 7 56 GetLastValid function on page 7 58 GetNextValid function on page 7 61 GetNthElem function on page 7 62 The ts_previous_valid function on page 9 45 GetStamp function The GetStamp function returns the time stamp associated with the supplied offset in a time series Offsets can be positive or negative integers Syntax GetStamp ts TimeSeries offset integer returns datetime year to fraction 5 ts The source time series offset The offset Description The equivalent API function is ts_time Returns The time stamp that begins the interval at the specified offset Example
113. the associated element is NULL Syntax TS_ELEM_NULL mi_integer STATUS STATUS The mi_integer argument previously passed to ts_elem ts_nth_elem ts_first_elem or a similar function Description This macro returns a nonzero value if the associated element is NULL This macro is often used in concert with TS ELEM HIDDEN Returns A nonzero value if the element returned by ts_elem ts_nth_elem ts_first_elem or similar function was NULL Related reference The ts_next function on page 9 41 The ts_previous_valid function on page 9 45 The ts_elem_to_row function 9 24 The ts_elem_to_row function converts a time series element into a new row Syntax MI_ROW ts_elem_to_row ts_tsdesc tsdesc ts_tselem elem mi_integer off IBM Informix TimeSeries Data User s Guide tsdesc The descriptor for a time series returned by ts_open elem A time series element It must agree in type with the time series described by tsdesc off If the time series is regular and off is non negative off is used to compute the time stamp value placed in the first column of the returned row If the time series is regular and off is negative column 0 of the resulting row will be taken from column 0 of the elem parameter which will be NULL if the element was created for or extracted from a regular time series If the time series is irregular the off parameter is ignored Returns A
114. the following functions specifying the container name The TSContainerUsage function returns the number of pages that contain time series data the number of elements and the number of pages that are allocated to the container The TSContainerTotalPages function returns the number of pages that are allocated to the container The TSContainerTotalUsed function returns the number of pages that contain time series data The TSContainerPctUsed function returns what percent of the container is full The TSContainerNElems function returns the number of time series data elements that are stored in the container If you specify NULL instead of a container name the functions return information about all containers in the database If you include wildcard characters for the MATCHES operator in the container name the functions return information about Related concepts Related reference TSContainerWindow Table on page 2 1 TSContainerUsageDormantWindowVTI Table on page 2 1 TSContainerUsageActiveWindowVTI Table on page 2 11 g page 7 114 4 page 7 112 TSContainerTotalUsed function on page 7 113 E w Q 5 2 J2 5 oO H n o Oo ae c 5 ch 5 lho 5 SS N 5 WN N e 5 5 5 5 oO 3 z rg o oO wn T 5 ie E D 5 e 5 TSContainerPctUsed function on page 7 10 TSContainerNElems function on page 7 10 J Time series storage on page 1 14 onch
115. the basis of a data mart and accelerate queries on time series data When you accelerate queries you can analyze large amounts of data faster than directly querying the base table or the virtual table Some operations are difficult or impossible in one interface but are easily accomplished in the other For example finding the average value of one of the fields in a time series over a time is easier with a query against a virtual table than by using TimeSeries functions The following query against a virtual table finds the average stock price over a year select avg vol from daily _stocks_no_ts where stock_name IBM and timestamp between datetime 2010 1 1 year to day and datetime 2010 12 31 year to day However aggregating from one calendar to another is easier using the AggregateBy routine Selecting the nth element in a regular time series is easy using the GetNthElem routine but difficult using a virtual table You can insert data into a virtual table that is based on a time series table which automatically updates the underlying base table You can use SELECT and INSERT statements with time series virtual tables You cannot use UPDATE or DELETE statements but you can update a time series element in the base table by inserting a new element for the same time point into the virtual table You can create a virtual table that is based on an expression that is performed on a time series table You can create a virtual table t
116. the database server checks for one or more containers that are appropriately configured for the time series If any matching containers exist the container with the most free space is assigned to the time series If no matching containers exist the database server creates a matching container in each of the dbspaces in which the table is stored For example if a table is not fragmented and is therefore stored in a single dbspace one container is created If a table is fragmented into three dbspaces three containers are created All containers that are created automatically by the database server belong to the default container pool called autopool A container pool is a group of containers You can create one or more container pools in which to include containers You can assign containers to container pools Alternatively you can create your own container pool policy function Strategies for using multiple dbspaces Strategies for storing time series data in multiple dbspaces depend on how the data is distributed how data is inserted into the appropriate container and how you delete old data Time series data is stored in multiple dbspaces in the following situations e The table is fragmented over multiple dbspaces and containers are created automatically in the same dbspaces as the table fragments e You create multiple containers in multiple dbspaces e You create a rolling window container that stores time series data in multiple
117. the whole timeseries scan ts_begin_scan descPtr 0 NULL NULL while scancode ts_next scan amp tselem TS_SCAN_EOS switch scancode case TS_SCAN_ELEM IBM Informix TimeSeries Data User s Guide if this element is not null expand its values noneYet MI_FALSE ts_get_all_cols descPtr tselem void values nulls curElem if minElem 1 save each element for i 1 i lt ncols i lastValues i values i else calculate the average for i 1 i lt ncols i newalues i values i lastValues i 2 0 lastValues i values i values i amp newValues i update the missing elements tselem ts_make elem descPtr void values nulls amp elem for elem minElem elem lt curElem elem ts_put_nth_elem descPtr tselem elem minElem 1 break case TS_SCAN_NULL if noneYet break remember the first null element if minElem 1 minElem curElem break curElemt ts_end_scan scan ts_close descPtr return tsPtr Appendix A The Interp function example A 3 A 4 IBM Informix TimeSeries Data User s Guide Appendix B The TSIncLoad procedure example The TSIncLoad procedure loads data into a database that contains a time series of corporate bond prices The TSIncLoad procedure loads time variant data from a file into a table that contains time series It assumes that the table is alr
118. time series data same timestamps as specified in the query regardless if the actual timestamps are the same You can specify that when you select data from a virtual table by timestamps only rows whose timestamps are exactly equal to the timestamps specified in the query are returned You control the display of data by setting the TSVTMode parameter in the TSCreateVirtualTab procedure or the TSCreateExpressionVirtualTab procedure Related concepts Related reference Insert data through virtual tables 4 4 You can insert data into a virtual table that is based on a time series table You can control whether to allow a new time series duplicate elements for the same timepoints which columns in the base table can be updated and how flexible the INSERT statement can be You can add a time series element to an existing time series through a virtual table You can specify to be able to add a time series element into an existing row that does not have any time series data or to add a row to the base table When you insert an element that has the same timepoint as an existing element the original element is replaced You can specify to allow multiple elements with the same timepoint If the base table has a primary key the primary key is used to find the row to update and updates to the base table do not require accurate values for columns that are not part of the primary key If the base table does not have a primary key all c
119. time series into another time series 7 82 InsSet function 3 36 7 70 Instance ID getting for a time series 7 71 Instanceld function 7 71 Intersect function 7 71 Intersection calendar patterns of 5 1 calendars of 6 1 time series of 7 71 Interval calendar pattern for 1 13 2 1 getting for a time series 7 55 number of between time stamps determining 9 9 Irregular time series 1 7 creating with metadata 7 118 creating with TSCreatelrr 7 118 determining if 9 37 specifying 3 24 IsRegular function 7 73 J jar file 8 3 Java class library 8 1 directory 8 3 sample programs 8 3 Java Developers Kit 8 3 javadoc 8 3 Javadoc 8 3 JDBC 8 1 JDBC 2 0 specification 8 1 Index X 3 JSON time series 1 12 JSON data loading 3 33 L Lag function 7 74 Lagging creating new time series 7 74 LessThan operator 1 26 load command 3 34 Loader program 3 31 Loading data 3 27 from a file 3 35 7 30 JSON 3 33 time series 1 23 using virtual tables 3 34 Loading data from a database 3 29 Loading data from a file 3 28 Loading time series data 3 1 command line loader application 3 29 3 30 Local time converting to 9 32 Logn function 7 74 M Mapping API functions to SQL functions 9 3 Mapping data types Java programs 8 4 Metadata adding to a time series 7 160 9 54 creating a time series with 7 116 7 118 9 18 creating for a time series 3 23 getting from a time series 7 59 9 31 getting the type name of 7 59 getting type ID fro
120. time stamp at the specified number of intervals after the start date Related reference The ts_tstamp_difference function on page 9 51 The ts_tstamp_minus function on page 9 52 The ts_update_metadata function The ts_update_metadata function adds the supplied user defined metadata to the specified time series Syntax ts_timeseries ts_update_metadata ts_timeseries ts mi_lvarchar metadata MI_TYPEID xmetadata_typeid s The time series for which to update metadata metadata The metadata to add to the time series Can be NULL metadata_typeid The type ID of the metadata Description The equivalent SQL function is UpdMetaData Returns A copy of the specified time series updated to contain the supplied metadata or if the metadata argument is NULL a copy of the specified time series with the metadata removed W o mu D o Qa a GetMetaData function on page 7 59 UpdMetaData function on page 7 159 GetMetaTypeName function on page 7 5 TSCreate function on page 7 116 TSCreatelrr function on page 7 118 The ts_create_with_metadata function on page 9 18 The ts_get_metadata function on page 9 31 The ts_upd_elem function 9 54 The ts_upd_elem function updates an element in an existing time series at a given timepoint Syntax ts_timeseries ts_upd_elem ts_tsdesc tsdesc ts_tsele
121. timepoint at which a price was either bid or asked Now consider a timepoint at which a bid was made but no price was asked The union at that timepoint contains the bid price offered at that timepoint along with the most recent asking price If an intersection involves one or more regular time series the resulting time series starts at the latest of the start points of the input time series and ends at the earliest of the end points of the regular input time series If all the input time series are irregular the resulting irregular time series starts at the latest of the start points of the input time series and ends at the latest of the end points If a union involves one or more time series the resulting time series starts at the first of the start points of the input time series and ends at the latest of the end points of the input time series Other than this difference in start and end points and of the resulting calendar there is no difference between union and intersection involving time series Apply also combines multiple time series into a single time series Therefore using Union within Apply is often unnecessary Returns The time series that results from the union Example The following query constructs the union of time series for two different stocks select Union sl stock_data s2 stock_data TimeSeries stock_bar_union from daily_stocks sl daily_stocks s2 where sl stock_name IBM and s2 stock_name HWP The
122. ts_last_elem 9 37 ts_last_valid 9 38 ts_make_elem 9 38 ts_make_elem_with_buf 9 39 ts_make_stamp 9 40 ts_nelems 9 41 ts_next 9 41 ts_next_valid 9 42 ts_nth_elem 9 43 ts_open 9 44 ts_previous_valid 9 45 ts_put_elem 9 46 ts_put_elem_no_dups 9 47 ts_put_last_elem 9 48 ts_put_nth_elem 9 48 ts_put_ts 9 49 ts_reveal_elem 9 50 ts_row_to_elem 9 50 ts_time 9 13 9 51 9 52 9 53 ts_upd_elem 9 54 ts_update_metadata 9 54 SQL calendar AndOp 6 1 CalIndex 6 2 CalRange 6 3 CalStamp 6 4 CalStartDate 6 5 OrOp 6 5 SQL calendar pattern AndOp 5 1 CalPattStartDate 5 2 Collapse 5 3 Routines continued SQL calendar pattern continued Expand 5 4 NotOp 5 4 OrOp 5 5 SQL time series Abs 7 11 Acos 7 11 AggregateBy 7 11 7 15 Apply 7 18 ApplyBinaryTsOp 7 23 ApplyCalendar 7 24 ApplyOpToTsSet 7 25 ApplyUnaryTsOp 7 26 Asin 7 27 Atan 7 27 Atan2 7 27 BulkLoad 3 35 7 30 Clip 7 31 ClipCount 7 35 ClipGetCount 7 37 Cos 7 38 CountIf 7 38 DelClip 7 42 DelElem 7 43 DelRange 7 44 DelTrim 7 45 Divide 7 46 Exp 7 48 GetCalendar 7 48 GetCalendarName 7 49 GetCompression 7 51 GetContainerName 7 51 GetElem 7 52 GetFirstElem 7 53 GetFirstElementStamp 7 54 GetHertz 7 54 GetIndex 7 55 GetInterval 7 55 GetLastElem 7 56 7 57 7 61 GetLastElementStamp 7 57 GetLastValid 7 58 GetMetaData 7 59 GetMetaTypeName 7 59 GetNelems 7 60 GetNextValid 7 61 GetNthElem 7 62 GetOrigin 7 64 GetPacked 7 64 GetPreviousValid 7 65 GetStamp 7 6
123. ts_open N The time series offset or position to read the element from This value must not be less than 0 STATUS A pointer to an mi_integer value that is set on return to indicate whether the element is NULL See The ts_hide_elem function on page 9 34 for a description of STATUS Description The equivalent SQL function is GetNthElem Returns The element at the nth position of the given time series and whether it was NULL This value must not be freed by the caller It is overwritten after two fetch calls Related reference The ts_elem function on page 9 22 The TS_ELEM_HIDDEN macro on page 9 23 The TS_ELEM_NULL macro on page 9 24 The ts_index function on page 9 35 The ts_last_elem function on page 9 3 Chapter 9 Time series API routines 9 43 The ts_open function 9 44 The ts_open function opens a time series Syntax ts_tsdesc ts_open MI_CONNECTION conn ts_timeseries ts MI_TYPEID xtype_id mi_integer flags conn A database connection This argument is unused in the server ts The time series to open type_id The ID for the type of the time series to be opened The ID is generally determined by looking in the MI_FPARAM structure flags Valid values for the flags parameter are defined in tseries h The flags argument values Valid values for the flags argument are defined in the file tseries h the integer value you use for the flags argum
124. used This function runs over a fixed number of elements not over a fixed length of time therefore it might not be appropriate for irregular time series This function is useful only when used within the Apply function Returns A SMALLFLOAT or DOUBLE PRECISION running sum of the last k values Chapter 7 Time series SQL routines 7 151 Example The following function calculates the volume accumulation percentage The columns represented by a through e are high low close volume and number_of_days respectively create function VAP a float b float c float d float e int returns int return cast 100 TSRunningSum d c b a c 0001 a b e 0001 TSRunningSum d e as int end function Related reference TSRunningAve function on page 7 147 TSRunningVar function 7 152 The TSRunningVar function computes the variance of a time series over a running window Syntax TSRunningVar value double precision num_values integer returns double precision TSRunningVar value real num_values integer returns double precision value The first input value to use to calculate the running correlation num_values The number of values to include in the running variance k Description Use the TSRunningVar function within the Apply function This function runs over a fixed number of elements not over a fixed length of time therefore it might not be appropriate for irregu
125. user defined type to be useful The value returned can be freed by the caller with mi_var_free Related reference The ts_update_metadata function on page 9 54 The ts_get_origin function The ts_get_origin function returns the origin of the given time series Syntax mi_datetime ts_get_origin ts_timeseries ts cH S The source time series Description The equivalent SQL function is GetOrigin Chapter 9 Time series API routines 9 31 Returns The origin of the given time series This value must be freed by the caller using mi_free Related reference GetOrigin function on page 7 64 The ts_get_packed function The ts_get_packed function returns whether the specified time series contains packed data Syntax mi_integer ts_get_packed ts_timeseries ts ts The name of the time series Description Use the ts_get_packed function to determine whether a time series stores either hertz data or compressed numeric data in packed elements Returns Returns 1 if the time series contains packed data returns 0 if the time series does not contain packed data Related reference GetPacked function on page 7 64 The ts_get_stamp_fields procedure The ts_get_stamp_fields procedure takes a pointer to an mi_datetime structure and returns the parameters with the year month day hour minute second and microsecond Syntax void ts_get_stamp_fields MI_CONNECTI
126. varchar returns int handle The table and column name combination returned by the TSL_Attach or the TSL_Init function Chapter 7 Time series SQL routines 7 139 Usage Use the TSL_SessionClose function to close the current database session The TSL_SessionClose function does not affect the global context Call the TSL_Flush function to flush data to disk before calling the TSL_SessionClose function Returns e 0 The session was closed e 1 An error occurred Example The following statement closes a database session for the table ts_data and the TimeSeries column raw_reads EXECUTE FUNCTION TSL_SessionClose ts_data raw_reads TSL_SetLogMode function The TSL_SetLogMode function specifies how messages about the loader are logged Syntax TSL_SetLogMode handle lvarchar log_type integer log_level integer logfile lvarchar returns integer TSL_SetLogMode handle lvarchar log_type integer log_level integer returns integer TSL_SetLogMode handle lvarchar log_level integer returns integer handle The table and column name combination returned by the TSL_Attach function log_type Optional The type of message log 0 No message log 1 Log all messages to the specified file 2 Log all messages in a queue for the loader program for retrieval by the TSL_GetLogMessage function 3 Default Log all messages to the server message log log_level The severity of information that i
127. 0 00 00000 2 2 2 3 2011 01 05 00 00 00 00000 2 2 3 3 2011 01 06 00 00 00 00000 2 2 3 Notice the null value for the final column on 2011 01 06 The SELECT query in the following example returns the closing price from the final column and the 4 day moving average from the stocks in the time series select stock_name Apply TSRunningAvg final 4 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 06 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries stock_bar TimeSeries one_real from first_stocks The query returns the following result IBM Informix TimeSeries Data User s Guide stock_name IBM expression origin 2011 01 03 00 00 00 00000 calendar daycal container threshold 20 regular 1 000000000000 1 500000000000 2 000000000000 2 000000000000 The fourth result is the same as the third result because the fourth value in the final column is null Related reference i Apply function on page 7 18 TSAddPrevious function on page 7 90 n TSCmp function on page 7 90 TSDecay function on page 7 122 TSPrevious function on page 7 141 n TSRunningSum function TSRunningCor function TSRunningMed function on page 7 150 TSRunningVar function on page 7 152 TSRunningCor function The TSRunningCor function computes the running correlation of two time series over a runn
128. 0 00000 datetime year to fraction 5 2011 01 08 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries one_real from daily stocks where stock_name IBM Related reference Apply function on page 7 18 TSAddPrevious function on page 7 90 TSDecay function on page 7 122 TSPrevious function on page 7 141 TSRunningAvg function on page 7 147 TSRunningSum function on page 7 151 TSCoINameToList function The TSColNameToList function takes a TimeSeries column and returns a list collection of rows containing the values of one of the columns in the elements of the time series Null elements are not added to the list Chapter 7 Time series SQL routines 7 91 Syntax TSColNameToList ts TimeSeries colname lvarchar returns list S The time series to act on ba colname The column to return Description Because this aggregate function can return rows of any type the return value must be explicitly cast at runtime Returns A list collection of rows Example This query returns a list of all values in the column high select from table select TSColNameToList stock_data high list real not null from daily_stocks Related reference Transpose function on page 7 86 TSColNumToList function TSRowNameToList function on page 7 145 TSRowNumToList function on page 7 146 TSSetToList function on page 7
129. 1 00 15 00 00000 year to fraction 5 1 0 reading TSL_PutSQL function 7 138 The TSL_PutSQL function loads time series data from a table Syntax TSL_PutSQL handle lvarchar statement lvarchar returns integer handle The table and column name combination that is returned by the TSL_Attach or the TSL_Init function statement An SQL statement that selects data from an existing database table The projection clause of the statement must consist of a primary key and the time series data The time series data can be multiple columns or a ROW data type and must be compatible with the data type of the TimeSeries column in the handle The project clause can be one of the following formats primary_key timestamp values primary_key row timestamp values primary_key The primary key column represented in character format Can consist of multiple column names concatenated and separated by a pipe symbol Cast to an LVARCHAR data type if necessary For example if the primary key in the source table consists of two columns named id1 and id2 the primary key column in the projection clause is idl id2 IBM Informix TimeSeries Data User s Guide timestamp The format of the timestamp is specified by the timestamp_format argument of the TSL_Init function The default format of the timestamp is YYYY mm dd HH MM SS year month day hour minute seconds values Values for the columns in the TimeSeries subtype S
130. 1 01 01 00 00 00 00000 ts_30min Once every 30 minutes 2011 01 01 00 00 00 00000 ts_lhour Once an hour 2011 01 01 00 00 00 00000 ts_lday Once a day 2011 01 01 00 00 00 00000 ts_lweek Once a week 2011 01 02 00 00 00 00000 ts_Imonth Once a month 2011 01 01 00 00 00 00000 ts_lyear Once a year 2011 01 01 00 00 00 00000 Related concepts CalendarTable table on page 2 8 Create a time series column To create a time series column Creating a TimeSeries subtype To create a column of type TimeSeries you must first create a row subtype to represent the data held in each element of the time series Subtypes for both regular and irregular time series are created in the same way To create the row subtype use the SQL CREATE ROW TYPE statement and specify that the first field has a DATETIME YEAR TO FRACTION 5 data type The row type must conform to the syntax of the TimeSeries data type Examples The following example creates a TimeSeries subtype called stock_bar create row type stock_bar timestamp datetime year to fraction 5 high real Chapter 3 Create and manage a time series through SQL 3 13 3 14 low real final real vol real J The following example creates a TimeSeries subtype called stock_trade create row type stock_trade timestamp datetime year to fraction 5 price double precision vol double precision trade int broker ints buyer int seller int Related concepts
131. 1 byte with some loss of precision Time series definition You define a compressed time series by running the TSCreatelIrr function with the compression parameter You must include a compression definition for every column in the TimeSeries subtype except the first timestamp column The compression definitions are associated with the columns in the same order If you do not want to compress a particular column include a compression definition of no compression for that column If you specify that none of the columns are compressed only the first timestamp column is compressed Besides the first timestamp column the TimeSeries subtype columns must have only the following data types SMALLINT INTEGER BIGINT SMALLFLOAT and FLOAT The calendar that you specify in the time series definition defines the size of the interval however off periods are not allowed One record per interval is accepted and the timestamp must be on the interval boundary For example if the calendar has an interval of minute a timestamp that has seconds values other than 00 00000 such as 2013 01 01 01 52 15 00000 is rejected Compressed records must be stored in containers However a compressed time series cannot be stored in rolling window containers Compression types You compress data with the following types of compression algorithms Quantization The quantization compression algorithm divides continuous values into discrete grids Each grid repr
132. 1 off 5 on 1 off day Table 1 6 When data collection is on or off Sunday Monday Tuesday Wednesday Thursday Friday Saturday OFF data ON data is ON data is ON data is ON data is ON data is OFF data is allowed allowed allowed allowed allowed is prohibited prohibited Chapter 1 Informix TimeSeries solution 1 13 1 14 Table 1 6 When data collection is on or off continued Sunday Monday Tuesday Wednesday Thursday Friday Saturday OFF data ON data is ON data is ON data is ON data is ON data is OFF data is allowed allowed allowed allowed allowed is prohibited prohibited For regular time series an interval represents the range of time in which one element is allowed You cannot enter more than one element into an interval The value of an element persists for the length of one interval If you query for a value in an off period you receive an error Intervals that are missing data are null You can query for null elements to find which elements are missing For irregular time series the interval is only relevant for designating off periods when data is not allowed The interval size for valid periods does not affect the number of elements that you can insert into a specific time range For example although the smallest interval size is a second you can enter subsecond frequency elements into an irregular time series The value o
133. 16 The ts_copy function makes and returns a copy of the given time series of the type in the type_id argument Syntax ts_timeseries ts_copy MI_CONNECTION conn ts_timeseries ts MI_TYPEID xtypeid conn A valid DataBlade API connection ts The time series to be copied typeid The ID of the row type of the time series to be copied Description Since values returned by mi_value are valid only until the next mi_next_row or mi_query_finish call it is sometimes necessary to use ts_copy to access a time series outside an mi_get_result loop On the client you must use the ts_copy function to make a copy of a time series before you pass the time series as an argument to the mi_exec_prepare statement Returns A copy of the given time series This value must be freed by the user by calling ts_free Related reference IBM Informix TimeSeries Data User s Guide The ts_free procedure on page 9 26 The ts_get_typeid function on page 9 34 The ts_create function The ts_create function creates a time series Syntax ts_timeseries ts_create MI_CONNECTION conn mi_string calname mi_datetime origin mi_integer threshold mi_integer flags MI_TYPEID xtypeid mi_integer nelem mi_string xcontainer ts_timeseries ts_create MI_CONNECTION conn mi_string calname mi_datetime origin mi_integer threshold MI_TYPEID xtypeid mi_integer nelem mi_string container
134. 2 amp amp L token 3 1 token 3 amp amp 0 token 4 End of is_null kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name read_day_data purpose Read in the daily data for one security returns Fills in the timestamp structure the col_data and col_is_null arrays notes Assumes that the col_is_null array is initialized to all TRUE kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void read_day_data loader_context_t context_ptr FILE_BUF buf_ptr mi_string token size_t token_buf_len mi_datetime tstamp_ptr register mi_integer i 0 register mi_integer c ValueDate DATETIME year to day c get_token buf_ptr token token_buf_len if STREAM _EOF c amp amp 0 strlen token n c returns tstamp_ptr gt dt_qual TU_DTENCODE TU_YEAR TU_DAY if is_null token tstamp_ptr gt dt_dec dec_pos DECPOSNULL else if 0 dtcvasc token tstamp_ptr Appendix B The TSIncLoad procedure example B 11 context_ptr gt col_is_nul1 MI_FALSE context_ptr gt col_data 0 MI_DATUM tstamp_ptr else mi_string err_buf 128 sprintf err_buf Illegal date on line d buf_ptr gt line_no mi_db_error_raise NULL MI_MESSAGE err_buf carryover char 1 c get_token buf_ptr token token_buf_len if STREAM_EOF c amp amp
135. 44 9 45 9 46 9 47 9 48 9 48 9 49 9 50 s 9 50 9 51 9 51 9 52 9 53 9 54 9 54 A 1 B 1 C 1 C 1 Notices a cs Privacy policy considerations be RA RM OR oy ee eh ote GID Trademarks 3 4 2 e Bok 4 4 b 6 eb ee ee eR ee eG Ge A Index Contents ix X IBM Informix TimeSeries Data User s Guide Introduction This introduction provides an overview of the information in this publication and describes the conventions it uses About this publication This publication contains information to assist you in using the time series data types and supporting routines These topics discuss the organization of the publication the intended audience and the associated software products that you must have to develop and use time series Types of users This publication is written for the following audience e Developers who write applications to access time series information stored in IBM Informix databases Assumptions about your locale IBM Informix products can support many languages cultures and code sets All the information related to character set collation and representation of numeric data currency date and time that is used by a language within a given territory and encoding is brought together in a single environment called a Global Language Support GLS locale The IBM Informix OLE DB Provider follows the ISO string formats for date time and money as defi
136. 5 default NULL end datetime year to fraction 5 default NULL returns row agge_express A comma separated list of these SQL aggregate operators MIN MAX MEDIAN SUM AVG FIRST LAST or Nth The MIN MAX MEDIAN SUM and AVG expressions can operate only on numeric columns s The time series to be aggregated flags optional See The flags argument values on page 7 16 You cannot use a flags argument value of 1 with this function start optional The date and time at which to start aggregation end optional The date and time at which to end aggregation Description The AggegateRange function converts the input section of a time series to a row of aggregate values Chapter 7 Time series SQL routines 7 15 7 16 The agg_express expressions operate on a column of the input time series which is specified by one of the following column identifiers colname The colname is the name of the column to aggregate in the TimeSeries data type For example if the column name is high the column identifier is high colnumber The colnumber is the position of the column to aggregate in the TimeSeries data type For example if the column number is 1 the column identifier is 1 bson_field_name The bson_field_name is the name of a field in at least one BOON document in the BSON column in the TimeSeries data type For example if the field name is v1 the column identifier is v1 If the BSON field name is
137. 6 GetThreshold 7 67 HideElem 7 67 InsElem 3 36 7 69 InsSet 3 36 7 70 Instanceld 7 71 Intersect 7 71 IsRegular 7 73 Lag 7 74 Logn 7 74 Minus 7 74 Mod 7 75 Negate 7 75 NullCleanup 7 75 Plus 7 77 Positive 7 77 Pow 7 77 Index X 5 Routines continued SQL time series continued PutElem 3 36 7 77 PutElemNoDups 7 79 PutNthElem 7 80 PutSet 3 36 7 81 PutTimeSeries 7 82 RevealElem 7 83 Round 7 84 SetContainerName 7 84 SetOrigin 7 85 Sin 7 85 Sqrt 7 86 Tan 7 86 Times 7 86 TimeSeriesRelease 7 86 Transpose 7 87 TSAddPrevious 7 90 TSCmp 7 91 TSContainerCreate 7 93 TSContainerDestroy 7 98 TSContainerLock 7 99 TSContainerManage 7 99 TSContainerPurge 7 108 TSCreate 7 116 TSCreatelrr 7 118 TSDecay 7 122 TSL_Attach 7 123 TSL_Commit 7 124 TSL_Flush 7 126 TSL_FlushAll 7 128 TSL_FlushInfo 7 129 TSL_FlushStatus 7 131 TSL_GetKeyContainer 7 131 TSL_GetLogMessage 7 132 TSL_Init 7 133 TSL_Put 7 135 TSL_PutRow 7 137 TSL_PutSQL 7 138 TSL_SessionClose 7 139 TSL_SetLogMode 7 140 TSL_Shutdown 7 141 TSPrevious 7 142 TSRollup 7 142 TSRunningAvg 7 92 7 148 TSRunningCor 7 149 TSRunningMed 7 150 TSRunningSum 7 151 TSRunningVar 7 152 TSSetToList 7 153 TSToXML 7 154 Union 7 157 UpdElem 7 159 UpdMetaData 7 160 UpdSet 7 160 WithinC 7 161 WithinR 7 161 Row converting to an element 9 50 RSS and time series 1 24 Running average computing 7 148 Running sum computing 7 151 S Scanning beginning for a time series 9 7
138. 66 CalStamp function 6 4 The CalStamp function returns the time stamp at a specified number of calendar intervals after a specified time stamp Syntax CalStamp cal_name lvarchar tstamp datetime year to fraction 5 num_stamps i nteger returns datetime year to fraction 5 cal_name The name of the calendar tstamp The input time stamp num_stamps The number of calendar intervals after the input time stamp Cannot be negative Description The equivalent API function is ts_cal_stamp Returns The time stamp that represents the specified offset Example The following example returns the time stamp that is two intervals after 2011 01 03 IBM Informix TimeSeries Data User s Guide execute function CalStamp daycal 2011 01 03 00 00 00 00000 2 Related reference Time series routines that run in parallel on page 7 7 The ts_cal_range function on page 9 10 The ts_cal_range_index function on page 9 11 The ts_cal_stamp function on page 9 11 CalStartDate function The CalStartDate function takes a calendar name and returns a DATETIME value that contains the start date of that calendar Syntax CalStartDate cal_name lvarchar returns datetime year to fraction 5 cal_name The name of the calendar Description The equivalent API function is ts_cal_startdate Returns The start date of the specified calendar Example The following example retur
139. 7 31 Clip some elements ClipCount function on page 7 35 Output values in XML format TSToXML function on page 7 154 Extract a period that includes the specified time or starts or ends at the specified time WithinC and WithinR functions on page 7 161 Apply a new calendar to a time series Apply a calendar ApplyCalendar function on page 7 24 Chapter 7 Time series SQL routines 7 3 Table 7 1 Time series SQL routines by task type continued Task type Description Create and load a time Load data from a client file BulkLoad function on page 7 30 series Create a regular empty time series a regular populated time series or a regular time series with metadata TSCreate function on page 7 116 Create an irregular empty time series an irregular populated time series or an irregular time series with metadata TSCreatelrr function on page 7 118 Load time series data Initialize a loader session TSL_Init function on page 7 133 through a loader program Open a database session TSL_Attach function on page 7 123 Load data into the database server TSL_Put function on page 7 135 Load data from a table into the database server TSL_PutSQL function on page 7 138 Load a row of data into the database server TSL_PutRow function on page 7 137 Flush loaded data to disk in a single transaction TSL_FlushAll function on page 7 128 Flush loaded data to disk in mult
140. 7 35 n GetElem function on page 7 52 The ts_next_valid function on page 9 42 GetMetaData function The GetMetaData function returns the user defined metadata from the given time series Syntax create function GetMetaData ts TimeSeries returns TimeSeriesMeta ts The time series to retrieve metadata from Returns This function returns the user defined metadata contained in the given time series If the time series does not contain user defined metadata then NULL is returned This return value must be cast to the source data type to be useful Related tasks Related reference TSCreatelrr function on page 7 118 The ts_create_with_metadata function on page 9 18 The ts_get_metadata function on page 9 31 The ts_update_metadata function on page 9 54 GetMetaTypeName function The GetMetaTypeName function returns the type name of the user defined metadata type stored in the given time series Syntax create function GetMetaTypeName ts TimeSeries returns lIvarchar Chapter 7 Time series SQL routines 7 59 ts The time series to retrieve the metadata from Returns The type name of the user defined metadata type stored in the given time series Returns NULL if the given time series does not have user defined metadata Related reference GetNelems function 7 60 The GetNelems function returns the number of elements stored
141. 8 s 7 139 TSL_SetLogMode function TSL_Shutdown procedure TSPrevious function TSRollup function TSRowNameToList function TSRowNumTolList function TSRowToList function TSRunningAvg function TSRunningCor function TSRunningMed function TSRunningSum function TSRunning Var function TSSetToList function TSToXML function Unary arithmetic functions Union function UpdElem function UpdMetaData function UpdSet function WithinC and WithinR functions Chapter 8 Time series Java class library Java class files and sample programs Preparing the server for Java classes Mapping time series data types i Querying time series data with the IfmxTimeSeries object Obtaining the time series Java class version Chapter 9 Time series API routines Differences in using functions on the server and on the client Data structures for the time series API The ts_timeseries structure The ts_tscan structure The ts_tsdesc structure The ts_tselem structure Time series API routines sorted by task The ts_begin_scan function The ts_cal_index function The ts_cal_pattstartdate function The ts_cal_range function The ts_cal_range_index function The ts_cal_stamp function The ts_cal_startdate function The ts_close function The ts_closest_elem function The ts_col_cnt function The ts_col_id function The ts_colinfo_name function The ts_colinfo_number function The ts_copy function The ts_crea
142. 9 47 Append an element regular time series The ts_put_last_elem function on page p15 Put an element in a place specified by an offset regular time series The ts_put_nth_elem function on page 9 48 Modify metadata Update metadata The ts_update_metadata function on page 9 54 Convert between an index Convert time stamp to index regular time series The ts_index function on page and a time stamp 9 35 Convert index to time stamp regular time series The ts_time function on page b 51 9 4 IBM Informix TimeSeries Data User s Guide Table 9 1 Time series API routines sorted by task continued Task type Description Transform an element Create an element from an array of values and nulls The ts_make_elem function on page 9 38 and The ts_make_elem_with_buf function on page 9 39 Convert an MI_ROW value to an element The ts_row_to_elem function on page 9 50 Convert an element to an MI_ROW value The ts_elem_to_row function on page 9 24 Free memory from a time series element created by ts_make_elem or ts_row_to_elem The ts_free_elem procedure on page 9 26 Extract column data from an Get a column from an element by name The ts_colinfo_name function on page element 9 15 Get a column from an element by number The ts_colinfo_number function o Pull columns from an element into values and nulls arra
143. 93 0 11 1 74 0 02 1 03 0 45 1 85 0 44 00 00 00 00000 IBM Informix TimeSeries Data User s Guide Table 1 5 How hertz data is packed in time series elements continued Values for Values for Values for Values for Values for Timestamp 00000 20000 40000 60000 80000 of element seconds seconds seconds seconds seconds 2014 01 01 2 00 0 02 1 99 0 05 1 53 0 03 NULL NULL 1 76 0 01 00 00 01 00000 Data requirements Hertz records must have timestamps that conform to the hertz subsecond boundaries within approximately 0 3 tolerance Only one record per subsecond boundary is allowed For example if the hertz value is 5 then there are 5 valid timestamps per second For the first second after midnight the following timestamps are valid 00 00 00 00000 e 00 00 00 20000 e 00 00 00 40000 00 00 00 60000 e 00 00 00 80000 The tolerance means that a timestamp has a margin of error of plus or minus 0 00003 seconds For example for the second record a subsecond value of 0 20003 is accepted and stored as 0 2 but a subsecond value of 0 20004 is rejected If the value of a subsecond boundary has more than 5 significant digits the value is rounded down For example the 127th subsecond value for a hertz of 255 has the value 0 498039216 which is rounded down to 0 49803 NULL values are allowed The size of the data for 1 second cannot exceed 32769 bytes Time series definition The T
144. 991 ts_data_h WHERE id 50 1 row s updated UPDATE tstable_h SET ts InsElem ts row 2014 01 01 00 00 00 02000 2 202 99992 ts_data_h WHERE id 50 1 row s updated UPDATE tstable_h SET ts InsElem ts row 2014 01 01 00 00 00 04000 3 203 99993 ts_data_h WHERE id 50 1 row s updated UPDATE tstable_h SET ts InsElem ts row 2014 01 01 00 00 00 06000 4 204 99994 ts_data_h WHERE id 50 1 row s updated UPDATE tstable_h SET ts InsElem ts row 2014 01 01 00 00 00 08000 5 205 99995 ts_data_h WHERE id 50 1 row s updated COMMIT Data committed You must insert records in chronological order Create a virtual table that is named vt_tstable_h_h that is based on the hertz time series by running the following SQL statement IBM Informix TimeSeries Data User s Guide EXECUTE PROCEDURE TSCreateVirtualTab vt_tstable_h_h tstable_h 4096 ts Query the virtual table to view the hertz data by running the following SQL statement SELECT FROM vt_tstable_h_h id 50 tstamp 2014 01 01 00 00 00 00000 tssmallint 1 tsint 201 tsbigint 99991 id 50 tstamp 2014 01 01 00 00 00 02000 tssmallint 2 tsint 202 tsbigint 99992 id 50 tstamp 2014 01 01 00 00 00 04000 tssmallint 3 tsint 203 tsbigint 99993 id 50 tstamp 2014 01 01 00 00 00 06000 tssmallint 4 tsint 204 tsbigint 99994 id 50 tstamp 2014 01 01 00 00 00 08000 tssmallint 5 tsint 205 tsbigint 99
145. 995 5 row s retrieved Related concepts Related reference TSContainerCreate procedure on page 7 9 TSCreatelrr function on page 7 118 InsElem function on page 7 69 TSCreateVirtualTab procedure on page 4 Example Create and load a compressed time series This example shows how to create load and query a time series that stores compressed numeric data In this example you create a time series that contains numeric data that is compressed You can create the time series table in any database that you choose The following table lists the time series properties that are used in this example Chapter 3 Create and manage a time series through SQL 3 7 Table 3 3 Time series properties used in this example Time series property Definition Calendar interval Second When elements are valid Always Data in the time series The following data e Timestamp Three columns with INTEGER values Time series table The following columns e An ID column of type INTEGER e A TimeSeries data type column Origin 2014 01 01 00 00 00 00000 Regularity Irregular Compression Three different compression definitions Metadata No metadata Where to store the data In a container that you create How to load the data The InsElem function How to access the data A virtual table To create load and view a compressed time series 1 Create a TimeSeries subtype that is name
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147. Creating a TimeSeries subtype on page 3 13 1 6 IBM Informix TimeSeries Data User s Guide Regular time series A regular time series stores data for regularly spaced timepoints A regular time series is appropriate for applications that record entries at predictable timepoints such as electricity power usage data that is recorded by smart meters every 15 minutes Regular time series are stored very efficiently because instead of storing the full time stamp of an element regular time series store the offset of the element The offset of an element is the relative position of the element to the origin of the time series The time stamp for an element is computed from its offset For example suppose you have a calendar that has an interval duration of a day The first element or origin is 2011 01 02 The offset for the origin is 0 The offset for the sixth element is 5 The time stamp for the sixth element is the origin plus 5 days 2011 01 07 The following table shows the relationship between elements and offset Table 1 4 Offsets for a daily time series Day of the month 1 2 3 4 5 6 7 Offset 0 1 2 3 4 5 You can use TimeSeries SQL routines to convert between a time stamp and an offset Some TimeSeries SQL routines require offset values as arguments For example you can return the 100th element in a time series with the GetNthElem function In a regular time series each interval between element
148. DelClip function Delete all elements and free space in a time series instance for a specified time range in any part of a time series DelRange function on page 7 44 Delete all elements and free space in a time series instance for a specified time range at the end of a time series DelTrim function on page 7 45 Free empty pages in a specified time range or throughout the time series instance NullCleanup function on page 7 75 Delete elements through a specified timestamp from one or more containers for one or more time series instances TSContainerPurge function on page 7 108 Modify metadata Update user defined metadata UpdMetaData function on page 7 159 Make elements visible or invisible to a scan Make an element invisible HideElem function on page 7 67 Make a range of elements invisible HideRange function on page 7 68 Make an element visible Make a range of elements visible RevealRange function on page 7 83 Check for null or hidden elements Determine whether an element is hidden ElemIsHidden function on page 7 47 Determine whether an element is null ElemIsNull function on page 7 47 Extract and use part of a time series Extract a period between two time stamps or corresponding to a set of values and run an expression or function on every entry Apply function on page 7 18 Extract data between two timepoints Clip function on page
149. E DISTINCT TYPE statement to define a new data type of type TimeSeries A TimeSeries column can contain either regular or irregular time series you specify regular or irregular when you create the time series The maximum allowable size for a single time series element is 32704 bytes You cannot put an index on a column of type TimeSeries After loading data into a TimeSeries column run the following commands update statistics high for table tsinstancetable update statistics high for table tsinstancetable id This improves performance for any subsequent load insert and delete operations Related concepts Hertz time series on page 1 8 Compressed numeric time series on page 1 10 TimeSeries data type technical overview on page 1 5 Related tasks Creating a TimeSeries subtype on page 3 13 Related reference Create the database table on page 3 14 Chapter 2 Data types and system tables 2 7 Create a time series on page 3 22 Time series return types When a routine returns a time series calendar information is preserved and if possible threshold and container information is preserved Some functions that return a TimeSeries subtype require that the return value be cast to a particular time series type For functions like Clip WithinC and WithinR the return type is always the same as the type of the argument time series and no cast is required However for other functions
150. EXECUTE FUNCTION statement You cannot run the TSL_Init function in a SELECT statement Returns e A session handle that consists of a table name and a TimeSeries column name e An exception or NULL if the session was not initialized Example The following statement initializes a loader session for a table named ts_data and a TimeSeries column named raw_reads EXECUTE FUNCTION TSL_Init ts_data raw_reads Y m d H M S tmp rejects log NULL The input data uses a single column timestamp format The rejected records are saved in a file Related tasks Writing a loader program on page 3 31 Related reference L DBTIME environment variable SQL Reference TSL_Put function The TSL_Put function loads time series data Syntax TSL_Put handle lvarchar elementlist lvarchar returns integer TSL_Put handle lvarchar elementlist CLOB returns integer handle The table and column name combination that is returned by the TSL_Attach or the TSL_Init function elementlist A buffer that contains the time series data to load That maximum size of a buffer is 32 KB Can be a file as a CLOB data type Chapter 7 Time series SQL routines 7 135 7 136 The element list must have the following format primary_key timestamp values primary_key One or more column values that comprise the primary key which are separated by pipe characters If this field is empty the value from the previous line
151. FRACTION 5 value Related reference Time series routines that run in parallel on page 7 7 GetLastNonNull function The GetLastNonNull function returns the last non null element on or before the date you specify Syntax GetLastNonNul ts TimeSeries tstamp datetime year to fraction 5 column_name lvarchar default null flags integer default 0 returns row ts The source time series tstamp The time stamp for the element you specify column_name optional If you specify a column using the column_name argument the GetLastNonNull function returns the last non null element on or before the specified date that has a non null value in the specified column Chapter 7 Time series SQL routines 7 57 If you do not specify the column_name argument the GetLastNonNull function returns the last non null element on or before the date It is possible that all the columns except the time stamp could be NULL flags Valid values for the ith argument are described in The flags argument alues on page 7 9 The default is 0 Description There are no null elements in an irregular time series Therefore when you use the GetLastNonNull function on an irregular time series always specify a column name If you use the GetLastNonNull function on an irregular time series without specifying a column name its effect is equivalent to that of the GetLastValid function Returns A non null element of the time series Relat
152. I_TYPEID typeid mi_integer nelem mi_string container mi_lvarchar metadata MI_TYPEID metadata_typeid mi_integer hertz ts_timeseries ts_create_with_metadata MI CONNECTION conn mi_string calname mi_datetime origin mi_integer threshold mi_integer flags MI_TYPEID typeid mi_integer nelem mi_string container mi_lvarchar metadata MI_TYPEID metadata_typeid mi_string compression conn A valid DataBlade API connection calname The name of the calendar origin The time series origin threshold The time series threshold If the time series stores this number or more elements it is stored in a container If the time series holds fewer than this number it is stored directly in the row that contains it threshold must be greater than or equal to 0 and less than 256 flags 0 Regular time series 1 Irregular time series 3 Hertz time series 5 Compressed time series typeid The ID of the new type for the time series to be created nelems The initial number of elements to create space for in the time series This space is reclaimed if not used after the time series is written into the database container The container for holding the time series This parameter can be NULL if the time series can fit in a row or is not going to be assigned to a table metadata The metadata to be put into the time series For more information about metadata see Creating a time series with metadata NULL metadata_typeid on
153. MI EXCEPTION mi_exec failed rc mi_get_result context_ptr gt conn if MI NO MORE RESULTS rc sprintf buf Security d line d not in s sec_id line_no mi_get_varlen context_ptr gt table_name mi_get_vardata context_ptr gt table_name mi_db_error_raise NULL MI MESSAGE buf Mi_db_error_raise returns after raising messages of type MI_MESSAGE Appendix B The TSIncLoad procedure example B 9 else if MI_ROWS rc mi_db_error_raise NULL MI_EXCEPTION mi_get_result failed else mi_integer err 0 MI_ROW row mi_next_row context_ptr gt conn amp err MI_DATUM ts_datum 0 mi_integer retlen 0 Save the row so that the time series column will not be erased when the query is finished if NULL row amp amp MI_NORMAL_VALUE mi_value row 0 amp ts_datum amp retlen if NULL row mi_save_set_insert context_ptr gt save_set row mi_db_error_raise NULL MI_EXCEPTION mi_save_set_insert failed if NULL row rc mi_value row 0 amp ts_datum amp retlen else rc MI_ERROR if MI_NORMAL_VALUE rc amp amp MI_NULL_VALUE rc if 0 err sprintf buf Look up of security ID d in s failed sec_id mi_get_varlen context_ptr gt table_name mi_get_vardata context_ptr gt table_name mi_db_error_raise NULL MI_EXCEPTION buf else sprintf buf Security d line d not in s sec_i
154. NULL The element must be freed by the caller using the ts_free_elem procedure IBM Informix TimeSeries Data User s Guide Related reference The ts_elem_to_row function on page 9 24 The ts_free_elem procedure on page 9 26 The ts_make_elem function on page 9 38 The ts_time function The ts_time function converts a regular time series offset to a time stamp Syntax mi_datetime ts_time ts_tsdesc xtsdesc mi_integer N ts_desc The time series descriptor returned by ts_open for the source time series Z The offset to convert Negative values are allowed Description For example for a daily time series that starts on Monday January 1 with a five day a week pattern starting on Monday this function returns Monday January 1 when the argument is set to 0 Tuesday January 2 when the argument is set to 1 Monday January 8 when the argument is 5 and so on The equivalent SQL function is GetStamp Returns The time stamp corresponding to the offset This value must be freed by the user with mi_free Related reference The ts_cal_range function on page 9 10 The ts_cal_range_index function on page 9 11 The ts_index function on page 9 35 The ts_tstamp_difference function The ts_tstamp_difference function subtracts one date from another and returns the number of complete intervals between the two dates Syntax mi_integer ts_tstamp_difference m
155. OAT to administer database objects that are related to a time series The following illustration shows how the Informix TimeSeries solution and the related products interact Chapter 1 Informix TimeSeries solution 1 3 Data loading IBM Data Studio Informix TimeSeries Plug in for Data Studio Informix database server Administration IBM OpenAdmin Tool for Informix Informix TimeSeries Plug in for OAT TimeSeries data types TimeSeries SQL routines TimeSeries API routines TimeSeries Java classes Application Figure 1 1 Informix TimeSeries architecture Time series concepts A time series as implemented by the Informix TimeSeries solution contains information about how the data is stored in the table column and information about valid data intervals and where the data is stored on disk Understand the following concepts when you create a time series TimeSeries data type The data type that defines the structure for the time series data Element A set of time series data for one timestamp For example a value of 1 01 for the time stamp 2011 1 1 00 45 00 00000 is an element for customer 1001 Packed element An element in which records for multiple timestamps are stored to save storage space Packed elements can store hertz data that is recorded at a subsecond frequency and compressed numeric data Timepoint The time period for a single element for example 15 minutes In some industries a timepoint is referre
156. ON conn mi_datetime dt mi_integer xyear mi_integer month mi_integer xday mi_integer xhour mi_integer minute mi_integer second mi_integer ms conn A valid DataBlade API connection dt The time stamp to convert year Pointer to year integer that the procedure sets Can be NULL month Pointer to month integer that the procedure sets Can be NULL day Pointer to day integer that the procedure sets Can be NULL hour Pointer to hour integer that the procedure sets Can be NULL 9 32 IBM Informix TimeSeries Data User s Guide minute Pointer to minute integer that the procedure sets Can be NULL second Pointer to second integer that the procedure sets Can be NULL ms Pointer to microsecond integer that the procedure sets Can be NULL Returns On return the non null year month day hour minute second and microsecond are set to the time that corresponds to the time indicated by the dt argument Related reference The ts_get_threshold function The ts_get_threshold function returns the threshold of the specified time series Syntax mi_integer ts_get_threshold ts_timeseries ts ts The source time series Description The equivalent SQL function is GetThreshold Returns The threshold of the given time series Related reference GetThreshold function on page 7 67 The ts_create function on page 9 17 The ts_get_ts function The ts_get_ts function returns a pointer to t
157. ON TSL_Put ts_data raw_reads 4727354321000111 KWH P 2010 11 10 00 00 00 00000 0 092 4727354321000111 KWH P 2010 11 10 00 15 00 00000 0 084 a le 2010 11 10 00 30 00 00000 0 09 4727354321000111 KWH P 2010 11 10 00 45 00 00000 0 085 e 2010 11 10 01 00 00 00000 0 088 4727354321000111 KWH P 2010 11 10 01 15 00 00000 0 088 4727354321000111 KWH P 2010 11 10 01 30 00 00000 0 085 a ala 2010 11 10 01 45 00 00000 net 4727354321046021 KWH P 2010 11 10 00 00 00 00000 0 041 lane 2010 11 10 00 15 00 00000 0 041 4727354321046021 KWH P 2010 11 10 00 30 00 00000 0 04 ekea 2010 11 10 00 45 00 00000 i 4727354321046021 KWH P 2010 11 10 01 00 00 00000 0 041 4727354321046021 KWH P 2010 11 10 01 15 00 00000 0 041 a 2010 11 10 01 30 00 00000 0 055 4727354321046021 KWH P 2010 11 10 01 45 00 00000 0 073 a ne 2010 11 10 02 00 00 00000 0 071 4727354321046021 KWH P 2010 11 10 02 15 00 00000 0 068 4727354321046021 KWH P 2010 11 10 02 30 00 00000 0 07 Js EXECUTE FUNCTION TSL_Put ts_data raw_reads 4727354321090954 KWH P 2010 11 10 00 00 00 00000 0 026 ae laa bane 11 10 00 15 00 00000 0 oa 4727354321090954 KWH P 2010 11 10 00 30 00 00000 0 062 4727354321090954 KWH P 2010 11 10 00 45 00 00000 0 092 reece en el 2010 11 10 01 00 00 00000 0 016 4727354321090954 KWH P 2010 11 10 01 15 00 00000 0 043 ae leh 2010 11 10 01 30 00 00000 0 038 2 4727354321090954 KWH P 2010 11 10 01 45 00 00000 0 037 3 32 IBM Informix TimeSeries D
158. Op call Calendar cal2 Calendar returns Calendar call The first calendar cal2 The second calendar Description This function returns a calendar that has every interval on that was on in both calendars the rest of the intervals are off The resultant calendar takes the later of the two start dates and the later of the two pattern start dates If the two calendars have different size interval units the resultant calendar has the smaller of the two intervals Returns A calendar that is the result of two other calendars that are combined with the AND operator Copyright IBM Corp 2006 2014 6 1 Example The following AndOp statement returns the intersection of an hourly calendar with a daily calendar that has a different start date select c_calendar from CalendarTable where c_name hourcal c_calendar startdate 2011 01 01 00 00 00 pattstart 2011 01 02 00 00 00 pattern 32 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour select c_calendar from CalendarTable where c_name daycal c_calendar startdate 2011 04 01 00 00 00 pattstart 2011 04 03 00 00 00 pattern 1 off 5 on 1 off day select AndOp cl c_calendar c2 c_calendar from CalendarTable cl CalendarTable c2 where cl c_name daycal and c2 c_name hourcal The query returns the following results expression startdate 2011 04 01 00 00 00 pattstart 2011 04 03 00 00 00 pattern 32 off 9 on 15 off 9 on 15
159. R The number of partitions in the active window dormantwindowsize INTEGER The number of partitions in the dormant window windowspaces LVARCHAR 4096 The list of dbspaces in which partitions are stored tstamp DATETIME YEAR TO FRACTION 5 The starting timestamp of the time range for the partition partition tsPartitionDesc_t The description of the partition container_size INTEGER The first extent size of the partitions container_grow INTEGER The next extent size of the partitions windowcontrol INTEGER A flag that indicates how active partitions are handled when they are manually or automatically detached e 0 Default Partitions are not automatically destroyed if they cannot fit into the dormant window e 1 Partitions are automatically destroyed if they cannot fit into the dormant window Related concepts Monitor containers on page 3 18 TSInstanceTable table The TSInstanceTable table contains one row for each large time series no matter how many times it is referenced Time series smaller than the threshold you specify when you create them are stored directly in a column and do not appear in the TSInstanceTable table 2 12 IBM Informix TimeSeries Data User s Guide Table 2 9 The columns in the TSInstanceTable table Column name Data type Description id BIGSERIAL The serial number of the time series and the primary key for this table You can use the Insta
160. SContainerSetPool procedure 3 Insert data into the time series by including the TSContainerPoolRoundRobin function with the container pool name or by including your user defined container pool policy function in the container argument Example This example uses a TimeSeries subtype named smartmeter_row that is in a column named rawreadings which is in a table named smartmeters Suppose you want to store the data for the time series in three containers in a container pool you created IBM Informix TimeSeries Data User s Guide The following statements create three containers for the TimeSeries subtype smartmeter_row EXECUTE PROCEDURE TSContainerCreate ctn_sm0 tsspaceO smartmeter_row 0 0 EXECUTE PROCEDURE TSContainerCreate ctn_sml tsspacel smartmeter_row 0 0 EXECUTE PROCEDURE TSContainerCreate ctn_sm2 tsspace2 smartmeter_row 0 0 The following statements add the containers to a container pool named readings EXECUTE PROCEDURE TSContainerSetPool ctn_sm0 readings EXECUTE PROCEDURE TSContainerSetPool ctn_sml readings EXECUTE PROCEDURE TSContainerSetPool ctn_sm2 readings The following statement inserts time series data into the column rawreadings The TSContainerPoolRoundRobin function that specifies the container pool named readings is used instead of a container name in the container argument INSERT INTO smartmeters meter_id rawreadings VALUES met 0001 origin 2006 01 01 00 00 00
161. TY READ The DIRTY READ isolation level ensures that a parallel query on a virtual table succeeds if the data in the base table is being modified at the same time Run parallel queries The following SQL statement selects the number of values the sum of the values and the average of the values for a 15 minute period on February 7 2011 for each customer who lives in the state of Arizona SELECT state COUNT value num_values SUM value sum AVG value average FROM ts_data_v v customer_ts_ data 1 customer c WHERE v loc_esi_id loc_esi_id AND 1 customer_num c customer_num AND state AZ AND v tstamp BETWEEN 2011 02 07 23 30 00 00000 AND 2011 02 07 23 45 00 00000 GROUP BY state value ORDER BY state value The result of the query displays the information for the three qualifying customers state count sum avg AZ 1 0 011 0 011 AZ 1 0 012 0 012 AZ 2 0 050 0 025 The explain output file parallelvtq out describes the query plan for this query The information Parallel fragments ALL for the second and third scans indicates that the scans ran in parallel and accessed all fragments Estimated Cost 14 Estimated of Rows Returned 1 Maximum Threads 5 Temporary Files Required For Order By Group By 1 informix c SEQUENTIAL SCAN Filters informix c state AZ 2 informix INDEX PATH 1 Index Keys customer_num Parallel fragments ALL Lower Index Filter informix customer_num informix c customer
162. Table 2 4 The CalendarTable table Column name Data type Description c_version INTEGER Internal The version of the calendar Currently only version 0 is supported IBM Informix TimeSeries Data User s Guide Table 2 4 The CalendarTable table continued Column name Data type Description c_refcount INTEGER Internal Counts the number of in row time series that reference this calendar The c_refcount column is maintained by the Assign and Destroy functions on TimeSeries Rules attached to this table allow updates only if c_refcount is 0 this restriction ensures that referential integrity is not violated c_name VARCHAR 255 The name of the calendar c_calendar Calendar The Calendar type for the calendar c_id SERIAL Internal The serial number of the calendar Related concepts Calendar data type on page 2 4 Related tasks Defining a calendar on page 3 12 Related reference Predefined calendars on page 3 13 TSContainerTable table The TSContainerTable table has one row for each container Table 2 5 The columns in the TSContainerTable table Column name Date type name VARCHAR 128 1 Description The name of the container of the time series The primary key of the table Containers that are created automatically are named autopoolunnnnnnn where n is a positive integer eight digits long with leading Zeros subtype VARCHAR 128 1 The name of the tim
163. Table 3 1 Time series properties used in this example Time series property Definition Timepoint size 15 minutes When timepoints are valid Every 15 minutes with no invalid times Data in the time series The following data Timestamp e A decimal value that represents electricity usage Time series table The following columns A meter ID column of type BIGINT e A TimeSeries data type column Origin All meter IDs have an origin of 2010 11 10 00 00 00 00000 Regularity Regular Copyright IBM Corp 2006 2014 3 1 Table 3 1 Time series properties used in this example continued Time series property Definition Metadata No metadata Amount of storage space Approximately 1 MB 8640 timepoints for each of the 28 rows Where to store the data In an automatically created container in the same dbspace as the stores_demo database which is in the root dbspace by default How to load the data The TimeSeries plug in How to access the data A virtual table Creating a TimeSeries data type and table You create a TimeSeries data type with columns for the timestamp and the electricity usage value Then you create a table that has primary key column for the meter ID and a TimeSeries column To create the TimeSeries data type and table 1 Create a TimeSeries subtype named my_meter_data in the stores_demo database by running the following SQL statement CREATE ROW TYPE my_me
164. The ts_ins_elem function i The TS_IS_INCONTAINER macro The TS_IS_IRREGULAR macro The ts_last_elem function The ts_last_valid function The ts_make_elem function The ts_make_elem_with_buf Aanction The ts_make_stamp function The ts_nelems function The ts_next function The ts_next_valid function The ts_nth_elem function The ts_open function The ts_previous_valid function The ts_put_elem function The ts_put_elem_no_dups fuseton The ts_put_last_elem function The ts_put_nth_elem function The ts_put_ts function The ts_reveal_elem function The ts_row_to_elem function The ts_time function The ts_tstamp_difference function The ts_tstamp_minus function The ts_tstamp_plus function The ts_update_metadata function The ts_upd_elem function Appendix A The Interp function example Appendix B The TSIncLoad procedure example Appendix C Accessibility a Accessibility features for IBM Informix products Accessibility features Keyboard navigation Related accessibility information IBM and accessibility Dotted decimal syntax diagrams viii IBM Informix TimeSeries Data User s Guide 9 25 9 25 9 26 9 26 9 27 lt 9 27 9 28 9 28 9 29 0 9229 9 30 9 30 lt 9 31 9 31 9 32 9 32 9 33 9 33 9 34 9 34 9 35 9 36 9 36 9 37 9537 9 38 9 38 9 39 9 40 9 41 9 41 9 42 9 43 9
165. ULL values can be used in the INSERT statement for columns other than the primary key columns e Whether hidden time series elements are displayed in a virtual table e Whether data selected by time stamp exactly matches the specified timestamps or includes the last rows that are equal to or earlier than the specified timestamps e Whether to quickly insert elements into existing time series instances that are stored in containers e Whether to reduce how many log records are generated when you insert data You use the TSVTMode parameter with the TSCreateExpression VirtualTab procedure to control e Whether NULL time series elements are displayed in a virtual table e Whether hidden time series elements are displayed in a virtual table e Whether data selected by time stamp exactly matches the specified timestamps or includes the last rows that are equal to or earlier than the specified timestamps IBM Informix TimeSeries Data User s Guide The default value of the TSVTMode parameter 0 sets the default behavior of the virtual table Each of the other values of the TSVTMode parameter reverses one aspect of the default behavior You can set the TSVTMode parameter to a combination of the values You can specify values for the TSVTMode parameter in the following formats e Numeric Sum the numeric values of the flags that you want to include For example if you want both null and hidden elements to be displayed in the virtual table se
166. WH 2010 11 10 00 15 00 00000 0 041 4727354321090954 KWH 2010 11 10 00 00 00 00000 0 026 4727354321090954 KWH 2010 11 10 00 15 00 00000 0 035 When you create a virtual table that is based on the results of an expression that is performed on a time series you specify the TimeSeries subtype appropriate for containing the results of the expression The virtual table is based on the specified TimeSeries data type and the other columns from the base table The display of data in virtual tables When you create virtual tables based on time series you can customize how time series data is shown in the virtual tables and in the results of queries on the virtual tables Null elements in a time series are not included in the virtual table If a base table has a null element at a specific timepoint the virtual table has no entry for that timepoint You can specify that null elements appear in the virtual table Hidden elements are not included in the virtual table A hidden element is marked as invisible in the base table You can specify if hidden elements appear as null values in the virtual table or if their values are visible in the virtual table When you select data from a virtual table by timestamps the rows whose timestamps are closest to being equal to or earlier than the timestamps specified in the query are returned If the time series is irregular the returned rows show the 4 3 Chapter 4 Virtual tables for
167. _num NESTED LOOP JOIN Chapter 4 Virtual tables for time series data 4 11 4 12 3 informix v VTI SCAN Parallel fragments ALL VTI Filters informix lessthanorequal informix v tstamp datetime 2011 02 07 23 45 00 00000 year to fraction 5 AND informix greaterthanorequal informix v tstamp datetime 2011 02 07 23 30 00 00000 year to fraction 5 Filters informix v loc_esi_id informix 1 loc_esi_id NESTED LOOP JOIN The following SQL statement selects the number of values the sum of the values and the average of the values after 11 30 PM on February 7 2011 for each customer SELECT state COUNT value num_values SUM value sum AVG value average FROM ts_data_v v customer_ts_ data 1 customer c WHERE v loc_esi_id loc_esi_id AND 1 customer_num c customer_num AND greaterthan v tstamp 2011 02 07 23 30 00 00000 GROUP BY state value ORDER BY state value The result of the query displays the information for all the qualifying customers state count sum avg AZ 1 0 011 0 011 AZ 1 0 025 0 025 CA 1 0 020 0 02 CA 1 0 023 0 023 CA 1 0 056 0 056 CA 1 0 065 0 065 CA 1 0 071 0 071 CA 1 0 073 0 073 CA 1 0 088 0 088 CA 1 0 162 0 162 CA 1 0 204 0 204 CA 1 0 226 0 226 CA 1 0 246 0 246 CA 1 0 277 0 277 CA 1 0 323 0 323 CA 1 0 340 0 34 CA 1 0 415 0 415 CA 1 0 469 0 469 CA 1 0 670 0 67 CA 1 1 412 1 412 co 1 0 118 0 118 DE 1 0 256 0 256 FL 1 3 470 3 47 MA 1 4 388 4 388 NJ 2 0 374 0 187 NY
168. a TimeSeries one_real from daily_stocks create table daily_avg of type simple series insert into daily_avg select 1 stock_id ApplyBinaryTSOp plus 1 data h data 2 from daily_low 1 daily_high h where 1 stock_id h stock_id create table log_high of type simple series insert into log high select stock_id ApplyUnaryTsOp logn data from daily_avg Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 IBM Informix TimeSeries Data User s Guide Asin function Atan function The Asin function returns the arc sine of its argument It is one of the unary arithmetic functions that work on time series The others are Abs Acos Atan Cos Exp Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 The Atan function returns the arc tangent of its argument It is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Cos Exp Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Atan2 function The Atan2 function returns the arc tangent of corresponding elements from two time series It is one of the binary arithmetic functions that work on time s
169. a of an arithmetic expression function on page 7 38 Select individual elements Get the element associated with the specified time stamyfGetElem function on page 7 52 Get the element at or before a time stamp GetLastValid function on page 7 58 Get the element after a time stamp Get the element before a time stamp Get the element at a specified position Get the first element Get the timestamp of the first element Get the last element Get the timestamp of the last element Get the last non null element GetLastNonNull function on page 7 57 Get the next non null element GetNextNonNull function on page 7 61 7 2 IBM Informix TimeSeries Data User s Guide Table 7 1 Time series SQL routines by task type continued Task type Description Modify elements or a set of elements Add or update a single element PutElem function on page 7 77 Add or update a single element PutElemNoDups function on page 7 79 Add or update a single element at a specified offset regular only PutNthElem function on page 7 80 Add or update an entire set Insert an element Insert a set Update an element Update a set Put se element of one time series into another time series PutTimeSeries function on Delete elements Delete an element at the specified timepoint DelElem function on page 7 43 Delete all elements in a time series instance for a specified time range
170. a pipe character between the date portion and the time portion The pipe character indicates that the date and the time are in separate fields For example you can use a two field format Y Yom d H M S reject_file Optional The path and file name for storing records that were not applied For example records with an incorrect number of fields or a formatting error are not applied By default only the number of rejected records is recorded where_clause Optional Additional predicate to append to the WHERE clause that is generated by loader The predicate can identify a subset of time series data to load Because the WHERE clause limits the entire loader session to a subset of IBM Informix TimeSeries Data User s Guide time series data use caution when you include the WHERE clause The predicate must start with the keyword AND For example AND meter_id A100 Usage Use the TSL_Init function to initialize a session as part of a loader program The TSL_Init function must be the first function that you run in your loader program The TSL_Init function creates a global context for the loader and opens a database session You can open additional database sessions by running the TSL_Attach function You can close a database session by running the TSL_SessionClose function The global context remains in effect until you run the TSL_Shutdown function or restart the database server You must run the TSL_Init function within an
171. a scan should work on the returned set begin_stamp Pointer to mi_datetime to specify where the scan should start If begin_stamp is NULL the scan starts at the beginning of the time series The begin_stamp_ argument acts much like the begin_stamp argument to the Clip function unless TS_SCAN_EXACT_START is set end_stamp Pointer to mi_datetime to specify where the scan should stop If end_stamp is NULL the scan stops at the end of the time series When end_stamp is set the scan stops after the data at end_stamp is returned Chapter 9 Time series API routines 9 7 9 8 Description This function starts a scan of a time series between two time stamps The scan descriptor is closed by calling ts_end_scan The flags argument values The flags argument determines how a scan should work on the returned set Valid values for the flags argument are defined in tseries h The integer value is the sum of the desired values from the following table Flag Value Meaning TS_SCAN_HIDDEN 512 Return hidden elements marked by 0x200 ts_hide_elem TS_SCAN_EXACT_START 256 Return NULL if the begin point is earlier 0x100 than the time series origin Normally a scan does not start before the time series origin TS_SCAN_EXACT_END 128 Return NULL until the end timepoint of the 0x80 scan is reached even if the end timepoint is beyond the end of the time series TS_SCAN_NO_NULLS 32 0x20 Affects the way elements a
172. abase The following tables in the stores_demo database contain time series data based on electricity usage data collected by smart meters Customer_ts_data Contains customer numbers and location references ts_data_location Contains spatial location information ts_data Contains location references and smart meter time series data ts_data_v Contains the data in the ts_data table in relational format as a virtual table The time series data is stored in a container named raw_container Chapter 1 Informix TimeSeries solution 1 27 Related concepts L dbaccessdemo command Create demonstration databases DB Access Guide Related reference L The stores_demo Database Map SQL Reference Setting up stock data examples 1 28 Set up the stock data examples Use the sample queries and sample programs to practice handling time series data To install the sample database schema and to compile the sample C programs 1 Set the following environment variables e MACHINE machine e PROD_VERSION version e USERFUNCDIR INFORMIXDIR extend TimeSeries version examples The version is the internal TimeSeries version number for example 5 00 UC1 Check the installation directory for the correct version number The machine is the name of the operating system as listed in the INFORMIXDIR incl dbdk makeinc file for example 1inux 2 Run the examples_setup sql command from the INFORMIXDIR extend TimeSeries version examp
173. ags argument determines how data points in the off periods of calendars are handled during aggregation and how hidden elements are managed It can have the following values 0 default Data in off periods is aggregated with the next output period IBM Informix TimeSeries Data User s Guide 2 Indicates that the scan runs with the TS_SCAN_HIDDEN flag set hidden elements are returned 4 Indicates that the scan runs with the TS_SCAN_SKIP_HIDDEN flag set hidden elements are not returned Returns A single element row Example Stock data The following example produces an average of the values in the column high of the time series called stock_data First the example creates the row type elemval as a cast for the result create row type elemval tstamp datetime year to fraction 5 high double precision select AggregateRange avg high stock_data elemval from daily_stocks Examples BSON data This example is based on the following row type and time series definition The TimeSeries row type contains an INTEGER column that is named v1 and the BSON column contains a field that is also named v1 CREATE ROW TYPE rb timestamp datetime year to fraction 5 data bson vl int INSERT INTO tj VALUES 1 origin 2011 01 01 00 00 00 00000 calendar ts_15min container kontainer threshold 0 regular v1 99 20 The following statement creates a TimeSeries data type for the results CREATE ROW TYPE outrow timest
174. al_elem ts_tsdesc tsdesc mi_datetime tstamp ts_desc The time series descriptor returned by ts_open for the source time series tstamp The time stamp to be made visible to the scan Description The equivalent SQL function is RevealElem Returns The modified time series No error is raised if there is no element at the given time stamp Related reference HideElem function on page 7 67 The ts_hide_elem function on page 9 34 RevealElem function on page 7 83 The ts_row_to_elem function 9 50 The ts_row_to_elem function converts an MI_ROW structure into a new ts_tselem structure The new element does not overwrite elements returned by any other time series API function Syntax ts_tselem ts_row_to_elem ts_tsdesc tsdesc MI_ROW row mi_integer offset_ptr tsdesc The descriptor for a time series returned by ts_open row A pointer to an MI_ROW structure The row must have the same type as the subtype of the time series offset_ptr If the time series is regular the offset of the element in the time series is returned in offset_ptr In this case column 0 the time stamp column must not be NULL If the time series is irregular 1 is returned in offset_ptr The offset_ptr argument can be NULL In this case calendar computations are avoided and column 0 can be NULL Returns An element and its offset If the time series is regular column 0 the time stamp column of the element is
175. al_range function returns a list of time stamps containing all valid timepoints in a calendar between two time stamps inclusive of the specified time stamps Syntax MI_COLLECTION ts_cal_range MI_CONNECTION conn mi_string cal_name mi_datetime begin_stamp mi_datetime end_stamp conn A valid DataBlade API connection cal_name The name of the calendar begin_stamp The begin point of the range It must not be earlier than the calendar origin end_stamp The end point of the range Description This function is useful if you must print out the time stamps of a series of regular time series elements If the range is known getting an array of all of the time stamps is more efficient than using ts_time on each element The caller is responsible for freeing the result of this function The equivalent SQL function is CalRange Returns A list of time stamps Related reference CalIndex function on page 6 2 The ts_cal_range_index function on page 9 11 9 10 IBM Informix TimeSeries Data User s Guide The ts_cal_range_index function The ts_cal_range_index function returns a list containing a specified number of time stamps starting at a given time stamp Syntax MI_COLLECTION ts_cal_range_index MI CONNECTION conn mi_string cal_name mi_datetime xbegin_stamp mi_integer num_stamps conn A valid DataBlade API connection cal_name The name of the calendar
176. alendarName function GetCalendarName function The GetCalendarName function returns the name of the calendar used by the given time series Syntax GetCalendarName ts TimeSeries returns lIvarchar ts The time series from which to obtain a calendar name Returns The name of the calendar used by the time series Example The following example returns the name of the calendar used by the time series for IBM select GetCalendarName stock_data from daily_stocks where stock_name IBM expression daycal Related reference Time series routines that run in parallel on page 7 7 GetCalendar function on page 7 48 GetClosestElem function The GetClosestElem function returns the first element that is non null and closest to the given time stamp Optionally you can specify which column within the time series element must be non null to satisfy the search Chapter 7 Time series SQL routines 7 49 Syntax GetClosestElem ts TimeSeries tstamp datetime year to fraction 5 cmp lvarchar column_list lvarchar default NULL flags integer default 0 returns ROW ts The time series to act on tstamp The time stamp to start searching from cmp A comparison operator used with tstamp to determine where to start the search Valid values for cmp are lt lt gt and gt column_list To search for an element with one or more columns non null specify a list of column names separated by a vertica
177. amp datetime year to fraction 5 x int If a column and a BSON field have the same name the column takes precedence The following statement returns the maximum value from the v1 INTEGER column SELECT AggregateRange max v1 ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 FROM tj The following two equivalent statements return the maximum value from the v1 field in the data BSON column which is column 1 in the TimeSeries row type SELECT AggregateRange max data vl ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 outrow FROM tj SELECT AggregateRange max 1 v1 ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 outrow FROM tj Chapter 7 Time series SQL routines 7 17 The aggregated time series that is returned has the TimeSeries data type outrow If you do not cast the result to a row type that has the appropriate columns for the results the statement fails Related reference Time series routines that run in parallel on page 7 7 AgeregateBy function on page 7 11 Apply function Apply function 7 18 The Apply function queries one or more time series and applies a user specified SQL expression or function to the selected tim
178. an Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 SetContainerName function 7 84 The SetContainerName function sets the container name for a time series even if the time series already has a container name Syntax SetContainerName ts TimeSeries container_name varchar 128 1 returns TimeSeries ts The time series to act on container_name The name of the container Description If a time series is stored in a container you can use the SetContainerName function to copy the time series from one container to another The time series is copied to the container that you specify with the container_name parameter The original time series is unaffected Returns A time series with a new container set Example The following example creates the container tsirr and sets a time series to it execute procedure TSContainerCreate tsirr rootdbs stock_bar_union 0 0 select SetContainerName Union s1 stock_data s2 stock_data TimeSeries stock_bar_union tsirr from daily_stocks sl daily_stocks s2 where sl stock_name IBM and s2 stock_name AAQ2 Related reference IBM Informix TimeSeries Data User s Guide TSContainerCreate procedure on page 7 93 SetOrigin function The SetOrigin function moves the origin of a time series back in time Syntax SetOrigin ts TimeSeries origin datetime year to fracti
179. and a compatible row operation on one or more type or number Apply Binany isp function on page 72a time series Apply a unary function to a time series ApplyUnaryTsOp function on page 7 26 Apply another function to a set of time series ApplyOpToTsSet function on page 7 25 Aggregate time series Aggregate values in a time series from a single row AggregateBy function on page 7 11 values Aggregate values in a time series from a single row over a specified time range AggregateRange function on page 7 15 Aggregate time series values across multiple rows TSRollup function on page 7 142 Create a time series that Create a time series that lags the source time series by a specified offset regular only lags Lag function on page 7 73 Reset the origin Reset the origin SetOrigin function on page 7 85 7 6 IBM Informix TimeSeries Data User s Guide Table 7 1 Time series SQL routines by task type continued Task type Description Manage containers Create a container TSContainerCreate procedure on page 7 93 Delete a container TSContainerDestroy procedure on page 7 98 Set the container name SetContainerName function on page 7 84 Specify the container pool for inserting data into a time series TSContainerPoolRoundRobin function on page 7 107 Add a container into a container pool or remove a container from a container pool TSContainerSetPool
180. and the server version of the time series API tsbeapi The client and server interfaces do not behave in exactly the same way when updating a time series This is because tsbeapi operates directly on a time series whereas tsfeapi operates on a private copy of a time series This means that updates through tsbeapi are always reflected in the database while updates through tsfeapi are not For changes made by tsfeapi to become permanent the client must write the updated time series back into the database Copyright IBM Corp 2006 2014 9 1 Another difference between the two interfaces is in how time series are passed as arguments to the mi_exec_prepare_statement function On the server no special steps are required a time series can be passed as is to this function However on the client you must make a copy of the time series with ts_copy and pass the copy as an argument to the mi_exec_prepare_statement function There can be a difference in efficiency between the client and the server APIs Functions built to run on the server take advantage of the underlying paging mechanism For instance if a function must scan across 20 years worth of data the tsbeapi interface keeps only a few pages in memory at any one time For a client program to do this the entire time series must be brought over to the client and kept in memory Depending on the size of the time series and the memory available this might cause swapping problems on the cli
181. anup function The NullCleanup function frees any pages in a time series instance that contain only null elements in a range or for the whole time series instance Syntax Nul1Cleanup ts TimeSeries begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries Nul1Cleanup ts TimeSeries flags integer default 0 returns TimeSeries Nul1Cleanup ts TimeSeries begin_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries Nul1Cleanup ts TimeSeries NULL end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries ts The time series to act on begin_stamp The begin point of the range Chapter 7 Time series SQL routines 7 75 7 76 end_stamp The end point of the range flags Valid values for the flags argument are described in The flags argument falues on page 7 5 lues on page 7 9 The default is 0 Description Use the NullCleanup function to free empty pages from a time series instance in one of the following time ranges e A specified begin point and a specified end point e The whole time series instance e A specified begin point and the end of the time series instance e The beginning of the time series instance and a specified end point If the begin point of the range falls before the origin of the time series instance an error is raised Returns A time series with all the empty pa
182. apter 8 Time series Java class library on page 8 1 Planning for data storage Time series data is stored in containers within dbspaces You can use the default containers that are created in the same dbspace as the table into which you are loading data or you can create containers in separate dbspaces You can estimate how much storage space you need Rolling window containers have specific storage requirements If you are loading high volumes of data you can improve the performance of loading the data if you use multiple dbspaces Similarly if you have multiple TimeSeries columns in the same table consider creating multiple containers that store data in different dbspaces Estimate the amount of storage space you need by using the following formulas Regular and irregular time series space in bytes primary_key index_entry timestamp ts_columns elements 4 elements table_rows B tree_size Hertz time series IBM Informix TimeSeries Data User s Guide space in bytes primary_key index_entry timestamp ts_columns records 4 11 elements table_rows B tree_size Compressed time series space in bytes primary_key index_entry timestamp ts_columns records 4 11 elements table_rows B tree_size B tree_size The size of the B tree index not including the index entries Typically the B tree index is approximately 2 of the size of the data for a regular tim
183. arallel You must run the TSL_Attach function in the context of a loader session that was initialized by the TSL_Init function Returns e A session handle that consists of a table name and a TimeSeries column name e An exception or NULL if the session was not initialized Example The following statement opens a database session for the table ts_data and the TimeSeries column raw_reads EXECUTE FUNCTION TSL_Attach ts_data raw_reads TSL_Commit function 7 124 The TSL_Commit function flushes data for all containers to disk in multiple transactions Syntax TSL_Commi t handle lvarchar writeflag integer DEFAULT 1 commit_interval integer DEFAULT NULL returns integer handle The table and column name combination that is returned by the TSL_Attach or the TSL_Init function writeflag Optional An integer that represents whether the duplicate elements are allowed for irregular time series and whether logging is reduced You must supply a value for the duplicate element behavior 1 or 5 and can optionally add the value for reduced logging 256 1 Default Duplicate elements are allowed IBM Informix TimeSeries Data User s Guide 5 Duplicate elements replace existing elements 1 256 257 Duplicate elements are allowed Logging is reduced See the description of the TSOPEN REDUCED_LOG flag in The flags argument values on page 7 9 5 256 261 Duplicate elements replace existing elements Log
184. arameter The examples use a base table with columns for the account number the meter identifier the time series data the meter owner and the meter address The account number and meter identifier columns are the primary key The TimeSeries column contains columns for the time stamp energy and temperature The owner column has a NOT NULL constraint Each of the virtual tables that is created in the examples has the following initial one row that represents one times series element acct_no 6546 meter_id 234 t 2011 01 01 00 00 00 00000 energy 33070 temperature 13 0000000000 owner John address 5 Nowhere Place 1 row s retrieved Default behavior The following statement creates a virtual table named smartmeters_vti_nn with the TSVTMode parameter set to 0 EXECUTE PROCEDURE TSCreateVirtualTab smartmeters_vti_nn smartmeters origin 2011 01 01 00 00 00 00000 calendar ts_15min regular threshold 20 container 0 The following statement inserts a new row into the virtual table and a new element in the time series in the base table INSERT INTO smartmeters_vti_nn acct_no meter_id t energy temperature owner address VALUES 6546 234 2011 01 01 00 45 00 00000 datetime year to fraction 5 3234 12 00 Ignored_value Ignored_value 1 row s inserted The values of the primary key columns match the original row The values of the owner and address columns are ignored they are not used to identify th
185. ard SQL statements on virtual tables to select and insert data TimeSeries data type technical overview The TimeSeries data type defines the structure for the time series data within a single column in the database The TimeSeries data type is a constructor data type that groups together a collection of ROW data type in time stamp order A ROW data type consists of a group of named columns The rows in a TimeSeries data type called elements each represent one or more data values for a specific time stamp The elements are ordered by time stamp The time stamp column must be the first column in the TimeSeries ROW data type and must be of type DATETIME YEAR TO FRACTION S5 even if your data does not have a scale of five fractional seconds Time stamps must be unique multiple entries in a single TimeSeries cannot have the same time stamp The following illustration shows a representation of the structure of a TimeSeries data type that is similar to the one used in the stores_demo database Chapter 1 Informix TimeSeries solution 1 5 Database table ts_data Time series data for ID 1000111 location_id reads tstamp DATETIME YEAR TO value DECIMAL 1000111 TimeSeries meter_data FRACTION S 1046021 TimeSeries meter_data 2010 11 10 00 00 00 00000 0 092 1090954 TimeSeries meter_data 2010 11 10 00 15 00 00000 0 082 2010 11 10 00 30 00 00000 0 090 Time series data
186. arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Minus function The Minus function subtracts one time series from another The Minus function is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Mod Plus Pow and Times Related reference 7 74 IBM Informix TimeSeries Data User s Guide Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 27 Mod function The Mod function computes the modulus or remainder of a division of one time series by another The Mod function is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Plus Pow and Times Related reference Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 2 Negate function The Negate function negates a time series The Negate function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 NullCle
187. artitions that are being moved to the dormant window are handled depends on the number of partitions that are being moved and the window_control parameter If the number of active partitions that are being detached is less than or equal to the dormant window size then the active partitions are moved to the dormant window If the number of dormant partitions exceeds the dormant window size the older partitions are destroyed If the number of partitions that must be detached is greater than the dormant windows size you specify the action taken by setting the window_control parameter To allow as any dormant partitions as necessary to be destroyed but prevent any active partitions from being destroyed set the window_control parameter to 0 An operation fails if it requires active partitions to be destroyed To allow as many as necessary active and dormant partitions to be destroyed set the window_control parameter to 1 To prevent active partitions from being destroyed and limit the number of dormant partitions that can be destroyed set the window control flag to 2 and set the destroy_count parameter to a positive integer To allow a specific number of active and dormant partitions to be destroyed set the window_control parameter to 3 and the destroy_count parameter to a positive integer You can manually destroy partitions in the dormant window Any partitions that are older than the date that you specify are destroyed
188. asure the wind speed air temperature air pressure and precipitation for each weather sensor every 15 minutes Packed data is loaded faster than data that is not packed because packed data generates fewer log records Each element that you insert into the database generates one or two log records depending on whether logging is reduced Packed data requires fewer elements When you create a virtual table or run a query packed data is indistinguishable from time series data that is not packed Each subelement that is shown in a virtual table has a row Each subelement that is returned by a query is shown as an individual element Hertz time series If your time series data is recorded at a regular subsecond frequency you can define a hertz time series to store the data efficiently In each element a hertz time series stores an 11 byte timestamp for the first record and a 1 byte timestamp for each of the other records An element contains records for one second For example if data is recorded 5 times a second then each element contains 5 sets of values If the TimeSeries subtype contains a timestamp column and two other columns the following table shows how the values are stored in each element Table 1 5 How hertz data is packed in time series elements Values for Values for Values for Values for Values for Timestamp 00000 20000 40000 60000 80000 of element seconds seconds seconds seconds seconds 2014 01 01 1 01 0 25 1
189. ata TimeSeries stock_trade from activity_load_tab where stock_id 600 Example Create a populated time series The following example creates a populated irregular time series insert into activity_stocks select 1234 TSCreateIrr daycal 2011 01 03 00 00 00 00000 datetime year to fraction 5 20 0 NULL set_data timeseries stock_trade from activity_load_tab Example Create a compressed time series When you create a compressed time series you need to know the structure of the TimeSeries subtype and characteristics about the data in each column The following statement creates a TimeSeries row type that has a timestamp column and six other columns of numeric data CREATE ROW TYPE irregular_t tstamp DATETIME YEAR TO FRACTION 5 keyl smallint key2 int key3 bigint key4 smallfloat key5 float key6 int s The following statement creates a compressed time series instance in the table tstable BEGIN INSERT INTO tstable VALUES 1 TSCreateIrr ts_lsec 2013 01 01 00 00 00 00000 container_2k n q 1 1 100 1s 0 10 1b 0 10 q1s 2 0 15 100 100000 qib 2 0 25 100 100000 COMMIT The columns in the irregular_t row type are compressed in the following ways e The key1 column is not compressed e The key2 column is compressed by the quantization compression type with a compression size of 1 byte a lower bound of 1 and an upper bound of 100 Chapter 7 Time series SQL routines 7 121
190. ata 3 37 deleting elements 7 108 Time series continued example of creating and loading 3 1 example of creating for compressed data 3 7 example of creating for hertz data 3 5 example of creating for JSON data 3 10 hardware and software requirements 1 25 Informix Warehouse Accelerator 1 24 insert through virtual tables 4 4 loading data 1 23 loading from a database 3 29 loading from a file 3 28 loading methods 3 27 loading with the plug in 3 27 overview 1 1 planning 1 19 properties 1 19 solution architecture 1 3 SQL restrictions for 1 26 SQL routines 3 31 storage planning 1 20 Time series data command line loader application 3 29 3 30 Time series functions TSContainerNElems 7 104 TSContainerPctUsed 7 106 TSContainerTotalPages 7 112 TSContainerTotalUsed 7 113 TSContainerUsage 7 114 TSCreateExpressionVirtualTab 4 13 Time Series Java class version 8 5 Timepoint 1 4 Timepoints arbitrary 1 7 Times function 7 86 TimeSeries database requirements 1 26 loading data 3 29 3 30 3 31 replicating 1 24 TimeSeries data type 1 5 2 6 3 1 TimeSeries plug in 1 3 3 1 3 27 3 28 3 29 command line loader application 3 29 3 30 TimeSeries routines parallelizable 7 7 TimeSeriesMeta distinct type 3 23 TimeSeriesRelease function 7 86 Timestamps calendar getting froma 9 11 comparing 9 21 current getting 9 21 getting after intervals 6 4 GMT converting to 9 40 local time converting to 9 32 offset associated with 1 7 offset converting fro
191. ata User s Guide 4727354321090954 KWH P 2010 11 10 02 15 00 00000 0 023 4727354321090954 KWH P 2010 11 10 02 30 00 00000 0 03 ee aero eal ea 02 45 00 00000 0 05 4727354321090954 KWH P 2010 11 10 03 00 00 00000 0 048 4727354321090954 KWH P 2010 11 10 03 15 00 00000 0 047 s begin EXECUTE FUNCTION TSL_FlushAll ts_data raw_reads commit peered tend ee rae 02 00 00 00000 0 034 EXECUTE FUNCTION TSL_SessionClose ts_data raw_reads EXECTUE PROCEDURE TSL_Shutdown ts_data raw_reads Related concepts Planning for loading time series data on page 1 23 Related tasks Loading JSON data Related reference TSL_ Put function on page 7 13 TSL_ Shutdown procedure on page 7 14 TSL_SessionClose function on page 7 13 TSL_ Init function on page 7 13 TSL_SetLogMode function on page 7 140 TSL_GetLogMessage function on page 7 132 TSL_GetKeyContainer function on page 7 13 page 7 13 a page 7 13 4 page 7 12 3 page 7 124 a page 7 128 m o m WN Z NO es 3 3 E a z D 2 5 O vllau CIECIE ziha ia JESE W Fo ZWOWS e e Oy fama EES alls IE JERE Fy Salo BESIN Ey o 74 5 Alle o o l gt 3 5 WN WN Z Ezi i 5 jo 5 g 5 Loading JSON data You can load JSON data into a time series with the time series input statement through a virtual table or by writing a loader program When you
192. ata mart so that you can quickly refresh the data mart You define time windows for the first three months of each year of data and load that data into the data mart You run analytic queries on the data through the accelerator Then you change the time window to the second three months of each year and run analytic queries on that data You can enhance the performance of your virtual tables by performing the following tasks e Create the virtual table as a fragmented table and enable PDQ so that queries are run in parallel The base table must be fragmented by expression e Create an index on the key column of the base table If the table has more than one column in the key create a composite index that consists of all key columns e Run UPDATE STATISTICS on the base table and on its key columns after any load or delete operation UPDATE STATISTICS HIGH FOR TABLE daily_stocks UPDATE STATISTICS HIGH FOR TABLE daily_stocks stock_id By default statistics are automatically updated once a week Related concepts Data marts for time series data Informix Warehouse Accelerator Guide The structure of virtual tables 4 2 A virtual table that is based on a time series has the same schema as the base table except for the TimeSeries column The TimeSeries column is replaced with the columns of the TimeSeries subtype A virtual table based on an expression on a time series displays the TimeSeries subtype that is the result of the e
193. ate a time series with the REST API or the MongoDB API If you have applications that handle time series data you can now create and manage a time series with the REST API or the MongoDB API Previously you created a time series by running SQL statements For example you can program sensor devices that do not have client drivers to load time series data directly into the database with HTTP commands from the REST API You create time series objects by adding definitions to time series collections You interact with time series data through a virtual table This feature is documented in the IBM Informix JSON Compatibility Guide Create time series through the wire listener ISON compatibility Replicate hertz and compressed time series data You can now replicate hertz and compressed time series data with Enterprise Replication Table 2 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC3 Overview Reference Efficient storage for hertz and numeric time series data You can save disk space by packing multiple time series records in each element If your data is recorded with a regular subsecond frequency you can define a hertz time series to pack records for a second of data in each time series element If all the columns in your TimeSeries data type are numeric you can define a compressed time series to pack and compress up to 4 KB of records in each time series element Packed time series
194. ateBy max high quarterlycal ts 1 Returns The aggregated time series which is always regular if you are aggregating to a new time interval The resulting time series has a time stamp column plus one column for each expression in the list Examples Stock data The following query aggregates the daily_stocks time series to a weekly time series insert into daily_stocks stock_id stock_name stock_data select stock_id stock_name AggregateBy max high min low last final sum vol weekcal stock_data TimeSeries stock_bar from daily_stocks The following query clause selects the second price from each week AggregateBy Nth price 2 weekly ts This query clause selects the second to the last price from each week AggregateBy Nth price 2 weekly ts Examples BSON data This example is based on the following row type and time series definition The TimeSeries row type contains an INTEGER column that is named v1 and the BSON column contains a field that is also named v1 CREATE ROW TYPE rb timestamp datetime year to fraction 5 data bson v1 int INSERT INTO tj VALUES 1 origin 2011 01 01 00 00 00 00000 calendar ts_ 15min container kontainer threshold 0 regular v1 99 20 The following statement creates a TimeSeries data type to hold the results of the aggregation on the BSON field in an INTEGER column CREATE ROW TYPE outrow timestamp datetime year to f
195. ated concepts The TSVTMode parameter on page 4 16 Related reference DelElem function on page 7 4 GetElem function on page 7 5 NTO TSCreate function on page 7 116 The ts_put_elem function on page 9 46 IBM Informix TimeSeries Data User s Guide PutNthElem function on page 7 80 UpdElem function on page 7 159 PutElemNoDups function The PutElemNoDups function inserts a single element into a time series If there is already an element at the specified timepoint it is replaced by the new element Syntax PutElemNoDups ts TimeSeries row_value row flags integer default 0 returns TimeSeries ts The time series to act on row_value The new row type value to store in the time series flags Valid values for the flags argument are described in fralues on page 7 9 lues on page 7 9 The default is 0 Description If the time stamp is NULL the data is appended to the time series for regular time series or an error is raised for irregular time series If there is data at the given timepoint it is updated with the new data otherwise the new data is inserted The row type passed in must match the subtype of the time series Hidden elements cannot be updated The API equivalent of PutElemNoDups is ts_put_elem_no_dups Returns A modified time series that includes the new values Example The following example updates a time
196. atically that detach operation fails You must first manually destroy the appropriate number of partitions e You can attach partitions into the active window from the dormant window The active window must have room for that partition and any partitions that belong in between that partition and the oldest partition in the active window For example you attach the January partition to the active window The February partition is attached automatically to the active window e You can change the size of the active window If you make the size of the active window larger you can attach partitions from the dormant window For example you change the active window size to 9 and attach the December partition to the active window The January partition also attaches to the active window If you make the size of the active window smaller the oldest partitions that exceed the new maximum size are detached to the dormant window However if the dormant window is full and partitions are not destroyed automatically the resizing operation fails You must first manually destroy the appropriate number of partitions Rules for destroying data e When you add data and one or more new active partitions are created so that the number of active partitions exceeds the active window size the appropriate number of older active partitions are detached and moved to the dormant Chapter 3 Create and manage a time series through SQL 3 17 3 18 window How the p
197. b_data and the field name is v1 the column identifier is b_data v1 colnumber bson_field_name For example if the BGON column number is 1 and the field name is v1 the column identifier is 1 v1 You must cast the results of the TSRollup function on a BSON field to a TimeSeries data type that has the appropriate type of columns for the result of the expression start start_time Optional Specifies the start of the time range on which to operate The value of start_time must be in the form of a DATETIME value No value indicates that the time range starts at the origin of the time series If you include the start argument more than once in the aggregate expression only the last start argument is used end end_time Optional Specifies the end of the time range on which to operate The value of end_time must be in the form of a DATETIME value No value indicates that the time range ends at the last element in the time series If you include the end argument more than once in the aggregate expression only the last end argument is used ts The name of the TimeSeries data type or a function that returns a TimeSeries data type such as AggregateBy Description Use the TSRollup function to run one or more aggregate operators on multiple rows of time series data in a table You can specify a time range on which to run the aggregate expression by including the start and end arguments in the aggregate expression Specifying the time
198. by the TSCreateVirtualTab procedure You cannot load data from a virtual table was created by the TSCreateExpressionVirtualTab procedure To load relational data through a virtual table 1 Create a virtual table that is based on a time series table 2 Put your input data in a single file IBM Informix TimeSeries Data User s Guide 3 Format the data according to the standard IBM Informix load file format 4 Use any of the Informix load utilities pload onpload dbload or the load command in DB Access to load the file into the virtual table See the IBM Informix Administrator s Guide for information about Informix load file formats and load utilities Related concepts Planning for loading time series data on page 1 23 Chapter 4 Virtual tables for time series data on page 4 1 Related reference Load data with the BulkLoad function You can load data into an existing time series with the BulkLoad function This SQL function takes an existing time series and a file name as arguments The file name is for a file on the client that contains row type data to be loaded into the time series The syntax for using BulkLoad with the UPDATE statement and the SET clause is update table_name set TimeSeries_col BulkLoad TimeSeries col filename where coll value The TimeSeries_col parameter is the name of the column that contains the row type The filename parameter is the name of the data file The WHERE clause
199. cH s The time series to act on IBM Informix TimeSeries Data User s Guide begin_stamp The begin point of the range end_stamp The end point of the range flags Valid values for the the argument are described in The flags argument alues on page 7 9 The default is 0 Description Use the DelRange function to delete elements in a time series instance from a specified time range and free any resulting empty pages For example you can remove data from the beginning of a time series instance to archive the data If you use the DelRange function to delete hidden elements or if the begin point of the range falls before the origin of the time series an error is raised Returns A time series with all elements in the range between the specified timepoints deleted Example The following example removes all elements in a one day range on the specified day for the specified time series instance UPDATE ts_data SET meter_data DelRange meter_data 2010 11 11 00 00 00 00000 datetime year to fraction 5 2010 11 11 00 00 00 00000 datetime year to fraction 5 WHERE loc_esi_id 4727354321000111 Related concepts Delete time series data on page 3 37 Related reference Time series routines that run in parallel on page 7 7 DelTrim function The DelTrim function deletes all elements in the specified time range in a time series instance including the delimiting timepoints The DelTrim functio
200. ced 7 126 IBM Informix TimeSeries Data User s Guide Usage Use the TSL_Flush function to write time series data to disk as part of a loader program You must run the TSL_Flush function in a transaction in the context of a loader session that was initialized by the TSL_Init function You run the TSL_Flush function to save data that is loaded by the TSL_Put TSL_PutSQL or TSL_PutRow function If you specify that duplicate elements are allowed for irregular time series with the writeflag argument value of 1 the default or 257 1 256 the TSL_Flush function inserts data in the same way as the PutElem function You can specify that duplicate elements replace existing elements that have the same timestamps with the writeflag argument value of 5 or 261 5 256 so that the TSL_Flush function inserts data in the same way as the PutElemNoDups function If you specify reduced logging with the writeflag argument value of 257 1 256 or 261 5 256 you must run TSL_Flush function in a transaction that can include only other functions that use reduced logging with the TSOPEN_REDUCED_LOG flag The elements that are saved are not visible by dirty reads until after the transaction commits The TSL_FlushInfo function returns eight categories of information about the last flush operation that saved data to disk Returns An integer that indicates the status of the function e Positive 4 byte number The top 16 bits represent the number of t
201. cedure on page 4 5 IBM Informix TimeSeries Data User s Guide Example Create and load a hertz time series This example shows how to create load and query a time series that stores hertz data In this example you create a time series that contains hertz data that is recorded 50 times a second You can create the time series table in any database that you choose The following table lists the time series properties that are used in this example Table 3 2 Time series properties used in this example Time series property Definition Element size 1 second When elements are valid Always Data in the time series The following data Timestamp e A SMALLINT value e An INTEGER value e A BIGINT value Time series table The following columns e An ID column of type INTEGER e A TimeSeries data type column Origin 2014 01 01 00 00 00 00000 Regularity Irregular Hertz 50 records per second Metadata No metadata Where to store the data In a container that you create How to load the data The InsElem function How to access the data A virtual table To create load and view a hertz time series 1 Create a TimeSeries subtype that is named ts_data_h in a database by running the following SQL statement CREATE ROW TYPE ts_data_h tstamp datetime year to fraction 5 tssmallint smallint tsint int tsbigint bigint Vs You can include only certain da
202. ch containing the ID name and high columns select TSRowNumToList d 1 list row id integer name lvarchar high real not null from daily_stocks d Related reference TSColNameToList function on page 7 91 TSColNumToList function on page 7 92 IBM Informix TimeSeries Data User s Guide TSRowToList function The TSRowToList function returns a list collection of rows containing the individual columns from a time series column plus the non time series columns of a table Null elements are not added to the list Syntax TSRowToList ts_row row returns list row not null ts_row A row value that contains a time series as one of its columns Description The TSRowToList function can only be used on rows with one TimeSeries column You must cast the return variable to match the names and types of the columns being returned exactly Returns A list collection of rows Example The query returns a list of rows each containing the following columns stock_id stock_name t high low final vol select TSRowToList d list row stock_id integer stock_name lvarchar t datetime year to fraction 5 high real low real final real vol real not null from daily_stocks d Related reference TSRowNameToList function on page 7 145 TSRowNumToList function on page 7 146 Transpose function on page 7 86 TSColNameToList function on page 7 91 TSColINumToList functio
203. column Both the position number and column number are zero based For example 0 means the element of the first input time series 0 0 represents its time stamp column and 0 1 is the column following the time stamp column Another way to refer to a column is to use the column name directly instead of the column number Suppose the second time series has a column called high then you can use 1 high to refer to it If the high column is the second column in the element 1 high is equivalent to 1 1 If Apply has only one time series argument you can refer to the column name without the time series position part hence 0 high is the same as high Notice that 0 always means the whole element of the first time series It does not mean the first column of the time series even if there is only one time series argument If you use a function as your expression then it must take the subtype of each input time series in that order as its arguments and return a row type that corresponds to the subtype of the result time series of Apply In most cases it is faster to evaluate a function than to evaluate a generic expression If performance is critical you should implement the calculation to be performed in a function and use the function syntax See Example on page 7 21 for how to achieve this Chapter 7 Time series SQL routines 7 19 7 20 The following examples show valid expressions for Apply to apply Assume two argument time s
204. column Each element is surrounded by parentheses and followed by an symbol and a time stamp The time stamp is optional for regular time series but mandatory for irregular time series Null data values or elements are indicated with the word NULL If no data elements are present the function creates an empty time series If you include the hertz or compress keywords you must run the input function in an explicit transaction Example Create a regular time series Following example shows an INSERT statement for a regular time series that is created in the table daily_stocks insert into daily_stocks values 1234 informix regular calendar daycal 350 310 340 1999 362 320 350 2500 This INSERT statement creates a regular time series that starts at the date and time of day that is specified by the calendar called daycal The first two elements in the time series are populated with the bracketed data Since the threshold parameter is not specified its default value is used Therefore if more than 20 elements are placed in the time series the database server attempts to move the data to a container but because there is no container that is specified an error is raised Example Create an irregular time series The following example shows an INSERT statement for an irregular time series that is created in the table activity_stocks insert into activity_stocks values 600 irregular container ctnr_stock orig
205. ction on page 7 85 The ts_get_origin function on GetPacked function 7 64 The GetPacked function returns whether the specified time series contains packed data Syntax GetPacked ts TimeSeries returns integer ts The name of the time series Description Use the GetPacked function to determine whether a time series stores either hertz data or compressed numeric data in packed elements IBM Informix TimeSeries Data User s Guide Returns Returns 1 if the time series contains packed data returns 0 if the time series does not contain packed data Example The following statement indicates that the time series that is named historic_measure contains packed data EXECUTE FUNCTION GetPacked historic_measure expression 1 Related concepts Manage packed data on page 3 37 Related reference The ts_get_packed function on page 9 32 GetPreviousValid function The GetPrevious Valid function returns the last element before the given time stamp Syntax GetPreviousValid ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns row ts The source time series tstamp The time stamp of interest flags Valid values for the flags argument are described in falues on page 7 9 lues on page 7 9 The default is 0 Description The equivalent API function is ts_previous_valid Returns A row containing the last element before the given time
206. ction argument indicates which periods other than the period containing the time stamp should be included if there is only one period the direction argument is moot For both WithinC and WithinR the requested timepoint is included in the output Returns A new time series with the same calendar as the original but containing only the requested values Example The following query retrieves data from the calendar week that includes Friday January 4 2011 select WithinC stock_data 2011 01 04 00 00 00 00000 week 1 PAST from daily_stocks where stock_name IBM The query returns the following results expression origin 2011 01 03 00 00 00 00000 calend ar daycal container threshold 20 re gular 356 0000000000 310 0000000000 340 000000000 999 0000000000 156 000000 0000 110 0000000000 140 0000000000 111 0000000000 NULL 99 00000000000 54 000 00000000 66 00000000000 888 0000000000 The following query returns two weeks worth of stock trades starting on January 4 2011 at 9 30 a m select WithinR activity_data 2011 01 04 09 30 00 00000 week 2 future from activity_stocks where stock_id 600 The following query returns the preceding three months worth of stock trades select WithinR activity_data 2011 02 01 00 00 00 00000 month 3 past from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel on pa
207. d function on page 9 14 The ts_colinfo_number function The ts_colinfo_number function The ts_colinfo_number function gets type information for a column in a time series Syntax ts_typeinfo ts_colinfo_number ts_tsdesc tsdesc mi_integer id tsdesc A time series descriptor returned by ts_open id The column number to return information for The id argument must be greater than or equal to 0 and less than the number of columns in a time series element An id of 0 corresponds to the time stamp column Chapter 9 Time series API routines 9 15 Description The resulting typeinfo structure and its ti_typename field must be freed by the caller Returns A pointer to a ts_typeinfo structure This structure is defined as follows typedef struct _ts_typeinfo MI_TYPEID ti_typeid type id mi_integer ti_typelen internal length mi_smallint ti_typealign internal alignment mi_smallint ti_typebyvalue internal byvalue flag mi_integer ti_typebound internal bound mi_integer ti_typeparameter internal parameter mi_string ti_typename type name of the column ts_typeinfo Example See the ts_interp function Appendix A The Interp function example on page A 1 for an example of ts_colinfo_number Related reference The ts_col_id function on page 9 14 The ts_colinfo_name function on page 9 15 The ts_copy function 9
208. d line_no mi_get_varlen context_ptr gt table_name mi_get_vardata context_ptr gt table_name mi_db_error_raise NULL MI_MESSAGE buf return entry_ptr if MI_NULL_VALUE rc entry_ptr gt in_row TS_IS_INCONTAINER ts_timeseries ts_datum l 0 else No time series has been created for this security yet Start one ts_datum ts_create context_ptr gt conn context_ptr gt calendar_name context_ptr gt origin context_ptr gt threshold context_ptr gt regular 0 TS_CREATE_IRR amp context_ptr gt ts_typeid context_ptr gt nelems context_ptr gt container_name entry_ptr gt in_row TS_IS_INCONTAINER ts_timeseries ts_datum 0 if entry_ptr gt in_row B 10 IBM Informix TimeSeries Data User s Guide increment_instances_created context_ptr entry_ptr gt tsdesc ts_open context_ptr gt conn ts_datum amp context_ptr gt ts_typeid 0 return entry_ptr End of get_sec_entry kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name is_null purpose Determine whether a token represents a null value returns 1 if so 0 if not kkkkxkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static int is_null register mi_string token n token 0 return N token 0 amp amp U token 1 u token 1 amp amp L token 2 token
209. d function 9 34 ts_hide_elem function 9 34 ts_index function 9 35 ts_ins_elem function 9 36 TS_IS INCONTAINER macro 9 36 TS_IS_ TRREGULAR macro 9 37 ts_last_elem function 9 37 ts_last_valid function 9 38 ts_make_elem function 9 38 ts_make_elem_with_buf function 9 39 ts_make_stamp function 9 40 ts_nelems function 9 41 ts_next function 9 41 ts_next_valid function 9 42 ts_nth_elem function 9 43 ts_open function 9 44 ts_previous_valid function 9 45 ts_put_elem function 9 46 ts_put_elem_no_dups function 9 47 ts_put_last_elem function 9 48 ts_put_nth_elem function 9 48 ts_put_ts function 9 49 TS_PUTELEM_NO_DUPS flag 7 9 ts_reveal_elem function 9 50 ts_row to_elem function 9 50 TS_SCAN_EXACT_END flag 9 7 TS_SCAN_EXACT_START flag 7 87 9 7 TS_SCAN_HIDDEN flag 7 37 7 87 9 7 TS_SCAN_SKIP_BEGIN flag 7 87 9 7 TS_SCAN_SKIP_END flag 7 87 9 7 ts_time function 9 13 9 51 9 52 9 53 Index X 7 ts_timeseries data structure 9 2 ts_tscan data structure 9 2 ts_tsdesc data structure 9 2 ts_tselem data structure 9 3 ts_upd_elem function 9 54 ts_update_metadata function 9 54 TS_VTI_DEBUG trace class 4 27 TSAddPrevious function 7 90 TSCmp function 7 91 TSColName parameter 4 5 TSContainerCreate procedure 7 93 TSContainerDestroy procedure 7 98 TSContainerLock procedure 7 99 TSContainerManage procedure 7 99 TSContainerNElems 7 104 TSContainerPctUsed 7 106 TSContainerPurge function 7 108 TSContainerTable system table 2 9 TSContainerTotalPag
210. d manage containers TimeSeriesContainer TimeSeriesContainer Builder Create query and manage TimeSeries row TimeSeriesRowType types TimeSeriesRowType Builder Instantiate time series IfmxTimeSeries IfmxTimeSeries Builder Query insert update or delete time series IfmxTimeSeries data Related concepts Connect to the database JDBC Driver Guide Planning for accessing time series data on page 1 24 Planning for creating a time series on page 1 19 Java class files and sample programs The time series Java class jar file Javadoc and sample programs are included with the database server The Java class file IfmxTimeSeries jar is in the INFORMIXDIR extend TimeSeries version java 1ib directory The Javadoc files are in the INFORMIXDIR extend TimeSeries version java doc directory To access the sample programs run the jar xvf IfmxTimeSeries jar command to expand the IfmxTimeSeries jar file The examples are in the resulting com informix docExamples directory The examples include the SQL scripts setup sql and clean sql to set up data for the examples and clean it up afterward The TimeSeriesExample java file provides a comprehensive example of using Builders to create load and query time series Preparing the server for Java classes You must prepare the database server so that you can include the time series Java classes in your application by setting the CLASSPATH e
211. d to as an interval Origin The earliest time stamp that is allowed Data that has a timestamp before the origin is not allowed time series instance For each TimeSeries data type value the set of elements that is stored in a container Each instance has a unique identifier that is stored in the TSInstanceTable table The time series instance ID is used by some time series routines and SQL statements The time series instance ID is used by some time series routines 1 4 IBM Informix TimeSeries Data User s Guide Calendar A set of valid timepoints in a time series as specified by the calendar pattern Calendar pattern The pattern of valid timepoints and when the pattern starts The calendar pattern can also specify the length of the timepoint For example if you collect electricity usage information every 15 minutes the calendar pattern specifies that timepoints have a length of 15 minutes and because you want to collect information continuously all timepoints are valid Container A named portion of a dbspace that contains the time series data for a specific TimeSeries data type and regularity The data is ordered by time stamp You can control in which containers your time series data is stored Regularity Whether a time series has regularly spaced timepoints or irregularly spaced timepoints Virtual table Virtual tables display a view of the time series data in a relational format without duplicating the data You can use stand
212. d ts_data_c in a database by running the following SQL statement CREATE ROW TYPE ts_data_c tstamp datetime year to fraction 5 tsl int ts2 int ts3 int s You can include only certain numeric data types in your TimeSeries subtype 2 Create a time series table that is named tstable_c by running the following SQL statement CREATE TABLE IF NOT EXISTS tstable_c id int not null primary key ts timeseries ts_data_c LOCK MODE ROW 3 Create a container that is named container_c by running the following SQL statement EXECUTE PROCEDURE TSContainerCreate container_c rootdbs ts_data_c 512 512 You can choose to create the container in a different dbspace than the root dbspace 4 Create a calendar by running the following SQL statement INSERT INTO CalendarTable c_name c_calendar VALUES ts_1sec startdate 2014 01 01 00 00 00 00000 pattern 1 on second You cannot include off periods in the calendar for a compressed time series 3 8 IBM Informix TimeSeries Data User s Guide 5 Create a compressed time series with compression definitions for the three data columns in the ts_data_c data type by running the following SQL statement in an explicit transaction BEGIN Started transaction INSERT INTO tstable_c VALUES 50 TSCreateIrr ts_lsec 2014 01 01 00 00 00 00000 container_c q 2 1 100 1b 20 1s 20 1 row s inserted COMMIT The first INTEGER column has a compression
213. d with the specified multiset of data The new time series can also have user defined metadata that is attached to it Syntax TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer hertz integer nelems integer container_name_ varchar returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer hertz integer container_name_ lvarchar multiset_rows multiset returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer hertz integer container_name_ lvarchar metadata TimeSeriesMeta returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer hertz integer container_name_ lvarchar metadata TimeSeriesMeta multiset_rows multiset returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 container_name_ lvarchar compression lvarchar returns TimeSeries with handlesnulls IBM Informix TimeSeries Data User s Guide TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 container_name_ lvarchar compression lvarchar multiset_rows multiset returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 container_name_ lvarchar compression lvarchar metadata TimeSeri
214. data 1 Round the time stamp up to the next second 2 Search backward for the first element less than the new time stamp 3 Insert the new data at this time stamp plus 10 microseconds The element passed in must match the subtype of the time series Hidden elements cannot be updated The equivalent SQL function is PutElem IBM Informix TimeSeries Data User s Guide Returns The original time series with the element added Related reference PutElem function on page 7 77 The ts_del_elem function on page 9 22 The ts_get_ts function on page 9 33 The ts_ins_elem function on page 9 36 The ts_make_elem function on page 9 38 The ts_elem function on page 9 22 The ts_next function on page 9 41 The ts_put_elem_no_dups function The ts_put_last_elem function on page 9 48 The ts_upd_elem function on page 9 54 The ts_put_ts function on page 9 49 The ts_put_elem_no_dups function The ts_put_elem_no_dups function puts a new element into an existing time series The element is inserted even if there is already an element with the given time stamp in the time series Syntax ts_timeseries ts_put_elem_no_dups ts_tsdesc tsdesc ts_tselem tselem mi_datetime tstamp tsdesc A descriptor of the time series to be modified returned by ts_open tselem The element to add tstamp The time stamp at which to put the element
215. data for the next time interval When you insert data that is after the latest partition a new partition is added and the active window moves ahead When the active window exceeds the maximum number of partitions the oldest partition is moved to the dormant window When the dormant window exceeds the maximum number of partitions the oldest partition is destroyed Chapter 1 Informix TimeSeries solution 1 17 The following illustration shows how an active window and a dormant window grow and move over time In this example the maximum size of both windows is two months Month 1 Active window Month 2 Active window Month 3 Dormant Active window window Month 4 Dormant Active window window Month 5 Dormant Active window window Figure 1 6 Example of an active window and a dormant window which move over time This illustration shows how the windows grow and move as data is inserted for each month 1 18 IBM Informix TimeSeries Data User s Guide When data is added for month 1 a partition is created in the active window When data is added for month 2 a second partition is created in the active window When data is added for month 3 the partition for month 1 moves out of the active window and into the dormant window When data is added for month 4 the active window adds a partition for month 4 and moves the partition for month 2 into the dormant window When data is added for month 5 the active wind
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217. e table_name The name of the time series table threshold The threshold for the time series If the time series stores more than this number of elements it is converted to a container Otherwise it is stored directly in the row that contains it The default is 20 The size of a row that contains an in row time series cannot exceed 1500 bytes timestamp The timestamp of the element The time stamp is optional for regular time series but mandatory for irregular time series All data types have an associated input function that is automatically run when ASCII data is inserted into the column For the TimeSeries data type the input has several pieces of data that is embedded in the text This information is used to convey the name of the calendar the time stamp of the origin the threshold the container the regularity and the initial time series data A time series is regular by default the regular keyword is optional To define an irregular time series you must include the irregular keyword Chapter 3 Create and manage a time series through SQL 3 25 3 26 If you did not specify a data file then you can supply the data to be placed in the time series the data element surrounded by square brackets after the parameters Elements consist of data values each separated by a comma The data values in each element correspond to the columns in the TimeSeries subtype not including the initial time stamp
218. e List the data type of the TimeSeries column as TimeSeries subtype_name where subtype_name is the name of the subtype that you created IBM Informix TimeSeries Data User s Guide Examples The following example creates a table that is called daily_stocks that contains a time series column of type TimeSeries stock_bar create table daily stocks stock_id int stock_name_ lvarchar stock_data TimeSeries stock_bar s Each row in the daily_stocks table can hold a stock_bar time series for a particular stock The following example creates a table that is called activity_stocks that contains a time series column of type TimeSeries stock_trade create table activity_stocks stock_id int activity_data TimeSeries stock_trade s Each row in the activity_stocks table can hold a stock trade time series for a particular stock Related concepts TimeSeries data type on page 2 6 Related reference Time series routines that run in parallel on page 7 7 L CREATE TABLE statement SQL Syntax Creating containers Containers are created automatically when they are needed in the same dbspaces in which the table is stored If you want to store your time series data in other dbspaces you can create containers You can move containers between container pools You must create containers if you want to replicate time series data with Enterprise Replication or create rolling window containers To create
219. e assuming that the base table has a primary key Whether the NewTimeSeries parameter is specified also affects the behavior of inserting data into the base table For information about the effect of the NewTimeSeries parameter see TSCreateVirtualTab procedure on page 4 5 Table 4 7 TSVTMode parameter settings that affect which columns are updated in the base table TSVTMode parameter Columns to update setting Update only the TimeSeries column You must specify valid 0 but not necessarily accurate values for non primary key columns Update only the TimeSeries column You can specify NULL 16 as the values for non primary key columns Update the TimeSeries column and all other non primary 32 key columns that do not have null values in the INSERT statement Chapter 4 Virtual tables for time series data 4 19 4 20 Table 4 7 TSVTMode parameter settings that affect which columns are updated in the base table continued TSVTMode parameter Columns to update setting Update the TimeSeries column and all other non primary 64 or 80 64 16 key columns You can set columns that do not have NOT NULL constraints to null values Update the TimeSeries column and all other non primary 48 32 16 key columns that have NOT NULL constraints You can specify null values for columns that have NOT NULL constraints The following examples illustrate some of the settings for the TSVTMode p
220. e series and is approximately 4 of the size of the data for an irregular time series elements The number of elements of time series data in each row For example the ts_data table in stores_demo database has 8640 elements for each of the 28 rows For hertz time series each element represents one second of data For compressed time series each element represents approximately 4 KB of data index_entry The size of an index entry which is approximately the size of the primary key columns plus 4 bytes primary_key The size of the data types of the primary key columns and other non time series columns in the time series table records For hertz and compressed time series the number of records table_rows The number of rows in the time series table ts_columns The size of the data types of the columns in the TimeSeries data type except the timestamp column The CHAR data type requires an additional 4 bytes when it is included in a TimeSeries data type timestamp The size of the timestamp per element Regular time series 0 Irregular time series 11 bytes The size of the timestamp per record Hertz time series 1 byte Compressed time series 2 bytes The 4 bytes per element is a slot entry For hertz and compressed time series each element has an 11 byte timestamp in addition to the 1 byte or 2 byte timestamp respectively for each record The equation is a guideline The amount of required space can be affect
221. e 0 No size limit e Positive integer The maximum number of partitions in the active window If you decrease the size the number of oldest active partitions that exceed the active window size are detached to the dormant window If you increase the size partitions in the dormant window are not attached into the active window The keywords window size and to are optional and do not change the command 7 100 IBM Informix TimeSeries Data User s Guide set dormant window size to number For rolling window containers sets the number of partitions in the dormant window e 0 Default No size limit e Positive integer The maximum number of partitions in the dormant window If you decrease the size the number of dormant partitions that exceed the dormant window size are destroyed The keywords window size and to are optional and do not change the command set pool containerpool Moves the container into the specified container pool set pool Removes the container from its container pool Same as the TSContainerSetPool procedure set extent container first first_ext next next_ext Sets the size in KB of the first and next extents for the container partition The keywords first and next are optional and do not change the command set extent first first_ext next next_ext Sets the sizes in KB of the first and next extents for the partitions that contain time series elements For rolling window containers changes the sizes
222. e 7 150 TSSetToList function The TSSetToList function takes a TimeSeries column and returns a list collection of rows containing all the elements in the time series Null elements are not added to the list Syntax TSSetToList ts TimeSeries returns list row not null ts The time series to act on Description Because this aggregate function can return rows of any type the return value must be explicitly cast at runtime Returns A list collection of rows Example The following query collects all the elements in all the time series in the stock_data column into a list and then selects out the high column from each element select high from table select TsSetToList stock_data list stock_bar not null from daily_stocks Chapter 7 Time series SQL routines 7 153 Related reference TSColNameToList function on page 7 91 TSColNumToList function on page 7 92 TSRowNameToList function on page 7 145 TSRowNumToList function on page 7 146 Transpose function on page 7 86 TSRowToList function on page 7 147 TSToXML function 7 154 The TSToXML function returns an XML representation of a time series Syntax TSTOXML doctype lvarchar id lvarchar ts timeseries output_max integer default 0 returns lvarchar TSToXML doctype lvarchar id lvarchar ts timeseries returns lvarchar doctype The name of the topmost XML element
223. e API connection cal_name The name of the calendar begin_stamp The beginning time stamp begin_stamp must not be earlier than the calendar origin end_stamp The time stamp whose offset from begin_stamp is to be determined This time stamp can be earlier than begin_stamp Description The equivalent SQL function is CalIndex Returns The number of valid intervals in the given calendar between the two time stamps If end_stamp is earlier than begin_stamp then the result is a negative number Related reference The ts_cal_range function on page 9 10 The ts_cal_range_index function on page 9 11 The ts_cal_stamp function on page 9 11 The ts_index function on page 9 35 The ts_cal_pattstartdate function The ts_cal_pattstartdate function takes a calendar name and returns the start date of the pattern for that calendar Syntax mi_datetime ts_cal_pattstartdate MI_CONNECTION conn mi_string cal_name conn A pointer to a valid DataBlade API connection structure cal_name The name of the calendar Description The equivalent SQL function is CalPattStartDate Chapter 9 Time series API routines 9 9 Returns An mi_datetime pointer that points to the start date of a calendar pattern You must free this value after use Related reference CalPattStartDate function on page 5 2 The ts_cal_startdate function on page 9 12 The ts_cal_range function The ts_c
224. e TSL_Put function and saved to disk by the TSL_Flush function IBM Informix TimeSeries Data User s Guide The TSL_GetLogMessage function is an iterator function You can retrieve the messages with an SQL cursor through a virtual table Returns The specified number of messages from the loader message queue If NULL is returned the loader message queue is empty Examples In the context of a loader program the following statement retrieves 50 messages from the loader message log EXECUTE FUNCTION TSL_GetLogMessage ts_data raw_reads 50 Related tasks Writing a loader program on page 3 31 TSL_Init function The TSL_Init function initializes a session for loading data Syntax TSL_Init table_name varchar 128 column_name varchar 128 returns lIvarchar TSL_Init table_name varchar 128 column_name varchar 128 tstamp_format varchar 25 returns lvarchar TSL_Init table_name varchar 128 column_name varchar 128 tstamp_format varchar 25 reject_file varchar 255 returns lIvarchar TSL_Init table_name varchar 128 column_name varchar 128 log_type integer log_level integer logfile lvarchar returns lIvarchar TSL_Init table_name varchar 128 column_name varchar 128 log_type integer log_level integer logfile lvarchar tstamp_format varchar returns lvarchar TSL_Init table_name varchar 128 column_name varchar 128 log_type integer log_level integer logfi
225. e calendar of the input time series to find the offset that corresponds to the time stamp If IBM Informix TimeSeries Data User s Guide there is no entry in the time series exactly at the requested time stamp Clip uses the time stamp that immediately follows the specified time stamp as the begin point of the range The end point is used in the same way as the begin point except that it specifies the end of the range rather than its beginning The begin and end points can be NULL in which case the beginning or end of the time series is used For irregular time series only time stamps are allowed for the begin and end points The timestamp of the first record in a resulting irregular time series is later than or equal to the begin point However the value of a record in an irregular time series persists until the next record Therefore by default the first record in the resulting time series can have a value that corresponds to an earlier timestamp than the begin point You can specify that the first record in the resulting time series is the first record whose timestamp is equal to or after the begin point by including the flag argument value of 16 You can specify that the resulting time series is irregular by including the flag argument value of 8 You can choose whether the origin of the resulting time series can be earlier than the origin of the input time series by setting the flag argument By default the origin of the resulting
226. e column identifier n A positive or negative number that indicates the position of the TimeSeries row within the aggregation period Positive values of n begin at the first row in the aggregation period therefore Nth col 1 is equivalent to FIRST col Negative values of n begin with the last row in the aggregation period therefore Nth col 1 is equivalent to LAST col If an aggregation period does not have a value for the nth row then the Nth function returns a null value for that period The Nth function is more efficient for positive values of the n argument than for negative values An aggregation time period is denoted by the start date and time of the period 7 12 IBM Informix TimeSeries Data User s Guide The origin of the aggregated output time series is the first period on or before the origin of the input time series Each output period is the aggregation of all input periods from the start of the output period up to but not including the start of the next output period For instance suppose you want to aggregate a daily time series that starts on Tuesday Jan 4 2011 to a weekly time series The input calendar named days starts at 12 00 a m and the output calendar named weeks starts at 12 00 a m on Monday The first output time is 00 00 Jan 3 2011 it is the aggregation of all input values from the input origin Jan 4 2011 to 23 59 59 99999 Jan 9 2011 The second output time is 00 00 Ja
227. e following SQL statement SELECT FROM vt_tstable_c id tstamp tsl ts2 ts3 50 2014 01 01 00 00 00 00000 1 201 99991 50 2014 01 01 00 00 01 00000 2 202 99992 50 2014 01 01 00 00 02 00000 3 203 99993 Chapter 3 Create and manage a time series through SQL 3 9 50 2014 01 01 00 00 03 00000 4 204 99994 50 2014 01 01 00 00 04 00000 5 205 99995 5 row s retrieved Related concepts Related reference Example Create and load a time series with JSON data This example shows how to create load and query a time series that stores JSON data In this example you create a time series that contains meter readings in JSON documents Readings are taken every 15 minutes The following table lists the time series properties that are used in this example Table 3 4 Time series properties used in this example Time series property Definition Timepoint size 15 minutes When timepoints are valid Every 15 minutes with no invalid times Data in the time series Timestamp e JSON documents that are stored in a BSON column in the TimeSeries subtype Time series table e A meter ID column of type INTEGER e A TimeSeries data type column Origin 2014 01 01 00 00 00 00000 Regularity Regular Metadata No metadata Where to store the data In a container that you create How to load the data Through an external table and a loader program How to access the data Through a virtual table To crea
228. e following values 2005 12 17 10 23 00 00000 26 46 2006 01 03 13 19 00 00000 27 30 2006 01 04 13 19 00 00000 28 67 2006 01 09 13 19 00 00000 30 56 The following statement extracts data from a time series over a five day period EXECUTE FUNCTION Transpose select Clip tsdata 2006 01 01 00 00 00 00000 datetime year to fraction 5 2006 01 05 00 00 00 00000 datetime year to fraction 5 0 from ts_tab where station_id 228820 The resulting irregular time series is as follows 2006 01 01 00 00 00 00000 26 46 2006 01 03 13 19 00 00000 27 30 2006 01 04 13 19 00 00000 28 67 The first record has a time stamp equal to the begin point of the clip and the value of the first original value Because the time series is irregular a record persists until the next record Therefore the value of 26 46 is still valid on 2006 01 01 However if the Clip function includes the flag argument value of 16 the first value of the resulting time series is later than the begin point of the clip The following statement extracts data that is after the begin point EXECUTE FUNCTION Transpose select Clip tsdata 2006 01 01 00 00 00 00000 datetime year to fraction 5 2006 01 05 00 00 00 00000 datetime year to fraction 5 16 from ts_tab where station_id 228820 The resulting irregular time series is as follows 2006 01 03 13 19 00 00000 27 30 2006 01 04 13 19 00 00000 28 67 Related reference Time serie
229. e not to compress a column Columns that are not compressed allow NULL values Lossiness The following equation describes margin of error that is allowed between the original and the compressed values for the different compression types Quantization type margin of error upper_bound lower_bound 2 compress_size 8 Linear types margin of error maximum_deviation Combination of quantization and linear types margin of error upper_bound lower_bound 2 compress_size 8 maximum_deviation compress_size The size of the compressed data in bytes lower_bound The lowest acceptable value maximum_deviation The absolute value of the margin of error upper_bound The highest acceptable value For example if the compression definition for quantization is q 1 1 100 the compression size is 1 byte the lower boundary is 1 and the upper boundary is 100 The following equation calculates the margin of error 100 1 256 0 387 The maximum difference between the original value and the compressed value is plus or minus 0 387 Chapter 1 Informix TimeSeries solution 1 11 1 12 For the linear compression type the margin of error is equal to the maximum deviation value For example if the original value is 20 and the maximum deviation value is 0 1 then the compressed value is in the range 19 9 20 1 If the compression definition for quantization linear boxcar is qlb 1 1 100 100000 the compression
230. e regular input time series If all the input time series are irregular the resulting irregular time series starts at the latest of the start points of the input time series and ends at the latest of the end points If a union involves one or more time series the resulting time series starts at the first of the start points of the input time series and ends at the latest of the end points of the input time series Other than this difference in start and end points and of the resulting calendar there is no difference between union and intersection involving time series In an intersection the resulting time series has a calendar that is the combination of the calendars of the input time series with the AND operator The resulting calendar is stored in the CalendarTable table The name of the resulting calendar is a string containing the names of the calendars of the input time series joined by an ampersand amp For example if two time series are intersected and mycal and yourcal are the names of their corresponding calendars the resulting calendar is named mycal amp yourcal To be certain of the order of the columns in the resultant time series when using Intersect with the set_ts argument use the ORDER BY clause Apply also combines multiple time series into a single time series Therefore using Intersect within Apply is often unnecessary IBM Informix TimeSeries Data User s Guide Returns The time series that results from the inter
231. e row that must be updated and those values are not updated in the base table After the INSERT statement the virtual table contains two rows and each contains the original values of the owner and address columns SELECT FROM smartmeters_vti_nn acct_no 6546 meter_id 234 IBM Informix TimeSeries Data User s Guide t 2011 01 01 00 00 00 00000 energy 33070 temperature 13 0000000000 owner John address 5 Nowhere Place acct_no 6546 meter_id 234 t 2011 01 01 00 45 00 00000 energy 3234 temperature 12 0000000000 owner John address 5 Nowhere Place 2 row s retrieved Fragment the virtual table When you run queries in parallel on fragmented virtual tables set the isolation level to Dirty Read Otherwise when a parallel query on the virtual table and a query that modifies data on the base table coincide the parallel query might fail with a 244 error The Dirty Read isolation level can result in returning phantom rows from other sessions that are never committed If you do not want to set the Dirty Read isolation level and you want to run a parallel query at a time when you know that the base table is being updated you can disable PDQ for the transaction The results of parallel queries on fragmented virtual tables are not necessarily ordered by the primary key values You can ensure that the results are ordered properly by including an ORDER BY clause in the query that specifies the primary key and the timestamp column If yo
232. e series elements Syntax Apply sql_express varchar ts TimeSeries returns TimeSeries Apply sql_express varchar multiset_ts multiset TimeSeries returns TimeSeries Apply sql_express varchar filter lvarchar ts TimeSeries returns TimeSeries Apply sql_express lvarchar filter lvarchar multiset_ts multiset TimeSeries returns TimeSeries Apply sql_express lvarchar begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 ts TimeSeries returns TimeSeries with handlesnulls Apply sql_express lvarchar begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 multiset_ts multiset TimeSeries returns TimeSeries with handlesnulls Apply sql_express lvarchar filter lvarchar begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 ts TimeSeries returns TimeSeries with handlesnulls Apply sql_express lvarchar filter lvarchar begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 multiset_ts multiset TimeSeries returns TimeSeries with handlesnulls sql_express The SQL expression or function to evaluate filter The filter expression used to select time series elements IBM Informix TimeSeries Data User s Guide begin_stamp The begin point of the range See Clip function on page 7 31 for more detail about range specifications end_stamp The end point o
233. e series subtype partitionDesc tsPartitionDesc_t The description of the partition for the container flags INTEGER Stores flags to indicate Whether the container is empty and always was empty Whether the time series is regular or irregular Whether the container is enabled for Enterprise Replication Whether the container is a rolling window container VARCHAR 128 1 DEFAULT NULL pool The name of the container pool to which the container belongs NULL indicates that the container does not belong to a container pool The default container pool is named autopool Chapter 2 Data types and system tables 2 9 The TSContainerTable table is managed by the database server Do not modify it directly Rows in this table are automatically inserted or deleted when containers are created or destroyed You can create or destroy containers by using the TSContainerCreate and TSContainerDestroy procedures which insert and delete rows in the TSContainerTable table To get a list of containers in the database run the following query SELECT NAME FROM TSContainerTable To get a list of the containers in the default container pool run the following query SELECT NAME FROM TSContainerTable WHERE pool autopool Related concepts Monitor containers on page 3 18 Related reference TSContainerWindow Table TSContainerCreate procedure on page 7 93 TSContainerDestroy proc
234. e table on page 2 9 Related tasks Creating containers on page 3 15 Configuring additional container Related reference TSContainerDestroy procedure 7 98 The TSContainerDestroy procedure deletes the container row from the TSContainerTable table and removes the container and its corresponding system catalog rows Syntax TSContainerDestroy container_name varchar 128 1 container_name The name of the container to destroy Description You can destroy a container only if no time series exist in that container even an empty time series prevents a container from being destroyed Only users with update privileges on the TSContainerTable table can run this The following example destroys the container ctnr_stock execute procedure TSContainerDestroy ctnr_stock Related concepts IBM Informix TimeSeries Data User s Guide TSInstanceTable table on page 2 12 TSContainerTable table on page 2 9 Related tasks Creating containers on page 3 15 Related reference TSContainerCreate procedure on page 7 93 TSContainerLock procedure The TSContainerLock procedure controls whether multiple sessions can write to a container at one time Syntax TSContainerLock container_name varchar 128 flag integer container_name The name of the container Must be an existing container name flag Controls whether multiple sessions can write to the container 0 Multiple sess
235. e time series data for one hour by using the Clip function from the TimeSeries subtype named actual to return in XML format SELECT TSToXML meterdata esi_id Clip actual 2010 09 08 12 00 00 datetime year to second 2010 09 08 13 00 00 datetime year to second FROM ts_data WHERE esi_id 2250561334 The following XML data is returned lt meterdata gt lt jd gt 2250561334 lt id gt lt Al1Data gt 1 lt Al1Data gt lt meter_data gt lt tstamp gt 2010 09 08 12 15 00 00000 lt tstamp gt lt value gt 0 9170000000 lt value gt lt meter_data gt lt meter_data gt lt tstamp gt 2010 09 08 12 15 00 00000 lt tstamp gt lt value gt 0 4610000000 lt value gt lt meter_data gt lt meter_data gt lt tstamp gt 2010 09 08 12 15 00 00000 lt tstamp gt lt value gt 4 1570000000 lt value gt lt meter_data gt lt meter_data gt lt tstamp gt 2010 09 08 12 15 00 00000 lt tstamp gt lt value gt 6 3280000000 lt value gt lt meter_data gt lt meter_data gt lt tstamp gt 2010 09 08 12 15 00 00000 lt tstamp gt lt value gt 2 6690000000 lt value gt lt meter_data gt lt meterdata gt The name of the TimeSeries subtype is meter_data and its columns are tstamp and value The value of 1 in the AllData tag indicates that for this example all data was returned Related concepts Planning for accessing time series data on page 1 24 Related reference Time series routines that run
236. eVirtualTab procedure Example of creating a virtual table 2 Example of creating a fragmented virtual table TSCreateExpressionVirtualTab procedure The TSVTMode parameter Drop a virtual table Trace functions The TSSetTraceFile function TSSetTraceLevel function Chapter 5 Calendar pattern routines AndOp function CalPattStartDate inet Collapse function Expand function NotOp function OrOp function Chapter 6 Calendar routines AndOp function CalIndex function CalRange function CalStamp function CalStartDate function OrOp function iv IBM Informix TimeSeries Data User s Guide 3 3 3 4 z oob 3 10 3 12 eie 3 13 18 3 14 3 15 3 16 3 18 wo lO 3 22 3 23 3 24 3 27 3 27 3 27 3 31 3 33 3 34 3 35 3 36 lt 3 37 3 37 4 1 4 2 4 2 4 3 4 5 4 5 48 4 10 4 13 4 16 4 26 4 26 4 26 4 27 5 1 5 2 5 3 5 4 5 5 6 1 6 2 6 3 6 5 6 5 Chapter 7 Time series SQL routines Time series SQL routines sorted by task Time series routines that run in parallel The flags argument values Abs function Acos function AggregateBy function AggregateRange function Apply function ApplyBinaryTsOp Aneto ApplyCalendar function ApplyOpToTsSet function ApplyUnaryTsOp function Asin function Atan function Atan2 function Binary arithmetic functions BulkLoad function Clip
237. eady populated with the time invariant data If the table already has time series data the new data overwrites the old data or is appended to the existing time series depending on the time stamps To set up the TSIncLoad example create the procedure the row subtype and the database table as shown in the following example The code for this example is in the INFORMIXDIR extend TimeSeries version examples directory create procedure if not exists TSIncLoad table name lvarchar file_name lvarchar calendar_name lvarchar origin datetime year to fraction 5 threshold integer regular boolean container_name lvarchar nelems integer external name tmp Loader bld TSIncLoad language C create row type day_info ValueDate datetime year to fraction 5 carryover char 1 spread integer pricing_bmk_id integer price float yield float priority char 1 create table corporates Secid integer UNIQUE series TimeSeries day_info create index if not exists corporatesIdx on corporates Secid execute procedure TSContainerCreate ctnr_daily rootdbs day_info 0 0 insert into corporates values 25000006 container ctnr_daily origin 2011 01 03 00 00 00 00000 calendar daycal threshold 0 execute procedure TSIncLoad corporates tmp daily dat daycal 2011 01 03 00 00 00 00000 0 ns Ue gt ctnr_daily 1 Any name can be used for the corporates table The corporates table can ha
238. eate a rolling window container a row is inserted in the TSContainerTable and the TSContainerWindowTable table As partitions are added for time series data rows are added to the TSContainerUsageActiveWindowVTI and the TSContainerUsageDormantWindowVTI tables Example 1 Create a rolling window container The following example creates a rolling window container execute procedure TSContainerCreate readings_ container containerdbs rt_raw_intvl 25600 12800 2011 01 01 00 00 00 00000 datetime year to fraction 5 month 4 10 dbsO dbs1 dbs2 dbs3 dbs4 1 16 8 The example configures a rolling window container that has the following properties e The container name is readings_container e The dbspace for the container partition is named containerdbs e The name of the time series is rt_raw_intvl e The first extent size of the partitions is 25600 KB e The next extent size of the partitions is 12800 KB e The active window contains up to 4 partitions e The dormant window contains up to 10 partitions e The origin of the container is 2011 01 01 00 00 00 00000 e Partitions each hold a month of data e The dbspaces for partitions are named dbs0 dbs1 dbs2 dbs3 and dbs4 e Partitions that no longer fit into the dormant window are automatically destroyed e The first extent size of the dbspace containerdbs is 16 KB e The next extent size of the dbspace containerdbs is 8 KB Example 2 Create a contai
239. eck pt and pT Display tblspaces for a Table or Fragment Administrator s Reference Manage container pools By default containers that are automatically created are added to the default container pool named autopool To add a container into a container pool or move a container from one container pool to another run the TSContainerSetPool procedure and specify the new container pool name If the container pool does not exist it is created To remove a container from a container pool run the TSContainerSetPool procedure without specifying a container pool name To delete a container pool remove all the containers from it Chapter 3 Create and manage a time series through SQL 3 19 3 20 Example 1 Creating a container and adding it to the default container pool Suppose that you have a TimeSeries subtype named smartmeter_row you want to store the time series data in a different dbspace than the table is in and you do not want to specify the container name when you insert data The following statements create a container that is called ctn_sm1 for the smartmeter_row time series and add the container to the default container pool EXECUTE PROCEDURE TSContainerCreate ctn_sm1 tsspacel smartmeter_row 0 0 EXECUTE PROCEDURE TSContainerSetPool ctn_sm1 autopool When you insert data for the smartmeter_row time series without specifying a container name the database server stores the data in the container na
240. ed by other factors such as the small amount of space that is needed for the slot table and the null bitmap for each element The equation might underestimate the amount of required space if the row size of your time series data size is small The maximum Chapter 1 Informix TimeSeries solution 1 21 1 22 number of elements that are allowed on a data page is 254 If the row size of your time series data is small the page might contain the maximum number of elements but have unused space especially if you are not using a 2 KB page size Rolling window container storage requirements Rolling window containers allow you to limit the amount of current data to a specific time range Rolling window containers have two different types of partitions with different storage requirements the container partition and the window partitions The container partition contains information about the rolling window intervals and partitions The window partitions store time series elements The container partition typically requires much less space than the window partitions To avoid allocating unnecessary space for the container partition store the container partition and the window partitions in different dbspaces that have different extent sizes A rolling window container has one container partition Use the following formula to estimate the size of the container partition Space container_name_length dbspace_name_length 48 active_windowsi
241. ed reference Time series routines that run in parallel on page 7 7 GetLastValid function 7 58 The GetLastValid function extracts the element for the given time stamp in a time series Syntax GetLastValid ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns row ts The source time series tstamp The time stamp for the element flags Valid values for the flags argument are described in falues on page 7 9 lues on page 7 9 The default is 0 Description For regular time series this function returns the element at the calendar s latest valid timepoint at or before the given time stamp For irregular time series it returns the latest element at or preceding the given time stamp The equivalent API function is ts_last_valid Returns A row type containing the nearest element at or before the given time stamp The type of the row is the same as the time series subtype If the time stamp is earlier than the origin of the time series NULL is returned Example The following query returns the last valid entry in a time series at or before a given time stamp IBM Informix TimeSeries Data User s Guide select GetLastValid stock_data 2011 01 08 00 00 00 00000 from daily stocks where stock_name IBM Related reference Time series routines that run in parallel on page 7 7 Clip function on page 7 3 ClipCount function on page
242. edure on page 7 98 TSContainerWindowTable The TSContainerWindowTable table has one row for each rolling window container Two virtual tables are based on the TSContainerWindowTable table The virtual tables contain a row for each partition e The TSContainerUsageActiveWindowVTI contains information about the partitions in the active window e The TSContainerUsageDormantWindowVTI contains information about the partitions in the dormant window Table 2 6 The columns in the TSContainerWindowTable table Column name Data type Description name VARCHAR 128 The name of the rolling window container windowinterval VARCHAR 8 The size of the interval of each partition year month week or day activewindowsize INTEGER The number of partitions in the active window dormantwindowsize INTEGER The number of partitions in the dormant window windowspaces LVARCHAR 4096 The list of dbspaces in which partitions are stored activewindow TimeSeries TSContainerWindow_r 2 10 IBM Informix TimeSeries Data User s Guide A row type that contains interval information for the partitions that are in the active window To view the starting timestamp and location information about the partitions query the TSContainerUsageActiveWindowVTI table Table 2 6 The columns in the TSContainerWindowTable table continued Column name Data type Description dormantwindow TimeSeries TSCon
243. eek_day expression 1 on 5 off 1 on day OrOp function The OrOp function returns the union of the two calendar patterns Syntax OrOp cal_pattl CalendarPattern cal_patt2 CalendarPattern returns CalendarPattern cal_patt1 The first calendar pattern cal_patt2 The second calendar pattern Description The OrOp function returns a calendar pattern that has every interval on that was on in either of the calendar patterns the rest of the intervals are off If the two patterns have different sizes of interval units the resultant pattern has the smaller of the two intervals Returns A calendar pattern that is the result of two others that are combined with the OR operator Example The examples use the following three calendar pattern definitions select from CalendarPatterns where cp_name workweek_day cp_name workweek_day cp_pattern 1 off 5 on 1 off day select from CalendarPatterns where cp_name fourday_day cp_name fourday_day cp_pattern 1 off 4 on 2 off day select from CalendarPatterns where cp_name workweek_hour Chapter 5 Calendar pattern routines 5 5 5 6 cp_name workweek_hour cp_pattern 32 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour The following OrOp statement returns the union of two daily calendar patterns select Or0p pl cp_pattern p2 cp_pattern from CalendarPatterns pl CalendarPatterns p2 where pl cp_name workweek_day
244. egation on the BSON field in an INTEGER column CREATE ROW TYPE outrow timestamp datetime year to fraction 5 x int If a column and a BSON field have the same name the column takes precedence The following statement returns the maximum value from the v1 INTEGER column SELECT TSRollup tsdata max v1 FROM tj TSRollup origin 2011 01 01 00 00 00 00000 calendar ts_15min container threshold regular 20 1 row s retrieved The following two equivalent statements return the maximum value from the v1 field in the data BSON column which is column 1 in the TimeSeries row type SELECT TSRollup tsdata max data v1 timeseries outrow FROM tj SELECT TSRollup tsdata max 1 v1 timeseries outrow FROM tj TSRollup origin 2011 01 01 00 00 00 00000 calendar ts_15min container threshold 0 regular 99 00000000000 1 row s retrieved The aggregated time series that is returned has the TimeSeries data type outrow If you do not cast the result to a row type that has appropriate columns for the results the statement fails Related reference TSRowNametToList function The TSRowNameToList function returns a list collection of rows containing one individual column from a time series column plus the non time series columns of a table Null elements are not added to the list Syntax TSRowNameToList ts_row row colname lvarchar returns list row not null ts_row The time series to ac
245. elated reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 15 Tan function The Tan function returns the tangent of its argument The Tan function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Positive Round and Sin Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Times function The Times function multiplies one time series by another The Times function is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Mod Plus and Pow Related reference Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 27 TimeSeriesRelease function The TimeSeriesRelease function returns an LVARCHAR string containing theTimeSeries extension version number and build date Syntax TimeSeriesRelease returns lvarchar Returns The version number and build date Example The following example shows how to get the version number using DB Access execute function TimeSeriesRelease Transpose function The Transpose function converts time series data for processing in a tabular format 7 86 IBM Informix TimeSeries Data User s Guide Syntax Transpose ts TimeSeries begin_stamp datetime year to f
246. elements are not included in the resulting time series e The resulting time series has the same regularity as the input time series e The first record in a resulting irregular time series has the timestamp of the begin point and the value of the first record from the input time series that is equal to or earlier than the begin point 1 The origin of the resulting time series is the earlier of the begin point and the origin of the input time series For regular time series timepoints that are before the origin of the time series are set to NULL For irregular time series has no effect 2 Hidden elements are included and kept hidden in the resulting time series 4 Hidden elements are included and revealed in the resulting time series 8 The resulting time series is irregular regardless of whether the input time series is irregular 16 For irregular time series the resulting time series begins with the first record that is equal to or later than the begin point For regular time series has no effect Chapter 7 Time series SQL routines 7 35 7 36 Description Begin points before the time series origin are permitted Negative counts with such time stamps result in time series with no elements Begin points before the calendar origin are not permitted If there is no entry in the calendar exactly at the requested time stamp ClipCount uses the calendar s first valid time stamp that immediately follows the given time stam
247. elements must be separated by a comma If no separator is given assume that you use a blank to separate each syntax element If a syntax element is preceded by the symbol that element is defined elsewhere The string that follows the symbol is the name of a syntax fragment rather than a literal For example the line 2 1 0P1 refers to a separate syntax fragment OP1 The following words and symbols are used next to the dotted decimal numbers Specifies an optional syntax element A dotted decimal number followed by the symbol indicates that all the syntax elements with a corresponding dotted decimal number and any subordinate syntax elements are optional If there is only one syntax element with a dotted decimal number the symbol is displayed on the same line as the syntax element for example 5 NOTIFY If there is more than one syntax element with a dotted decimal number the symbol is displayed on a line by itself followed by the syntax elements that are optional For example if you hear the lines 5 5 NOTIFY and 5 UPDATE you know that syntax elements NOTIFY and UPDATE are optional that is you can choose one or none of them The symbol is equivalent to a bypass line in a railroad diagram Specifies a default syntax element A dotted decimal number followed by the symbol and a syntax element indicates that the syntax element is the default option for all syntax elements that share the same dotted decimal number
248. ement 9 26 Function output creating time series with 3 27 G GetCalendar function 7 48 GetCalendarName function 7 49 GetCompression function 7 51 GetContainerName function 7 51 GetElem function 7 52 GetFirstElem function 7 53 GetFirstElementStamp function 7 54 GetHertz function 7 54 GetIndex function 7 55 getInt method 8 4 GetInterval function 7 55 GetLastElem function 7 56 7 57 7 61 GetLastElementStamp function 7 57 GetLastValid function 7 58 GetMetaData function 7 59 GetMetaTypeName function 7 59 GetNelems function 7 60 GetNextValid function 7 61 GetNthElem function 7 62 GetOrigin function 7 64 GetPacked function 7 64 GetPreviousValid function 7 65 GetStamp function 7 66 GetThreshold function 7 67 getTimestamp method 8 4 getVersion method 8 5 GMT converting to 9 40 H Hardware requirements 1 25 HDR and time series 1 24 Hertz data 1 8 creating time series 7 118 deleting 3 37 inserting 3 37 specifying 3 24 Hidden elements 4 3 HideElem function 7 67 Hiding an element 7 67 9 34 IfmxTimeSeries object 8 4 IfmxTimeSeries jar 8 3 Indexes base tables 4 2 industry standards xviii Informix JDBC Driver 8 1 Informix Warehouse Accelerator virtual tables 4 2 Input function creating time series with 3 24 InsElem function 3 36 7 69 INSERT statement 3 24 Inserting element 7 69 7 77 7 79 9 36 9 38 9 46 9 47 element at an offset 7 80 9 48 element at end of a time series 9 48 elements set of 7 70 7 81
249. ements If the beginning point is NULL the scan starts at the first element of the time series unless the TS_SCAN_EXACT_START value of the flags parameter is set If the end point is NULL the scan ends at the last element of the time series unless the TS_SCAN_EXACT_END value of the flags parameter is set The flags argument values The flags argument determines how a scan works on the returned set The value of flags is the sum of the wanted flag values from the following table Chapter 7 Time series SQL routines 7 87 Table 7 3 The flags argument values Flag Value Meaning TS_SCAN_HIDDEN 512 Return hidden elements marked by HideElem see HideElem function on page 7 67 TS_SCAN_EXACT_START 256 Return the element at the beginning timepoint adding null elements if necessary TS_SCAN_EXACT_END 128 Return elements up to the end point return NULL if necessary TS_SCAN_NULLS_OK 64 Return null time series elements by default time series elements that are NULL are not returned TS_SCAN_NO_NULLS 32 Instead of returning a null row return a row with the time stamp set and the other columns set to NULL TS_SCAN_SKIP_END 16 Skip the element at the end timepoint of the scan range TS_SCAN_SKIP_BEGIN 8 Skip the element at the beginning timepoint of the scan range TS_SCAN_SKIP_HIDDEN 4 Used by ts_begin_scan to tell ts_next not to return hidden elements Returns Mult
250. en elements are not included in the resulting time series e The resulting time series has the same regularity as the input time series e The first record in a resulting irregular time series has the timestamp of the begin point and the value of the first record from the input time series that is equal to or earlier than the begin point 1 The origin of the resulting time series is the earlier of the begin point and the origin of the input time series For regular time series timepoints that are before the origin of the time series are set to NULL For irregular time series has no effect 2 Hidden elements are included and kept hidden in the resulting time series 4 Hidden elements are included and revealed in the resulting time series 8 The resulting time series is irregular regardless of whether the input time series is irregular 16 For irregular time series the resulting time series begins with the first record that is equal to or later than the begin point For regular time series has no effect Description The Clip functions all take a time series a begin point and an end point for the range For regular time series the begin and end points can be either integers or time stamps If the begin point is an integer it is the absolute offset of an entry in the time series Data at the beginning and ending offsets is included in the resulting time series If the begin point is a time stamp the Clip function uses th
251. enotes the level of nesting For example if a syntax element with dotted decimal number 3 is followed by a series of syntax elements with dotted decimal number 3 1 all the syntax elements numbered 3 1 are subordinate to the syntax element numbered 3 Copyright IBM Corp 2006 2014 C 1 C 2 Certain words and symbols are used next to the dotted decimal numbers to add information about the syntax elements Occasionally these words and symbols might occur at the beginning of the element itself For ease of identification if the word or symbol is a part of the syntax element the word or symbol is preceded by the backslash character The symbol can be used next to a dotted decimal number to indicate that the syntax element repeats For example syntax element FILE with dotted decimal number 3 is read as 3 FILE Format 3 FILE indicates that syntax element FILE repeats Format 3 FILE indicates that syntax element FILE repeats Characters such as commas which are used to separate a string of syntax elements are shown in the syntax just before the items they separate These characters can appear on the same line as each item or on a separate line with the same dotted decimal number as the relevant items The line can also show another symbol that provides information about the syntax elements For example the lines 5 1 5 1 LASTRUN and 5 1 DELETE mean that if you use more than one of the LASTRUN and DELETE syntax elements the
252. ent However performance depends on many factors including the pattern of usage and distribution of your hardware If hundreds of users are performing complex analysis in the server it can overwhelm the server whereas if each client does their portion of the work the load can be better balanced Data structures for the time series API 9 2 The time series API uses four data structures The ts_timeseries structure A ts_timeseries structure is the header for a time series It can be stored in and retrieved from a time series column of a table The ts_timeseries structure contains pointers so it cannot be copied directly Use the ts_copy function to copy a time series When you pass a binary time series value ts of type ts_timeseries to mi_exec_prepared_statement you must pass ts in the values array and 0 in the lengths array The ts_tscan structure A ts_tscan structure allows you to look at no more than two time series elements at a time It maintains a current scan position in the time series and has two element buffers for creating elements An element fetched from a scan is overwritten after two ts_next calls A ts_tscan structure is created with the ts_begin_scan function and destroyed with the ts_end_scan procedure The ts_tsdesc structure A ts_tsdesc structure contains a time series ts_timeseries and data structures for working with it Among other things ts_tsdesc tracks the current element and ho
253. ent is hidden and FALSE if it is not Related reference Time series routines that run in parallel on page 7 7 ElemIsNull function FindHidden function on page 7 48 ElemlsNull function The ElemIsNull function determines if an element contains no data Syntax ElemIsNull ts TimeSeries offset integer returns Boolean ElemIsNull ts TimeSeries tstamp datetime year to fraction 5 returns Boolean ts The time series to act on offset The offset of the element to examine tstamp The time stamp of the element to examine Description Use either offset or time stamp to locate the element you want to examine Returns Returns TRUE if the element has never been written to or was written to and the data has since been deleted returns FALSE if the element contains data or is hidden Chapter 7 Time series SQL routines 7 47 Related reference Time series routines that run in parallel on page 7 7 ElemIsHidden function on page 7 47 Find Hidden function Exp function The Exp function exponentiates the time series It is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 FindHidden function The FindHidden function scans a time series and returns a
254. ent is the sum of the desired values Valid options are TSOPEN_RDWRITE The default mode for opening a time series Indicates that the time series can be read and written to TSOPEN_READ_HIDDEN Indicates that hidden elements should be treated as if they are not hidden TSOPEN_READ_ONLY Indicates that the time series can only be read TSOPEN_WRITE_HIDDEN Allows hidden elements to be written to without first revealing the element TSOPEN_WRITE_AND_HIDE Causes any elements written to a time series also to be marked as hidden TSOPEN_WRITE_AND_REVEAL Reveals any hidden element that is written TSOPEN_NO_NULLS Affects the way elements are returned that have never been allocated TS_NULL_NOTALLOCATED Usually if an element has not been allocated it is returned as NULL If TSOPEN NO_NULLS is set an element that has each column set to NULL is returned instead These flags can be used in any combination except the following four combinations e TSOPEN_WRITE_HIDDEN and TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_HIDDEN and TSOPEN_WRITE_AND_REVEAL e TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_AND_HIDE and TSOPEN_WRITE_AND_REVEAL IBM Informix TimeSeries Data User s Guide The TSOPEN_WRITE_ HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_ HIDDEN Description Almost all other functions depend on this function being called first Use ts_close
255. ep to create a time series for each meter ID with one SQL statement instead of running a separate SQL statement for each meter ID 3 Create a regular empty time series for each meter ID that uses the pre defined calendar ts_15min by running the following SQL statement which uses the time series input function INSERT INTO my_ts_data SELECT id origin 2010 11 10 00 00 00 00000 calendar ts_15min threshold regular FROM my_tmp Because you did not specify a container name the time series for each meter ID is stored in a container in the same dbspace in which the table resides The container is created automatically and is a member of the default container pool Related reference Creating the data load file You create a time series data load file by creating a virtual table based on the ts_data table and then unloading some of the columns To create the data load file 1 Create a virtual table based on the raw_reads time series column of the ts_data table by running the following SQL statement EXECUTE PROCEDURE TSCreateVirtualTab my_vt ts_data 0 raw_reads You use the virtual table to create a data load file 2 Unload the data from the tstamp and value columns from the virtual table into a file named my_meter_data un by running the following SQL statement UNLOAD TO my_meter_data unl SELECT loc_esi_id tstamp value FROM my_vt Related reference TSCreateVirtualTab procedure on pa
256. eparate multiple values with commas Usage Use the TSL_PutSQL function to load time series data from another table as part of a loader program You must run the TSL_PutSQL function in the context of a loader session that was initialized by the TSL_Init function You can run the TSL_PutSQL function multiple times in the same session The data is stored in the database server until you run the TSL_Flush function to write the data to disk Returns e An integer that indicates the number of records that were inserted e An exception if no records were inserted Examples These examples run in the context of an initialized loader session Example 1 Load a primary key and multiple columns The following example selects data from a table named dataload from a primary key column a timestamp column and a column with other values EXECUTE FUNCTION TSL_PutSQL meter readings SELECT id lvarchar tstamp value FROM dataload Example 2 Load a primary key and a ROW data type The following example selects data from a table named dataload from a primary key column and a ROW data type that consists of a timestamp field and a value field EXECUTE FUNCTION TSL_PutSQL meter readings SELECT id lvarchar row tstamp value FROM dataload Related tasks Writing a loader program on page 3 31 TSL_SessionClose function The TSL_SessionClose function closes the current database session Syntax TSL_SessionClose handle
257. er of duplicate values that were inserted for irregular time series if the writeflag value was 1 or 257 instance_ids The number of time series instances that were affected by the flush commits The number of committed transactions for the TSL_Commit function For the TSL_Flush and TSL_FlushAll functions this number is always 0 rollbacks For the TSL_Commit function whether transactions were rolled back 0 no rolled back transactions 1 a transaction was interrupted by the user exceptions The number of exceptions that were raised during the flush For example attempting to insert an element that has a timestamp that is before the origin results in an exception Attempting to save data into a container that does not have enough available space also results in an exception errors The number of errors in the load operation For example incorrectly formatted input data results in an error Example The following statement returns the results of the TSL_FlushInfo function as a table SELECT mr FROM TABLE TSL_FlushInfo ts_data raw_reads AS TAB mr containers 4 elements 198458 duplicates 0 instance_ids 522 7 130 IBM Informix TimeSeries Data User s Guide commits rollbacks exceptions errors Related tasks Writing a loader program on page 3 31 Related reference ooof TSL_Commit function on page 7 12 TSL_FlushAll function on page 7 128 TSL_FlushStatus function The TSL_F
258. er s Guide U Unary arithmetic functions Abs 7 11 Acos 7 11 Asin 7 27 Atan 7 27 Cos 7 38 description 7 156 Exp 7 48 Logn 7 74 Negate 7 75 Positive 7 77 Round 7 84 Sin 7 85 Sqrt 7 86 Tan 7 86 Union function 7 157 Union of time series 7 157 UPDATE statement 3 35 UPDATE STATISTICS statement 4 2 Updating element 9 46 9 47 element in a time series 9 54 metadata in a time series 9 54 Updating a set of elements 7 160 Updating an element 7 159 UpdElem function 7 159 UpdMetaData function 7 160 UpdSet function 7 160 V Version TimeSeries Java class 8 5 Virtual table 4 1 Virtual table interface 4 8 Virtual tables creating 4 16 creating fragmented tables 4 10 creating with expressions 4 13 display of data 4 3 insert time series data 4 4 performance 4 2 structure 4 2 VirtualTableName parameter 4 5 Visual disabilities reading syntax diagrams C 1 W WithinC function 7 161 WithinR function 7 161 Printed in USA SC27 4535 03 p ny S Jasf Qq s n g w XIWOJUY INI Ol 2 UOlsi9 XILUOJU AWW JONPOAd XIWOJUT gt UOLZEWNOJUL JULAS
259. erence TSCreateVirtualTab procedure Creating a time series virtual table You can create a virtual table that is based on a time series or based on the results of an expression on a time series You can update or insert data through a virtual table that is based on a time series table You cannot update or insert data through a virtual table that is based on an expression on a time series To create a virtual table that is based on a table that contains a TimeSeries column run the TSCreateVirtualTab procedure To create a virtual table that is based on the results of an expression that is performed on a time series run the TSCreateExpressionVirtualTab procedure If you alter the base table you must drop and re create the virtual table Related reference TSCreateVirtualTab procedure TSCreateExpressionVirtualTab procedure on page 4 13 TSCreateVirtualTab procedure The TSCreateVirtualTab procedure creates a virtual table that is based on a table that contains a TimeSeries column Syntax TSCreateVirtualTab VirtualTableName varchar BaseTableName lvarchar NewTimeSeries lvarchar TSVTMode integer default 0 TSColName lvarchar default NULL TSCreateVirtualTab VirtualTableName varchar BaseTableName lvarchar NewTimeSeries lvarchar TSVTMode lvarchar TSColName lvarchar default NULL Virtual TableName The name of the new virtual table BaseTableName The name of the base table NewTimeSeries optio
260. eries The others are Divide Minus Mod Plus Pow and Times Related reference Time series routines that run in parallel on page 7 Binary arithmetic functions Binary arithmetic functions The standard binary arithmetic functions Atan2 Plus Minus Times Divide Mod and Pow can operate on time series data The Plus Minus Times and Divide functions can also be denoted by their standard operators and Syntax Function ts TimeSeries ts TimeSeries returns TimeSeries Function number scalar ts TimeSeries returns TimeSeries Function ts TimeSeries number scalar returns TimeSeries Function row row ts TimeSeries returns TimeSeries Chapter 7 Time series SQL routines 7 27 7 28 Function ts TimeSeries row row returns TimeSeries ts The source time series One of the two arguments must be a time series for this variant of the functions The two inputs must be compatible under the function number A scalar number Must be compatible with the source time series row A row type Must be compatible with the source time series Description In the first format both arguments are time series The result is a time series that starts at the later of the starting times of the inputs The end point of the result is the later of the two input end points if both inputs are irregular The result end point is the earlier of the input regular time series end points if one or more of t
261. eries with the same subtype daybar t DATETIME YEAR TO FRACTION 5 high REAL low REAL close REAL vol REAL The expression could be any of e O high 1 high 2 0 low 1 1ow 2 0 1 1 1 2 0 2 1 2 2 e O high 1 high e avghigh The signature of avghigh is avghigh argl daybar arg2 daybar returns one_real The syntax for the filter argument is similar to the previous expression except that it must evaluate to a single column Boolean result Only those elements that evaluate to TRUE are selected 0 vol gt 1 vol and 0 close gt 0 high 0 low 2 Apply with the multiset_ts argument assigns parameter numbers by fetching TimeSeries values from the set and processing them in the order in which they are returned by the set management code Since sets are unordered parameters might not be assigned numbers predictably Apply with the multiset_ts argument is useful only if you can guarantee that the TimeSeries values are returned in a fixed order There are two ways to guarantee this e Write a C function that creates the set and use the function as the multiset_ts argument to Apply The C function can return the TimeSeries values in any order you want e Use ORDER BY in the multiset_ts expression Apply with the multiset_ts argument evaluates the expression once for every timepoint in the resulting union of time series values When all the data in the clipped period has been exhausted Apply r
262. ern routines can be useful when comparing time series that are based on different calendars For example to compare peak time business usage of a computer network across multiple countries requires accounting for different sets of public holidays in each country An efficient way to handle this is to define a calendar for each country and then create the calendar intersections to perform business day comparisons Calendar pattern routines can be run in SQL statements or sent from an application using the DataBlade API function mi_exec Related concepts Related reference CalendarPattern data type on page 2 1 AndOp function The AndOp function returns the intersection of two calendar patterns Syntax AndOp cal_patt1 CalendarPattern cal_patt2 CalendarPattern returns CalendarPattern cal_patt1 The first calendar pattern cal_patt2 The second calendar pattern Description This function returns a calendar pattern that has every interval on that was on in both calendar patterns the rest of the intervals are off If the specified patterns do not have the same interval unit the pattern with the larger interval unit is expanded to match the other Returns A calendar pattern that is the result of two others that are combined by the AND operator Copyright IBM Corp 2006 2014 5 1 Example The first AndOp statement returns the intersection of two daily calendar patterns and the second AndOp statement returns t
263. es on page 6 1 Time series storage Time series data is stored in a container unless the data remains small enough to fit in a single row of a table When a time series is stored in a container the data is stored contiguously and is retrieved with a minimum number of disk reads If you do not create time series containers before you insert time series data the containers are created automatically as needed You can manage storage by controlling in which dbspaces the time series data is stored and by deleting old time series data IBM Informix TimeSeries Data User s Guide A container is mapped to a disk partition in a dbspace A dbspace is a logical grouping of physical storage chunks The following illustration shows the architecture of containers in the database A database usually contains multiple dbspaces A dbspace can contain multiple containers along with tables and free space A container can contain data for one or more sources for example electricity meters The time series data for a particular source is stored on pages in time stamp order Database dbspaces dbspace1 container1 container 1 other tables data for data for data for i meter1 meter2 meter3 Data storage Multiple dbspaces Partitions in dbspace1 Data pages in container1 on disk in the database Figure 1 3 Architecture of the default configuration of a container in a database When you insert data into a time series and you do not specify a container name
264. es 7 112 TSContainerTotalUsed 7 113 TSContainerUsage 7 114 TSContainerUsageActiveWindowVTI virtual table 2 11 TSContainerUsageDormantWindowVTI virtual table 2 12 TSContainerWindowTable system table 2 10 TSCreate function 7 116 TSCreateExpressionVirtualTab 4 5 4 13 TSCreatelrr function 7 118 TSCreateVirtualTab procedure 4 5 TSDecay function 7 122 TSInstanceTable system table 2 12 TSL_Attach function 7 123 TSL_Commit function 7 124 TSL_Flush function 7 126 TSL_FlushAll function 7 128 TSL_FlushInfo function 7 129 TSL_FlushStatus function 7 131 TSL_GetKeyContainer function 7 131 TSL_GetLogMessage function 7 132 TSL_Init function 7 133 TSL_Put function 7 135 TSL_PutRow function 7 137 TSL_PutSQL function 7 138 TSL_SessionClose function 7 139 TSL_SetLogMode function 7 140 TSL_Shutdown procedure 7 141 TSOPEN_NO_NULLS flag 7 9 TSOPEN_RDWRITE flag 7 9 TSOPEN_READ_HIDDEN flag 7 9 TSOPEN_WRITE_AND_HIDE flag 7 9 TSOPEN_WRITE_HIDDEN flag 7 9 TSPrevious function 7 142 TSRollup function 7 142 TSRowNameToList function 7 145 TSRowNumToList function 7 146 TSRowToList function 7 147 TSRunningAvg function 7 92 7 148 TSRunningCor function 7 149 TSRunningMed function 7 150 TSRunningSum function 7 151 TSRunningVar function 7 152 TSSetToList function 7 153 TSSetTraceFile function 4 26 TSSetTraceLevel function 4 26 4 27 TSToXML function 7 154 TSVTMode parameter 4 16 TSWRITE_AND_REVEAL flag 7 9 Type map 8 4 X 8 IBM Informix TimeSeries Data Us
265. es data and that is fragmented by expression Prerequisites Before you run the statements in this example create three dbspaces named dbs1 dbs2 and dbs3 Otherwise substitute your dbspace names in the example About this example When you create the stores_demo database all the setup tasks for creating and loading a time series table are complete The ts_data table in the stores_demo database contains time series data The ts_data_v table is a virtual table that is based on the ts_data table The ts_data table and the ts_data_v virtual table are not fragmented In this example you alter the ts_data table to fragment it by expression into three dbspaces You re create the ts_data_v virtual table as a fragmented virtual table Then you run queries in parallel against the ts_data_v virtual table Preparing for parallel queries Run the SQL statements in these steps from an SQL editor such as DB Access or IBM OpenAdmin Tool OAT for Informix To prepare for running parallel queries on the ts_data_v virtual table 1 If necessary create the stores_demo database by running the following command dbaccessdemo 2 If necessary set the PDQPRIORITY environment variable to a value other than OFF to enable parallel database queries For example run the following SQL statement SET PDQPRIORITY 100 3 Specify an explain output file and enable explain output so that you can determine whether your queries run in parallel by running the fol
266. es data from the json un file CREATE EXTERNAL TABLE ext_tstable_j id INT tstamp DATETIME YEAR TO FRACTION 5 json_doc JSON USING FORMAT DELIMITED DATAFILES DISK path json un1 Substitute path with the directory for the json un file Initialize a global context and open a database session by running the TSL_Init function EXECUTE FUNCTION TSL_Init tstable_j ts Load the data by running the TSL_PutSQL function with an SQL statement that selects the data from the external table and casts the JSON column to BSON EXECUTE FUNCTION TSL_PutSQL tstable_j ts SELECT id tstamp sensor_data bson FROM ext_tstable_j Save the data to disk by running the TSL_FlushAl function BEGIN EXECUTE FUNCTION TSL_FlushAl1 tstable_j ts COMMIT WORK Close the session and remove the global context by running the TSL_SessionClose and TSL_Shutdown functions EXECUTE FUNCTION TSL_SessionClose tstable_j ts EXECUTE FUNCTION TSL_Shutdown tstable_j ts Chapter 3 Create and manage a time series through SQL 3 11 11 Create a virtual table that is named virt_tstable_j by running the TSCreateVirtualTab procedure EXECUTE PROCEDURE TSCreateVirtualTab virt_tstable_j tstable_j 12 View the virtual table by running a SELECT statement Cast the sensor_data column to JSON so that you can view the data SELECT id tstamp sensor_data json FROM virt_tstable j expression id tstamp sensor_data
267. esMeta returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 container_name_ lvarchar compression lvarchar metadata TimeSeriesMeta multiset_rows multiset returns TimeSeries with handlesnulls cal_name The name of the calendar for the time series origin The origin of the time series which is the first valid date from the calendar for which data can be stored in the series threshold The threshold for the time series If the time series stores more than this number of elements it is converted to a container Otherwise it is stored directly in the row that contains it The default is 20 The size of a row that contains an in row time series cannot exceed 1500 bytes If a time series has too many bytes to fit in a row before this threshold is reached the time series is put into a container hertz Optional An integer that specifies whether the times series stores hertz data 0 The time series does not contain hertz data 1 255 The number of records per second If you set the hertz parameter to a value other than 0 the values of the threshold parameter must be 0 nelems Optional The number of elements that are allocated for the resultant time series If the number of elements exceeds this value the time series is expanded through reallocation container_name The name of the container that is used to store the time series Can be NULL co
268. esc dst_tsdesc mi_boolean nodups src_tsdesc The source time series descriptor dst_tsdesc The destination time series descriptor nodups Determines whether to overwrite an element in the destination time series if there is an element at the same time stamp in the source time series This argument is ignored if the destination time series is regular Description The two descriptors must meet the following conditions e The origin of the source time series must be after or equal to that of the destination time series e The two time series must have the same calendar If nodups is MI_TRUE the element from the source time series overwrites the element in the destination time series For irregular time series if nodups is MI_FALSE and there is already a value at the existing timepoint the update is made at the next microsecond after the last element in the given second If the last microsecond in the second already contains a value an error is raised The equivalent SQL function is PutTimeSeries Returns The time series associated with the destination time series descriptor Related reference PutTimeSeries function on page 7 82 The ts_put_elem function on page 9 46 Chapter 9 Time series API routines 9 49 The ts_reveal_elem function The ts_reveal_elem function makes the element at a given time stamp visible to a scan It reverses the effect of ts_hide_elem Syntax ts_timeseries ts_reve
269. esents a range of values Fewer bytes are needed to represent a grid than a numeric value The quantization algorithm can be lossy The quantization algorithm allows NULL values The quantization compression algorithm is suitable when records are frequent and the values are highly variable You specify the upper and lower boundaries of the values of the data and the number of bytes to store for each value The larger the difference between the upper and lower boundaries and the smaller the compressed size that you specify the more compact and possibly lossy the data becomes IBM Informix TimeSeries Data User s Guide Linear The linear compression algorithm represents values as line segments which are defined by two end points If the values are within the supplied deviation the values are not recorded The linear compression algorithm records a value only when a new value deviates too much from the last recorded value The linear compression algorithm does not allow NULL values The linear compression algorithm is suitable when values vary little The larger the maximum deviation that you specify the more compact and possibly lossy the data becomes Use the boxcar variant if you need fast reading and writing performance Use the swing door variant if you need a higher compression ratio You can combine compression types and choose the quantization linear boxcar or quantization linear swing door compression algorithm You can choos
270. etInterval function on page 7 55 GetOrigin function on page 7 64 GetCompression function The GetCompression function returns the compression string if the time series data is compressed 7 50 IBM Informix TimeSeries Data User s Guide Syntax GetCompression ts TimeSeries returns string ts The name of the time series Description Use the GetCompression function to determine the type of compression that is used in a time series that contains compressed numeric data Returns Returns a string that represents the compression type if the time series contains compressed data returns NULL if the time series does not contain compressed data Example The following statement indicates that the time series that is named compress_test uses the compression type Quantization SELECT GetCompression compress test FROM tstable expression n q 1 1 100 1s 0 10 1b 0 10 q1s 2 0 15 100 100000 qlb 2 0 25 100 100000 1 row s retrieved Related concepts Manage packed data on page 3 37 Related reference The ts_get_compressed function on page 9 29 GetContainerName function The GetContainerName function returns the name of the container for the given time series Syntax GetContainerName ts TimeSeries returns lIvarchar ts The time series from which to obtain the container name Description The API equivalent of this function is ts_get_containername Returns The name
271. etMetaTypeName function on page 7 5 id page 7 11 dMetaData function on page 7 15 on page 7 116 The ts_free procedure on page 9 26 14 Sic Q a o mi a g mh c QO a O 5 oo Ne E WN 0 a o S oO ua fan 5 2 a 5 Q 5 The ts_open function on page 9 44 The ts_get_metadata function on page 9 3 m The ts_get_typeid function on page 9 3 The ts_update_metadata function on page 9 54 The ts_current_offset function 9 20 The ts_current_offset function returns the offset for the last element returned by ts_next Syntax mi_integer ts_current_offset ts_tscan tscan tscan The scan descriptor returned by ts_begin_scan IBM Informix TimeSeries Data User s Guide Returns The offset of the last element returned If no element has been returned yet the offset of the first element is returned For irregular time series ts_current_offset always returns 1 Related reference The ts_begin_scan function on page 9 7 The ts_current_timestamp function The ts_current_timestamp function finds the time stamp that corresponds to the current element retrieved from the scan Syntax mi_datetime ts_current_timestamp ts_tscan scan scan The scan descriptor returned by ts_begin_scan Returns If no elements have been retrieved the value returned is the time stamp of the first e
272. et_stamps The multiset of time stamps to be made visible to a scan Returns The modified time series Example The following example hides the element at 2011 01 03 in the IBM time series and then reveals it select HideElem stock_data 2011 01 03 00 00 00 00000 from daily_stocks where stock_name IBM select RevealElem stock_data 2011 01 03 00 00 00 00000 from daily_stocks where stock_name IBM Related reference Time series routines that run in parallel on page 7 7 HideElem function on page 7 67 The ts_reveal_elem function on page 9 50 RevealRange function The RevealRange function makes hidden elements in a specified date range visible It reverses the effect of HideRange Syntax Reveal Range ts TimeSeries start datetime year to fraction 5 end datetime year to fraction 5 returns TimeSeries ts The time series to act on start The time stamp at the start of the range end The time stamp at the end of the range Chapter 7 Time series SQL routines 7 83 Returns The modified time series Related reference Time series routines that run in parallel on page 7 7 HideRange function on page 7 68 Round function The Round function rounds a time series to the nearest whole number The Round function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Positive Sin Sqrt and T
273. eturns a time series of a given stock s data for each of the four working days select ApplyCalendar stock_data fourdaycal from daily_stocks where stock_name IBM Related reference ApplyOpToTsSet function The ApplyOpToTsSet function applies a binary arithmetic function to a set of time series Syntax ApplyOpToTsSet func_name lvarchar multiset_ts multiset TimeSeries returns TimeSeries func_name The name of a binary function See Binary arithmetic functions on page for more information multiset_ts A multiset of time series that are compatible with the function All the time series in the multiset must have the same type Description All the time series must have the same type If the multiset is empty then ApplyOpToTsSet returns NULL If the multiset contains only one time series then ApplyOpToTsSet returns a copy of that time series If the multiset contains exactly two time series ts1 and ts2 then ApplyOpToTsSet returns ApplyBinaryTsOp func_name ts1 ts2 If the multiset contains three time series ts1 ts2 and ts3 then ApplyOpToTsSet returns ApplyBinaryTsOp func_name ApplyBinaryTsOp func_name ts1 ts2 ts3 and so on Returns A time series of the same type as the time series in the multiset The calendar of the resulting time series is the union of the calendars of the input time series The resulting time series is regular if all the input times series are regular and irregular i
274. eturns the resulting series Apply uses the optional clip time range to restrict the data to a particular time period If the beginning timepoint is NULL then Apply uses the earliest valid timepoint of all the input time series If the ending timepoint is NULL then Apply uses the latest valid timepoint of all the input time series When the optional clip time range is not used it is equivalent to both the beginning and ending timepoints being NULL Apply considers all elements If both the clip time range and filter expression are given then clipping is done before filtering If you use a string literal or NULL for the clip time range you should cast to DATETIME YEAR TO FRACTION 5 on at least the beginning timepoint to avoid ambiguity in function resolution When more than one input time series is specified a union of all input time series is performed to produce the source of data to be filtered and evaluated by Apply Hence Apply acts as a union function with extra filtering and manipulation of union results For details on how the Union function works see on page 7 157 IBM Informix TimeSeries Data User s Guide Returns A new time series with the results of evaluating the expression on every selected element from the source time series Example The following example uses Apply without a filter argument and without a clipped range select Apply high low datetime 2011 01 01 year to day datetime 2011 01 06 yea
275. f Within queries calibrated WithinC and relative WithinR WithinC or within calibrated takes a time stamp and finds the period that includes that time Weeks have natural boundaries Sunday through Saturday as do years January 1 through December 31 months first day of the month through the last 24 hour days 60 minute hours and 60 second minutes WithinC allows you to specify a time stamp and find the corresponding period or periods that include it For example July 2 2010 fell on a Friday Given an hourly time series WithinC allows you to ask for all the hourly values in the series beginning on Sunday morning at midnight of that week and ending on Saturday night at 11 59 59 Of course the calendar might not mark all of those hours as active only data from active periods is returned by the Within functions WithinR or within relative takes a time stamp from the user and finds the period beginning or ending at that time For example given a weekly time series WithinR can extract all the weekly values for two years beginning on June 3 2008 WithinR is able to convert weeks to years and count forward or backward from the supplied date for the number of intervals requested Relative means that you supply the exact time stamp of interest as the begin point or end point of the range WithinR behaves slightly differently for irregular than for regular time series With regular time series the time stamp argument is always ma
276. f an element persists until the next element If you query for a value in an off period you receive the value of the last element Irregular time series have no null elements You can use a predefined calendar or define your own calendar The seven predefined calendars each have a different interval duration that ranges from one minute to one month All the predefined calendars start at the beginning of 2011 but you can alter the start date You create a calendar by inserting a row into the CalendarTable table in the format of a Calendar data type You can include the calendar pattern in the calendar definition or create a separate calendar pattern by inserting a row into the CalendarPatterns table in the format of a CalendarPattern data type You can aggregate information by selecting data and changing the calendar for the results of the query Use the AggregateBy function to aggregate data For example if you collect electricity usage information every 15 minutes but you want to know the total usage per customer per day you can specify a daily calendar in the AggregateBy function to aggregate the data You can use calendar and calendar pattern routines to manipulate calendars and calendar patterns For example you can create the intersection of calendars or calendar patterns Related reference Chapter 2 Data types and system tables on page 2 1 Chapter 5 Calendar pattern routines on page 5 1 Chapter 6 Calendar routin
277. f any of the inputs are irregular Related reference ApplyBinaryTsOp function on page 7 23 Binary arithmetic functions on page 7 27 Chapter 7 Time series SQL routines 7 25 ApplyUnaryTsOp function 7 26 The ApplyUnaryTsOp function applies a unary arithmetic function to a time series Syntax ApplyUnaryTsOp func_name lvarchar ts TimeSeries returns TimeSeries func_name The name of the unary arithmetic function ts The time series to act on Description This function operates in an analogous fashion to the unary arithmetic functions that have been overloaded to operate on time series See the description of these functions in the section Unary arithmetic functions on page 7 156 for more information For example Logn ts1 is equivalent to ApplyUnaryTsOp Logn ts1 Returns A time series of the same type as the supplied time series Example The following example uses ApplyUnaryTSOp with the Logn function create row type simple series stock_id int data TimeSeries one_real create table daily_high of type simple series insert into daily_high select stock_id Apply 0 high NULL datetime year to fraction 5 NULL datetime year to fraction 5 stock_data TimeSeries one_real from daily_stocks create table daily_low of type simple series insert into daily_low select stock_id Apply 0 low NULL datetime year to fraction 5 NULL datetime year to fraction 5 stock_dat
278. f the range See Clip function on page 7 31 for more detail about range specifications ts The first ts argument is the first series the second ts argument is the second series and so on This function can take up to eight ts arguments The order of the arguments must correspond to the desired order in the SQL expression or function There is no limit to the number of parameters in the expression multiset_ts A multiset of time series Description This function runs a user specified SQL expression on the given time series and produces a new time series containing the result of the expression at each qualifying element of the input time series You can qualify the elements from the input time series by specifying a time period to clip and by using a filter expression The sql_express argument is a comma separated list of expressions to run for each selected element There is no limit to the number of expressions you can run The results of the expressions must match the corresponding columns of the result time series minus the first time stamp column Do not specify the first time stamp as the first expression the first time stamp is generated for each expression result The parameters to the expression can be an input element or any column of an input time series You should use followed by the position of a given time series on the input time series list to represent its data element plus a dot then the number of the
279. f type smallfloat and that there are less than 100 columns in each element ts_timeseries ts_interp tsPtr fParamPtr ts_timeseries tsPtr MI_FPARAM fParamPtr ts_tsdesc descPtr ts_tselem tselem ts_tscan scan MI_CONNECTION conn ts_typeinfo xtypeinfo int scancode mi_real values TS_MAX_COLS mi_real lastValues TS_MAX_COLS newValues TS_MAX_COLS mi_boolean nulls TS_MAX_COLS mi_integer minElem curElem elem mi_integer i mi_boolean noneYet mi_integer ncols char strbuf 100 get a connection for libmi conn mi_open NULL NULL NULL open a descriptor for the timeseries descPtr ts_open conn tsPtr mi_fp_rettype fParamPtr 0 0 if ncols mi_integer mi_fp_funcstate fParamPtr 0 ncols ts_col_cnt descPtr if ncols gt TS_MAX_COLS sprintf strbuf Timeseries elements have too many columns 100 is the max got d instead ncols mi_db_error_raise NULL MI_FATAL strbuf 0 for i 1 i lt ncols i typeinfo ts_colinfo_number descPtr i if strlen typeinfo gt ti_typename strlen DATATYPE amp amp memcmp typeinfo gt ti_typename DATATYPE strlen DATATYPE 0 sprintf strbuf column was not a s got s instead DATATYPE typeinfo gt ti_typename mi_db_error_raise NULL MI_FATAL strbuf 0 mi_fp_setfuncstate fParamPtr void ncols noneYet MI_TRUE minElem 1 curElem 0 begin a scan of
280. f you create a container with the TSContainerCreate procedure the container does not belong to a container pool until you run the TSContainerSetPool procedure to move it into a container pool If the container pool specified in the TSContainerSetPool procedure does not exist the procedure creates it To move a container from one container pool to another run the TSContainerSetPool procedure and specify the destination container pool name To move a container out of a container pool run the TSContainerSetPool procedure without a container pool name The TSContainerTable table contains a row for each container and the container pool to which the container belongs Example 1 Move a container into a container pool The following statement moves a container named ctn_1 into a container pool that is named smartmeter_pool EXECUTE PROCEDURE TSContainerSetPool ctn_1 smartmeter_pool Example 2 Remove a container from a container pool The following statement removes a container named ctn_1 from its container pool EXECUTE PROCEDURE TSContainerSetPool ctn_1 Related concepts TSInstanceTable table on page 2 12 Related tasks Chapter 7 Time series SQL routines 7 111 Creating containers on page 3 15 Configuring additional container pools on page 3 20 TSContainerTotalPages function The TSContainerTotalPages function returns the total number of pages that are allocated to the specified contai
281. f your primary key has a data type of CHAR n and each value is not n bytes long you must pad the values to be n bytes long or change the data type to VARCHAR 20 Connectivity information for the Informix database server that contains the time series table The connection is created through JDBC A file of time based data that you want to load into the database or a query to select data from a database e The data must be compatible with Informix data types A load job consists of the following definitions Chapter 3 Create and manage a time series through SQL 3 27 3 28 e Record reader definition that describes the source of the data The data can be in a file or in a database including a database other than Informix e Table definition that describes the schema of the Informix table that has the TimeSeries column e Mapping definition that maps the source data to the time series table If you change your table definition you must also update the corresponding mapping definition e A connection profile for the Informix database e A connection profile for the source database if you are loading data from another database e Load properties that describe how the data is loaded Load jobs have the following default properties Existing records can be updated The timestamp date format is yyyy MM dd HH mm ss Missing rows are created and populated with NULL values Data loading is distributed among five threads
282. for ID 1046021 tstamp DATETIME YEAR TO value DECIMAL FRACTION 5 2010 11 10 00 00 00 00000 0 041 2010 11 10 00 15 00 00000 0 041 2010 11 10 00 30 00 00000 0 040 Time series data for ID 1090954 tstamp DATETIME YEAR TO value DECIMAL FRACTION 5 2010 11 10 00 00 00 00000 0 026 2010 11 10 00 15 00 00000 0 035 2010 11 10 00 30 00 00000 0 062 Figure 1 2 TimeSeries data type architecture The figure shows the ts_data table which has two columns the location_id column that identifies the source of the time series data and the reads column that contains the time series data The reads column has a data type of TimeSeries meter_data The TimeSeries meter_data data type has two columns tstamp and value The tstamp column as the first column in a TimeSeries data type has a data type of DATETIME YEAR TO FRACTION S For a regular time series such as the one in the illustration the timestamp is actually replaced by the offset from the first element The value column has a data type of DECIMAL For each source of data the reads column contains multiple rows of time series data which are ordered by time stamp All time series data for a particular source is in the same row of the table Each value of the reads column in the ts_data table is a different time series instance Related concepts TimeSeries data type on page 2 6 Related tasks
283. from daily_stocks where stock_name IBM Related reference Apply function on page 7 18 TSCmp function TSDecay function on page 7 122 TSPrevious function on page 7 141 TSRunningAvg function on page 7 14 N TSRunningSum function on page 7 151 TSCmp function The TSCmp function compares two values 7 90 IBM Informix TimeSeries Data User s Guide Syntax TSCmp valuel smallfloat valueZ smallfloat returns ints TSCmp valuel double precision valueZ double precision returns int valuel The first value to be compared value2 The second value to be compared Description Use the TSCmp function within the Apply function The TSCmp function takes either two SMALLFLOAT values or two DOUBLE PRECISION values both values must be the same type The TSCmp function can take parameters that are columns of a time series Use the same parameter format that the Apply function accepts Returns 1 If the first argument is less than the second 0 If the first argument is equal to the second 1 If the first argument is greater than the second Example The following example uses the TSCmp function to calculate the on balance volume a continuous summation that adds the daily volume to the running total if the stock or index advances and subtracts the volume if it declines select Apply TSAddPrevious TSCmp final TSPrevious final vol 2011 01 03 00 00 0
284. function The ts_nelems function returns the number of elements in the time series Syntax mi_integer ts_nelems ts_tsdesc tsdesc tsdesc The time series descriptor returned by ts_open Description The equivalent SQL function is GetNelems Returns The number of elements in the time series Related reference ClipGetCount function on page 7 37 GetNelems function on page 7 60 The ts_next function After a scan has been started with ts_begin_scan elements can be retrieved from the time series with ts_next Syntax mi_integer ts_next ts_tscan tscan ts_tselem tselem tscan The specified scan tselem A pointer to an element that ts_next fills in Description On return the ts_tselem contains the next element in the time series if there is one When ts_tselem is valid it can be passed to other routines in the time series API such as ts_put_elem ts_get_col_by_name and ts_get_col_by_number Returns TS_SCAN_ELEM The tselem parameter contains a valid element TS_SCAN_NULL The value in the element was NULL or hidden if tselem is not NULL then the element was hidden otherwise the element was NULL TS_SCAN_EOS The scan has completed tselem is not valid The return value must not be freed by the caller it is freed when the scan is ended It is overwritten after two ts_next calls Chapter 9 Time series API routines 9 41 Example See the ts_interp function A
285. function is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Mod Pow and Times Related reference Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 2 Positive function i The Positive function returns the argument It is bound to the unary operator The Positive function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Pow function The Pow function raises the first argument to the power of the second The Pow function is one of the binary arithmetic functions that work on time series The others are Atan Divide Minus Mod Plus and Times Related reference Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 27 PutElem function The PutElem function adds an element to a time series at the timepoint indicated in the supplied row type Syntax PutElem ts TimeSeries row_value row flags integer default 0 returns TimeSeries ts The time series to act on row_value The new row type value to store in the time series flags Valid values for the flags argument are described in The flags argument
286. function with reduced logging and no duplicate elements The following statement saves the data to disk with reduced logging and replaces existing elements that have the same timestamps as new elements EXECUTE FUNCTION TSL_Commit ts_data raw_reads 261 200000 Related tasks Writing a loader program on page 3 31 Related reference TSL_FlushInfo function on page 7 129 TSL_Flush function The TSL_Flush function flushes data for one or all containers to disk in a single transaction Syntax TSL_Flush handle lvarchar container integer DEFAULT NULL writeflag integer DEFAULT 1 returns integer handle The table and column name combination that is returned by the TSL_Attach or the TSL_Init function container Optional The container identifier Default is NULL which indicates all containers writeflag Optional An integer that represents whether the duplicate elements are allowed for irregular time series and whether logging is reduced You must supply a value for the duplicate element behavior 1 or 5 and can optionally add the value for reduced logging 256 1 Default Duplicate elements are allowed 5 Duplicate elements replace existing elements 1 256 257 Duplicate elements are allowed Logging is reduced See the description of the TSOPEN REDUCED_LOG flag in The flags argument values on page 7 9 5 256 261 Duplicate elements replace existing elements Logging is redu
287. g for loading time series data When you plan to load time series data you must choose the loading method and where to store the data on disk The following table summarizes the methods of loading data that you can use depending on how much data you need to load and the format of the data Table 1 9 Data loading methods Data to load Methods Bulk data from a file that Use IBM Data Studio and the IBM Informix TimeSeries Plug in is created by your data for Data Studio to create a load job for a delimited file collection application Write a custom loader program that runs time series SQL routines Create a virtual table and load data that is in standard relational format Use the BulkLoad SQL function The file must be formatted according to the BulkLoad function requirements Data in a database Use IBM Data Studio and the IBM Informix TimeSeries Plug in including databases in for Data Studio to create a load job for data in a database database servers other than Informix Write a custom loader program that runs time series SQL routines Alter or add one or more Use the InsElem SQL function to insert an element or the elements to edit incorrect PutElem SQL function to update an element Use the InsSet data or insert missing SQL function to insert multiple elements or the PutSet SQL values function to update multiple elements Create a virtual table and use a standard SQL INSERT statement You can add or update e
288. ge 4 5 Loading the time series data You use the TimeSeries plug in to load the data from the my_meter_data unl file into the my_ts_data table The TimeSeries plug in has a cheat sheet that you use to guide you through the process of loading the data To load time series data 1 If you are using Data Studio or Optim Developer Studio on a different computer move the INFORMIXDIR my_meter_data un file to that computer and start Data Studio or Optim Developer Studio 2 From the main menu choose Help gt Cheat Sheets expand the TimeSeries Data category choose Loading from a File and click OK 3 Open the TimeSeries perspective Chapter 3 Create and manage a time series through SQL 3 3 3 4 4 Create a project area named my_test 5 Create the Informix table definition and define the columns of the table Name the table definition my_table and save the definition in the my_test project directory Define the following table columns e meter_id choose the Big Integer type and specify that it is the primary key e raw_reads choose the TimeSeries type 6 Define the following subcolumns for the raw_reads column and then save the project e timestamp choose the Timestamp type e data choose the Numeric type 7 Create a record format and define the format of the data file Name the record format definition my_format and save it in the my_test project directory Define the following record formats e meter_id choose the Big I
289. ge 7 11 AgeregateRange function on page 7 15 ClipCount function on page 7 35 TSAddPrevious function on page 7 90 TSPrevious function on page 7 141 TSRunningAve function on page 7 147 7 22 IBM Informix TimeSeries Data User s Guide A d TSRunningCor function on page 7 149 TSRunningMed function on page 7 15 n TSRunningVar function on page 7 152 Unary arithmetic functions on page 7 15 ApplyBinaryTsOp function The ApplyBinaryTsOp function applies a binary arithmetic function to a pair of time series or to a time series and a compatible row type or number Syntax ApplyBinaryTsOp func_name_ lvarchar ts TimeSeries ts TimeSeries returns TimeSeries ApplyBinaryTsOp func_name lvarchar number_or_row scalar row ts TimeSeries returns TimeSeries ApplyBinaryTsOp func_name lvarchar ts TimeSeries number_or_row scalar row returns TimeSeries func_name The name of a binary arithmetic function ts The time series to use in the operation The second and third arguments can be a time series a row type or a number At least one of the two must be a time series number_or_row A number or a row type to use in the operation The second and third arguments can be a time series a row type or a number The second two arguments must be compatible under the function See Binary arithmetic functions on page 7 27 for a
290. ge 7 7 CalendarPattern data type on page 2 1 Clip function on page 7 31 Chapter 7 Time series SQL routines 7 163 7 164 IBM Informix TimeSeries Data User s Guide Chapter 8 Time series Java class library You can use the time series Java class library to create and manage a time series from within Java applications or applets The time series Java class library uses the JDBC 2 0 specification for supporting user defined data types in Java When you write a Java application for time series data you use the IBM Informix JDBC Driver to connect to an IBM Informix database as shown in the following figure See your IBM Informix JDBC Driver Programmer s Guide for information about how to set up your Java programs to connect to Informix databases Java application Calls to JDBC JDBC Driver Query results Results from the database Queries and other SQL statements Figure 8 1 Runtime architecture for Java programs that connect to a database The Java application makes calls to the JDBC driver which sends queries and other SQL statements to the IBM Informix database The database sends query results to the JDBC driver which sends them on to the Java application You can also use the time series Java classes in Java applets and servlets as shown in the following figures Copyright IBM Corp 2006 2014 8 1 8 2 Web server Browser JDBC driver Figure 8 2 Runtime architecture for
291. ger The maximum number of partitions that can be destroyed in an operation If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count parameter the operation fails Usage for rolling window containers The rolling window container stores information about the properties of the windows and information about the partitions The partitions store the time series data for specific date ranges The TSContainerCreate procedure creates the rolling window container when the procedure completes Partitions are created as needed when you insert time series elements To create a rolling window container that stores data in multiple dbspaces and automatically deletes old data set the following arguments to non default values e active_windowsize Set to a positive value to limit to the size of the active window e dormant_windowsize Set to a positive value to limit to the size of the dormant window e window_spaces Set to a list of dbspaces IBM Informix TimeSeries Data User s Guide If you use the default size of 0 for the active window you create a container that grows until you manually detach partitions into the dormant window and manually destroy partitions from the dormant window The container partitions and the partitions in the active and dormant windows can require significantly different amounts of storage space Plan the storage for rolling window containers carefully When you cr
292. ges in the range freed Examples Example 1 Free empty pages between specified begin and end points The following example frees the empty pages in a one day range on the specified day in the time series instance for the location ID of 4727354321000111 UPDATE ts_data SET meter_data NullCleanup meter_data 2010 11 11 00 00 00 00000 datetime year to fraction 5 2010 11 11 00 00 00 00000 datetime year to fraction 5 WHERE loc_esi_id 4727354321000111 Example 2 Free all empty pages in the time series instance The following example frees all empty pages in the time series instance for the location ID of 4727354321000111 UPDATE ts_data SET meter_data Nul1Cleanup meter_data WHERE loc_esi_id 4727354321000111 Example 3 Free empty pages from the beginning of the time series instance to a specified date The following example frees empty pages from the beginning of the time series instance to the specified end point in the time series instance for the location ID of 4727354321000111 UPDATE ts_data SET meter_data Nul lCleanup meter_data NULL 2010 11 11 00 00 00 00000 datetime year to fraction 5 WHERE loc_esi_id 4727354321000111 Related concepts IBM Informix TimeSeries Data User s Guide Delete time series data on page 3 37 Related reference Time series routines that run in parallel on page 7 7 Plus function The Plus function adds two time series together The Plus
293. ging is reduced commit_interval Optional A positive integer that represents the maximum number of elements to insert per transaction The default value if the argument is omitted or set to NULL is 10000 Usage Use the TSL_Commit function to write time series data to disk as part of a loader program You must run the TSL_Commit function in the context of a loader session that was initialized by the TSL_Init function You run the TSL_Commit function to save data that is loaded by the TSL_Put TSL_PutSQL or TSL_PutRow function The TSL_Commit function is useful when the amount of data that you are loading is too large for your logical log to flush as one transaction The TSL_Commit function commits transactions after flushing the specified number of elements or when all elements for a container are flushed For example suppose that you set the commit_interval argument to 5000 If you load 10 000 elements into a container the TSL_Commit function commits two transactions If you insert 10 000 elements in equal amounts into five containers the TSL_Commit function commits five transactions of 2000 elements If you run the TSL_Commit function within an explicit transaction then the TSL_Commit function does not commit transactions If you specify that duplicate elements are allowed for irregular time series with the writeflag argument value of 1 the default or 257 1 256 the TSL_Commit function inserts data in the same way as the PutEle
294. greater than the dormant windows size e 0 Default As many as necessary dormant partitions are destroyed If the operation requires more new active partitions than the value of the active_windowsize parameter the operation fails e 1 As many as necessary existing dormant partitions and older active partitions are destroyed Use this setting with caution Destroyed data cannot be recovered e 2 Dormant partitions are destroyed but limited to the number specified by the destroy_count parameter If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count parameter the operation fails e 3 2 1 Existing dormant partitions and older active partitions are destroyed but limited to the number specified by the destroy_count parameter If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count parameter the operation fails When you destroy a partition the data that is stored in the partition is deleted rwi_firstextsize Optional The first extent size in KB for the container partition The default size is 16 KB rwi_nextextsize Optional The next extent size in KB for the container partition The default size is 16 KB destroy_count Optional How many partitions can be destroyed in an operation Valid if the value of the window_control parameter is 2 or 3 0 Default No dormant partitions are destroyed A positive inte
295. grows in KB The value must be equivalent to at least 4 pages If you specify 0 or a negative number 16 KB is used Usage By default containers are created automatically as needed when you insert data into a time series However you can create additional containers by using the TSContainerCreate procedure You can create multiple containers in the same dbspace When you create a container a row is inserted in the TSContainerTable table Example The following example creates a container that is called new_cont in the space rootdbs for the time series type stock_bar execute procedure TSContainerCreate new_cont rootdbs stock_bar 0 0 Syntax for rolling window containers TSContainerCreate container_name varchar 128 1 dbspace_name varchar 128 1 ts_type varchar 128 1 container_size integer container_grow integer window_origin datetime year to fraction 5 TSContainerCreate container_name varchar 128 1 dbspace_name varchar 128 1 ts_type varchar 128 1 container_size integer container_grow integer window_origin datetime year to fraction 5 window_interval lvarchar default month active_windowsize integer default 0 dormant_windowsize integer default 0 window_spaces lvarchar 4096 default null window_control integer default 0 rwi_firstextsize integer default 16 rwi_nextextsize integer default 16 destroy count integer default 0 container_name The name of the new container The container
296. gure the sessions to load into different containers You must run the TSL_GetKeyContainer function in the context of a loader session that was initialized by the TSL_Init function Returns The name of the container Examples The following statement returns the name of the container associated with the time series data for the primary key value of 1 in the meter table EXECUTE FUNCTION TSL_GetKeyContainer meter readings 1 TSL_GetLogMessage function 7 132 The TSL_GetLogMessage function specifies how many messages are retrieved from the loader message queue Syntax TSL_GetLogMessage handle lvarchar max_messages integer returns lvarchar handle The table and column name combination returned by the TSL_Attach function max_messages 0 All messages are retrieved A positive integer The number of messages to retrieve If the number is greater than the total number of messages in the queue all messages are retrieved Usage Use the TSL_GetLogMessage function to retrieve loader messages as part of a loader program To retrieve messages the log mode must be set to 2 so that messages are sent to the loader program The log mode is set by the TSL_Init function or reset by the TSL_SetLogMode function You must run the TSL_GetLogMessage function in the context of a loader session that was initialized by the TSL_Init function You can use the TSL_GetLogMessage function to monitor that data that is loaded by th
297. h can be provided by the AggregateBy function Elements are added to the second time series by calling ts_put_elem if the TS_PUTELEM_NO_DUPS value of the flags parameter is not set The API equivalent of this function is ts_put_ts Returns A version of the second time series into which the first time series was inserted Example The following example converts a regular time series to an irregular one The daily_stocks table holds regular time series data and the activity_stocks table holds irregular time series data Additionally the elements in the daily_stocks time series are converted from stock_bar to stock_trade update activity_stocks set activity_data PutTimeSeries activity_data calendar daycal irregular TimeSeries stock_trade where stock_id 600 Related reference AgeregateBy function on page 7 11 PutSet function on page 7 80 The ts_put_ts function on page 9 49 SetOrigin function on page 7 85 IBM Informix TimeSeries Data User s Guide RevealElem function The RevealElem function makes an element at a specified time stamp available for a scan It reverses the effect of HideElem Syntax RevealElem ts TimeSeries tstamp datetime year to fraction 5 returns TimeSeries RevealElem ts TimeSeries set_stamps multiset datetime year to fraction 5 returns TimeSeries ts The time series to act on tstamp The time stamp to be made visible to a scan s
298. hat hidden elements are treated as if they are not hidden Cannot be used in combination with the following flag values e TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_AND_REVEAL e TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_HIDDEN 2 TSOPEN_WRITE_AND_HIDE 4 Allows hidden elements to be written to without first revealing them The element remains hidden afterward Marks as hidden any elements that are written to a time series Cannot be used in combination with the following flag values e TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_AND_REVEAL e TSOPEN_READ_HIDDEN Cannot be used in combination with the following flag values e TSOPEN_WRITE_HIDDEN e TSOPEN_WRITE_AND_REVEAL e TSOPEN_READ_HIDDEN TSWRITE_AND_REVEAL 8 Reveals any hidden element that is written to TSOPEN_NO_NULLS 32 Prevents elements that were never allocated TS_NULL_NOTALLOCATED from being returned as NULL By default if an element is not allocated it is returned as NULL If this flag is set an element that has each column set to NULL is returned instead TS_PUTELEM_NO_DUPS 64 Prevents duplicate elements By default the function adds elements with the PutElem function If this flag is set the function uses the PutElemNoDups function Can be used only in the following functions BulkLoad e PutTimeSeries PutSet TSOPEN_REDUCED_LOG 256 Reduces how many log records are generated when you insert data By default every element
299. hat is based on only one TimeSeries column at a time If the base table has multiple TimeSeries columns you can create a virtual table for each of them Related concepts Copyright IBM Corp 2006 2014 4 1 Related tasks Loading data from a file into a virtual table on page 3 34 Performance of queries on virtual tables The performance of queries on virtual tables depends on the type of query and whether the query is accelerated The performance of queries on virtual tables is similar in most cases to the performance of queries that run TimeSeries functions on base tables For example the Clip function is faster applied through a virtual table than directly on a time series However it is faster to run the Apply or the Transpose routines on a time series than to run them through a virtual table by using the TSCreateExpressionVirtualTab procedure If you want to query large amounts of time series data the performance of queries on virtual tables that are accelerated by Informix Warehouse Accelerator is significantly faster than any other type of query You can control how much of the virtual table is loaded into the data mart to further improve query performance You can easily change which parts of the virtual table are in the data mart For example you create a virtual table that contains three years of time series data You create a data mart that is based on the virtual table You define virtual partitions for the d
300. he row data type The number of fields in a subtype is not restricted Must follow the Identifier syntax For more information see Identifier SQL Syntax IBM Informix TimeSeries Data User s Guide Table 2 3 TimeSeries data type parameter values continued Value Description data_type Can be any data type except the following data types e SERIAL SERIAL8 or BIGSERIAL data types e Types that have Assign or Destroy functions assigned to them including large object types and some user defined types JSON A hertz time series must have columns of only the following data types SMALLINT INT BIGINT SMALLFLOAT FLOAT DATE INT8 CHAR VARCHAR NCHAR NVCHAR LVARCHAR DATETIME DECIMAL and MONEY A compressed time series must have only the following data types SMALLINT INTEGER BIGINT SMALLFLOAT and FLOAT You can include only one BSON column A BSON document cannot exceed 4 KB in size subtype_name The name of the TimeSeries subtype Can be a maximum of 128 bytes Must follow the Identifier syntax For more information see Identifier SQL Syntax timestamp_field The name of the field that contains the time stamp Must be unique for the row data type Must follow the Identifier syntax For more information see Identifier SQL Syntax After you create the TimeSeries subtype you create the table containing the TimeSeries column using the CREATE TABLE statement You can also use the CREAT
301. he inputs is a regular time series The result time series has one time point for each input time point in the interval The element at time t in the resulting time series is formed from the last elements at or before time t in the two input time series Normally the function is applied column by column to the input columns except for the time stamp to produce the output element In this case the two input row types must have the same number of columns and the corresponding columns must be compatible under the function However if there is a variant of the function that operates directly on the row types of the two input time series then that variant is used Then the input row types can have different numbers of columns and the columns might be incompatible The time stamp of the resulting element is ignored the element placed in the resulting time series has the later of the time stamps of the input elements The resulting calendar is the union of the calendars of the input time series If the input calendars are the same then the resulting calendar is the same as the input calendar Otherwise a new calendar is made The name of the resulting calendar is a string that contains the names of the calendars of the input time series separated by a vertical line For example if two time series are joined and mycal and yourcal are the names of their corresponding calendars the resulting calendar is named mycal yourcal The resulti
302. he time stamp column as column 1 When you clip a time series the resultant time series is read only The sample programs demonstrate how to retrieve and update time series data Obtaining the time series Java class version If requested by IBM Software Support you can obtain the version stamp for the time series Java classes To retrieve the version stamp for the time series Java classes use one of the following methods e Run the following command from the command line java com informix timeseries TimeSeriesBuildInformation e Construct an instance of com informix timeseries TimeSeriesBuildInformation from within an application import com informix TimeSeries TimeSeriesBuildInformation TimeSeriesBuildInformation buildInfo new TimeSeriesBuildInformation String version buildInfo getVersion Chapter 8 Time series Java class library 8 5 8 6 IBM Informix TimeSeries Data User s Guide Chapter 9 Time series API routines The time series application programming interface routines allow application programmers to directly access a time series datum You can scan and update a set of time series elements or a single element referenced by either a time stamp or a time series index These routines can be used in client programs that fetch time series data in binary mode or in registered server or client routines that have an argument or return value of a time series type If there is a failure these routines rai
303. he Informix TimeSeries solution loads and queries time stamped data faster requires less storage space and provides more analytical capability than a standard relational table implementation Although relational database management systems can store time series data for standard types by storing one row per time stamped Copyright IBM Corp 2006 2014 1 1 data entry performance is poor and storage is inefficient The Informix TimeSeries solution saves disk space by not storing duplicate information from the columns that do not contain the time based data The Informix TimeSeries solution loads and queries time series data quickly because the data is stored on disk in order by time stamp and by source For example the following table shows a relational table that contains time based information for two sources or customers whose identifiers are 1000111 and 1046021 Table 1 1 Relational table with time based data Customer Time Value 1000111 2011 1 1 00 00 00 00000 0 092 1000111 2011 1 1 00 15 00 00000 0 082 1000111 2011 1 1 00 30 00 00000 0 090 1000111 2011 1 1 00 45 00 00000 0 085 1046021 2011 1 1 00 00 00 00000 0 041 1046021 2011 1 1 00 15 00 00000 0 041 1046021 2011 1 1 00 30 00 00000 0 040 1046021 2011 1 1 00 45 00 00000 0 041 The following table shows a representation of the same data stored in an Informix TimeSeries table The information about the customer is stored once All the time ba
304. he database result set There must be an entry in the type map for every TimeSeries type that your program uses You must also add entries for the Calendar and CalendarPattern data types if your program selects those types from the database for example customTypeMap put calendarpattern Class forName com informix timeseries IfmxCalendarPattern customTypeMap put calendar Class forName com informix timeseries IfmxCalendar e Create a custom map for the time series data types and reference the map in your application Use the Builder pattern from the TimeSeriesTypeMap class to create a PatternClassMap for the TimeSeries Calendar and CalendarPattern data types For example the following map provides case insensitive access to time series data types Map lt String Class lt gt gt typeMap TimeSeriesTypeMap builder caseSensitive false build connection setTypeMap typeMap You can include the type map in your getObject method call for example ts IfmxTimeSeries rSet getObject 1 typeMap Related reference UpdElem function on page 7 159 Querying time series data with the IfmxTimeSeries object 8 4 You can use a SELECT statement your Java program to retrieve TimeSeries data with the IfmxTimeSeries object For example the following code selected time series data into an IfmxTimeSeries object IBM Informix TimeSeries Data User s Guide String sqlCmd SELECT ts FROM test WHERE id 1
305. he intersection of one hourly and one daily calendar pattern select from CalendarPatterns where cp_name workweek_day cp_name workweek_day cp_pattern 1 off 5 on 1 off day select from CalendarPatterns where cp_name fourday_day cp_name fourday_day cp_pattern 1 off 4 on 2 off day select from CalendarPatterns where cp_name workweek_hour cp_name workweek_hour cp_pattern 32 of f 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour select AndOp pl cp_pattern p2 cp_pattern from CalendarPatterns pl CalendarPatterns p2 where pl cp_name workweek_day and p2 cp_name fourday_day expression 1 off 4 on 2 off day select AndOp pl cp_pattern p2 cp_pattern from CalendarPatterns pl CalendarPatterns p2 where pl cp_name workweek_hour and p2 cp_name fourday_day expression 32 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 55 off hour Related reference AndOp function on page 6 1 CalPattStartDate function 5 2 The CalPattStartDate function takes a calendar name and returns a DATETIME containing the start date of the pattern for that calendar Syntax CalPattStartDate calname lvarchar returns datetime year to fraction 5 calname The name of the source calendar Description The equivalent API function is ts_cal_pattstartdate Returns The start date of the pattern for the specified calendar IBM Informix TimeSeries Data User s Guide Example
306. he time series associated with the given time series descriptor Syntax ts_timeseries ts_get_ts ts_tsdesc tsdesc tsdesc The time series descriptor from ts_open Description The ts_get_ts function is useful when you must call a function that takes a time series argument for example ts_get_calname but you only have a tsdesc time series descriptor Returns A pointer to the time series associated with the given time series descriptor This value can be freed by the caller after ts_close has been called if the original time series was created by ts_create or ts_copy To free it use ts_free Chapter 9 Time series API routines 9 33 Related reference The ts_free procedure on page 9 26 The ts_put_elem function on page 9 46 The ts_put_elem_no_dups function on page 9 47 The ts_put_last_elem function on page 9 48 The ts_put_nth_elem function on page 9 48 The ts_get_typeid function The ts_get_typeid function returns the type ID of the specified time series Syntax mi_typeid ts_get_typeid MI_CONNECTION conn ts_timeseries ts conn A valid DataBlade API connection Laat S The source time series Description This function returns the type ID of the specified time series Usually a time series type ID is located in an MI_FPARAM structure This function is useful when there is no easy access to an MI_FPARAM structure Returns A poi
307. her the time series is regular use TS_IS_IRREGULAR Returns An integer containing the flags for the given time series Related reference IsRegular function on page 7 73 The TS_IS IRREGULAR macro on page 9 37 The ts_get_hertz function 9 30 The ts_get_hertz function returns the frequency for packed hertz data Syntax mi_integer ts_get_hertz ts_timeseries ts ts The name of the time series Description Use the ts_get_hertz function to determine how many records per second the time series can store Returns Returns an integer 1 255 if the time series contains packed hertz data returns 0 if the time series does not contain packed hertz data IBM Informix TimeSeries Data User s Guide Related reference GetHertz function on page 7 54 The ts_get_metadata function The ts_get_metadata function returns the user defined metadata and its type ID from the specified time series ts_get_metadata ts_timeseries ts MI_TYPEID xxmetadata_typeid cH S The time series to retrieve the metadata from metadata_typeid The return parameter to hold the type ID of the user defined metadata Description The equivalent SQL function is GetMetaData Returns The user defined metadata contained in the specified time series If the time series does not contain any user defined metadata then NULL is returned and the metadata_typeid pointer is set to NULL This return value must be cast to the real
308. his format row timestamp set row value value row value value value The tab format is to separate the values by tabs It is only recommended for single level rows that do not contain collections or row data types The first two lines of a typical file in this format look like this 2011 01 03 00 00 00 00000 1 1 2 2 2011 01 04 00 00 00 00000 10 1 20 2 IBM Informix TimeSeries Data User s Guide The spaces between entries represent a tab In both formats the word NULL indicates a null entry When BulkLoad encounters data with duplicate time stamps in a regular time series the old values are replaced by the new values In an irregular time series when BulkLoad encounters data with duplicate time stamps the following algorithm is used to determine where to place the data belonging to the duplicate time stamp 1 Round the time stamp up to the next second 2 Search backwards for the first element less than the new time stamp 3 Insert the new data at this time stamp plus 10 microseconds This is the same algorithm as used by the PutElem function described in Returns A time series containing the new data Example The following example adds data from the sam dat file to the stock_data time series update daily_stocks set stock_data BulkLoad stock_data sam dat where stock_name IBM Related reference Load data with the BulkLoad function on page 3 35 Clip function The Clip function extracts da
309. i_datetime datel mi_datetime date2 mi_integer interval date1 The first date date2 The date to subtract from the first date interval The interval as described next Chapter 9 Time series API routines 9 51 Description Before the difference is calculated both time stamps are truncated to the given interval For example if the interval is an hour and the first date is 2011 01 03 01 02 03 12345 its truncated value is 2011 01 03 01 00 00 00000 Valid values for the interval parameter can be found in tseries h They are e TS_SECOND e TS_MINUTE e TS_HOUR e TS_DAY e TS_WEEK e TS_MONTH e TS_YEAR Returns The number of intervals of the type you specify between the two dates Example For example if the interval is day and the dates are 2011 01 01 00 00 00 00000 and 2011 01 01 00 00 00 00001 the result is 0 If the dates are 2011 01 01 00 00 00 0000 and 2011 01 02 00 10 00 12345 the result is 1 Related reference The ts_tstamp_minus function The ts_tstamp_plus function on page 9 53 The ts_tstamp_minus function 9 52 The ts_tstamp_minus function returns a time stamp at a specified number of intervals before a starting date you specify Syntax mi_datetime ts_tstamp_minus mi_datetime startdate mi_integer cnt mi_integer interval mi_datetime result startdate The date to start from cnt The number of intervals to subtract from the start date interval The interval
310. ication development tool or SQL API see the documentation for your product Additional documentation Documentation about this release of IBM Informix products is available in various formats You can access Informix technical information such as information centers technotes white papers and IBM Redbooks publications online at http www ibm com software data sw library Compliance with industry standards IBM Informix products are compliant with various standards IBM Informix SQL based products are fully compliant with SQL 92 Entry Level published as ANSI X3 135 1992 which is identical to ISO 9075 1992 In addition many features of IBM Informix database servers comply with the SQL 92 Intermediate and Full Level and X Open SQL Common Applications Environment CAE standards Syntax diagrams Syntax diagrams use special components to describe the syntax for statements and commands Table 5 Syntax Diagram Components Component represented in PDF Component represented in HTML Meaning Statement begins Statement continues on next line AAE OAOA Statement continues from previous line Statement ends SELECT Required item L LOCAL Optional item xviii IBM Informix TimeSeries Data User s Guide Table 5 Syntax Diagram Components continued Component represented in PDF Component represented in HTML Meaning
311. id 50 tstamp 2014 01 01 00 00 00 06000 tssmallint 2 tsint 50 tsbigint 5050 4 row s retrieved Related concepts Hertz time series on page 1 8 Compressed numeric time series on page 1 10 Related reference GetPacked function on page 7 64 GetHertz function on page 7 54 GetCompression function on page 7 50 Chapter 3 Create and manage a time series through SQL 3 39 3 40 IBM Informix TimeSeries Data User s Guide Chapter 4 Virtual tables for time series data A virtual table provides a relational view of your time series data Virtual tables are useful for viewing time series data in a simple format An SQL SELECT statement against a virtual table returns data in ordinary data type format rather than in the TimeSeries data type format Many of the operations that TimeSeries SQL functions and API routines perform can be done using SQL statements against a virtual table Some SQL queries are easier to write for the virtual table than for an underlying time series table especially SQL queries with qualifications on a TimeSeries column The virtual table is not a real table stored in the database The data is not duplicated At any moment data visible in the virtual table is the same as the data in the base table The data in the virtual table is updated to reflect changes to the data in the base table You cannot create an index on a time series virtual table You can use a virtual table as
312. id The primary key value in the time series table that uniquely identifies the time series ts The name of the TimeSeries subtype output_max The maximum size in bytes of the XML output If the parameter is absent the default value is 32 768 The following table describes the results for each possible value of the output_max parameter Value Result no value 32 768 bytes negative integer 2 1 bytes 1 through 4096 4096 bytes 4096 through 2 1 the specified number of bytes Description Use the TSToXML function to provide a standard representation for information exchange in XML format for small amounts of data The top level tag in the XML output is the first argument to the TSTOXML function The id tag must uniquely identify the time series and refer the XML output to the row on which it is based The Al1Data tag indicates whether all the data was returned or the data was truncated because it exceeded the size set by the output_max parameter IBM Informix TimeSeries Data User s Guide The remaining XML tags represent the TimeSeries subtype and its columns including the time stamp The special characters lt gt amp and are replaced by their XML predefined entities Returns The specified time series in XML format up to the size set by the output_max parameter The AllData tag indicates whether all the data was returned 1 or whether the data was truncated 0 Example The following query selects th
313. ies from function output 3 27 time series subtype 3 13 time series with input function 3 24 time series with metadata 3 23 virtual tables 4 5 D Data file formats 3 35 loading from a file 7 30 loading into a time series with BulkLoad 3 35 Data structures ts_timeseries 9 2 X 2 IBM Informix TimeSeries Data User s Guide Data structures continued ts_tscan 9 2 ts_tsdesc 9 2 ts_tselem 9 3 Data Studio 1 3 TimeSeries plug in 3 27 3 28 3 29 Data type mapping 8 4 Data types Calendar 2 4 CalendarPattern 2 1 DATETIME 3 13 restrictions for time series 3 13 TimeSeriesMeta 3 23 Database requirements 1 26 DATETIME data type 3 13 dbload utility 3 34 dbspace time series containers 1 15 Decay computing 7 122 DelClip function 7 42 DelElem function 7 43 Deleting element 7 43 9 22 elements in a clip 7 42 7 45 elements ina range 7 44 null elements 7 75 Deleting time series data 3 37 7 108 DelRange function 7 44 DelTrim function 7 45 Directory 1 27 1 28 Disabilities visual reading syntax diagrams C 1 Disability C 1 Divide function 7 46 Dividing one time series by another 7 46 Documentation files Java 8 3 Dotted decimal format of syntax diagrams C 1 DROP statement virtual tables 4 26 E Eclipse command line loader application 3 29 3 30 Element 1 4 Elements columns in getting number of 9 14 converting toa row 9 24 data from one column in getting 9 28 deleting 7 43 9 22 deleting from a clip 7 42 7 45 deleting fr
314. ies to the power of another 7 77 readSQL method 8 4 Regular time series 1 7 creating with metadata 7 116 creating with TSCreate 7 116 determining if 7 73 specifying 3 24 Regularity 1 4 Relative search type 7 161 Replicating time series data 1 24 ResultSet interface 8 4 Retrieving time series data Java 8 4 RevealElem function 7 83 Revealing a hidden element 7 83 9 50 Rolling window containers 3 16 Round function 7 84 Rounding a time series to a whole number 7 84 Routines API ts_begin_scan 9 7 ts_cal_index 9 9 ts_cal_pattstartdate 9 9 ts_cal_range 9 10 ts_cal_range_index 9 11 ts_cal_stamp 9 11 9 12 ts_close 9 12 ts_col_cnt 9 14 ts_col id 9 14 ts_colinfo_name 9 15 ts_colinfo_number 9 15 ts_copy 9 16 ts_create 9 17 Routines continued API continued ts_create_with_metadata 9 18 ts_current_offset 9 20 ts_current_timestamp 9 21 ts_datetime_cmp 9 21 ts_del_elem 9 22 ts_elem 9 22 TS_ELEM_HIDDEN 9 23 TS_ELEM_NULL 9 24 ts_elem_to_row 9 24 ts_end_scan 9 25 ts_first_elem 9 25 ts_free 9 26 ts_free_elem 9 26 ts_get_all_cols 9 27 ts_get_calname 9 27 ts_get_col_by_name 9 28 ts_get_col_by_number 9 28 ts_get_compressed 9 29 ts_get_containername 9 29 ts_get_flags 9 30 ts_get_hertz 9 30 ts_get_metadata 9 31 ts_get_origin 9 31 ts_get_packed 9 32 ts_get_stamp_fields 9 32 ts_get_threshold 9 33 ts_get_ts 9 33 ts_get_typeid 9 34 ts_hide_elem 9 34 ts_index 9 35 ts_ins_elem 9 36 TS_IS INCONTAINER 9 36 TS_IS IRREGULAR 9 37
315. ily calendar pattern with hourly intervals must contain exactly 24 intervals If your calendar pattern length is not correct your time series data might not match your requirements For example the pattern 1 off 4 on 1 off appears to repeat every week but the pattern repeats every six days because there are only six intervals When the calendar pattern begins is specified by the calendar pattern start date Calendars that have the same calendar pattern length but different interval sizes are not equivalent For example a calendar can be built around a normal five day work week with the time unit in days and Saturday and Sunday as days off Assuming that the calendar pattern start date is for a Sunday the syntax for this calendar pattern is INSERT INTO CalendarPatterns VALUES workweek_day 1 off 5 on 1 off day In the next example the calendar is built around the same five day work week with the time unit in hours IBM Informix TimeSeries Data User s Guide INSERT INTO CalendarPatterns VALUES workweek_hour 32 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour Both examples have a calendar pattern length of seven days or one week However the number of allowed records for regular time series and the on and off periods are different between the calendar patterns The workweek_day calendar pattern allows a maximum of five records per week for regular time series and prohibits
316. ime interval for each partition is one month dbs0 dbs1 Container Partition 1 Partition 2 Data for Data for Information about rolling month 1 for month 4 for window intervals and partitions all meters all meters Other tables Other tables dbs2 dbs3 Partition 1 Partition 2 Partition 1 Partition 2 Data for Data for Data for Data for month 2 for month 5 for month 3 for month 6 for all meters all meters all meters all meters Other tables Other tables Figure 1 5 Architecture of dbspaces for a rolling window container In this illustration the dbspace named dbs0 contains the container Container1 and other tables Container1 stores the information about the time interval of the data in each partition and the location of each partition The dbspaces named dbs1 dbs2 and dbs3 store time series elements in partitions and other tables Each partition stores the data for one month for all meter IDs Active and dormant windows Partitions make it easy to remove old data You can configure a rolling window container to automatically delete old data after a specified amount of data is stored You specify the number of partitions to keep in the active window and in the dormant window The active window contains the partitions into which you can insert data The dormant window contains partitions that you no longer need to query but are not yet ready to delete The active window moves ahead in time when you insert
317. ime series on page 1 7 Irregular time series on page 1 7 Related reference TSCreate function on page 7 116 TSCreatelrr function on page 7 118 Creating a time series with metadata You can create an empty or populated time series that also contains user defined metadata A time series column includes a header that holds information about the time series and can also contain user defined metadata User defined metadata allows the time series to be self describing The metadata can be information usually contained in additional columns in the table such as the name of a stock or the type of the time series The advantage of keeping this type of information in the time series is that when using an API routine it is easier to retrieve the metadata than to pass additional columns to the routine Metadata is stored in a distinct type based on the TimeSeriesMeta data type The TimeSeriesMeta data type is an opaque data type of variable length up to a maximum length of 512 bytes The routines that accept the TimeSeriesMeta data type also accept its distinct type The distinct type requires support functions such as input output send receive and so on To create a time series with metadata 1 Create a distinct data type based on the TimeSeriesMeta data type with the following SQL statement Substitute MyMetaData with a name you choose create distinct type MyMetaData as TimeSeriesMeta 2 Create support functions for
318. ime series instances that were affected The lower 16 bits represent the number of elements that were inserted The returned number of elements that were inserted does not exceed 65535 even if more than 65535 elements were successfully inserted You can run the TSL_Flush function through the TSL_FlushStatus function to return the number of containers that are affected and the number of elements that are inserted as integers e 1 Process was interrupted or encountered a severe error Example Run the TSL_Flush function The following statement saves the data to disk for the table ts_data and the TimeSeries column raw_reads BEGIN WORK EXECUTE FUNCTION TSL_Flush ts_data raw_reads COMMIT WORK Example Run the TSL_Flush function with reduced logging The following statement saves the data to disk with reduced logging BEGIN WORK EXECUTE FUNCTION TSL_Flush ts_data raw_reads NULL 257 COMMIT WORK Example Run the TSL_Flush function with reduced logging and no duplicate elements The following statement saves the data to disk with reduced logging and replaces existing elements that have the same timestamps as new elements Chapter 7 Time series SQL routines 7 127 BEGIN WORK EXECUTE FUNCTION TSL_Flush ts_data raw_reads NULL 261 COMMIT WORK Related reference TSL_FlushInfo function on page 7 129 TSL_FlushStatus function on page 7 131 TSL_FlushAll function 7 128 The TSL_FlushAll function
319. ime series with a calendar given by the cal_name argument The agg_express expressions operate on a column of the input time series which is specified by one of the following column identifiers colname The colname is the name of the column to aggregate in the TimeSeries data type For example if the column name is high the column identifier is high colnumber The colnumber is the position of the column to aggregate in the TimeSeries data type For example if the column number is 1 the column identifier is 1 bson_field_name The bson_field_name is the name of a field in at least one BOON document in the BSON column in the TimeSeries data type For example if the field name is v1 the column identifier is v1 If the BSON field name is the same as another column in the TimeSeries data type you must qualify the field name in one of the following ways e colname bson_field_name For example if the BSON column name is b_data and the field name is v1 the column identifier is b _data v1 colnumber bson_field_name For example if the BOON column number is 1 and the field name is v1 the column identifier is 1 v1 You must cast the results of the AggregateBy function on a BSON field to a TimeSeries data type that has the appropriate type of columns for the result of the expression The Nth expression returns the value of a column for the specified aggregation period using the following syntax Nth col n col Th
320. imeSeries tstamp datetime year to fraction 5 interval lvarchar num_intervals integer direction lvarchar returns TimeSeries WithinR ts TimeSeries tstamp datetime year to fraction 5 interval lvarchar num_intervals integer direction lvarchar returns TimeSeries ts The source time series tstamp The timepoint of interest interval The name of an interval second minute hour day week month or year num_intervals The number of intervals to include in the output direction The direction in time to include intervals Possible values are e FUTURE or F or f e PAST or P or p Chapter 7 Time series SQL routines 7 161 7 162 Description Every time series has a calendar that describes the active and inactive periods for the time series and how often they occur A regular time series records one value for every active period of the calendar Calendars can have periods of a second a minute an hour a day a week a month or a year Given a time series you might want to pose calendar based queries on it such as Show me all the values in this daily series for six years beginning on May 31 2004 or Show me the values in this hourly series for the week including December 27 2010 The Within functions are the primary mechanism for queries of this form They convert among time units and do the calendar math to extract periods of interest from a time series value There are two fundamental varieties o
321. imeSeries class has the following enhancements for managing time series data e You can insert data into a time series with the new IfmxTimeSeries Builder class e You can easily modify data and process query results because the results of queries on time series data are now JDBC updatable result sets e You can distinguish between case sensitive and case insensitive databases and make multiple updates within a row e You can convert the time series data to the appropriate time zone on the client e You can select and update data by specifying similar data types instead of the exact data types Data is implicitly cast during read and write operations Previously transactions that did not specify the exact data types failed Chapter 8 Time series Java class library on page 8 1 xii IBM Informix TimeSeries Data User s Guide Table 1 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC4 continued Overview Reference Include JSON documents in time series You can include JSON documents that are associated with timestamps in time series For example weather monitoring sensors that return 2 50 values in JSON documents through the REST API every 10 minutes You store JSON documents with time series data as BRON documents in a BSON column in the TimeSeries data type JSON time series on page 1 12 Example Create and load a time series with JSON data on page 3 10 Cre
322. imeSeries subtype that you define for hertz data must have columns of only the following data types SMALLINT INT BIGINT SMALLFLOAT FLOAT DATE INT8 CHAR VARCHAR NCHAR NVCHAR LVARCHAR DATETIME DECIMAL and MONEY You can use a calendar with any interval size because the interval is significant only in defining on and off periods The interval size for a hertz time series is defined by the hertz parameter when you create the time series You define a hertz time series by running the TSCreatelrr function with the hertz parameter Hertz data must be stored in containers that are not rolling window containers Related concepts Manage packed data on page 3 37 TimeSeries data type on page 2 6 Related tasks Example Create and load a hertz time series on page 3 5 Chapter 1 Informix TimeSeries solution 1 9 1 10 Related reference TSCreatelrr function on page 7 118 Compressed numeric time series If your time series data is recorded at a regular frequency and all the time series values are numeric you can define a compressed time series to store the data efficiently In each element a compressed time series stores an 11 byte timestamp for the first record and a 2 byte timestamp for each of the other records The compression ratio of the rest of the time series data varies depending on the type of data and the compression definitions For example you can compress an 8 byte BIGINT value down to
323. in 2005 10 06 00 00 00 00000 calendar daycal 6 25 1000 1 7 2 1 2005 10 06 12 58 09 12345 6 50 2000 1 8 3 1 2005 10 06 12 58 09 23456 The INSERT statement creates an irregular time series that starts on 06 October 2005 at the time of day specified by the calendar called daycal Two rows of data are inserted with the specified time stamps Example Create a time series for hertz data The following statement creates an empty irregular time series that stores hertz data with a frequency of 50 records per second BEGIN INSERT INTO tstable VALUES origin 2013 01 01 00 00 00 00000 calendar ts_lsec container container_2k threshold hertz 50 irregular COMMIT Example Create a time series for compressed data The following statement creates an empty irregular time series that compresses the time series records for a TimeSeries subtype that has six numeric columns in addition to the timestamp column IBM Informix TimeSeries Data User s Guide BEGIN INSERT INTO tstable VALUES 0 origin 2013 01 01 00 00 00 00000 calendar ts_lsec container container_4k threshold 0 compress n q 1 1 100 1s 0 10 1b 10 qls 2 0 15 100 100000 qlb 2 0 25 100 100000 irregular COMMIT Related tasks Loading JSON data on page 3 33 Related reference Load small amounts of data with SQL functions on page 3 36 Create a time series with the output of a function Many functi
324. in a time series Syntax GetNelems ts TimeSeries returns integer ts The source time series Description For regular time series GetNelems also counts null elements before the last non null element so GetNelems might not return the same results as ClipGetCount which does not count null elements Returns The number of elements in the time series Example The following query returns all stocks containing fewer than 355 elements select stock_name from daily _stocks where GetNelems stock_data lt 355 The following query returns the last five elements of each time series select Clip stock_data GetNelems stock_data 4 GetNelems stock_data from daily_stocks where stock_name IBM This example only works if the time series has more than four elements Related reference Time series routines that run in parallel on page 7 7 ClipGetCount function on page 7 37 GetIndex function on page 7 55 GetNthElem function on page 7 62 GetStamp function on page 7 66 IBM Informix TimeSeries Data User s Guide The ts_nelems function on page 9 41 GetNextNonNull function The GetNextNonNull function returns the next non null element on or after the date you specify Syntax GetNextNonNul1 ts TimeSeries tstamp datetime year to fraction 5 column_name lvarchar default null flags integer default 0 returns row ts The source time series tstamp The
325. indow Size L to set dormant number Lwindow size to set pool _containterpool set extent First eki T oo ee C eontainer Lfirst next set wcontrol flag C destroy_count container_name The name of the container The container name must exist command A command that changes the properties of the container detach active partitions before datetime For rolling window containers detaches any active partitions that are before the specified timestamp to the dormant window The keywords active partitipants and before are optional and do not change the command attach dormant partitions after datetime For rolling window containers attaches any dormant partitions that are after the specified timestamp into the active window However the maximum number of partitions in the active window is not exceeded even if more partitions fit the timestamp criteria The keywords dormant partitions and after are optional and do not change the command destroy dormant partitions before datetime For rolling window containers destroys any dormant partitions that are before the specified timestamp The partitions can contain data Any active partitions that meet the timestamp criteria are not destroyed The keywords dormant partitions and before are optional and do not change the command set active window size to number For rolling window containers sets the number of partitions in the active window
326. indow containers specifies for which partitions to return the sum of storage space usage 0 The partitions in the active window 1 The partitions in the dormant window 2 The container partition 3 All partitions IBM Informix TimeSeries Data User s Guide Description Use the TSContainerUsage function to monitor how full the specified container is For rolling window containers the TSContainerTotalUsage function returns summary values for how full the specified set of partitions is You can use the information from this function to determine how quickly your containers are filling and whether you must allocate more storage space Returns The number of pages that contain time series data in the pages column the number of elements in the slots column and the number of pages that are allocated to the container in the total column Example Monitor a container The following statement returns the information for the container that is named mult_container EXECUTE FUNCTION TSContainerUsage mult_container pages slots total 30 26 50 1 row s retrieved This container has 26 time series data elements that use 30 pages out of the total 50 pages of space Although the container is almost half empty the container can probably accommodate fewer than 20 more time series elements Example Monitor all containers The following statement returns the information for all containers EXECUTE FUNCTION TSContainerUsage NULL
327. ing statement changes the behavior when partitions are destroyed to allow up to seven dormant and active partitions to be destroyed in an operation execute function TSContainerManage readings_container set wcontrol 3 7 Related reference Rules for rolling window containers on page 3 16 TSContainerNElems function 7 104 The TSContainerNElems function returns the number of time series data elements stored in the specified container or in all containers Syntax TSContainerNElems container_name varchar 128 1 TSContainerNElems container_name_ varchar 128 1 rw_flag integer default 0 container_name Specifies which container to return information about Must be an existing container name You can include wildcard characters from the MATCHES operator The function returns information for all containers that have names that match the expression See MATCHES operator The value NULL returns information about all containers for the database IBM Informix TimeSeries Data User s Guide rw_flag For rolling window containers specifies for which partitions to return the sum of number of elements 0 The partitions in the active window 1 The partitions in the dormant window 3 The container partition The number of elements equals the number of intervals in the active and dormant windows 4 All partitions Description Use the TSContainerNElems function to view the number of elements stored in
328. ing the time series data and time stamp of the last entry in the time series If the time series is empty NULL is returned The type of the row is the same as the time series subtype Example The following query returns the final entry in a time series select GetLastElem stock_data from daily_stocks where stock_name IBM The following query retrieves the final entries on a daily stocks table IBM Informix TimeSeries Data User s Guide select GetLastElem stock_data from daily_stocks Related reference Time series routines that run in parallel on page 7 7 GetElem function on page 7 52 GetFirstElem function on page 7 53 GetLastValid function on page 7 58 GetNthElem function on page 7 62 PutElem function on page 7 77 The ts_last_elem function on page 9 3 GetPreviousValid function on page 7 65 GetLastElementStamp function The GetLastElementStamp function returns the timestamp of the last element in the time series Syntax GetLastElementStamp ts TimeSeries flag integer DEFAULT 0 returns lIvarchar ts The source time series flag 0 Default Return the timestamp of the last element regardless of whether the element is null 1 Return the timestamp of the last element that has data which is stored on the last page of the time series in a container Returns The timestamp of the last element in the time series as a DATETIME YEAR TO
329. ing window The TSRunningCor function returns NULL if the variance of either input is zero or NULL over the window on page 7 151 Syntax TSRunningCor valuel double precision value2 double precision num_values integer returns double precision TSRunningCor valuel real value2 real num_values integer returns double precision valuel The column of the first time series to use to calculate the running correlation value2 The column of the second time series to use to calculate the running correlation num_values The number of values to include in the running correlation k Description Use the TSRunningCor function within the Apply function The TSRunningCor function runs over a fixed number of elements not over a fixed length of time therefore it might not be appropriate for irregular time series The first set of num_values 1 outputs result from shorter windows the first output is derived from the first input time the second output is derived from the first two input times and so on Null elements in the input also result in shortened windows Chapter 7 Time series SQL routines 7 149 The TSRunningCor function can take parameters that are columns of a time series Use the same parameter format that the Apply function accepts Returns A DOUBLE PRECISION running correlation of the last k values Example This statement finds the running correlation between stock data for IBM and AA1 over a
330. instance update activity_stocks set activity_data DelTrim activity_data 2011 01 05 00 00 00 00000 datetime year to fraction 5 2011 01 06 00 00 00 00000 datetime year to fraction 5 where stock_id 600 Related reference Time series routines that run in parallel on page 7 DelClip function on page 7 42 DelElem function on page 7 4 Clip function on page 7 31 HideElem function on page 7 67 InsSet function on page 7 7 PutSet function on page 7 8 Le N UpdSet function on page 7 16 Divide function 7 46 The Divide function divides one time series by another It is one of the binary arithmetic functions that work on time series The others are Atan2 Minus Mod Plus Pow and Times Related reference Time series routines that run in parallel on page 7 7 Binary arithmetic functions on page 7 27 IBM Informix TimeSeries Data User s Guide ElemlsHidden function The ElemIsHidden function determines if an element is hidden Syntax ElemIsHidden ts TimeSeries offset integer returns Boolean ElemIsHidden ts TimeSeries tstamp datetime year to fraction 5 returns Boolean ts The time series to act on offset The offset of the element to examine tstamp The time stamp of the element to examine Description Use either offset or time stamp to locate the element you want to examine Returns Returns TRUE if the elem
331. ion on page 7 15 The ts_get_metadata function on page 9 31 Oo id 2 Q oO o 2 oO Poe O 00 The ts_update_metadata function on page 9 54 TSDecay function 7 122 The TSDecay function computes a decay function over its arguments Syntax TSDecay current_value smallfloat initial_value smallfloat decay_factor smallfloat returns smallfloat TSDecay current_value double precision initial_value double precision decay_factor double precision returns double precision current_value The current datum v in the sum shown next IBM Informix TimeSeries Data User s Guide initial_value The initial value initial in the sum shown next decay_factor The decay factor decay in the sum shown next Description All three arguments must be of the same type The function maintains a sum of all the arguments it has been called with so far Every time it is called the sum is multiplied by the supplied decay factor Given a decay factor between 0 and 1 this causes the importance of older arguments to fall off over time The first time that TSDecay is called it includes the supplied initial value in the running sum The actual function that TSDecay computes is decay initial 3 v decay In this computation i is the number of times the function has been called so far and v is the value it was called with in its jth invocation This function is useful only when used within the Apply f
332. ion on page 7 31 ClipCount function on page 7 3 ClipGetCount function on page 7 37 GetElem function on page 7 52 GetIndex function on page 7 55 GetLastElem function on page 7 56 GetLastValid function on page 7 58 GetNelems function on page 7 60 GetNextValid function on page 7 61 GetPreviousValid function on page 7 65 ion on page 7 77 PutElem function Transpose function on page 7 8 GetStamp function on page 7 Chapter 7 Time series SQL routines 7 63 GetOrigin function The GetOrigin function returns the origin of the time series Syntax GetOrigin ts TimeSeries returns datetime year to fraction 5 s The source time series Description Every time series value has a corresponding calendar and an origin within the calendar The calendar describes how often data values appear in the time series The origin of the time series is the first timepoint within the calendar for which the time series can contain data however the time series does not necessarily have data for that timepoint The origin is set when the time series is created and it can be changed with SetOrigin Returns The following example returns the time stamp of the origin of the time series for a given stock select GetOrigin stock_data from daily_stocks where stock_name IBM Related reference SetOrigin fun
333. ions can write to the container at the same time Multiple locks are available for the container 1 Only one session at a time can write to the container One lock is available for the container Usage By default multiple sessions can write to a container at the same time You can prevent more than one session from writing to a container by setting the flags argument to 1 Data is loaded faster when a single session writes to a container and the flags argument is set to 1 If your application enforces that one session writes to a container at a time set the flags argument to 1 to improve performance Example The following statement restricts the number of sessions that can write to the container named ctn_sm0 to 1 EXECUTE PROCEDURE TSContainerLock ctn_sm0 1 Related tasks Creating containers on page 3 15 TSContainerManage function The TSContainerManage function changes the properties of containers Only users with update privileges on the TSContainerTable table and the TSContainerWindowTable table can run this function Syntax TSContainerManage container_name varchar command lvarchar Chapter 7 Time series SQL routines 7 99 Syntax of command gt gt detach datetime gt lt active partitions _before attach datetime _dormant partitions lafter destroy datetime dormant partitions L before set active number w
334. iple rows that contain a time stamp and the other columns of the time series elements Example 1 Convert time series data to a table The following statement converts the data from stock_data for IBM to tabular form execute function Transpose select stock_data from daily_stocks where stock_name IBM Example 2 Transpose clipped data The following statement converts data for a clipped range into tabular form execute function Transpose select stock_data from daily_stocks where stock_name IBM datetime 2011 01 05 year to day NULL datetime year to fraction 5 The statement returns the following data in the form of a row data type ROW 2011 01 06 00 00 00 00000 99 00000 000000 54 00000000000 66 00000000000 888 0000000000 Example 3 Convert time series and other data into tabular format The following example returns the time series columns together with the non time series columns in tabular form execute function Transpose select from daily_stocks NULL row stock_id int stock_name lvarchar t datetime year to fraction 5 high real low real final real volume real 7 88 IBM Informix TimeSeries Data User s Guide Example 4 Display specific data as multiple fields within a single column The following statement selects the time and energy readings from a time series SELECT mr t mr energy FROM TABLE transpose SELECT readings FROM smartmeters WHERE meter_id 13243 smartmeter_ro
335. iple transactions TSL_Commit function on page 7 124 View information about the last data flush operation TSL_FlushInfo function on page 7 129 Reset the logging mode TSL_SetLogMode function on page 7 140 Monitor loading and saving data TSL_GetLogMessage function on page 7 132 Get the container name for a specific primary key TSL_GetKeyContainer function on page 7 131 7 131 Close a database session TSL_SessionClose function on page 7 139 Shut down the loader session TSL_Shutdown procedure on page 7 141 Find the intersection or Build the intersection of multiple time series and optionally clip the result union of time series function on page 7 71 Build the union of multiple time series and optionally clip the result Union function on page 7 157 Iterator functions Convert time series data to tabular form Transpose function on page 7 86 Aggregate functions Return a list collection of rows containing all elements in a time series TSSetToList function on page 7 153 Return a list of values from the specified column name in the time series TSCoINameToList function on page 7 91 Return a list of values from the specified column number in the time series TSColNumToList function on page 7 92 Return a list of values that contains the columns of the time series plus non time series columns TSRowToList function on page 7 147 Return a list of
336. it can have a different origin and number of entries Example The following example clips the first 30 elements at or after March 14 2011 at 9 30 a m for the stock with ID 600 and it returns the entire resulting time series select ClipCount activity_data 2011 01 01 09 30 00 00000 30 from activity_stocks where stock_id 600 The following example clips the previous 60 elements at or before August 22 2011 at 12 00 midnight for the stock with ID 600 select ClipCount activity_data 2011 08 22 00 00 00 00000 60 from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel on page 7 7 Apply function on page 7 18 Clip function on page 7 31 IBM Informix TimeSeries Data User s Guide ClipGetCount function The ClipGetCount function returns the number of elements in the current time series that occur in the period delimited by the time stamps Syntax ClipGetCount ts TimeSeries begin_stamp datetime year to fraction 5 default NULL end_stamp datetime year to fraction 5 default NULL flags integer default 0 returns integer S The source time series begin_stamp The begin point of the range Can be NULL end_stamp The end point of the range Can be NULL flags Valid values for the flags argument are described later in this topic Description For an irregular time series deleted elements are not counted For a regular time se
337. ith a time series The ts_get_calname function on page 9 27 Return the number of elements in a time series The ts_nelems function on page 9 41 Return the flags associated with the time series The ts_get_flags function on page 9 30 Get the name of the container The ts_get_containername function on page 9 29 Determine whether the time series is in a container The TS_IS INCONTAINE macro on page 9 36 Get the origin of the time series The ts_get_origin function on Get the metadata associated with the time series The ts_get_metadata function on page 9 31 Determine whether the time series is irregular The TS_IS_IRREGULAR macro on page 9 37 Determine whether a time series contains packed data The ts_get_packed function page 9 32 Get the frequency of hertz data The ts_get_hertz function on page 9 30 Get the compression type of compressed data The ts_get_compressed function on page 9 29 Return the number of valid intervals between two time stamps The ts_cal_index function on page 9 9 Return all valid timepoints between two time stamps The ts_cal_range function on page 9 1 e 3 Return a specified number of time stamps starting at a specified time stamp The s_cal_range_index function on page 9 1 Return the time stamp at a specified number of intervals after a specified time stamp The ts_cal_stamp
338. ithout including the NewTimeSeries and the TSVTMode parameters The NewTimeSeries parameter The NewTimeSeries parameter specifies whether the virtual table allows elements to be inserted into a time series that does not yet exist in the base table either because the row does not exist or because the row does not yet have a time series element To allow inserts if a time series does not yet exist use the NewTimeSeries parameter to specify the time series input string To prohibit inserts if a time series does not yet exist omit the NewTimeSeries parameter when you create the virtual table The following table describes the results of attempting to update the base table for different goals Table 4 3 Behavior of updates to the base table Need to use the NewTimeSeries Goal Result parameter Add a time series element into A new time series is inserted in Yes an existing row that does not the existing row have any time series data For example add the first meter reading for a specific meter IBM Informix TimeSeries Data User s Guide Table 4 3 Behavior of updates to the base table continued Need to use the NewTimeSeries Goal Result parameter Add a time series element to an If the timepoint is not the same as No existing time series For an existing element the new example add a meter reading element is inserted to the time for a meter that has previous series If the timepoint is the same
339. ivity_stocks set activity_data UpdElem activity_data row 2011 01 04 12 58 09 12345 6 75 2000 2 007 3 1 stock_trade where stock_id 600 Related tasks Related reference The ts_upd_elem function on page 9 5 N N 4 UpdMetaData function The UpdMetaData function updates the user defined metadata in the specified time series Chapter 7 Time series SQL routines 7 159 Syntax create function UpdMetaData ts TimeSeries metadata TimeSeriesMeta returns TimeSeries s The time series for which to update metadata metadata The metadata to be added to the time series Can be NULL This function adds the supplied user defined metadata to the specified time series If the metadata argument is NULL then the time series is updated to contain no metadata If it is not NULL then the user defined metadata is stored in the time series Returns The time series updated to contain the supplied metadata or the time series with metadata removed if the metadata argument is NULL Related tasks Creating a time series with metadata on page 3 23 Related reference GetMetaData function on page 7 59 GetMeta TypeName function on page 7 59 TSCreate function on page 7 116 ioe TSCreatelrr function on page 7 11 The ts_create_with_metadata function on page 9 18 The ts_get_metadata function on page 9 31 The ts_update_metadata func
340. ix TimeSeries solution Informix TimeSeries solution architecture Time series concepts TimeSeries data type technical overview Regular time series Irregular time series Packed time series JSON time series Calendar Time series storage F Getting started with the Informix TimeSeries solution Planning for creating a time series Planning for data storage Planning for loading time series data Planning for replication of time series data Planning for accessing time series data Hardware and software requirements Installing the IBM Informix TimeSeries Plug in foi Data Studio Database requirements for time series data SQL restrictions for time series data Time series global language support Sample smart meter data Setting up stock data examples Chapter 2 Data types and ii tables CalendarPattern data type Calendar data type TimeSeries data type Time series return types CalendarPatterns table CalendarTable table TSContainerTable table TSContainerWindowTable P TSContainerUsageActiveWindowVTI Table TSContainerUsageDormantWindowVTI Table TSInstanceTable table Bh ods she et Chapter 3 Create and manage a time series through SQL Example Create and load a regular time series Creating a TimeSeries data type and table Creating regular empty time series Copyright IBM Corp 2006 2014 X Xi Xi Xi xii xvii xviii xviii xviii xix XX XXi XXi
341. kcal startdate 2011 01 02 00 00 00 00000 pattstart 2011 01 02 00 00 00 00000 pattname workweek_day This calendar starts on 2011 01 02 and uses a pattern named workweek_day The following example creates an hourly calendar with the specified pattern Chapter 2 Data types and system tables 2 5 INSERT INTO CalendarTable c_name c_calendar VALUES my_cal startdate 2011 01 01 00 00 00 00000 pattstart 2011 01 02 00 00 00 00000 pattern 24 off 120 on 24 off hour The calendar start date is 24 hours before the pattern start date The pattern length is 168 hours or one week Related concepts CalendarTable table on page 2 8 Related tasks Defining a calendar on page 3 12 Related reference hapter 6 Calendar routines on page 6 1 CalendarPattern data type on page 2 1 HideElem function on page 7 67 TimeSeries data type 2 6 The TimeSeries data type is constructed from a row data type and is a collection of row subtypes To create a TimeSeries column first you create the TimeSeries subtype using the CREATE ROW TYPE statement CREATE ROW TYPE subtype_name gt gt timestamp_field DATETIME YEAR TO FRACTION 5 gt _field_name data_type NOT NULL Table 2 3 TimeSeries data type parameter values Value Description field_name The name of the field in the row data type Must be unique for t
342. kely to change storing that data as unstructured data in JSON documents prevents the need to update your schema or your application For example if you have sensors that monitor every machine in a factory when you add a machine the new sensors might collect different types of data than existing sensors Simplified schema If your schema for time based data includes more columns than each record typically uses or your records typically contain many NULL values you can easily load data as unstructured JSON documents For example if you have 50 different measurements but each sensor collects only 5 of those measurements each record has 45 NULL values IBM Informix TimeSeries Data User s Guide Storing data as JSON documents might require more storage space and result in slower queries than storing data in individual columns in the TimeSeries row type Time series definition Although you load JSON documents into your time series table internally the database server stores the JSON documents as BSON Therefore when you create a TimeSeries row type you include a BSON column to store the JSON documents You can include only one BSON column in a TimeSeries data type You can also include columns that have other data types The maximum size of a document in a BSON column in a TimeSeries data type is 4 KB Loading and querying JSON time series You can load JSON documents into a time series with the time series input statement through
343. l bar An error is raised if any of the column names does not exist in the time series type To search for a null element set column_list to NULL flags Determines whether hidden elements should be returned Valid for the flags parameter values are defined in tseries h They are e TS_CLOSEST_NO_FLAGS no special flags e TS_CLOSEST_RETNULLS_FLAGS return hidden elements Description The search algorithm ts_closest_elem is as follows e If cmp is any of lt or gt the search starts at tstamp e If cmp is lt the search starts at the first element before tstamp e If cmp is gt the search starts at the first element after tstamp The tstamp and cmp parameters are used to determine where to start the search The search continues in the direction indicated by cmp until an element is found that qualifies If no element qualifies the return value is NULL Important For irregular time series values in an irregular element persist until the next element This means that any of the equals operations on an irregular time series look for lt first If cmp is gt and the lt operation fails the operation then looks forward for the next element otherwise NULL is returned Returns An element meeting the described criteria that is non null and closest to the given time stamp Related reference Time series routines that run in parallel on page 7 7 GetCalendar function on page 7 48 G
344. lar time series The first num_values 1 outputs are exceptions because they result from shorter windows the first output is derived from the first input time the second output is derived from the first two input times and so on Null elements in the input also result in shortened windows The TSRunning Var function can take parameters that are columns of a time series Use the same parameter format that the Apply function accepts IBM Informix TimeSeries Data User s Guide Returns A DOUBLE PRECISION running variance of the last k values Example This statement produces a time series with the same length and calendar as stock_data but with one data column other than the time stamp Element n of the output is the variance of column 1 of stock_bar elements n 19 n 18 n The first 19 elements of the output are a bit different the first element is NULL because variance is undefined for a series of 1 The second output element is the variance of the first two input elements and so on If element i of stock_data is NULL or if column 1 of element i of stock _data is NULL output elements i i 1 i 19 are variances of just 19 numbers assuming that there are no other null values in the input window select stock_name Apply TSRunningVar 0 high 20 stock_data TimeSeries stock_bar TimeSeries one_real from daily_stocks Related reference Apply function on page 7 18 TSRunningMed function on pag
345. ld Q container TSContainerPoolRoundRobin readings Chapter 7 Time series SQL routines 7 107 33070 13 00 100 00 9 98e 34 19347 4 00 100 00 1 007e 35 17782 18 00 100 00 9 83e 34 When the INSERT statement runs the TSContainerPoolRoundRobin function runs with the following values TSContainerPoolRoundRobin smartmeters rawreadings smartmeter_row 0 readings The TSContainerPoolRoundRobin function sorts the container names alphabetically and returns the first container name to the INSERT statement The next time an INSERT statement is run the TSContainerPoolRoundRobin function returns the second container name and so on Related tasks Configuring additional container pools on page 3 20 Related reference User defined container pool policy on page 3 21 TSContainerPurge function 7 108 The TSContainerPurge function deletes time series data through a specified timestamp from one or more containers Syntax TSContainerPurge control_file varchar location lvarchar default client flags integer default 0 returns lvarchar control_file The name of the text file that contains information about which elements to delete from which containers The file must have one or more lines in the following format container_name instance_id end_range container_name The name of the container from which to delete elements instance_id The unique identifier of a time series insta
346. lds two element buffers for creating two elements Important The two element buffers are shared by the element fetching functions An element that is fetched is overwritten two fetch calls later Elements fetched by functions like ts_elem should not be explicitly freed They are freed when the ts_tsdesc is closed IBM Informix TimeSeries Data User s Guide If you must look at more than two elements at a time open a scan or use the ts_make_elem or ts_make_elem_with_buf routines to make a copy of one of your elements A ts_tsdesc structure is created by the ts_open function and destroyed by the ts_close procedure It is used by most of the time series API routines The ts_tselem structure A ts_tselem structure is a pointer to one element row of a time series When you use ts_tselem with a regular time series the time stamp column in the element is left as NULL allowing you to avoid the expense of computing the time stamp if it is not required The time stamp is computed on demand in the ts_get_col_by_name ts_get_col_by_number and ts_get_all_cols routines For irregular time series the time stamp column is never NULL You can convert a ts_tselem structure to and from an MI_ROW structure with the ts_row_to_elem and ts_elem_to_row routines If the element was created by the ts_make_elem or ts_make_elem_with_buf procedure you must use the ts_free_elem procedure to free the memory allocated for a ts_tsele
347. le 3 7 Time series input function elements Element Description calendar_name The name of the calendar IBM Informix TimeSeries Data User s Guide Table 3 7 Time series input function elements continued Element Description comp_definition A string that includes a compression definition for each column in the TimeSeries row type except the first column For the syntax of the compression definition see TSCreatelrr function on page 7 118 The irregular keyword must be included and the value of the threshold parameter must be 0 column_value A value of any column in the table except the TimeSeries column container_name The name of an existing container data_value The value of a column in a time series element except the timestamp column datafile_name The name of a file that contains time series data For the format of the file see BulkLoad function on page 7 30 hertz An integer 1 255 that specifies the number of records per second The irregular keyword must be included and the value of the threshold parameter must be 0 metadata_type The user defined_metadata type For more information see Creating a time series with metadata on page 3 23 metadata_value The metadata values Can be NULL For more information see Creating a time series with metadata on page 3 23 origin The origin of the time series The default origin is the calendar start dat
348. le lvarchar Chapter 7 Time series SQL routines 7 133 7 134 tstamp_format lvarchar reject_file lvarchar returns lvarchar TSL_Init table_name varchar 128 column_name varchar 128 log_type integer log_level integer logfile lvarchar tstamp_format lvarchar reject_file lvarchar where_clause lvarchar returns lvarchar table_name The name of the time series table Must not contain uppercase letters The table must contain a TimeSeries column and have a primary key column_name The name of the TimeSeries column Must not contain uppercase letters log_type Optional The type of message log 0 No message log 1 Log all messages to the specified file 2 Log all messages in a queue for the loader program for retrieval by the TSL_GetLogMessage function 3 Default Log all messages to the server message log log_level Optional The severity of information that is included in the message log file 2 Warning messages and error messages 4 Default Error messages logfile Optional If the value of the log_type argument is set to 1 the path and file name of the loader message log file If the Jog_type argument is set to a value other than 1 the logfile argument is ignored tstamp_format Optional The string that describes the format of the timestamp The default format is Y Yom od H M S The string must conform to the format specified by the DBTIME environment variable but can include
349. le_name lvarchar column_name lvarchar subtype lvarchar irregular integer pool_name lvarchar returns lIvarchar table_name The table into which the time series data is being inserted column_name The name of the time series column into which data is being inserted subtype The name of the TimeSeries subtype irregular Whether the time series is regular 0 or irregular 1 pool_name The name of the container pool Description Use the TSContainerPoolRoundRobin function to select containers in which to insert time series data from the specified container pool The container pool must exist before you can insert data into it and at least one container within the container pool must be configured for the same TimeSeries subtype as used by the data being inserted Set the TSContainerPoolRoundRobin function to a container pool name and use it as the value for the container argument in the VALUES clause of an INSERT statement The TSContainerPoolRoundRobin function returns container names to the INSERT statements in round robin order Returns The container name in which to store the time series value Example The following statement inserts data into a time series The TSContainerPoolRoundRobin function specifies that the container pool named readings is used in the container argument INSERT INTO smartmeters meter_id rawreadings VALUES met00001 origin 2006 01 01 00 00 00 00000 calendar smartmeter regular thresho
350. lement This value cannot be freed by the user with mi_free Related reference The ts_datetime_cmp function The ts_datetime_cmp function compares two time stamps and returns a value that indicates whether tstamp1 is before equal to or after tstamp2 Syntax mi_integer ts_datetime_cmp mi_datetime xtstampl mi_datetime tstamp2 tstamp1 The first time stamp to compare tstamp2 The second time stamp to compare Returns lt 0 If tstamp1 comes before tstamp2 0 If tstamp1 equals tstamp2 gt 0 If tstamp1 comes after tstamp2 Related reference The ts_get_all_cols procedure on page 9 27 The ts_get_col_by_name function on page 9 28 The ts_get_col_by_number function on page 9 28 Chapter 9 Time series API routines 9 21 The ts_del_elem function The ts_del_elem function deletes an element from a time series at a given timepoint Syntax ts_timeseries ts_del_elem ts_tsdesc tsdesc mi_datetime tstamp tsdesc The time series descriptor returned by ts_open tstamp The timepoint from which to delete the element Description If there is no element at the timepoint no error is raised and no change is made to the time series It is an error to delete a hidden element The equivalent SQL function is DelElem Returns The original time series minus the element deleted if there was one Related reference DelElem function on page 7 43 The ts_i
351. lements Related concepts Chapter 4 Virtual tables for time series data on page 4 1 IBM Informix TimeSeries Plug in for Data Studio on Related tasks Loading data from a file into a virtual table on page 3 34 Writing a loader program on page 3 31 Related reference Load data with the BulkLoad function on page 3 35 Load small amounts of data with SQL functions on page 3 36 Planning for replication of time series data You can replicate time series data with high availability clusters and Enterprise Replication Review the restrictions and requirements before you set up replication Chapter 1 Informix TimeSeries solution 1 23 1 24 Restrictions You cannot replicate time series data with high availability secondary servers that allow updates You cannot replicate time series data with the Change Data Capture API High availability clusters You can replicate time series data between the following types of high availability clusters that have read only secondary servers e High Availability Data Replication HDR e Shared disk secondary servers e Remote stand alone secondary servers High availability clusters do not require any prerequisites or have any other restrictions for replicating time series data Because some time series calendar and container information is kept in memory stop replication before you drop and then re create your calendar or container defini
352. les directory make f Makefile MY_DATABASE dbname The dbname is the name of a database Sample queries and programs are located in the same examples directory Precede queries with the BEGIN WORK statement and follow them with the ROLLBACK WORK statement IBM Informix TimeSeries Data User s Guide Chapter 2 Data types and system tables Specialized data types and system tables handle time series data You create time series objects that have the following data types e Calendar e CalendarPattern e TimeSeries You can also create a user defined data type to store time series metadata Other time series data types store information in the time series system tables The time series system tables are created in the database in which you create time series objects Important Do not delete time series system tables unless you do not have any time series data Related concepts Calendar on page 1 13 CalendarPattern data type The CalendarPattern data type defines the interval duration and the pattern of valid and invalid intervals in a calendar pattern The CalendarPattern data type is an opaque data type that has the following format v gt num_intervals 2 interval gt lt Copyright IBM Corp 2006 2014 2 1 2 2 Table 2 1 CalendarPattern data type parameter values Value Description interval For regular time series defines the size of one element One of the
353. ling window container The active window has space for the new partition which is created automatically The dormant window does not have data that has the same or a more recent timestamp than the data that is being added For example the dormant window is empty and you insert an element for December 31 of the previous year A new partition is created for December in the active window e If you add data that is more than one interval later than the latest partition intermediate empty partitions are also created Empty partitions are counted in the window size For example you insert an element for May 15 New partitions are created for April and May The April partition remains empty until you insert data for April The active window has 6 partitions e When you add data for a new partition and the active window is full the oldest partition automatically moves to the dormant window For example you add data for July Partitions are created automatically for June and July The partitions for December and January are moved to the dormant window Rules for managing partitions e You can manually detach partitions from the active window to the dormant window Any partitions older than the partition you choose to detach are also detached For example if you detach the March partition to the dormant window the February partition is detached automatically However if the dormant window is full and partitions are not destroyed autom
354. ll elements that are hidden Syntax FindHidden ts TimeSeries start datetime year to fraction 5 default NULL end datetime year to fraction 5 default NULL returns multiset ts The time series to act on start optional The date from which to start the scan end optional The date at which to end the scan Description You can scan the whole time series or specify a start date and an end date for the scan Returns A multiset containing all the hidden elements in the date range you specify Related reference ElemIsHidden function on page 7 47 ElemIsNull function on page 7 47 GetCalendar function The GetCalendar function returns the calendar associated with the given time series Syntax GetCalendar ts TimeSeries returns Calendar ts The time series from which to obtain a calendar 7 48 IBM Informix TimeSeries Data User s Guide Returns The calendar used by the time series Example The following example returns the calendar used by the time series for IBM select GetCalendar stock_data from daily_stocks where stock_name IBM expression startdate 2011 01 01 00 00 00 pattstart 2011 01 02 00 00 00 pattern 1 off 5 on 1 off day Related reference Time series routines that run in parallel on page 7 7 GetClosestElem function GetInterval function on page 7 55 GetOrigin function on page 7 64 TSCreate function on page 7 116 GetC
355. ller The element is overwritten after two calls to fetch elements using this tsdesc time series descriptor The equivalent SQL function is GetFirstElem Returns The first element in the time series Related reference The TS_ELEM_HIDDEN macro on page 9 23 The TS _ ELEM NULL macro on page 9 24 GetElem function on page 7 5 N The ts_begin_scan function on page 9 The ts_elem function on page 9 22 The ts_next function on page 9 41 The ts_next_valid function on page 9 42 The ts_free procedure The ts_free procedure frees all memory associated with the given time series argument The time series argument must have been generated by a call to either ts_create or ts_copy Syntax void ts_free ts_timeseries ts S The source time series Related reference The ts_copy function on page 9 1 The ts_create function on page 9 17 The ts_create_with_metadata function on page 9 18 The ts_get_ts function on page 9 3 The ts_ins_elem function on page 9 36 The ts_free_elem procedure 9 26 The ts_free_elem procedure frees a time series element releasing its resources It is used to free elements created by ts_make_elem or ts_row_to_elem It must not be called to free elements returned by ts_elem ts_first_elem ts_last_elem ts_last_valid ts_next ts_next_valid ts_nth_elem or ts_previou
356. llowing the given time stamp For irregular time series it returns the earliest element following the given time stamp Tip The ts_next_valid function is less efficient than ts_next so it is better to iterate through a time series using ts_begin_scan and ts_next rather than using ts_first_elem and ts_next_valid The equivalent SQL function is GetNextValid IBM Informix TimeSeries Data User s Guide Returns The element following the given time stamp If no valid element exists or the time series is regular and the next valid interval contains a null element NULL is returned The value pointed to by off is either 1 if the time series is irregular or the offset of the element if the time series is regular The element returned must not be freed by the caller It is overwritten after two fetch calls See The ts_hide_elem function on page 9 34 for an explanation of STATUS Related reference The TS_ELEM_HIDDEN macro on page 9 23 The TS_ELEM_NULL macro on page 9 24 The ts_first_elem function on page 9 25 GetLastValid function on page 7 58 The ts_next function on page 9 41 The ts_previous_valid function on page 9 45 The ts_nth_elem function The ts_nth_elem function returns the element at the nth position of the given time series Syntax ts_tselem ts_nth_elem ts_tsdesc tsdesc mi_integer N mi_integer STATUS tsdesc The descriptor returned by
357. lowing SQL statements SET EXPLAIN FILE TO c test parallelvtq out SET EXPLAIN ON You can specify a different directory and file name for the explain output file 4 Drop the existing virtual table on time series data ts_data_v in the stores_demo database by running the following SQL statement DROP TABLE ts_data_v 5 Alter the ts_data table to fragment it by expression by running the following SQL statement ALTER FRAGMENT ON TABLE ts_data init FRAGMENT BY EXPRESSION PARTITION part1 loc_esi_id lt 4727354321355594 in dbs1 PARTITION part2 loc_esi_id lt 4727354321510846 in dbs2 REMAINDER IN dbs3 6 Create the fragmented virtual table ts_data_v by running the following SQL statement 4 10 IBM Informix TimeSeries Data User s Guide EXECUTE PROCEDURE TSCreateVirtual Tab ts_data_v ts data origin 2010 11 10 00 00 00 00000 calendar call5min container raw_container threshold 0 regular fragment raw_reads The difference between this definition of the ts_data_v virtual table and the original definition is the value of the fourth parameter the TSVTMode parameter In this definition the TSVTMode parameter is set to fragment to fragment the virtual table In the original definition of the ts_data_v virtual table the TSVTMode parameter is set to 0 which indicates the default behavior 7 Set the isolation level to DIRTY READ by running the following SQL statement SET ISOLATION TO DIR
358. lue of the owner column is updated to Jim The value of the address column is not changed because null values are ignored The virtual table now contains two rows each of which have the new value for the owner column and the existing value for the address column SELECT FROM smartmeters_vti_nn_nk_nn acct_no 6546 meter_id 234 t 2011 01 01 00 00 00 00000 energy 33070 temperature 13 0000000000 owner Jim address 5 Nowhere Place acct_no 6546 meter_id 234 t 2011 01 01 00 45 00 00000 energy 3234 temperature 12 0000000000 owner Jim address 5 Nowhere Place 2 row s retrieved Update values that are not in the primary key and allow null values The following statement creates a virtual table named smartmeters_vti_nn_nk_in with the TSVTMode parameter set to 64 EXECUTE PROCEDURE TSCreateVirtualTab smartmeters_vti_nn_nk_in smartmeters origin 2011 01 01 00 00 00 00000 calendar ts_15min regular threshold 20 container 64 The following statement inserts a new row into the virtual table and a new element in the time series in the base table INSERT INTO smartmeters_vti_nn_nk_in acct_no meter_id t energy temperature owner address VALUES 6546 234 2011 01 01 00 45 00 00000 datetime year to fraction 5 3234 12 00 Jim NULL 1 row s inserted The value of the owner column is updated to Jim The value of the address column is updated to a null value The virtual table now contains two
359. lushStatus function presents the return value of the TSL_Flush function in a format that is easier to read Syntax TSL_FlushStatus value integer returns integer integer value The TSL_Flush function Returns Two 16 bit integers that indicate the status of the TSL_Flush function e The number of containers into which elements are inserted e The total number of elements that are inserted A return value of 1 indicates that the process was interrupted or encountered a severe error Example The following statement runs the TSL_Flush function and returns the number of containers that are affected and the number of elements that are inserted as integers EXECUTE FUNCTION TSL_FlushStatus TSL_FLush ts_data raw_reads Related reference TSL_GetKeyContainer function The TSL_GetKeyContainer function returns the name of the container that stores the time series values for the specified primary key value Syntax TSL_GetKeyContainer handle lvarchar key_value lvarchar returns varchar 128 handle The table and column name combination returned by the TSL_Attach or the TSL_Init function Chapter 7 Time series SQL routines 7 131 key_value The primary key value If the primary key contains multiple columns separate each value by a pipe character Usage Use the TSL_GetKeyContainer function to determine into which container the specified data is loaded If you load data in multiple sessions you can confi
360. lution at https www ibm com support ddocview wss rs 630 amp uid swe27020937 Installing the IBM Informix TimeSeries Plug in for Data Studio The IBM Informix TimeSeries Plug in for Data Studio is included with the database server installation Install the TimeSeries plug in by specifying its location from within IBM Data Studio IBM Data Studio version or IBM Optim Developer Studio must be installed and running IBM Data Studio is included with the Informix product To install the TimeSeries plug in 1 Move the plug in file ts_datastudio zip from the INFORMIXDIR extend TimeSeries version plugin directory to the computer where you are running Data Studio 2 From Data Studio choose Help gt Software Updates 3 From the Available Software tab click Add Site and then click Archive to select the plug in file 4 Select the plug in directory from the Available Software list and click Install 5 After the installation is complete restart Data Studio 6 To verify that the plug in is installed select Help gt About IBM Data Studio and click Plugin Details Look for Informix TimeSeries Loader in the Plug in Name column Chapter 1 Informix TimeSeries solution 1 25 1 26 Related concepts IBM Informix TimeSeries Plug in for Data Studio on page 3 27 Database requirements for time series data To implement the Informix TimeSeries solution the Scheduler must be running and the database must conform to re
361. ly stocks where stock_name IBM This example along with many others is supplied in the INFORMIXDIR extend TimeSeries version directory To use the Interp function create a server function create function Interp TimeSeries returns TimeSeries external name tmp Interpolate bld ts_interp language c not variant You can now use the Interp function in a DB Access statement For example consider the difference in output between the following two queries the output has been reformatted the actual output you would see would not be in tabular format select stock_data from daily_stocks where stock_name IBM 2011 01 03 00 00 00 L i A 2011 01 04 00 00 00 2 2 2 2 NULL 2011 01 06 00 00 00 3 3 3 3 select Interp stock_data from daily_stocks where stock_name IBM 2011 01 03 00 00 00 2011 01 04 00 00 00 2011 01 05 00 00 00 2011 01 06 00 00 00 WNNe Sz WNN a WwWNNr on WNNrR on SETUP create function Interp TimeSeries returns TimeSeries external name Interpolate so ts_interp language c not variant USAGE select Interp stock_data from daily_stocks where stock_id 901 include lt stdio h gt include lt mi h gt include lt tseries h gt define TS _MAX_COLS 100 define DATATYPE smallfloat Copyright IBM Corp 2006 2014 A 1 A 2 This example interpolates between values to fill in null elements It assumes that all columns are o
362. m tselem mi_datetime tstamp tsdesc A descriptor of the time series to be updated returned by ts_open IBM Informix TimeSeries Data User s Guide tselem The element to update tstamp The timepoint at which to update the element Description There must already be an element at the given time stamp For irregular time series hidden elements cannot be updated The equivalent SQL function is UpdElem Returns An updated copy of the original time series Related reference The ts_del_elem function on page 9 22 The ts_ins_elem function on page 9 36 The ts_put_elem function on page 9 46 The ts_put_elem_no_dups function on page 9 47 UpdElem function on page 7 159 The ts_elem function on page 9 22 The ts_last_elem function on page 9 3 The ts_make_elem function on page 9 38 The ts_make_elem_with_buf function on page 9 39 The ts_next function on page 9 41 Chapter 9 Time series API routines 9 55 9 56 IBM Informix TimeSeries Data User s Guide Appendix A The Interp function example The Interp function is an example of a server function that uses the time series API This function interpolates between values of a regular time series to fill in null elements This function does not handle individual null columns It assumes that all columns are of type FLOAT Interp might be used as follows select Interp stock_data from dai
363. m 9 51 offset getting for 7 66 offset getting from 9 35 range getting from a calendar 9 10 9 11 returning set of valid in range 6 3 traceFileName parameter 4 26 traceLevelSpec parameter 4 27 Tracing virtual tables 4 26 Transpose function 7 87 ts_begin_scan function 9 7 ts_cal_index function 9 9 ts_cal_pattstartdate function 9 9 ts_cal_range function 9 10 ts_cal_range_index function 9 11 ts_cal_stamp function 9 11 9 12 ts_close procedure 9 12 ts_col_cnt function 9 14 ts_col_id function 9 14 ts_colinfo_name function 9 15 ts_colinfo_number function 9 15 ts_copy function 9 16 ts_create function 9 17 TS_CREATE_IRR flag 9 17 9 18 ts_create_with_metadata function 9 18 ts_current_offset function 9 20 ts_current_timestamp function 9 21 ts_datetime_cmp function 9 21 ts_del_elem function 9 22 ts_elem function 9 22 TS_ELEM_HIDDEN macro 9 23 TS_ELEM_NULL macro 9 24 ts_elem_to_row 9 24 ts_end_scan procedure 9 25 ts_first_elem function 9 25 ts_free procedure 9 26 ts_free_elem procedure 9 26 ts_get_all_cols procedure 9 27 ts_get_calname function 9 27 ts_get_col_by_name function 9 28 ts_get_col_by_number function 9 28 ts_get_compressed function 9 29 ts_get_containername function 9 29 ts_get_flags function 9 30 ts_get_hertz function 9 30 ts_get_metadata function 9 31 ts_get_origin function 9 31 ts_get_packed function 9 32 ts_get_stamp_fields procedure 9 32 ts_get_threshold function 9 33 ts_get_ts function 9 33 ts_get_typei
364. m a time series 9 31 using distinct type TimeSeriesMeta 3 23 mi_set_trace_file API routine virtual tables 4 26 mi_set_trace_level API routine virtual tables 4 27 Minus function 7 74 Mod function 7 75 Modulus computing of division of two time series 7 75 Multiplying one time series by another 7 86 N Natural logarithm determining 7 74 Negate function 7 75 Negating a time series 7 75 next method 8 4 NotOp function 5 4 Null elements 4 3 NullCleanup function 7 75 O Offsets 1 7 converting to time stamp 9 51 determining 9 20 element getting for 9 43 inserting an element at 7 80 9 48 timestamp getting for 7 55 7 66 9 35 onpload utility 3 34 OpenAdmin Tool for Informix 1 3 Opening a time series 9 44 X 4 IBM Informix TimeSeries Data User s Guide Operators LessThan 1 26 Optim Developer Studio TimeSeries plug in 3 27 ORDER BY clause virtual tables 4 8 Origin 1 4 Origin of a time series changing 7 85 getting 7 64 9 31 specifying 3 24 OrOp function 5 5 6 5 Output of a function creating time series with 3 27 P Packed elements compressed data 1 10 Packed time series 1 8 Patterns 1 13 Performance virtual tables 4 2 planning 1 19 pload utility 3 34 Plus function 7 77 Positive function 7 77 Pow function 7 77 Properties of time series 1 19 PutElem function 3 36 7 77 PutElemNoDups function 7 79 PutNthElem function 7 80 PutSet function 3 36 7 81 PutTimeSeries function 7 82 R Raising one time ser
365. m function You can specify that duplicate elements replace existing elements that have the same timestamps with the writeflag argument value of 5 or 261 5 256 so that the TSL_Commit function inserts data in the same way as the PutElemNoDups function If you specify reduced logging with the writeflag argument value of 257 1 256 or 261 5 256 you must run TSL_Commit function in a transaction that can include only other functions that use reduced logging with the TSOPEN_REDUCED_LOG flag The elements that are saved are not visible by dirty reads until after the transaction commits The TSL_FlushInfo function returns eight categories of information about the last flush operation that saved data to disk Returns An integer that indicates the status of the function e A positive integer The number of elements that were inserted e 1 Process was interrupted or encountered a severe error Chapter 7 Time series SQL routines 7 125 Example Run the TSL_Commit function The following statement saves the data to disk after every 5000 elements are inserted for the table ts_data and the TimeSeries column raw_reads EXECUTE FUNCTION TSL_Commit ts_data raw_reads 1 5000 Example Run the TSL_Commit function with reduced logging The following statement saves the data to disk with reduced logging after every 20 000 elements are inserted EXECUTE FUNCTION TSL_Commit ts_data raw_reads 257 20000 Example Run the TSL_Commit
366. m size of the input file is set by your Java implementation 1 Open the appropriate cheat sheet by choosing Help gt Cheat Sheets expand the TimeSeries Data category choose Loading from a File and click OK IBM Informix TimeSeries Data User s Guide 2 Follow the instructions in the cheat sheet to create the load job Related tasks Running a load job from the command line Create a load job to load data from a database Use the IBM Informix TimeSeries Plug in for Data Studio to create a load job that loads time based data from a database into existing time series instances Loading data from databases has the following additional requirements e The data is in a database including databases on database servers other than Informix e Connectivity information for the database server that contains the source data The database server must support the Informix or DRDA connectivity protocols The connection is created through JDBC 1 Open the appropriate cheat sheet by choosing Help gt Cheat Sheets expand the TimeSeries Data category choose Loading from a Database and click OK 2 Follow the instructions in the cheat sheet to create the load job Related tasks Running a load job from the command line Running a load job from the command line After you create a load job in the IBM Informix TimeSeries Plug in for Data Studio you can run the load job from the command line with the command line loader applicatio
367. m structure Time series API routines sorted by task Time series API routines are sorted into logical areas based on the type of task The following table shows the time series interface routines listed by task type An uppercase routine name such as TS_ELEM_NULL denotes a macro Table 9 1 Time series API routines sorted by task Task type Description Open and close a time series Open a time series The ts_open function on page 9 44 Close a time series The ts_close function on page 9 12 Return a pointer to the time series associated with the specified time series descriptor The ts_get_ts function on page 9 33 Create and copy a time series Create a time series The ts_create function on page 9 17 Create a time series with metadata The ts_create_with_metadata function on page Copy a time series The ts_copy function on page 9 16 Free all memory associated with a time series created with ts_copy or ts_create The ts_free procedure on page 9 26 gt all elements of one time series into another The ts_put_ts function on page 9 49 Chapter 9 Time series API routines 9 3 Table 9 1 Time series API routines sorted by task continued Task type Description Scan a time series Start a scan Retrieve the next element from a scan The ts_next function on page 9 41 Find the time stamp of the last element retrieved from a scan Return
368. me memset buf_ptr 0 sizeof buf_ptr buf_ptr gt fd mi_file_open file_name_str O _RDONLY 0 mi_free file_name_str buf_ptr gt file_name file_name if MI_ERROR buf_ptr gt fd char buf 356 mi_integer name_len 256 lt mi_get_varlen file_name 256 mi_get_varlen file_name sprintf buf mi_file_open s failed name_len mi_get_vardata file_name mi_db_error_raise NULL MI_EXCEPTION buf buf_ptr gt buf_index buf_ptr gt buf_len buf_ptr gt ine_no End of open_buf 0 0 1 kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name get_char B 6 IBM Informix TimeSeries Data User s Guide purpose Get the next character from a buffered file returns The character or STREAM_EOF t t H kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static mi_integer get_char FILE_BUF buf_ptr register mi_integer c STREAM_EOF if buf_ptr gt buf_index gt buf_ptr gt buf_len buf_ptr gt buf_index 0 buf_ptr gt buf_len mi_file_read buf_ptr gt fd buf_ptr gt data sizeof buf_ptr gt data if MI_ERROR buf_ptr gt buf_len char buf 356 mi_integer name_len 256 lt mi_get_varlen buf_ptr gt file_name 256 mi_get_varlen buf_ptr gt file_name sprintf buf mi_file_read s failed name_len mi_get_vardata buf_ptr gt file_name mi_db_error_raise NULL
369. med cnt_sm1 in the dbspace named tsspace1 instead of creating a container in the same dbspace as the table Example 2 Removing a container from the default container pool Suppose that a container was automatically created for your time series but you want to stop automatically inserting data into that container After you create the container for the time series using the process in the first example you can remove the original container from the default container pool The following statement removes a container named ctn_sm4 from the default container pool EXECUTE PROCEDURE TSContainerSetPool ctn_sm4 The container ctn_sm4 still exists but data is inserting into it only if the INSERT statement explicitly names ctn_sm4 with the container argument Configuring additional container pools You can create a container pool to manage how time series data is inserted into multiple containers You can insert data into containers in round robin order or by using a user defined method If you want to use a container pool policy other than round robin order you must write the user defined container pool policy function before you insert data into the container pool For more information see User defined container pool palicy To create a container pool and store data into containers by using a container pool policy 1 Create containers by running the TSContainerCreate procedure 2 Add each container to the container pool by using the T
370. mes that match the expression See MATCHES operator The value NULL returns information about all containers for the database a For rolling window containers specifies for which partitions to return the percentage of the used space 0 The partitions in the active window 1 The partitions in the dormant window 2 The container partition 3 All partitions Description Use the TSContainerPctUsed function to view the percentage of used space in a container or in all containers For rolling window containers the TSContainerPctUsed function returns the percentage of the space in the specified set of partitions that is used Returns The percentage of used space Example The following statement returns the percentage of used space in the container named mult_container EXECUTE FUNCTION TSContainerPctUsed mult_container percent 60 000 1 row s retrieved The following statement returns the percentage of used space in all containers EXECUTE FUNCTION TSContainerPctUsed NULL percent 93 545 1 row s retrieved Related concepts Monitor containers on page 3 18 Related reference TSContainerUsage function on page 7 114 7 106 IBM Informix TimeSeries Data User s Guide TSContainerPoolRoundRobin function The TSContainerPoolRoundRobin function provides a round robin policy for inserting time series data into containers in the specified container pool Syntax TSContainerPool RoundRobin tab
371. most half Run one or more of the PutElem PutElemNoDups PutNthElem InsElem BulkLoad or PutTimeSeries functions with the TSOPEN_REDUCED_LOG 256 flag or the TSL_Flush function with the 257 flag within a transaction without other functions or SQL statements If you insert data through a virtual table run the TSCreateVirtualTab procedure with the TS_VTILREDUCED_LOG 256 flag and then insert data within a transaction without other types of statements The flags argument values on page 7 9 The TSVTMode parameter on page 4 16 Replicate time series data You can replicate time series data with Enterprise Replication For example if you collect time series data in multiple locations you can consolidate the data to a central server Planning for replication of time series data on page 1 23 Faster writing to time series containers By default multiple sessions can now write to a time series container simultaneously However you can limit the number of sessions to one Data is loaded faster if only one session writes to the container Use the TSContainerLock procedure to control whether multiple sessions are allowed Previously you wrote your application to prevent more than one session from writing to a container at one time TSContainerLock procedure on page 7 99 XVi IBM Informix TimeSeries Data User s Guide Table 4 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC1
372. mpression Optional Syntax of the compression parameter Chapter 7 Time series SQL routines 7 119 7 120 ik aos gt lt L q compress_size lower_bound upper_bound 1b max_deviation Lis qlb compress_size max_deviation lower_bound upper_bound Lqls A string that includes a compression definition for each column in the TimeSeries subtype except the first column List the compression definition for each field in the order of the fields in the subtype which are separated by commas The compression definition consists of the compression type and the corresponding compression attributes Compression types e n None The column is not compressed e q Quantization e Ib Linear boxcar e Is Linear swing door e qlb Quantization linear boxcar e qls Quantization linear swing door Compression attributes e compress_size The number of bytes to store 1 2 or 4 The value must be smaller than the size of the associated column e lower_bound A number that represents the lower boundary of acceptable values The range of values is the same as the data type of the associated column e upper_bound A number that represents the upper boundary of acceptable values The range of values is the same as the data type of the associated column e max_deviation A positive floating point number that represents the maximum deviation between the
373. n The supplied row type values must have a time stamp as their first attribute This time stamp is used to determine where in the time series the insertions are to be performed For example to insert into a time series that stores a single double precision value the row type values passed to InsSet would have to contain a time stamp and a double precision value If there is already an element at the specified timepoint the entire insertion is void and an error is raised You cannot insert an element at a time stamp that has been hidden Returns The time series with the multiset inserted Example The following example inserts a set of stock_trade items into a time series update activity_stocks set activity_data select InsSet activity_data set_data from activity_load_tab where stock_id 600 where stock_id 600 Related reference IBM Informix TimeSeries Data User s Guide Instanceld function The Instanceld function determines if the time series is stored in a container and if it is returns the instance ID of that time series Syntax InstanceId ts TimeSeries returns bigint ts The source time series Description The instance ID is used as an index in the container It can also be used to lookup information from the TSInstanceTable table Returns The instance ID associated with the specified time series unless the time series is stored in a row rather than in a container in which ca
374. n on page 7 92 TSSetToList function on page 7 153 TSRunningAvg function The TSRunningAvg function computes a running average over SMALLFLOAT or DOUBLE PRECISION values Chapter 7 Time series SQL routines 7 147 7 148 Syntax TSRunningAvg value double precision num_values integer returns double precision TSRunningAvg value real num_values integer returns double precision value The value to include in the running average num_values The number of values to include in the running average k Description Use the TSRunningAvg function within the Apply function A running average is the average of the last k values where k is supplied by the user If a value is NULL the previous value is used The running average for the first k 1 values is NULL The TSRunningAvg function can take parameters that are columns of a time series Use the same parameter format that the Apply function accepts This function runs over a fixed number of elements not over a fixed length of time therefore it might not be appropriate for irregular time series Returns A SMALLFLOAT or DOUBLE PRECISION running average of the last k values Example The example is based on the following row type create row type if not exists stock_bar timestamp datetime year to fraction 5 high real low real final real vol real The example uses the following input data 2011 01 03 00 00 00 00000 3 2 1 3 2011 01 04 00 0
375. n on page 9 22 The ts_make_elem_with_buf function The ts_put_elem function on page 9 46 The ts_put_elem_no_dups function on The ts_put_last_elem function on page 9 48 The ts_put_nth_elem function on page 9 48 The ts_row_to_elem function on page 9 50 The ts_upd_elem function on page 9 54 The ts_make_elem_with_buf function The ts_make_elem_with_buf function creates a time series element using the buffer in an existing time series element The initial data in the element is overwritten Syntax ts_tselem ts_make_elem_with_buf ts_tsdesc tsdesc MI_DATUM values mi_boolean nulls mi_integer off ts_tselem elem tsdesc The descriptor for a time series returned by ts_open values An array of data to be placed in the element Data that is by value is placed in the array and data that is by reference stores pointers nulls Stores which columns in the element should be NULL Chapter 9 Time series API routines 9 39 off For a regular time series off contains the offset of the element on return For an irregular time series off is set to 1 This argument can be NULL elem The time series element to be overwritten It must agree in type with the subtype of the time series If this argument is NULL a new element is created Returns A time series element If the elem argument is non null that is returned containing the new values If the elem
376. n Sqrt function Tan function Times function Pe TimeSeriesRelease function Transpose function TSAddPrevious function TSCmp function TSColNameToList function TSColNumToList function TSContainerCreate procedure TSContainerDestroy procedure TSContainerLock procedure TSContainerManage function TSContainerNElems function TSContainerPctUsed function f TSContainerPoolRoundRobin function TSContainerPurge function TSContainerSetPool procedure TSContainerTotalPages function TSContainerTotalUsed function TSContainerUsage function TSCreate function TSCreatelrr function TSDecay function TSL_Attach function TSL_Commit function TSL_Flush function TSL_FlushAll function TSL_FlushInfo function TSL_FlushStatus function TSL_GetKeyContainer function TSL_GetLogMessage function TSL_Init function p48 TSL_Put function TSL_PutRow function TSL_PutSQL function TSL_SessionClose function Vi IBM Informix TimeSeries Data User s Guide 7 70 7 71 7 71 7 73 7 73 7 74 7 74 LTD 779 7 75 7 77 7 77 7 77 7 77 lt 7 79 7 80 7 80 7 82 7 83 7 83 7 84 7 84 7 85 7 85 7585 7 86 7 86 7 86 7 86 7 90 7 90 fd 7 92 7 93 7 98 lt 7 99 lt 7 99 7 104 7 105 7 107 7 108 7 111 7 112 7 113 7 114 7 116 7 118 7 122 7 123 7 124 7 126 7 128 7 129 7 131 7 131 7 132 7 133 7 135 7 137 7 13
377. n 10 2011 it is the aggregation of all input values from 00 00 Jan 10 2011 to 23 59 59 99999 Jan 16 2011 Typically the AggregateBy function is used to aggregate from a fine grained regular time series to a coarser grained one However the following scenarios are also supported e Converting from a regular time series to a time series with a calendar of the same granularity In this case AggregateBy shifts the times back to accommodate differences in the calendar start times for example 00 00 from 8 00 Elements can be removed or null elements added to accommodate differences in the on off pattern e Converting from a regular time series to one with a calendar of finer granularity In this case AggregateBy replicates values e The input time series is irregular Because the granularity of an irregular time series does not depend on the granularity of the calendar this case is treated like aggregation from a fine grained time series to a coarser grained one This type of aggregation always produces a regular time series The flags argument values The flags argument determines how data points in the off periods of calendars are handled during aggregation and how hidden elements are managed It can have the following values 0 Default Data in off periods is aggregated with the next output period 1 Data in off periods is aggregated with the previous output period 2 Indicates that the scan runs with the TS_SCAN_HIDDEN flag set
378. n The command line loader application is useful if you want to load data without using the IBM Data Studio or Eclipse user interface The command line loader requires a minimal Eclipse platform The following software is required on the computer on which you run the command line loader e The Eclipse Platform Runtime Binary or a larger Eclipse or Data Studio installation with all necessary dependencies e TimeSeries plug in To load data by running the command line loader on a computer with a minimal Eclipse platform 1 Create load job definition files by running the TimeSeries plug in through Data Studio or the Eclipse IDE 2 Copy the five definition files e Record reader definition file e Table definition file e Mapping definition file e The connection profile file for the Informix database that has the TimeSeries column e Load properties file If you did not edit the load properties and want to use the default properties you can create an empty file with a ts p extension 3 Move the copies of the five definition files to the computer on which you want to load the data 4 Extract the TimeSeries plug in files into the top level Eclipse directory 5 Start Eclipse with the clean and initialize flags to install the plug in eclipse clean initialize 6 Run the command line loader application Chapter 3 Create and manage a time series through SQL 3 29 3 30 Related tasks Creating a load job to load data f
379. n element for 2011 01 18 select from inst_vt0 where code AA and t between datetime 2011 01 14 year to day and datetime 2011 01 19 year to day order by t code AA t 2011 01 14 00 00 00 00000 high 69 25000000000 low 68 37500000000 final 68 62500000000 vol 462 0000000000 code AA t 2011 01 19 00 00 00 00000 high 69 62500000000 low 69 12500000000 final 69 62500000000 vol 96 69999700000 2 row s retrieved The following query shows that the inst_vt2 table contains null elements select from inst_vt2 where code AA and t between datetime 2011 01 14 year to day and datetime 2011 01 19 year to day order by t code AA t 2011 01 14 00 00 00 00000 high 69 25000000000 low 68 37500000000 final 68 62500000000 vol 462 0000000000 code AA t 2011 01 17 00 00 00 00000 high low final vol code AA t 2011 01 18 00 00 00 00000 high low final vol code AA t 2011 01 19 00 00 00 00000 high 69 62500000000 low 69 12500000000 final 69 62500000000 vol 96 69999700000 4 row s retrieved The following query shows that the inst_vt512 table does contain the hidden element select from inst_vt512 where code AA and t between datetime 2011 01 14 year to day and datetime 2011 01 19 year to day IBM Informix TimeSeries Data User s Guide order by t code AA t 2011 01 14 00 00 00 00000 high 69 25000000000 low 68 37500000000 final 68 62500000000 vol 462 0000000000
380. n is similar to the DelClip function except that the DelTrim function deletes elements and reclaims space from the end of a regular time series instance whereas the DelClip function replaces elements with null values The DelTrim function is also similar to the DelRange function except that the DelRange function deletes elements and reclaims space from any part of a regular time series instance Syntax DelTrim ts TimeSeries begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries ts The time series to act on Chapter 7 Time series SQL routines 7 45 begin_stamp The begin point of the range end_stamp The end point of the range flags Valid values for the ithe argument are described in The flags argument alues on page 7 9 The default is 0 Description If you use the DelTrim function to delete elements from the end of a time series instance DelTrim trims off all null elements from the end of the time series and thus reduces the number of elements in the time series If you use the DelTrim function to delete hidden elements or if the begin point of the range falls before the origin of the time series instance an error is raised Returns A time series with all elements in the range between the specified timepoints deleted Example The following example removes all elements in a one day range on the specified day for the specified time series
381. n on the products and services currently available in your area Any reference to an IBM product program or service is not intended to state or imply that only that IBM product program or service may be used Any functionally equivalent product program or service that does not infringe any IBM intellectual property right may be used instead However it is the user s responsibility to evaluate and verify the operation of any non IBM product program or service IBM may have patents or pending patent applications covering subject matter described in this document The furnishing of this document does not grant you any license to these patents You can send license inquiries in writing to IBM Director of Licensing IBM Corporation North Castle Drive Armonk NY 10504 1785 U S A For license inquiries regarding double byte DBCS information contact the IBM Intellectual Property Department in your country or send inquiries in writing to Intellectual Property Licensing Legal and Intellectual Property Law IBM Japan Ltd 19 21 Nihonbashi Hakozakicho Chuo ku Tokyo 103 8510 Japan The following paragraph does not apply to the United Kingdom or any other country where such provisions are inconsistent with local law INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION AS IS WITHOUT WARRANTY OF ANY KIND EITHER EXPRESS OR IMPLIED INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF NON INFRINGEMENT MERCHANT
382. n page 7 2 Atan2 function on page 7 27 CalStamp function on page 6 A Clip function on page 7 3 ClipCount function on page 7 35 ClipGetCount function on page 7 3 Cos function on page 7 3 Countlf function on page 7 3 N 0 NX DelClip function on page 7 4 Qo DelElem function on page 7 4 DelRange function on page 7 DelTrim function on page 7 4 Divide function on page 7 46 ElemIsHidden function on page 7 4 GetCalendar function on page 7 48 GetContainerName function on page 7 51 tS N GetElem function on page 7 52 GetFirstElem function on page 7 53 GetFirstElementStamp function on page 7 5 GetIndex function on page 7 55 GetInterval function on page 7 55 3 GetLastElem function on page 7 56 7 8 IBM Informix TimeSeries Data User s Guide GetLastElementStamp function on page 7 57 GetLastNonNull function on page 7 57 GetLastValid function on page 7 58 GetNelems function on page 7 60 GetNextNonNull function on page 7 61 GetNextValid function on page 7 61 GetNthElem function on page 7 62 GetOrigin function on page 7 64 GetPreviousValid function on page 7 65 GetStamp function on page 7 66 GetThreshold function on page 7 67 N HideElem function
383. n takes a column name and returns the associated column number Syntax mi_integer ts_col_id ts_tsdesc tsdesc mi_string colname tsdesc A time series descriptor returned by ts_open colname The name of the column Description Column numbers start at 0 therefore the first time stamp column is always column 0 IBM Informix TimeSeries Data User s Guide Returns The number of the column associated with colname Related reference The ts_colinfo_name function The ts_colinfo_number function The ts_colinfo_name function The ts_colinfo_name function gets type information for a column in a time series Syntax ts_typeinfo ts_colinfo_name ts_tsdesc tsdesc mi_string colname tsdesc A time series descriptor returned by ts_open colname The name of the column to return information for Description The resulting typeinfo structure and its ti_typename field must be freed by the caller Returns A pointer to a ts_typeinfo structure This structure is defined as follows typedef struct _ts_typeinfo MI_TYPEID ti_typeid type id mi_integer ti_typelen internal length mi_smallint ti_typealign internal alignment mi_smallint ti_typebyvalue internal byvalue flag mi_integer ti_typebound internal bound mi_integer ti_typeparameter internal parameter mi_string ti_typename type name of the column ts_typeinfo Related reference The ts_col_i
384. nal The definition of the new time series to create TSVTMode optional Sets the virtual table mode as described in The TSVTMode parameter on Chapter 4 Virtual tables for time series data 4 5 4 6 page 4 16 Can be an integer or a string of one or more flag names that are separated by one of the following delimiters plus sign pipe 1 or comma TSColName optional For base tables that have more than one TimeSeries column specifies the name of the TimeSeries column to be used to create the virtual table The default value for the TSColName parameter is NULL in which case the base table must have only one TimeSeries column Usage Use the TSCreateVirtualTab procedure to create a virtual table that is based on a table that contains a time series Because the column names in the TimeSeries row type are used as the column names in the resulting virtual table you must ensure that these column names do not conflict with the names of other columns in the base table The total length of a row in the virtual table non time series and TimeSeries columns combined must not exceed 32 KB You can configure the time series virtual table to allow updating data in the base table through the virtual table If you specify any of the optional parameters you must include them in the order that is shown in the syntax but you can use any one of them without using the others For example you can specify the TSColName parameter w
385. name must be unique dbspace_name The name of the dbspace that contains the container partition If you do not specify additional dbspaces with the window_spaces argument this dbspace also contains the partitions for time series elements ts_type The name of the TimeSeries subtype that is stored by the container This argument must be the name of an existing row type that begins with a time stamp 7 94 IBM Informix TimeSeries Data User s Guide container _size The first extent size for partitions in KB The value must be equivalent to at least 4 pages If you specify 0 or a negative number 16 KB is used The maximum size of a partition depends on the page size e For 2 KB pages the maximum size is 32 GB e For 4 KB pages the maximum size is 64 GB e For 8 KB pages the maximum size is 128 GB e For 16 KB pages the maximum size is 256 GB container_grow The next extent size for partitions in KB The value must be equivalent to at least 4 pages If you specify 0 or a negative number 16 KB is used window_origin The first timestamp that is allowed for the rolling window container The rolling window container rejects time series values if the origin of the time series is before the origin of the container window_interval The range of time for which data is stored in each partition By default the window interval is one month which means that each window partition contains data from one calendar month Possible values are
386. nce An instance is a row in a table that includes a TimeSeries column end_range The ending time of the deletion range For a regular time series the index of the last timestamp to delete For irregular time series the last timestamp to delete location Optional The location of the control file Can be either of the following values client Default The control file is on the client computer server The control file is on the same computer as the database server flags Optional Determines delete behavior Can be either of the following values IBM Informix TimeSeries Data User s Guide 0 Elements that match the delete criteria are deleted only if all elements on a page match the criteria The resulting empty pages are freed 1 Elements on pages where all the elements match the delete criteria are deleted and the pages are freed Remaining elements that match the delete criteria are set to NULL Usage Use the TSContainerPurge function to remove old data from containers The TSContainerPurge function deletes pages where all elements have a timestamp that is equal to or older than the specified ending time in the specified containers for the specified time series instances The resulting empty pages are freed You can use the TSContainerPurge function to remove data from row that use a different TimeSeries subtype than the subtype specified in the container definition However the TimeSeries subtype that is named in
387. nceld function to return this number The instance ID is a required argument in some time series routines The instance ID is also used to order results if an SQL query includes an ORDER BY clause on a TimeSeries column cal_id INTEGER The identification of the CalendarTable row for the time series flags SMALLINT Stores various flags for the time series e 0x01 TSFLAGS_IRR The time series is irregular e 0x10 TSFLAGS_PACKED The time series contains packed elements e 0x20 TSFLAGS_COMPRESSED The time series is compressed e 0x40 TSFLAGS_HERTZ The time series contains hertz data vers SMALLINT The version of the time series container_name VARCHAR 128 1 The name of the container of the time series This is a reference to the primary key of the TSContainerTable table ref_count INTEGER The number of different references to the same time series instance The TSInstanceTable table is managed by the database server Do not modify it directly Rows in this table are automatically inserted or deleted when large time series are created or destroyed Related reference Instanceld function on page 7 71 TSContainerCreate procedure on page 7 93 TSContainerSetPool procedure on page 7 111 TSContainerDestroy procedure on page 7 98 Chapter 2 Data types and system tables 2 13 2 14 IBM Informix TimeSeries Data User s Guide Chapter 3 Create and manage a time series thr
388. nction Free any empty pages in a time series instance by running the NullCleanup function Related reference Rules for rolling window containers on page 3 16 DelRange function on page 7 44 TSContainerPurge function on page 7 108 DelElem function on page 7 43 NullCleanup function on page 7 75 Manage packed data You can insert delete and select packed data You must follow these rules when you insert or delete packed data e Records must be inserted in chronological order Existing records cannot be replaced or updated Missing records are allowed but cannot be updated e Functions that insert or delete compressed data must be run in an explicit transaction You can determine whether a time series contains packed data by running the GetPacked function You can determine the frequency of hertz data by running the GetHertz function You can determine the compression definition of compressed data by running the GetCompression function Insert packed data You can insert data into an existing hertz or compressed time series by running the following functions Chapter 3 Create and manage a time series through SQL 3 37 e InsElem e InsSet e TSL_Put e TSL_PutRow e TSL_PutSQL You can also insert data through a virtual table The data is saved to disk when an element reaches maximum size or the transaction is committed Delete packed data You can delete elements of hertz data by
389. ndlesnulls TSCreate cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar metadata TimeSeriesMeta returns TimeSeries with handlesnulls TSCreate cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar metadata TimeSeriesMeta set_rows set returns TimeSeries with handlesnulls cal_name The name of the calendar for the time series The origin of the time series This is the first valid date from the calendar for which data can be stored in the series origin 7 116 IBM Informix TimeSeries Data User s Guide threshold The threshold for the time series If the time series stores more than this number of elements it is converted to a container Otherwise it is stored directly in the row that contains it not in a container The default is 20 The size of a row containing an in row time series should not exceed 1500 bytes If a time series has too many bytes to fit in a row before this threshold is reached the time series is put into a container at that point zero Must be 0 nelems The number of elements allocated for the resultant time series If the number of elements exceeds this value the time series is expanded through reallocation container_name The name of the container used to store the time series Can be NULL metadata The user defined metadata to be p
390. ned by the Microsoft OLE DB standards You can override that default by setting an Informix environment variable or registry entry such as GL_DATE If you use Simple Network Management Protocol SNMP in your Informix environment note that the protocols GNMPv1 and SNMPv2 recognize only English code sets For more information see the topic about GLS and SNMP in the IBM Informix SNMP Subagent Guide The examples in this publication are written with the assumption that you are using one of these locales en_us 8859 1 ISO 8859 1 on UNIX platforms or en_us 1252 Microsoft 1252 in Windows environments These locales support U S English format conventions for displaying and entering date time number and currency values They also support the ISO 8859 1 code set on UNIX and Linux or the Microsoft 1252 code set on Windows which includes the ASCII code set plus many 8 bit characters such as and You can specify another locale if you plan to use characters from other locales in your data or your SQL identifiers or if you want to conform to other collation rules for character data For instructions about how to specify locales additional syntax and other considerations related to GLS locales see the IBM Informix GLS User s Guide Copyright IBM Corp 2006 2014 xi What s new in TimeSeries data for Informix Version 12 10 This publication includes information about new features and changes in existing functions
391. ner or in all containers Syntax TSContainerTotalPages container_name varchar 128 1 TSContainerTotalPages container_name varchar 128 1 rw_flag integer default 0 container_name Specifies which container to return information about Must be an existing container name You can include wildcard characters from the MATCHES operator The function returns information for all containers that have names that match the expression See MATCHES operator The value NULL returns information about all containers for the database os For rolling window containers specifies for which partitions to return the sum of the total pages that are allocated 0 The partitions in the active window 1 The partitions in the dormant window 2 The container partition 3 All partitions Description Use the TSContainerTotalPages function to view the size of a container or all containers For rolling window containers the TSContainerTotalPages function returns the sum of size of the specified set of partitions Returns The number of allocated pages Example The following statement returns the number of pages that are allocated to the container named mult_container EXECUTE FUNCTION TSContainerTotalPages mult_container total 50 1 row s retrieved The following statement returns the number of pages allocated to all the containers EXECUTE FUNCTION TSContainerTotal Pages NULL total 7 112 IBM Informix TimeSerie
392. ner with multiple dbspaces The following example creates a container that stores data in multiple dbspaces but does not use a purging policy execute procedure TSContainerCreate readings container containerdbs rt_raw_intvl 25600 12800 2011 01 01 00 00 00 00000 datetime year to fraction 5 month 0 0 dbsO dbsl dbs2 dbs3 dbs4 0 16 8 The example configures a container that has the following properties e The container name is readings_container e The dbspace for the container partition is named containerdbs e The name of the time series is rt_raw_intvl Chapter 7 Time series SQL routines 7 97 The first extent size of the partitions is 25600 KB The next extent size of the partitions is 12800 KB The active window size is unlimited The dormant window size is unlimited The origin of the container is 2011 01 01 00 00 00 00000 Partitions each hold a month of data The dbspaces for partitions are named dbs0 dbs1 dbs2 dbs3 and dbs4 Partitions are not automatically destroyed The first extent size of the dbspace containerdbs is 16 KB The next extent size of the dbspace containerdbs is 8 KB The container stores each month of data in a partition in one of the five dbspaces for partitions Because the active window size is unlimited all partitions are active until they are manually detached and then destroyed Related concepts TSInstanceTable table on page 2 12 TSContainerTabl
393. nformix TimeSeries Data User s Guide for 12 10 xC1 Overview Reference Manage time series data in rolling window containers You can control the amount of time series data that is stored in containers by specifying when to delete obsolete data You create a rolling window container that has multiple partitions that are stored in multiple dbspaces You configure a rolling window container to define the time interval for each partition and how many partitions are allowed for example 12 partitions that each store a month of data When you insert data for a new month a new partition is created and if the number of partitions exceed the maximum that is allowed the oldest partition becomes dormant You specify when to destroy dormant partitions Previously you had to delete obsolete data manually Time series storage on page 1 14 Rules for rolling window containers on page 3 16 Load time series data faster by reducing logging If you load time series elements into containers in a single transaction you can save time by specifying a reduced amount of logging By default every time series element that you insert generates two log records one for the inserted element and one for the page header update However you can specify that page header updates are logged for each transaction instead For example you can insert a set of daily meter readings for a meter in one transaction and reduce the amount of logging by al
394. ng time series is regular if both the input time series are regular and irregular if either of the inputs is irregular One of the inputs can be a scalar number or a row type In this case the resulting time series has the same calendar sequence of time stamps and regularity as the input time series If one of the inputs is a scalar number then the function is applied to the scalar number and to each non time stamp column of each element of the input time series If an input is a row type then that row type must be compatible with the time series row type The function is applied to the input row type and each element of the input time series It is applied column by column or directly to the two row IBM Informix TimeSeries Data User s Guide types depending on whether there is a variant of the function that handles the row types directly Returns The same type of time series as the first time series input unless the function is cast then it returns the type of time series to which it is cast For example suppose that time series tsi has type TimeSeries ci and that time series tsr has type TimeSeries cr where ci is a row type with INTEGER columns and cr is a row type with SMALLFLOAT columns Then Plus tsi tsr has type TimeSeries ci the fractional parts of the resulting numbers are discarded This is generally not the wanted effect Plus tsi tsr TimeSeries cr has type TimeSeries cr and does not discard the fractional part
395. ns the start dates of all the calendars in the CalendarTable table select c_name CalStartDate c_name from CalendarTable Related reference CalPattStartDate function on page 5 2 The ts_cal_startdate function on page 9 12 OrOp function The OrOp function returns the union of the two calendars Syntax Or0p call Calendar cal2 Calendar returns Calendar call The first calendar to be combined cal2 The second calendar to be combined Description This function returns a calendar that has every interval on that was on in either calendar the rest of the intervals are off The resultant calendar takes the earlier of the two start dates and the two pattern start dates Chapter 6 Calendar routines 6 5 6 6 If the two calendars have different sizes of interval units the resultant calendar has the smaller of the two intervals Returns A calendar that is the result of two others that are combined with the OR operator Example The following OrOp function returns the union of an hourly calendar with a daily calendar that has a different start date select c_calendar from CalendarTable where c_name hourcal c_calendar startdate 2011 01 01 00 00 00 pattstart 2011 01 02 00 00 00 pattern 32 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 15 off 9 on 31 off hour select c_calendar from CalendarTable where c_name daycal c_calendar startdate 2011 04 01 00 00 00 pattstart 2011 04 03 00 00
396. ns_elem function on page 9 36 The ts_put_elem function on page 9 46 The ts_upd_elem function on page 9 54 The ts_elem function 9 22 The ts_elem function returns an element from the time series at the given time Syntax ts_tselem ts_elem ts_tsdesc tsdesc mi_datetime tstamp mi_integer STATUS mi_integer off tsdesc The time series descriptor returned by ts_open tstamp A pointer to the time stamp for the desired element STATUS Set on return to indicate whether the element is NULL or hidden See ts_hide_elem function on page 9 34 for an explanation of the isNull argument G ff For regular time series off is set to the offset on return If the time series is irregular or if the time stamp is not in the calendar off is set to 1 The offset can be NULL IBM Informix TimeSeries Data User s Guide Description On return off is filled in with the offset of the element for a regular time series or 1 for an irregular time series The element is overwritten after two calls to fetch elements using this tsdesc time series descriptor The equivalent SQL function is GetElem Returns An element its offset and whether it is hidden NULL or both This element must not be freed by the caller Related reference The ts_last_elem function on page 9 3 The ts_first_elem function on page 9 2 N rs O1 The ts_ins_elem function on page
397. nt are described in The flags argument falues on page 7 5 lues on page 7 9 The default is 0 Description For regular time series GetNextValid returns the element at the calendar s earliest valid timepoint following the given time stamp For irregular time series it returns the earliest element following the given time stamp The equivalent API function is ts_next_valid Returns A row type containing the nearest element after the given time stamp The type of the row is the same as the time series subtype NULL is returned if the time stamp is later than that of the last time stamp in the The following example gets the first element that follows time stamp 2011 01 03 in a regular time series select GetNextValid stock_data 2011 01 03 00 00 00 00000 from daily_stocks where stock_name IBM The following example gets the first element that follows time stamp 2011 01 03 in an irregular time series select GetNextValid activity_data 2011 01 03 00 00 00 00000 from activity_stocks where stock_id 600 Related reference GetElem function on page 7 52 GetLastValid function on page 7 58 GetNthElem function GetPrevious Valid function on page 7 6 The ts_next_valid function on page 9 42 GetNthElem function 7 62 The GetNthElem function extracts the entry at a particular offset or position in a time series Syntax GetNthElem ts TimeSeries N integer
398. nteger type and specify the pipe delimiter e timestamp choose the Timestamp type and specify the pipe delimiter e data choose the Numeric type and specify the pipe delimiter 8 Create a table map named my_map and map the data formats of the data file to the columns of the Informix table and then save it in the my_test project directory 9 Create a connection profile to the Informix database server named my_ifx 10 Define and start a load job Specify the following values e File format file my_format udrf e Table definition file my_table tbl e Mapping file my_map tbImap e Data file my_meter_data unl e Connection profile my_ifx When you click OK the load job starts and you see the status Accessing time series data through a virtual table You create a virtual table to view the time series data in relational data format To create a virtual table based on the time series table Use the TSCreateVirtualTab procedure to create a virtual table named my_vt2 that is based on the my_ts_data table by running the following SQL statement EXECUTE PROCEDURE TSCreateVirtualTab my_vt2 my_ts_data calendar ts_15min origin 2010 11 10 00 00 00 00000 You can query the virtual table by running standard SQL statements For example the following query returns the first value for each of the 28 meter IDs SELECT FROM my_vt2 WHERE timestamp 2010 11 10 00 00 00 00000 Related reference TSCreateVirtualTab pro
399. nter to an MI_TYPEID structure that contains the type ID of the specified time series You must not free this value after use Related reference The ts_copy function on page 9 16 The ts_create function on page 9 17 The ts_create_with_metadata function on page 9 18 The ts_open function on page 9 44 The ts_hide_elem function 9 34 The ts_hide_elem function marks the element at the given time stamp as invisible to a scan unless TS_SCAN_HIDDEN is set Syntax ts_timeseries ts_hide_elem ts_tsdesc tsdesc mi_datetime tstamp tsdesc The time series descriptor returned by ts_open for the source time series tstamp The time stamp to be made invisible to the scan Description When an element is hidden element retrieval API functions such as ts_elem and ts_nth_elem return the hidden element however their STATUS argument has the TS_NULL_HIDDEN bit set The values for the element s STATUS argument are IBM Informix TimeSeries Data User s Guide If STATUS is TS_NULL_HIDDEN the element is hidden If STATUS is TS_NULL_NOTALLOCED the element is NULL If STATUS is both TS_NULL_HIDDEN and TS NULL _NOTALLOCED the element is both hidden and NULL If STATUS is 0 zero the element is not hidden and is not NULL The TS_ELEM_HIDDEN and TS_ELEM_NULL macros are provided to check the value of STATUS Hidden elements cannot be modified they must be revealed first using ts_reveal_elem
400. ntry purpose Get the security entry for a security ID returns A pointer to security entry notes If the entry is not found in the hash table then the security is looked up in the table and a new entry made in the hash table A warning message will be emitted if the security ID cannot be found In this case the security entry will have a NULL tsdesc kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkxk static sec_entry_t get_sec_entry loader_context_t context_ptr mi_integer sec_id mi_integer line_no mi_unsigned_integer i mi_unsigned_integer sec_id context_ptr gt hash_size sec_entry_t entry_ptr context_ptr gt hash i mi_string buf 256 mi_integer rc 0 Look the security ID up in the hash table for NULL entry_ptr entry_ptr entry_ptr gt next if sec_id entry_ptr gt sec_id return entry_ptr This is the first time this security ID has been seen entry_ptr mi_zalloc sizeof entry_ptr entry_ptr gt sec_id sec_id entry_ptr gt next context_ptr gt hash i context_ptr gt hash i entry_ptr Look up the security ID in the database table sprintf buf select series from s where Secid d mi_get_varlen context_ptr gt table_name mi_get_vardata context_ptr gt table_name sec_id if MI_OK mi_exec context_ptr gt conn buf MI_QUERY_BINARY mi_db_error_raise NULL
401. nts in the range between the specified timepoints deleted Example The following example removes all elements on the specified day for the specified time series instance update activity_stocks set activity_data DelClip activity_data 2011 01 05 00 00 00 00000 datetime year to fraction 5 2011 01 06 00 00 00 00000 datetime year to fraction 5 where stock_id 600 w mmi er a m a Time series routines that run in parallel on page 7 7 Clip function on page 7 3 DelElem function DelTrim function on page 7 4 77 T a oO es oO 3 g 5 O ct 5 o 5 ge o oa oO D a 7 5 5 nN N T a c 5 2 5 5 q page 7 70 77 E g Ja oO Q g 5 Q ct aj 5 o 5 ge o og oO N ee S amp S 7 gel Q ee oO on 3 Q a O 5 5 ge o og oO gt ion j DelElem function The DelElem function deletes the element at the specified timepoint in the specified time series instance Syntax DelElem ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns TimeSeries fa S The time series to act on tstamp The time stamp of the element to be deleted Chapter 7 Time series SQL routines 7 43 flags Valid values for the flags parameter are described in The flags argument falues on page 73 lues on page 7 9 The default is 0
402. nvironment variable to the location of the Java class files Your Java program must use the following versions of Java and JDBC e IBM Java Developer Kit 1 6 or later e IBM Informix JDBC Driver Version 4 10 JC4 or later To prepare the database server for using Java classes Chapter 8 Time series Java class library 8 3 Set the CLASSPATH environment variable to the location of the jar file or its contents For example if you leave the jar file in the default location use the following setting CLASSPATH INFORMIXDIR extend TimeSeries version java 1lib IfmxTimeSeries jar CLASSPATH export CLASSPATH If you move the jar file or expand it set the CLASSPATH environment variable to the appropriate directory Mapping time series data types To enable your Java program to retrieve or send a Calendar CalendarPattern orTimeSeries data type to or from an IBM Informix database you must create a custom type map Create a type map with one of the following methods e Create an entry for each time series data type in the type map for each database connection Entries are valid only for the current connection The following example makes an entry in the type map of a connection for handling TimeSeries data java util Map customTypeMap customTypeMap conn getTypeMap customTypeMap put timeseries stock_bar Class forName com informix timeseries IfmxTimeSeries The readSQL method extracts the time series data from t
403. o elements The type of the row is the same as the time series subtype Example The following example gets the first element in the time series for the stock with ID 600 select GetFirstElem activity_data from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel on page 7 7 The ts_first_elem function on page 9 25 GetFirstElementStamp function The GetFirstElementStamp function returns the timestamp of the first element in the time series Chapter 7 Time series SQL routines 7 53 Syntax GetFirstElementStamp ts TimeSeries flag integer DEFAULT 0 returns lvarchar ts The source time series flag 0 Default Return the timestamp of the first element regardless of whether the element is null 1 Return the timestamp of the first element that has data which is stored on the first page of the time series in a container Returns The timestamp of the first element in the time series as a DATETIME YEAR TO FRACTION 5 value Related reference Time series routines that run in parallel on page 7 7 GetHertz function The GetHertz function returns the frequency for packed hertz data Syntax GetHertz ts TimeSeries returns integer ts The name of the time series Description Use the GetHertz function to determine how many records per second the time series can store Returns Returns an integer 1 255 if the time series contain
404. o fraction 5 355 309 341 999 stock_bar 0 where stock_name IBM Related reference PutElem function on page 7 77 PutSet function The PutSet function updates a time series with the supplied multiset of row type values 7 80 IBM Informix TimeSeries Data User s Guide Syntax PutSet ts TimeSeries multiset_ts set flags integer default 0 returns TimeSeries ts The time series to act on multiset_ts The multiset of new row type values to store in the time series flags Valid values for the the argument are described in The flags argument alues on page 7 9 The default is 0 Description For each element in the multiset of rows if the time stamp is NULL the data is appended to the time series for regular time series or an error is raised for irregular time series For regular time series if there is data at a specified timepoint it is updated with the new data otherwise the new data is inserted For irregular time series if there is no data at a specified timepoint the new data is inserted If there is data at the specified timepoint the following algorithm is used to determine where to place the data 1 Round the time stamp up to the next second 2 Search backward for the first element less than the new time stamp 3 Insert the new data at this time stamp plus 10 microseconds The row type that is passed in must match the subtype of the time series Hidden elements cannot be u
405. oader program that uses multiple database sessions Chapter 3 Create and manage a time series through SQL 3 31 1 Initialize a global context and open a database session by running the TSL_Init function 2 Open additional database sessions by running the TSL_Attach function 3 Determine how to distribute the data among database sessions by running the TSL_GetKeyContainer function to find into which container each primary key value belongs Loading is faster if each database session loads data into a different container 4 Within the context of each database session run the TSL_Put TSL_PutRow or TLS_PutSQL function and the TSL_FlushAll or TSL_Commit function to load and save data 5 Within the context of each database session monitor the progress of loaded and saved data by running the TSL_GetLogMessage function 6 If necessary change the logging mode of all database sessions by running the TSL_SetLogMode function 7 Within the context of each database session close the database session by running the TSL_SessionClose function 8 Remove the global context and shut down the loader by running the TSL_Shutdown procedure Examples The following loader session uses one database session to load data into the ts_data table in the stores_demo database EXECUTE PROCEDURE ifx_allow_newline t EXECUTE FUNCTION TSL_Init ts_data raw_reads 3 4 NULL Y m d H M S tmp rejects 1og NULL EXECUTE FUNCTI
406. of the existing partitions in the active and dormant windows and sets the size of new partitions The keywords first and next are optional and do not change the command set wcontrol flagdestroy_count Sets a flag that indicates whether active partitions can be destroyed when the number of partitions that must be detached is greater than the dormant windows size and optionally sets how many partitions can be destroyed The values for flag are e 0 Default As many as necessary dormant partitions are destroyed If the operation requires more new active partitions than the value of the active_windowsize parameter the operation fails e 1 As many as necessary existing dormant partitions and older active partitions are destroyed Use this setting with caution Destroyed data cannot be recovered e 2 Dormant partitions are destroyed but limited to the number specified by the destroy_count value If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count value the operation fails e 3 2 1 Existing dormant partitions and older active partitions are destroyed but limited to the number specified by the destroy_count value If the number of partitions that must be destroyed for an operation exceeds the value of the destroy_count value the operation fails The destroy_count value is valid if the value of the flag parameter is 2 or 3 Chapter 7 Time series SQL routines 7 101 7 102
407. off 9 on 15 off 9 on 15 off 9 on 31 off hour Related reference AndOp function on page 5 1 OrOp function on page 6 5 Callndex function 6 2 The CalIndex function returns the number of valid intervals in a calendar between two given time stamps Syntax CalIndex cal_name lvarchar begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 returns integer cal_name The name of the calendar begin_stamp The begin point of the range Must not be earlier than the calendar start date end_stamp The end point of the range Description The equivalent API function is ts_cal_index IBM Informix TimeSeries Data User s Guide Returns The number of valid intervals in the specified calendar between the two time stamps Example The following query returns the number of intervals in the calendar daycal between 2011 01 03 and 2011 01 05 select CalIndex daycal 2011 01 03 00 00 00 00000 2011 01 05 00 00 00 00000 from systables where tabid 1 Related reference The ts_cal_range function on page 9 10 The ts_cal_range_index function on page 9 11 The ts_cal_stamp function on page 9 11 GetIndex function on page 7 55 GetStamp function on page 7 66 CalRange function The CalRange function returns a set of valid time stamps within a range Syntax CalRange cal_name lvarchar begin_stamp datetime year to fracti
408. ollowing statement returns the number of pages that are used by time series data in the container named mult_container Chapter 7 Time series SQL routines 7 113 EXECUTE FUNCTION TSContainerTotalUsed mult_container pages 30 1 row s retrieved The following statement returns the number of pages that are used by time series data in all containers EXECUTE FUNCTION TSContainerTotalUsed NULL pages 2029 1 row s retrieved Related concepts Monitor containers on page 3 18 Related reference TSContainerUsage function TSContainerTotalPages function on page 7 112 TSContainerPctUsed function on page 7 10 TSContainerNElems function on page 7 104 Time series storage on page 1 14 TSContainerUsage function 7 114 The TSContainerUsage function returns information about the size and capacity of the specified container or of all containers Syntax TSContainerUsage container_name varchar 128 1 TSContainerUsage container_name varchar 128 1 rw_flag integer default 0 container_name Specifies which container to return information about Must be an existing container name You can include wildcard characters from the MATCHES operator The function returns information for all containers that have names that match the expression See MATCHES operator The value NULL returns information about all containers for the database rw_flag For rolling w
409. olumns in the table except the TimeSeries column are used to identify the row to be updated and updates to the base table require accurate values for every column in the base table other than the TimeSeries column You can only update the values in the TimeSeries column You can specify the rules for the INSERT statement and which columns can be updated e You can update only the TimeSeries column but you can specify NULL as the values for non primary key columns e You can update the TimeSeries column and all other non primary key columns that do not have null values in the INSERT statement e You can update the TimeSeries column and all other non primary key columns You can set columns that do not have NOT NULL constraints to null values e You can update the TimeSeries column and all other non primary key columns that have NOT NULL constraints You can specify null values for columns that have NOT NULL constraints You can speed data insertion by reducing the amount of logging If you reduce the amount of logging INSERT statements must be run in a transaction without other types of SQL statements and the elements that are inserted are not visible until the transaction commits IBM Informix TimeSeries Data User s Guide Control the rules for inserting data by setting the NewTimeSeries parameter and the TSVTMode parameter in the TSCreateVirtualTab procedure Related concepts The TSVTMode parameter on page 4 16 Related ref
410. om a range 7 44 deleting null 7 75 first in a time series getting 7 53 9 25 freeing memory for 9 26 getting 7 52 9 22 9 27 hidden determining if 9 23 hidden revealing 7 83 9 50 hiding 7 67 9 34 inserting 7 69 7 77 7 79 9 36 9 38 9 46 9 47 inserting a set of 7 70 7 81 inserting at an offset 7 80 9 48 inserting at end of a time series 9 48 last valid getting 7 58 last getting 7 56 9 37 next valid getting 7 61 next getting 9 41 null determining if 9 24 number in time series clip getting 7 37 Elements continued number of getting 7 60 9 41 offset getting for an 7 62 9 20 9 43 timestamp getting for an 9 38 timestamp getting last before 7 65 9 45 timestamp getting nearest to an 9 42 updating 7 159 9 46 9 47 9 54 updating a set of 7 160 Enterprise Replication and time series 1 24 Examples directory 1 27 1 28 stock data 1 28 virtual tables 4 8 Exp function 7 48 Expand function 5 4 Expanding a calendar pattern 5 4 Exponentiating a time series 7 48 F Flags argument 7 9 getting for a time series 9 30 TS_CREATE_ IRR 9 17 9 18 TS_PUTELEM_NO_DUPS 7 9 TS_SCAN_EXACT_START 7 87 9 7 TS_SCAN_HIDDEN 7 37 7 87 9 7 TS_SCAN_SKIP_BEGIN 7 87 9 7 TS_SCAN_SKIP_END 7 87 9 7 TSOPEN_NO_NULLS 7 9 TSOPEN_RDWRITE 7 9 TSOPEN_READ_HIDDEN 7 9 TSOPEN_REDUCED_LOG 7 9 TSOPEN_WRITE_AND_HIDE 7 9 TSOPEN_WRITE_HIDDEN 7 9 TSWRITE_AND_REVEAL 7 9 Freeing memory for a time series 9 26 Freeing memory for a time series el
411. ome cases payment of a fee The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement IBM International Program License Agreement or any equivalent agreement between us Any performance data contained herein was determined in a controlled environment Therefore the results obtained in other operating environments may vary significantly Some measurements may have been made on development level systems and there is no guarantee that these measurements will be the same on generally available systems Furthermore some measurements may have been estimated through extrapolation Actual results may vary Users of this document should verify the applicable data for their specific environment Information concerning non IBM products was obtained from the suppliers of those products their published announcements or other publicly available sources IBM has not tested those products and cannot confirm the accuracy of performance compatibility or any other claims related to non IBM products Questions on the capabilities of non IBM products should be addressed to the suppliers of those products All statements regarding IBM s future direction or intent are subject to change or withdrawal without notice and represent goals and objectives only All IBM prices shown are IBM s suggested retail prices are current and are subject to change without notice Deale
412. on The ts_get_compressed function returns the compression string if the time series data is compressed Syntax mi_string ts_get_compressed ts_timeseries ts ts The name of the time series Description Use the ts_get_compressed function to determine the type of compression that is used in a time series that contains compressed numeric data Returns Returns a string that represents the compression type if the time series contains compressed data returns NULL if the time series does not contain compressed data Related reference GetCompression function on page 7 50 The ts_get_containername function The ts_get_containername function gets the container name of the given time series Syntax mi_string ts_get_containername ts_timeseries ts Chapter 9 Time series API routines 9 29 ts The source time series Description The equivalent SQL function is GetContainerName Returns The name of the container for the given time series This value must not be freed by the user Related reference GetContainerName function on page 7 51 The ts_get_flags function The ts_get_flags function returns the flags associated with the given time series Syntax mi_integer ts_get_flags ts_timeseries ts ts The source time series Description The return value is a collection of flag bits The possible flag bits set are TSFLAGS_IRR TSFLAGS_INMEM and TSFLAGS_ASSIGNED To check whet
413. on 5 end_stamp datetime year to fraction 5 returns list datetime year to fraction 5 CalRange cal_name lvarchar begin_stamp datetime year to fraction 5 num_stamps integer returns list datetime year to fraction 5 cal_name The name of the calendar begin_stamp The begin point of the range Must be no earlier than the first time stamp in the calendar end_stamp The end point of the range num_stamps The number of time stamps to return Description The first syntax specifies the range as between two given time stamps The second syntax specifies the number of valid time stamps to return after a specified time stamp The equivalent API function is ts_cal_range Chapter 6 Calendar routines 6 3 Returns A list of time stamps Example The following query returns a list of all the time stamps between 2011 01 03 and 2011 01 05 in the calendar daycal execute function CalRange daycal 2011 01 03 00 00 00 00000 2011 01 05 00 00 00 00000 datetime year to fraction 5 The following query returns a list of the two time stamps that follow 2011 01 03 in the calendar daycal execute function CalRange daycal 2011 01 03 00 00 00 00000 2 Related reference The ts_cal_range function on page 9 1 The ts_cal_range_index function on page 9 1 The ts_cal_stamp function on page 9 1 ka GetIndex function on page 7 55 GetStamp function on page 7
414. on 5 returns TimeSeries S The time series to act on origin The new origin of the time series Description If the supplied origin is not a valid timepoint in the given time series calendar the first valid timepoint following the supplied origin becomes the new origin The new origin must be earlier than the current origin To move the origin forward use the Clip function Returns The time series with the new origin Example The following example sets the origin of the stock_data time series update daily_stocks set stock_data SetOrigin stock_data 2011 01 02 00 00 00 00000 Related reference Apply function on page 7 18 Clip function on page 7 31 GetOrigin function on page 7 64 PutTimeSeries function on page 7 82 Sin function The Sin function returns the sine of its argument The Sin function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Positive Round and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 Sqrt function The Sqrt function returns the square root of its argument Chapter 7 Time series SQL routines 7 85 The Sqrt function is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Positive Round Sin and Tan R
415. ons return a time series The container for a time series that is created by the output of a function is often implicitly determined For example if part of a time series is extracted using the Clip function and the result is stored in the database the container for the original time series is used for the new time series If a time series returned by one of these functions cannot use the container of the original time series and a container name is not specified the resulting time series is stored in a container associated with the matching TimeSeries subtype and regularity If no matching container exists a new container is created Load data into an existing time series After you create a time series you can use one of several methods to load data into the time series Choose the data loading method according to the amount of data and the format of the data IBM Informix TimeSeries Plug in for Data Studio You can load time based data into existing time series instances by creating load jobs in the IBM Informix TimeSeries Plug in for Data Studio You must have the following prerequisites to create load jobs in the Informix TimeSeries Plug in for Data Studio e IBM Data Studio or IBM Optim Developer Studio with the Informix TimeSeries Plug in for Data Studio installed e An existing table with a TimeSeries column Primary key values in your table and a time series instance that is stored in a container defined for each row I
416. ormantwindowsize INTEGER The number of partitions in the dormant window windowspaces LVARCHAR 4096 The list of dbspaces in which partitions are stored tstamp DATETIME YEAR TO FRACTION 5 The starting timestamp of the time range for the partition partition tsPartitionDesc_t The description of the partition container_size INTEGER The first extent size of the partitions container_grow INTEGER The next extent size of the partitions windowcontrol INTEGER A flag that indicates how active partitions are handled when they are manually or automatically detached e 0 Default Partitions are not automatically destroyed if they cannot fit into the dormant window e 1 Partitions are automatically destroyed if they cannot fit into the dormant window Chapter 2 Data types and system tables 2 11 Related concepts Monitor containers on page 3 18 TSContainerUsageDormantWindowVTI Table The TSContainerUsageDormantWindowVTI table is a virtual table that is based on the TSContainerWindowTable table The TSContainerUsageDormantWindowVTI table has one row for each partition in the dormant window of a rolling window container Table 2 8 The columns in the TSContainerUsageDormantWindowVT table Column name Data type Description name VARCHAR 128 The name of the rolling window container windowinterval VARCHAR 8 The size of the interval of each partition year month week or day activewindowsize INTEGE
417. ough SQL Before you can load time series data into the database you must configure database objects specific to your time series You can manage data storage and remove data as necessary You can run SQL statements to create and manage time series To create and load a time series 1 Create a calendar or choose a predefined calendar 2 3 4 5 Related concepts Create a time series column If necessary create containers Create the time series Load data into the time series Example Create and load a regular time series This example shows how to create a TimeSeries data type create a time series table create a regular time series by running the TSCreate function and load data into the time series through the IBM Informix TimeSeries Plug in for Data Studio Prerequisites e IBM Data Studio or IBM Optim Developer Studio must be running and the Informix TimeSeries Plug in for Data Studio must be installed Data Studio can be installed on a different computer than the database server e The stores_demo database must exist You create the stores_demo database by running the dbaccessdemo command In this example you create a time series that contains electricity meter readings Readings are taken every 15 minutes The table and TimeSeries data type you create are similar to the examples in the ts_data table in the stores_demo database The following table lists the time series properties used in this example
418. oundaries that surround the given starting timepoint Examples The following statement creates a pattern that is named hour that has a timepoint every hour INSERT INTO CalendarPatterns VALUES hour 1 on hour The following statement creates a pattern that is named fifteen_min that has a 15 minute timepoint INSERT INTO CalendarPatterns VALUES fifteen_min 1 on 14 off minute The following statement creates a pattern that is named fourday_day that has a weekly pattern of four days on and three days off INSERT INTO CalendarPatterns VALUES fourday_day 1 off 4 on 2 off day Related concepts Calendar data type on page 2 4 Chapter 2 Data types and system tables 2 3 CalendarPatterns table on page 2 8 Related tasks Defining a calendar on page 3 12 Related reference WithinC and WithinR functions on page 7 161 HideElem function on page 7 67 hapter 5 Calendar pattern routines on page 5 1 GetInterval function on page 7 55 O Calendar data type 2 4 The Calendar data type controls the times at which time series data can be stored The Calendar data type is an opaque data type that is composed of e A calendar starting time stamp e A calendar pattern e A calendar pattern starting time stamp For regular time series calendars are also used to convert the time periods of interest to offsets of values in the vector and vice versa
419. ow and the dormant window move forward The partition for month 1 is destroyed You can move partitions between the active and dormant windows and change the size of the windows ie Creating containers on page 3 1 a Rules for rolling window containers on page 3 1 g 4 page 7 11 g Z page 7 112 Getting started with the Informix TimeSeries solution N 5 vw A 3 7A AZ 2 Ol else O D joja oO alel 3 a ajgja 2 ABS 3 Sirle allele islels lz Pi ao e a Cc o for oO s 5 5S R IP e al a T A 5 mi Fpj g D ag la 8 oa iS p N 5 5 fad 5 io 2 fo 3 a rg o oO n 5 ap O 5 e 5 Before you can create a time series decide on the properties of the time series and where to store the time series data After you create a time series you load the data and query the data Planning for creating a time series When you create a time series you define a set of properties You can perform the necessary tasks for creating a time series in the following ways e Running SQL commands e Writing an application with the time series Java class library e Writing an application with the REST API or the MongoDB API that runs through a wire listener The following table lists the properties of a time series Table 1 8 Properties of a time series Time series property Description How to define Timepoint size For a regular time series Define a calendar
420. owing statement loads data from a file name tsdata unl that is converted to a CLOB data type EXECUTE FUNCTION TSL_Put tsdata pkcol FileToClob data tsdata unl server Example 3 Element list format The following element list contains a value for every primary key and date field even if the values are the same as the previous element IBM Informix TimeSeries Data User s Guide MX230001 MX230001 MX230001 MX230001 MX672382 MX672382 MX672382 2011 01 01 00 2011 01 01 01 2011 01 01 02 2011 01 01 03 2011 01 01 00 2011 01 01 01 2011 01 01 02 The following element list is equivalent to the previous list but requires less room in the input buffer because duplicate primary key and date values are omitted MX230001 2011 01 01 00 00 00 23 4 56 7 01 00 00 34 7 57 8 lee 12 8 58 8 03 00 00 18 4 59 1 MX672382 2011 01 01 00 00 00 3 2 0 0 01 00 00 4 7 02 00 00 5 8 03 00 00 1 3 MX672382 2011 01 01 03 0 0 0 0 0 0 The following element list has a primary key that has multiple columns The values in the primary key that repeat are omitted MX23001 AQ74D 2011 01 01 00 00 00 23 11 98 43 AQ74E 22 71 97 65 00 01 00 22 69 94 56 MX23002 AV9QA 2011 01 01 Related tasks Writing a loader program on page 3 31 Related reference L ALLOW_NEWLINE configuration parameter Administrator s Reference L ITEX_ALLOW_NEWLINE Function SQL Syntax beaien TSL_PutRow f
421. p as the begin point of the range If the begin point is NULL the origin of the time series is used The timestamp of the first record in a resulting irregular time series is later than or equal to the begin point However the value of a record in an irregular time series persists until the next record Therefore by default the first record in the resulting time series can have a value that corresponds to an earlier timestamp than the begin point You can specify that the first record in the resulting time series is the first record whose timestamp is equal to or after the begin point by including the flag argument value of 16 You can specify that the resulting time series is irregular by including the flag argument value of 8 You can choose whether the origin of the resulting time series can be earlier than the origin of the input time series by setting the flag argument By default the origin of the resulting time series cannot be earlier than the origin of the input time series You can also control how hidden elements are handled with the flag argument By default hidden elements from the input time series are not included in the resulting time series You can include hidden element in the resulting time series and specify whether those elements remain hidden or are revealed in the resulting time series Returns A new time series containing only data from the requested range The new series has the same calendar as the original but
422. page 3 23 Can be The type ID of the metadata Can be NULL if the metadata argument is NULL hertz Optional An integer 1 255 that specifies the number of records per second Implicitly creates an irregular time series 9 19 Chapter 9 Time series API routines The value of the threshold parameter must be 0 The value of the flags parameter must be 3 compression Optional A string that includes a compression definition for each column in the TimeSeries subtype except the first column For the syntax of the compression parameter and descriptions of the compression types and attributes see TSCreatelrr function on page 7 118 The value of the threshold parameter must be 0 The value of the flags parameter must be 5 Description This function behaves the same as ts_create plus it saves the supplied metadata in the time series The metadata can be NULL or a zero length LVARCHAR if either ts_create_with_metadata acts exactly like ts_create If the metadata pointer points to valid data the metadata_typeid parameter must be a valid pointer to a valid type ID for a user defined type If you include the hertz or compression parameter you must run the ts_create function within an explicit transaction The equivalent SQL function is TSCreate or TSCreatelrr Returns A pointer to a new time series The user can free this value by calling ts_free Related reference GetMetaData function on page 7 59 G
423. pdated Returns A modified time series that includes the new values Example The following example updates a time series with a multiset update activity_stocks set activity_data select PutSet activity_data set_data from activity_load_tab where stock_id 600 where stock_id 600 Related reference Chapter 7 Time series SQL routines 7 81 PutTimeSeries function 7 82 The PutTimeSeries function puts every element of the first time series into the second time series Syntax PutTimeSeries ts1 TimeSeries ts2 TimeSeries flags integer default 0 returns TimeSeries ts1 The time series to be inserted ts2 The time series into which the first time series is to be inserted flags Valid values for the ithe argument are described in The flags argument alues on page 7 9 The default is 0 Description If both time series contain data at the same timepoint the rule of PutElem is followed see PutElem function on page 7 77 unless the TS_PUTELEM_NO_DUPS value of the flags parameter is set Both time series must have the same calendar Also the origin of the time series that is specified by the first argument must be later than or equal to the origin of the time series that is specified by the second argument This function can be used to convert a regular time series to an irregular one Important Converting an irregular time series to regular often requires aggregation information whic
424. ple partitions or a rolling window container which controls the amount of data that is stored Only users with update privileges on the TSContainerTable table and the TSContainerWindowTable can run this procedure Rolling window containers are a special type of container that requires additional arguments See Syntax for rolling window containers on page 7 94 Use the rolling window container syntax to create a container that spans multiple partitions Syntax TSContainerCreate container_name varchar 128 1 dbspace_name varchar 128 1 ts_type varchar 128 1 container_size integer container_grow integer container_name The name of the new container The container name must be unique dbspace_name The name of the dbspace that holds the container ts_type The name of the TimeSeries subtype that is stored in the container This argument must be the name of an existing row type that begins with a time stamp container_size The first extent size of the container in KB The value must be equivalent to at least 4 pages If you specify 0 or a negative number 16 KB is used The maximum size of a container depends on the page size e For 2 KB pages the maximum size is 32 GB Chapter 7 Time series SQL routines 7 93 e For 4 KB pages the maximum size is 64 GB e For 8 KB pages the maximum size is 128 GB e For 16 KB pages the maximum size is 256 GB container_grow The increments by which the container
425. plicate elements replace existing elements that have the same timestamps with the writeflag argument value of 5 or 261 5 256 so that the TSL_FlushAll function inserts data in the same way as the PutElemNoDups function If you specify reduced logging with the writeflag argument value of 257 1 256 or 261 5 256 you must run TSL_FlushAll function in a transaction that can include only other functions that use reduced logging with the TSOPEN_REDUCED_LOG flag The elements that are saved are not visible by dirty reads until after the transaction commits IBM Informix TimeSeries Data User s Guide The TSL_FlushInfo function returns eight categories of information about the last flush operation that saved data to disk Returns An integer that indicates the status of the function e A positive integer The number of elements that were inserted e 1 Process was interrupted or encountered a severe error Example Run the TSL_FlushAll function The following statement saves the data to disk for the table ts_data and the TimeSeries column raw_reads BEGIN WORK EXECUTE FUNCTION TSL_FlushAl1 ts_data raw_reads COMMIT WORK Example Run the TSL_FlushAll function with reduced logging The following statement saves the data to disk with reduced logging BEGIN WORK EXECUTE FUNCTION TSL_FlushAl1 ts_data raw_reads 257 COMMIT WORK Example Run the TSL_FlushAll function with reduced logging and no duplicate elements The
426. pped to a timepoint in accordance with the argument time series calendar interval Relative offsetting is then performed starting with that point In irregular time series the corresponding calendar interval does not indicate where time series elements are and therefore offsetting begins at exactly the time stamp specified Also since irregular elements can appear at any point within the calendar time interval WithinR returns elements with time stamps up to the last instant of the argument interval For example assume an irregular time series with a daily calendar turning on all weekdays The following function returns elements in the following interval excluding the endpoint WithinR stock_data 2010 07 11 07 37 18 day 3 future 2010 07 11 07 37 18 2010 07 14 07 37 18 In a regular time series the interval is as follows since each timepoint corresponds to the period containing the entire following day 2010 07 11 00 00 00 2010 07 13 00 00 00 IBM Informix TimeSeries Data User s Guide Both functions take a time series a time stamp an interval name a number of intervals and a direction The supplied interval name is not required to be the same as the interval stored by the time series calendar but it cannot be smaller than that interval For example given an hourly time series the Within functions can count forward or backward for hours days weeks months or years but not for minutes or seconds The dire
427. ppendix A The Interp function example on page A 1 for an example of ts_next Related reference The TS_ELEM_HIDDEN macro on page 9 23 The TS_ELEM_NULL macro on page 9 24 The ts_first_elem function on page 9 25 The ts_next_valid function The ts_previous_valid function on page 9 45 The ts_put_elem function on page 9 46 The ts_put_elem_no_dups function on page 9 47 The ts_put_last_elem function on page 9 48 The ts_put_nth_elem function on page 9 48 The ts_upd_elem function on page 9 54 The ts_next_valid function 9 42 The ts_next_valid function returns the nearest entry after a given time stamp Syntax ts_tselem ts_next_valid ts_tsdesc tsdesc mi_datetime tstamp mi_integer STATUS mi_integer off tsdesc The time series descriptor returned by ts_open tstamp Points to the time stamp that precedes the element returned STATUS Points to an mi_integer value that is filled in on return See the discussion of ts_hide_elem The ts_hide_elem function on page 9 34 for a description of STATUS off For regular time series off points to an mi_integer value that is filled in on return with the offset of the returned element For irregular time series off is set to 1 Can be NULL Description For regular time series this function returns the element at the calendar s earliest valid timepoint fo
428. pplyBinaryTSOp plus 1 data h data 2 from daily_low 1 daily_high h where 1 stock_id h stock_id You can receive the same results by substituting l data h data for ApplyBinaryTSOp plus 1 data h data Related reference ApplyCalendar function 7 24 The ApplyCalendar function applies a new calendar to a time series Syntax ApplyCalendar ts TimeSeries cal_name l varchar flags integer default 0 returns TimeSeries ts The specified time series from which specific timepoints are projected cal_name The name of the calendar to apply flags Valid values for the ith argument are described in The flags argument alues on page 7 9 The default is 0 Description If the calendar specified by the argument has an interval smaller than the calendar attached to the original time series and the original time series is regular then the resulting time series has a higher frequency and can therefore have more elements than the original time series For example applying an hourly calendar with eight valid timepoints per day to a daily time series converts each daily entry in the new time series into eight hourly entries IBM Informix TimeSeries Data User s Guide Returns A new time series that uses the named calendar and includes entries from the original time series on active timepoints in the new calendar Example Assuming fourdaycal is a calendar that contains four day workweeks the following query r
429. procedure on page 7 111 Delete elements through a specified timestamp from one or more containers TSContainerPurge function on page 7 108 Control whether multiple sessions write to the container at the same time TSContainerLock procedure on page 7 99 Change the properties of rolling windows containers TSContainerManage function on Change the extent sizes of containers TSContainerManage function on page 7 99 Monitor containers Return the number of elements in one or all containers TSContainerNElems function on page 7 10 Return the percentage of space that is used in one or all containers TSContainerPctUsed unction on page 7 105 Return the total number of pages that are allocated to one or all containers TSContainerTotalPages function on page 7 112 Return the number of pages that are used by one or all containers TSContainerTotalUsed function on page 7 113 Return the number of elements the number of pages that are used and the total number of pages that are allocated for one or all containers TSContainerUsage function on page 11 BE Time series routines that run in parallel Some time series routines can run in parallel Running in parallel is faster than running serially For time series routines to run in parallel the following conditions are required The table that contains the time series data is fragmented by any fragmentation method
430. quirements The Scheduler must be running in the database server If the Scheduler is not running when you create the TimeSeries data type or run a time series routine a message that the data type is not found or the routine cannot be resolved is returned The database that contains the time series data must meet the following requirements e The database must be logged e The database must not be defined as an ANSI database e Table and column names cannot be delimited identifiers The DELIMIDENT environment variable must be not set or set to n If you attempt to create a TimeSeries data type or run a time series routine in an unlogged or ANSI database the message DataBlade registration failed is printed in the online message log Related reference L scheduler argument Stop or start the scheduler SQL administration API Administrator s Reference L DELIMIDENT environment variable SQL Reference Mode Administrator s Reference SQL restrictions for time series data Some SQL statements cannot operate on time series data You cannot use the following SQL statements or keywords on TimeSeries columns e Boolean operators lt lt lt gt gt or gt e SELECT UNIQUE statement e GROUP BY clause e FRAGMENT BY clause e PRIMARY KEY clause You cannot use the MERGE statement on a table with time series data You cannot use the ALTER TYPE statement on the TimeSeries data type Time series global lang
431. r prices may vary This information is for planning purposes only The information herein is subject to change before the products described become available This information contains examples of data and reports used in daily business operations To illustrate them as completely as possible the examples include the names of individuals companies brands and products All of these names are fictitious and any similarity to the names and addresses used by an actual business enterprise is entirely coincidental COPYRIGHT LICENSE IBM Informix TimeSeries Data User s Guide This information contains sample application programs in source language which illustrate programming techniques on various operating platforms You may copy modify and distribute these sample programs in any form without payment to IBM for the purposes of developing using marketing or distributing application programs conforming to the application programming interface for the operating platform for which the sample programs are written These examples have not been thoroughly tested under all conditions IBM therefore cannot guarantee or imply reliability serviceability or function of these programs The sample programs are provided AS IS without warranty of any kind IBM shall not be liable for any damages arising out of your use of the sample programs Each copy or any portion of these sample programs or any derivative work must include a copyright notice a
432. r that is the combination of the calendars of the input time series with the OR operator The resulting calendar is stored in the CalendarTable table The name of the resulting calendar is a string containing the names of the calendars of the input time series separated by a vertical bar For example if two time series are combined and mycal and yourcal are the names of their corresponding calendars the resulting calendar is named mycal yourcal If all the time series have the same calendar then Union does not create a new calendar For a regular time series if a time series does not have a valid element at a timepoint of the resulting calendar the value for that time series element is NULL To be certain of the order of the columns in the resultant time series when using Union over a set use the ORDER BY clause Chapter 7 Time series SQL routines 7 157 7 158 For the purposes of Union the value at a given timepoint is that of the most recent valid element For regular time series this is the value corresponding to the current interval which can be NULL it is not necessarily the most recent non null value For irregular time series this condition never occurs since irregular time series do not have null intervals For example consider the union of two irregular time series one containing bid prices for a certain stock and one containing asking prices The union of the two time series contains bid and ask values for each
433. r to day stock_data TimeSeries one_real from daily_stocks where stock_name IBM The following example shows Apply without a filter and with a clipped range select Apply 0 hight 1 high 2 0 lowt 1 low 2 0 final 1 final 2 0 vol 1 vol 2 datetime 2011 01 04 year to day datetime 2011 01 05 year to day tl stock_data t2 stock_data TimeSeries stock_bar from daily_stocks tl daily_stocks t2 where tl stock_name IBM and t2 stock_name HWP The following example shows Apply with a filter and without a clip range The resulting time series contains the closing price of the days that the trading range is more than 10 of the low create function ts_sum a stock_bar returns one_real return row null datetime year to fraction 5 a high a low a final a vol one_real end function select Apply ts_sum 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 03 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries one_real from daily_stocks where stock_id 901 The following example uses a function as the expression to evaluate to boost performance The first step is to compile the following C function into applyfunc so begin applyfunc c include mi h MI_ROW high_low_diff MI_ROW row MI_FPARAM fp MI_ROW_DESC rowdesc MI_ROW result void xvalues 2 mi_boolean nulls 2 mi_real high low mi_real f mi_integer len
434. raction 5 x int If a column and a BSON field have the same name the column takes precedence The following statement returns the maximum value from the v1 INTEGER column SELECT AggregateBy max v1 ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 FROM tj IBM Informix TimeSeries Data User s Guide The following two equivalent statements return the maximum value from the v1 field in the data BSON column which is column 1 in the TimeSeries row type SELECT AggregateBy max data vl ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 timeseries outrow FROM tj SELECT AggregateBy max 1 v1 ts_lyear tsdata 0 2011 01 01 00 00 00 00000 datetime year to fraction 5 2012 01 01 00 00 00 00000 datetime year to fraction 5 timeseries outrow FROM tj The aggregated time series that is returned has the TimeSeries data type outrow If you do not cast the result to a row type that has appropriate columns for the results the statement fails Related reference AggregateRange function The AggregateRange function produces an aggregate over each element for a time range that is specified by start and end DATETIME parameters Syntax AggregateRange agg_express lvarchar ts TimeSeries flags integer default 0 start datetime year to fraction
435. raction 5 default NULL end_stamp datetime year to fraction 5 default NULL flags integer default 0 returns row Transpose query lvarchar dummy row begin_stamp datetime year to fraction 5 default NULL end_stamp datetime year to fraction 5 default NULL col_name lvarchar default NULL flags integer default 0 returns row with iterator ts The time series to transpose begin_stamp The begin point of the range Can be NULL end_stamp The end point of the range Can be NULL flags Determines how a scan works on the returned set query A string that contains a SELECT statement that can return multiple columns but only one time series column The non time series columns are concatenated with each time series element in the returned rows dummy A row type that must be passed in as NULL and cast to the expected return type of each row that is returned by the query string version of the Transpose function col_name If col_name is not NULL only the column that is specified with this parameter is used from the time series element plus the non time series columns Description The Transpose function is an iterator function You can run the Transpose function with the EXECUTE FUNCTION statement or in a table expression Normally the transpose function skips NULL elements when returning the rows found in a time series If the TS_SCAN_NULLS_OK 0x40 bit of the flags parameter is set the Transpose function returns NULL el
436. range in the TSRollup function is faster than first clipping the data by running the Clip or AggregateBy function Returns A TimeSeries data type that is the result of the expression or expressions Chapter 7 Time series SQL routines 7 143 7 144 Example Sum of all electricity usage in a postal code The following statement adds all the electricity usage values for each time stamp in the ts_data table in the stores_demo database for the customers that have a postal code of 94063 SELECT TSRollup raw_reads sum value FROM ts_data customer customer_ts data WHERE customer zipcode 94063 AND customer_ts_data customer_num customer customer_num AND customer_ts_data loc_esi_id ts_data loc_esi_id Example Sum of daily electricity usage by postal code Suppose that you have a table named ts_table that contains a user ID the postal code of the user and the electricity usage data for each customer which is collected every 15 minutes and stored in a column named value in a time series named ts_col The following query returns the total amounts of electricity used daily for each postal code for one month SELECT zipcode TSRol lup AggregateBy SUM value callday ts_col 0 2011 01 01 00 00 00 00000 2011 01 31 23 45 00 00 00000 SUM value FROM ts_table GROUP BY zipcode The first argument to the TSRollup function is an AggregateBy function which sums the electricity usage for each customer for each day
437. re y returns 1 if there was more data in the file 0 if the end of the file was found notes Creates a new time series if the series column for the Secid is NULL kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkxk int read_line loader_context_t context_ptr FILE_BUF buf_ptr ts_tsdesc tsdesc_ptr ts_tselem day_elem_ptr int null_line mi_datetime tstamp_ptr sec_entry_t sec_entry_ptr_ptr mi_integer sec_id 1 sec_entry_t sec_entry_ptr NULL mi_string token 256 mi_integer c 0 Next character from file mi_integer i 0 sec_entry_ptr_ptr NULL null_line 1 for i 0 i lt DAILY_COL_COUNT i context_ptr gt col_is_null i MI_TRUE c get_token buf_ptr token sizeof token if STREAM _EOF c amp amp 0 strlen token return 0 sec_id atoi token sec_entry_ptr_ptr sec_entry_ptr get_sec_entry context_ptr sec_id buf_ptr gt line_no read_day_data context_ptr buf_ptr token sizeof token tstamp_ptr xtsdesc_ptr sec_entry_ptr gt tsdesc if NULL sec_entry_ptr gt tsdesc An invalid security ID return 1 if context_ptr gt col_is_nul1 0 amp amp TS_IS_IRREGULAR ts_get_ts sec_entry_ptr gt tsdesc mi_string err_buf 128 sprintf err_buf Missing date on line d buf_ptr gt line_no mi_db_error_raise NULL MI_MESSAGE err_buf return 1 null_line Q
438. re returned that have never been allocated TS_NULL_NOTALLOCATED Usually if an element has not been allocated it is returned as NULL If TS_SCAN_NO_NULLS is set an element is returned that has each column set to NULL instead TS_SCAN_SKIP_END 16 0x10 Skip the element at the end timepoint of the scan range TS_SCAN_SKIP_BEGIN 8 0x08 Skip the element at the beginning timepoint of the scan range TS_SCAN_SKIP_HIDDEN 4 0x04 Skip hidden elements Returns An open scan descriptor or NULL if the scan times are both before the origin of the time series or if the end time is before the start time Example See the ts_interp function in Appendix A The Interp function example on for an example of the ts_begin_scan function Related reference HideElem function on page 7 67 The ts_next function on page 9 41 The ts_open function on page 9 44 The ts_first_elem function on page 9 25 IBM Informix TimeSeries Data User s Guide The ts_current_offset function on page 9 20 The ts_current_timestamp function on page 9 21 The ts_end_scan procedure on page 9 25 The ts_cal_index function The ts_cal_index Q function returns the number of valid intervals in a calendar between two given time stamps Syntax mi_integer ts_cal_index MI_CONNECTION conn mi_string cal_name mi_datetime begin_stamp mi_datetime end_stamp conn A valid DataBlad
439. re the specified time stamp and a positive number to indicate an offset after the specified time stamp Description The equivalent SQL function is CalStamp Returns The time stamp representing the given offset which must be freed by the caller Related reference The ts_cal_index function on page 9 9 The ts_cal_range_index function on page 9 11 The ts_cal_range function on page 9 10 The ts_cal_startdate function The ts_cal_startdate function returns the start date of a calendar ts_cal_startdate MI_CONNECTION conn mi_string cal_name conn A pointer to a valid DataBlade API connection structure cal_name The name of the calendar Description The equivalent SQL function is CalStartDate Returns An mi_datetime pointer that points to the start date of a calendar You must free this value after use Related reference CalStartDate function on page 6 5 The ts_cal_pattstartdate function on page 9 9 The ts_close function The ts_close procedure closes the associated time series 9 12 IBM Informix TimeSeries Data User s Guide Syntax void ts_close ts_tsdesc tsdesc tsdesc A time series descriptor returned by ts_open Description After a call to this procedure tsdesc is no longer valid and so should not be passed to any routine requiring the tsdesc argument Example See the ts_interp function Appendix A The Interp function example on page
440. readings as an existing element the existing element is updated with the new value If the TSVTMode parameter includes the value 1 or putelem multiple elements for the same timepoint can coexist therefore the new element is inserted and the existing element is also retained Add a row For example add a A new row is inserted into the Yes row for a new meter ID base table If you do not include the NewTimeSeries parameter and attempt to insert a time series element into an existing row that does not have any time series elements or into a new row you receive an error Example The following example creates a virtual table that is called daily_stocks_virt based on the table daily_stocks Because this example specifies a value for the NewTimeSeries parameter the virtual table daily_stocks_virt allows inserts if a time series does not exist for an element in the underlying base table If you perform such an insert the database server creates a new empty time series that uses the calendar daycal and has an origin of January 3 2011 EXECUTE PROCEDURE TSCreateVirtualTab daily_stocks_virt daily_stocks calendar daycal origin 2011 01 03 00 00 00 00000 The following statement creates a virtual table with the same characteristics as the previous statement except that the TSVTMode parameter specifies to allow duplicate timepoints and to reduce logging EXECUTE PROCEDURE TSCreateVirtualTab daily_stocks_virt
441. records on weekends The workweek_hour calendar pattern allows 45 records per week for regular time series and prohibits records on weekends and 15 hours per day For irregular time series the number of on periods and the number of records do not correspond The calendar pattern is stored in the CalendarPatterns table and can be used in multiple calendars When a calendar is inserted into the CalendarTable table it draws information from the CalendarPatterns table The database server refers only to CalendarTable for calendar and calendar pattern information changes to the CalendarPatterns table have no effect unless CalendarTable is updated or recreated You can manage exceptions to your calendar pattern by hiding elements for which there is no data by using the HideElem function Calendar patterns can be combined with functions that form the Boolean AND OR and NOT of the calendar patterns The resulting calendar patterns can be stored in a calendar pattern table or used as arguments to other functions You can use the calendar pattern interval with the WithinR and WithinC functions to search for data around a specified timepoint The WithinR function performs a relative search Relative searches search forward or backward from the starting timepoint traveling the specified number of intervals into the future or past The WithinC function performs a calibrated search A calibrated search proceeds both forward and backward to the interval b
442. ries only entries that are non null are counted so ClipGetCount might return a different value than GetNelems If the begin point is NULL the time series origin is used If the end point is NULL the end of the time series is used See Clip function on page 7 31 for more information about the begin and end points of the range The flags argument values The flags argument determines how a scan should work on the returned set If you set the flags argument to 0 the default null and hidden elements are not part of the count If the flags argument has a value of 512 0x200 the TS_SCAN_HIDDEN bit is set all non null elements are counted whether they are hidden or not Flag Value Meaning TSOPEN_RDWRITE 0 Default Hidden elements are not included in the count TS_SCAN_HIDDEN 512 Hidden elements marked by HideElem are included in the count see HideElem CEEA nction on page 7 67 Chapter 7 Time series SQL routines 7 37 Cos function Returns The number of elements in the given time series that occur in the period delimited by the time stamps Example The following statement returns the number of elements between 10 30 a m on March 14 2011 and midnight on March 19 2011 inclusive select ClipGetCount activity_data 2011 03 14 10 30 00 00000 2011 03 19 00 00 00 00000 from activity_stocks where stock_id 600 Related reference Time series routines that run in parallel
443. rm it is important to ensure that the order of the time series in the set is deterministic so that elements remain in the correct order Since the resulting time series is a different type from the input time series the result of the intersection must be cast Intersect can be thought of as a join on the time stamp columns If any of the input time series is irregular the resulting time series is irregular For the purposes of Intersect the value at a specified timepoint is that of the most recent valid element For regular time series this is the value corresponding to the current interval which can be NULL it is not necessarily the most recent non null value For irregular time series this condition never occurs because irregular time series do not have null intervals For example consider the intersection of two irregular time series one containing bid prices for a certain stock and one containing asking prices The intersection of the two time series contains bid and ask values for each timepoint at which a price was either bid or asked Now consider a timepoint at which a bid was made but no price was asked The intersection at that timepoint contains the bid price offered at that timepoint along with the most recent asking price If an intersection involves one or more regular time series the resulting time series starts at the latest of the start points of the input time series and ends at the earliest of the end points of th
444. rom a file on page 3 28 Create a load job to load data from a database on page 3 29 Related reference Command line loader application Command line loader application You run the command line loader application as an argument to the eclipse command The command line loader application uses the parameter files from a load job that you create with the IBM Informix TimeSeries Plug in for Data Studio Syntax eclipse application com ibm informix timeseries loader recordReader RecordReader_file table Table_file tb1 map Map_file fcmap connection Connection_file xml properties LoadProps_file tsIp RecordReader_file The name of the file that contains the record reader definition including the database connection information if necessary The file extension depends on from where the data is loaded udrf User defined record format The data is loaded from a file drr Database record reader The data is loaded from a database Table_file The name of the file that contains the table definition Map_file The name of the file that contains the mapping definition Connection_file The name of the file that contains the Informix database connection profile LoadProps_file The name of the file that contains the load properties Usage Use the command line loader application to load time series data into a TimeSeries column The Eclipse Platform Runtime Binary or a larger Eclipse program the TimeSerie
445. rows each of which have the new value for the owner column and a null value for the address column SELECT FROM smartmeters_vti_nn_nk_in acct_no 6546 meter_id 234 t 2011 01 01 00 00 00 00000 IBM Informix TimeSeries Data User s Guide energy 33070 temperature 13 0000000000 owner Jim address acct_no 6546 meter_id 234 t 2011 01 01 00 45 00 00000 energy 3234 temperature 12 0000000000 owner Jim address 2 row s retrieved Duplicate timepoints By default the database server uses the PutElemNoDups function to add an element to the underlying time series If an element exists at the same timepoint the existing element is updated You can perform bulk updates of the underlying time series without producing duplicate elements for the same timepoints When the TSVTMode parameter includes the value 1 the database server uses the PutElem function to add an element to the underlying time series The PutElem function handles updates to existing data in an underlying irregular time series differently than does the PutElemNoDups function Null and hidden elements The TSVTMode parameter includes options to display null or hidden time series elements in the virtual table By default if a base table has a null element at a specific timepoint the virtual table has no entries for that timepoint You can use the TSVTMode parameter to display null elements as a row of null values plus the timestamp column and any non time series
446. rt of the primary key regardless if those columns have NOT NULL constraints in the base table NOT NULL constraints are not included in the virtual table but are enforced in the base table For new rows you can specify null values for columns that are not part of the primary key and do not have NOT NULL constraints update_nonkey_not_nulls 32 This setting is valid only if the base table has a primary key You can update the value of columns in an existing row that are not part of the primary key You can specify NULL for non primary key columns that you do not want to update All columns that have non NULL values in the INSERT statement are updated in the base table except the primary key columns See Update values that are not in the primary key on page 4 21 update_nonkey_include_nulls 64 This setting is valid only if the base table has a primary key You can update the value of all the columns in an existing row that are not part of the primary key including setting null values for columns that allow null values Columns that are not part of the primary key are updated to the value included in the INSERT statement Columns that allow null values can be set to NULL See Update values that are not in the primary key and allow null values on page 4 22 4 18 IBM Informix TimeSeries Data User s Guide Table 4 6 Settings for the TSVTMode parameter continued Flag name Value Description
447. s with this tsdesc time series descriptor Related reference GetLastElem function on page 7 56 The ts_elem function on page 9 22 Chapter 9 Time series API routines 9 37 The ts_last_valid function The ts_last_valid function extracts the entry for a particular timepoint Syntax ts_tselem ts_last_valid ts_tsdesc tsdesc mi_datetime tstamp mi_integer STATUS mi_integer off tsdesc The descriptor for a time series returned by ts_open tstamp The time stamp of interest STATUS A pointer to an mi_integer value See The ts_hide_elem function on age 9 34 for a description of STATUS off If the time series is regular off is set to the offset of the returned element If the time series is irregular or if the time series is empty off is set to 1 This argument can be passed as NULL Description For regular time series this function returns the first element with a time stamp less than or equal to tstamp For irregular time series it returns the latest element at or preceding the given time stamp Returns The nearest element at or before the given time stamp If there is no such element before the time stamp NULL is returned NULL is returned if The element at the timepoint is NULL and the time series is regular The timepoint is before the origin The time series is irregular and there are no elements at or before the given time stamp This element must not be freed by
448. s Data User s Guide 2169 1 row s retrieved Related concepts Related reference TSContainerPctUsed function on page 7 105 TSContainerTotalUsed function The TSContainerTotalUsed function returns the total number of pages that contain time series data in the specified container or in all containers Syntax TSContainerTotalUsed container_name varchar 128 1 TSContainerTotalUsed container_name varchar 128 1 rw_flag integer default 0 container_name Specifies which container to return information about Must be an existing container name You can include wildcard characters from the MATCHES operator The function returns information for all containers that have names that match the expression See MATCHES operator The value NULL returns information about all containers for the database ee For rolling window containers specifies for which partitions to return the sum of the total number of pages that contain time series elements 0 The partitions in the active window 1 The partitions in the dormant window 2 The container partition 3 All partitions Description Use the TSContainerTotalUsed function to view the amount of data in a container or in all containers For rolling window containers the TSContainerTotalUsed function returns the sum of the amount of data in the specified set of partitions Returns The number of pages that contain time series data Example The f
449. s follows your company name year Portions of this code are derived from IBM Corp Sample Programs Copyright IBM Corp _enter the year or years_ All rights reserved If you are viewing this information softcopy the photographs and color illustrations may not appear Privacy policy considerations IBM Software products including software as a service solutions Software Offerings may use cookies or other technologies to collect product usage information to help improve the end user experience to tailor interactions with the end user or for other purposes In many cases no personally identifiable information is collected by the Software Offerings Some of our Software Offerings can help enable you to collect personally identifiable information If this Software Offering uses cookies to collect personally identifiable information specific information about this offering s use of cookies is set forth below This Software Offering does not use cookies or other technologies to collect personally identifiable information If the configurations deployed for this Software Offering provide you as customer the ability to collect personally identifiable information from end users via cookies and other technologies you should seek your own legal advice about any laws applicable to such data collection including any requirements for notice and consent For more information about the use of various technologies inclu
450. s included in the message log file 2 Warning messages and error messages 7 140 IBM Informix TimeSeries Data User s Guide 4 Default Error messages logfile Optional If the value of the log_type argument is set to 1 the path and file name of the loader message log file If the Jog_type argument is set to a value other than 1 the logfile argument is ignored Usage Use the TSL_SetLogMode function in a loader program to specify the severity of messages that are saved in the loader message log You must run the TSL_SetLogMode function in the context of a loader session that was initialized by the TSL_Init function The TSL_SetLogMode function changes the log mode that was specified by the TSL_Init function for all loading sessions Returns e 0 The log mode was set e An error if the function failed Examples The following statement saves loader warning and error messages to a file EXECUTE FUNCTION TSL_SetLogMode tsdata raw_reads 1 2 tmp messag log TSL_Shutdown procedure The TSL_Shutdown procedure shuts down the loader application and releases all resources Syntax TSL_Shutdown handle _1varchar handle The table and column name combination returned by the TSL_Init function Usage Use the TSL_Shutdown procedure when data loading is complete The TSL_Shutdown procedure closes the global context and releases the associated resources that are used by the loader application Any virtual tables that
451. s is the same length Regular elements persist only for the length of an interval as defined by the calendar associated with the time series If a value for a timepoint is missing that element is null You can update null elements Related reference Create a time series on page 3 22 Irregular time series An irregular time series stores data for a sequence of arbitrary timepoints Irregular time series are appropriate when the data arrives unpredictably such as when the application records every stock trade or when electricity meters record random events such as low battery warnings or low voltage indicators Irregular time series are also required for packed data which includes hertz data and compressed numeric data Irregular time series store the time stamps for each element instead of storing offsets because the interval between each element can be a different length Irregular elements persist until the next element by default and cannot be null For example if you query for the value of a stock price at noon but the last recorded trade was at 11 59 AM the query returns the value of the price at 11 59 AM because that value is the nearest value equal to or earlier than noon However you can also create a query to return null if the specified time stamp does not exactly match the time stamp of an element For example if you query for the price that a stock traded for at noon but the stock did not have a trade at noon the query
452. s of containers with wildcard characters You can monitor groups of containers that have similar names Include the wildcard characters for the MATCHES operator in the parameter for the container name in the TSContainerUsage TSContainerTotalPages TSContainerTotalUsed TSContainerPctUsed TSContainer and TSContainerNElems functions TSContainerUsage function on page 7 114 TSContainerTotalPages function on page 7 11 TSContainerTotalUsed function on page 7 11 TSContainerPctUsed function on page 7 10 TSContainerNElems function on page 7 104 ol N Faster queries with IN conditions through virtual tables Access methods that are created through the virtual table interface now process IN conditions in query predicates that operate on simple columns Processing through a virtual table interface is generally faster than SQL processing For example queries with IN conditions that you run on time series virtual tables now run faster than in previous releases Table 3 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC2 Overview Reference Accelerate queries on time series data You accelerate queries on time series data by creating data marts that are based on time series virtual tables You can define virtual partitions so that you can quickly refresh the data in part of the data mart or continuously refresh the data You can make queries faster by limiting the amoun
453. s of the resulting numbers Example Suppose that you want to know the average daily value of stock prices The following statements separate the daily high and low values for the stocks into separate time series in a daily_high table and a daily_low table create row type price timestamp datetime year to fraction 5 val real create row type simple_series stock_id int data TimeSeries price create table daily_high of type simple_series insert into daily_high select stock_id Apply high 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 10 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries one_real from daily_stocks create table daily_low of type simple series insert into daily_low select stock_id Apply low 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 10 00 00 00 00000 datetime year to fraction 5 stock_data TimeSeries price from daily_stocks The following query uses the symbol form of the Plus and Divide functions to produce a time series of daily average stock prices in the daily_avg table create table daily_avg of type simple series insert into daily_avg select 1 stock_id 1 data h data 2 from daily_low 1 daily_high h where 1 stock_id h stock_id Related reference Load small amounts of data with SQL functions on page 3 36 ApplyBinaryTsOp function on page 7 23 Chapter 7 Time series SQL rou
454. s packed hertz data returns 0 if the time series does not contain packed hertz data Example The following statement indicates that the time series that is named hertz_test can store 60 records per second EXECUTE FUNCTION GetHertz hertz_test expression 60 Related concepts Manage packed data on page 3 37 Related reference The ts_get_hertz function on page 9 30 7 54 IBM Informix TimeSeries Data User s Guide GetIndex function The GetIndex function returns the index offset of the time series entry associated with the supplied time stamp Syntax Get Index ts TimeSeries tstamp datetime year to fraction 5 returns integer ts The source time series tstamp The time stamp of the entry Description The data extracted is associated with the time period that the time stamp is in For example if you have a time series set to hourly 8 00 a m to 5 00 p m the time stamp 3 15 p m would return the index associated with 3 00 p m The API equivalent of this function is ts_index Returns The integer offset of the entry for the given time stamp in the time series NULL is returned if the time stamp is not a valid day in the calendar or if it falls before the origin of the time series Example The following example returns the offset for the supplied time stamp select stock_name GetIndex stock_data 2011 01 05 00 00 00 00000 from daily_stocks Related reference GetNelems
455. s performed on time series data typically include a time criteria and often include aggregations of data over a longer period of time For example you might want to know which day of the week your customers use the most electricity The Informix TimeSeries solution provides the following capabilities to store and analyze time series data e Define the structure of the data e Control when and how often data is accepted Set the frequency for regularly spaced records Handle arbitrarily spaced records e Control data storage Specify where to store data Change where data is stored Monitor storage usage e Load data from a file or individually e Query data Extract values for a time range Find null data Modify data Display data in standard relational format e Analyze data Perform statistical and arithmetic calculations Aggregate data over time Make data visible or invisible Find the intersection or union of data The Informix TimeSeries solution stores time series data in a special format within a relational database in a way that takes advantage of the benefits of both non relational and standard relational implementations of time series data The Informix TimeSeries solution is more flexible than non relational time series implementations because the Informix TimeSeries solution is not specific to any industry is easily customizable and can combine time series data with information in relational databases T
456. s plug in and the load files that are generated by the TimeSeries plug in are required You can load time series data in parallel by running multiple loader applications simultaneously As the data is loaded statistics about the data are printed to the console When the load job is complete cumulative statistics are printed Example The following command starts a load job that specifies the files that are named for loadjob1 IBM Informix TimeSeries Data User s Guide eclipse application com ibm informix timeseries loader recordReader customerl udrf table loadjobl_table tbl map loadjobl_map fcmap connection loadjobl_conn xml properties loadjobl_prop tsIp Related tasks Running a load job from the command line on page 3 29 Writing a loader program You can write a program to load time series data by using time series SQL routines You must have the following prerequisites before you load data e An existing table with a TimeSeries column The name of the table and the name of the TimeSeries column must not contain uppercase letters e Primary key values in your table and a time series instance that is defined for each row The time series definition must include a threshold value of 0 which means that all elements are stored in containers e A container that is associated with the TimeSeries column e Data that consists of a primary key and time based data The data can have the form of a buffer a
457. s routines that run in parallel on page 7 7 Apply function on page 7 1 ClipCount function on page 7 3 ClipGetCount function on page 7 3 GetElem function on page 7 5 GetLastValid function on page 7 5 GetNthElem function on page 7 62 IBM Informix TimeSeries Data User s Guide ClipCount function The ClipCount function is a variation of Clip in which the first integer argument is interpreted as a count of entries to clip If the count is positive ClipCount begins with the first element at or after the time stamp and clips the next count entries If the count is negative ClipCount begins with the first element at or before the time stamp and clips the previous count entries Syntax ClipCount ts TimeSeries begin_stamp datetime year to fraction 5 num_stamps integer flag integer default 0 returns TimeSeries ts The time series to clip begin_stamp The begin point of the range Can be NULL num_stamps The number of elements to be included in the resultant time series flag optional The configuration of the resulting time series Each flag value other than 0 reverses one aspect of the default behavior The value of the flag argument is the sum of the flag values that you want to use 0 Default behavior e The origin of the resulting time series is the later of the begin point and the origin of the input time series e Hidden
458. s to existing rows only the TimeSeries column can be updated Hidden elements are not included in the virtual table When you select data from a virtual table by timestamps the rows whose timestamps are closest to being equal to or earlier than the timestamps specified in the query are returned If the time series is irregular the returned rows show the same timestamps as specified in the query regardless if the actual timestamps are the same See Default behavior on page 4 20 putelem Multiple elements for the same timepoint are allowed Updates to the underlying time series insert elements even if elements exist for the timepoints Uses the PutElem function See Duplicate timepoints on page 4 23 Chapter 4 Virtual tables for time series data 4 17 Table 4 6 Settings for the TSVTMode parameter continued Flag name Value Description show_nulls 2 Null elements are displayed in the virtual table Hidden elements are displayed as null elements unless the value 512 is also set See Null and hidden elements on page 4 23 fragment 4 This setting is only valid if the base table is fragmented by expression The virtual table is fragmented by expression using the same distribution scheme as the base table Queries on the virtual table are run in parallel if PDQ is enabled disable_not_null_constraints 16 For existing rows you can specify NULL values for columns that are not pa
459. s utility copies the contents of a table into a client file or a client file into a table When copying a file into a table the time series is created and then the data is written into the new time series See the IBM Informix Performance Guide for more information about onload Example Load data with BulkLoad The following example uses BulkLoad in the SET clause of an UPDATE statement to populate the existing time series in the daily_stocks table insert into daily_stocks values 999 IBM TSCreate daycal 2011 01 03 00 00 00 00000 20 0 0 NULL update daily_stocks set stock_data BulkLoad stock_data sam dat where stock_name IBM Load small amounts of data with SQL functions You can load individual elements or sets of elements by using time series SQL functions Use any of the following functions to load data into a time series PutElem Updates a time series with a single element PutSet Updates a time series with a set of elements InsElem Inserts an element into a time series InsSet Inserts every element of a specified set into a time series These functions add or update an element or set of elements to the time series They must be used in an SQL UPDATE statement with the SET clause update table_name set TimeSeries_col FunctionName TimeSeries_type data where coll value The TimeSeries_col argument is the name of the column in which the time series is located The FunctionName argument is
460. s void and an error is raised After the insertion is done the time series must be assigned to a row in a table or the insertion is lost InsElem should be used only within UPDATE and INSERT statements If it is used within a SELECT statement or a qualification unpredictable results can occur You cannot insert an element at a time stamp that is hidden The API equivalent of InsElem is ts_ins_elem Returns The new time series with the element inserted Example The following example inserts an element into a time series update activity_stocks set activity_data InsElem activity_data row 2011 10 06 08 06 56 00000 6 50 2000 1 007 3 1 stock_trade where stock_id 600 Related reference DelElem function on page 7 43 Chapter 7 Time series SQL routines 7 69 GetElem function on page 7 52 TInsSet function PutElem function on page 7 7 The ts_ins_elem function on page 9 36 UpdElem function on page 7 159 InsSet function 7 70 The InsSet function inserts every element of a specified set into a time series Syntax InsSet ts TimeSeries multiset_rows multiset flags integer default 0 returns TimeSeries fma S The time series to act on multiset_rows The multiset of new row type values to store in the time series flags Valid values for the flags argument are described in Falues on page 75 lues on page 7 9 The default is 0 Descriptio
461. s_no_ts virtual table stock_id stock_name timestamp high low final vol 900 AA01 t1 7 25 6 75 7 1000000 900 AA01 t2 7 5 6 875 7 125 1500000 901 IBM t1 97 94 25 g5 2000000 901 IBM t2 97 95 5 96 3000000 905 FNM t1 49 25 47 75 48 2500000 905 FNM t2 48 75 48 48 25 3000000 In this column t1 and t2 are DATETIME values Query the virtual table Certain SQL queries are much easier to write for a virtual table than for a base table For example the query to obtain the closing price is much simpler select stock_id final from daily_stocks_no_ts The query to obtain price and volume within a specific time range is select from daily_stocks_no_ts where timestamp between tl and t5 Some tasks that are complex for time series functions to accomplish such as use of the ORDER BY clause are now simple select from daily_stocks_no_ts where timestamp between tl and t5 order by volume Inserting data into the virtual table is also simple To add an element to the IBM stock use the following query insert into daily_stock_no_ts values IBM t6 55 53 54 2000000 The element t6 55 53 54 2000000 is added to daily_stocks Related concepts The TSVTMode parameter on page 4 16 Chapter 4 Virtual tables for time series data 4 9 Example of creating a fragmented virtual table This example shows how to create a fragmented virtual table that is based on a table that contains time seri
462. s_valid those elements are overwritten with subsequent calls or freed when the corresponding scan or time series descriptor is closed Syntax void ts_free_elem ts_tsdesc tsdesc ts_tselem elem IBM Informix TimeSeries Data User s Guide tsdesc The descriptor for a time series returned by ts_open elem A time series element It must agree in type with the time series described by tsdesc Related reference The ts_elem_to_row function on page 9 24 The ts_make_elem function on page 9 38 The ts_make_elem_with_buf function on page 9 3 9 The ts_row_to_elem function on page 9 50 The ts_get_all_cols procedure The ts_get_all_cols procedure loads the values in the element into the values and nulls arrays Syntax void ts_get_all_cols ts_tsdesc tsdesc ts_tselem tselem MI_DATUM values mi_boolean nulls mi_integer off tsdesc A time series pointer returned by ts_open tselem The element to extract data from values The array to put the column data into This array must be large enough to hold data for all the columns of the time series nulls An array that indicates null values off For a regular time series off is the offset of the element For an irregular time series off is ignored Returns None The values and nulls arrays are filled in with data from the element The values array is filled with values or pointers to values depending on whether the
463. se an error condition and do not return a value On UNIX these routines exist in two archives tsfeapi a and tsbeapi a To use any of these routines include the tsbeapi a file when producing a shared library for the server or use tsfeapi a when compiling a client application The tseries h header file must be included when there are calls to any of the time series interface routines On UNIX tsfeapi a tsbeapi a and tseries h are all in the lib directory in the database server installation On Windows these routines exist in two archives tsfeapi lib and tsbeapi 1ib To use any of these routines include the tsbeapi 1ib file when producing a shared library for the server or use tsfeapi 1ib when compiling a client application The tseries h header file must be included when there are calls to any of the time series interface routines On Windows tsfeapi lib tsbeapi lib and tseries h are all in the lib directory in the database server installation Important Because values returned by mi_value are valid only until the next mi_next_row or mi_query_finish call it might be necessary to put time series in save sets or to use ts_copy to access time series outside an mi_get_results loop Related concepts Planning for accessing time series data on page 1 24 Differences in using functions on the server and on the client There are significant differences between using the client version of the time series API tsfeapi
464. se the return value is 1 Example The following example gets the instance IDs for each stock in the activity_stocks table select stock_id InstanceId activity_data from activity stocks Related concepts TSInstanceTable table on page 2 12 Intersect function The Intersect function performs an intersection of the specified time series over the entire length of each time series or over a clipped portion of each time series Syntax Intersect ts TimeSeries ts TimeSeries returns TimeSeries Intersect set_ts set TimeSeries returns TimeSeries Intersect begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 ts TimeSeries ts TimeSeries returns TimeSeries Intersect begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 set_ts set TimeSeries returns TimeSeries ts The time series that form the intersection Intersect can take from two to eight time series arguments set_ts Indicates the intersection of a set of time series begin_stamp The begin point of the clip Chapter 7 Time series SQL routines 7 71 7 72 end_stamp The end point of the clip Description The second and fourth forms of the function intersect a set of time series The resulting time series has one DATETIME YEAR TO FRACTION 5 column followed by each column in each time series in order not including the other time stamps When using the second or fourth fo
465. section Example The following example returns the intersection of two time series select Intersect d1l stock_data d2 stock_data TimeSeries stock_bar_union from daily_stocks dl daily_stocks d2 where dl stock_name IBM and d2 stock_name HWP The following query intersects two time series and returns data only for time stamps between 2011 01 03 and 2011 01 05 select Intersect 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 05 00 00 00 00000 datetime year to fraction 5 d1 stock_data d2 stock_data TimeSeries stock_bar_union from daily_stocks dl daily_stocks d2 where dl stock_name IBM and d2 stock_name HWP Related reference Time series routines that run in parallel on page 7 7 Apply function on page 7 18 Union function on page 7 157 IsRegular function The IsRegular function tells whether a specified time series is regular Syntax IsRegular ts TimeSeries returns boolean ts The source time series Returns TRUE if the time series is regular otherwise FALSE Example The following query gets stock IDs for all stocks in irregular time series select stock_id from activity_stocks where not IsRegular activity_data Related reference Time series routines that run in parallel on page 7 7 The ts_get_flags function on page 9 30 Lag function The Lag function creates a new regular time series in which the data values lag
466. sed information for a customer is stored together in a single row Table 1 2 Informix TimeSeries table with time based data Customer Time Value 1000111 2011 1 1 00 00 00 00000 0 092 2011 1 1 00 15 00 00000 0 082 2011 1 1 00 30 00 00000 0 090 2011 1 1 00 45 00 00000 0 085 1046021 2011 1 1 00 00 00 00000 0 041 2011 1 1 00 15 00 00000 0 041 2011 1 1 00 30 00 00000 0 040 2011 1 1 00 45 00 00000 0 041 The following table summarizes the advantages of using the Informix TimeSeries solution for time based data over using a standard relational table 1 2 IBM Informix TimeSeries Data User s Guide Table 1 3 Comparison of time series data stored in a standard relational table and in an Informix TimeSeries table Standard relational table issue Informix TimeSeries table benefit Storage space Stores one row for every record Duplicates the information in non time series columns Stores timestamps Null data takes as much space as actual data The index typically includes the time stamp column and several other columns Significant reduction in disk space needed to store the same data The index size on disk is also smaller Stores all time series data for a single source in the same row No duplicate information Calculates instead of stores the time stamp Null data does not require any space The index does not include the time stamp column Query speed Data for a single
467. series update activity_stocks set activity_data PutElemNoDups activity_data row 2011 08 25 09 06 00 00000 6 25 1000 1 007 2 1 stock_trade where stock_id 600 Related concepts The TSVTMode parameter on page 4 16 Related reference PutElem function on page 7 77 The ts_put_elem_no_dups function on page 9 47 Chapter 7 Time series SQL routines 7 79 PutNthElem function The PutNthElem function puts the supplied row at the supplied offset in a regular time series Syntax PutNthElem ts TimeSeries row_value row N integer flags integer default 0 returns TimeSeries ts The time series to act on row_value The new row type value to store in the time series N The offset Must be greater than or equal to 0 flags Valid values for the flags argument are described in falues on page 7 3 lues on page 7 9 The default is 0 Description This function is similar to PutElem except PutNthElem takes an offset instead of a time stamp If there is data at the given offset it is updated with the new data otherwise the new data is inserted The row type passed in must match the subtype of the time series Hidden elements cannot be updated Returns A modified time series that includes the new values Example The following example puts data in the first element of the IBM time series update daily_stocks set stock_data PutNthElem stock_data row NULL datetime year t
468. size is 1 byte the maximum deviation is 1 the lower boundary is 100 and the upper boundary is 100000 The following equation calculates the margin of error 100000 100 256 390 235 1 391 235 The maximum difference between the original value and the compressed value is plus or minus 391 235 Related concepts Manage packed data on page 3 37 TimeSeries data type on page 2 6 Related tasks Example Create and load a compressed time series on page 3 7 Related reference TSCreatelrr function on page 7 118 JSON time series You can create a time series that contains JSON documents Because JSON documents do not have a rigid structure you avoid schema changes when the structure of the data changes You can easily load and query the data The advantage of having time based data unstructured is that the schema of the TimeSeries data type is simple and does not need to be altered as the data changes A JSON time series has the following advantages over creating a column for every type of value in the TimeSeries data type MongoDB and REST API clients You can load JSON documents directly from MongoDB and REST API clients without formatting the data Application compatibility The changes that you need to make to your application when you move your data into time series is minimized because you do not need to perform schema migration Variable schema If the structure of your time based data is li
469. source can be intermixed on multiple data pages in no particular order Queries that use a time criteria require many fewer disk reads and significantly less I O Data is loaded very efficiently Data for a single source is stored together in time stamp order Query complexity Queries that aggregate data or apply an expression can be difficult or impossible to perform with SQL Much of the query logic must be provided by the application Less application coding and faster queries Allows complex SQL queries and analysis Allows custom analytics written using the TimeSeries API Informix TimeSeries solution architecture The Informix TimeSeries solution consists of built in data types and routines You can use other Informix tools to administer and load time series data The Informix database server includes the following functionality for managing time series data e The TimeSeries data type and other related data types to configure the data e TimeSeries SQL routines to run queries on time series data e TimeSeries API routines and Java classes to use in your applications to manipulate and analyze time series data You can use IBM Data Studio or IBM Optim Developer Studio along with the IBM Informix TimeSeries Plug in for Data Studio to load data from a file into an Informix TimeSeries table You can use the IBM OpenAdmin Tool OAT for Informix along with the Informix TimeSeries Plug in for
470. ssion lvarchar subtype lvarchar TSVTMode integer default 0 TSColName lvarchar default NULL Virtual TableName The name of the new virtual table BaseTableName The name of the base table expression The expression to be evaluated on time series data The expression must result in a time series value that has a TimeSeries subtype that is specified by the subtype parameter subtype The name of the TimeSeries subtype for the values that are the results of the expression Chapter 4 Virtual tables for time series data 4 13 4 14 TSVTMode optional Sets the virtual table mode as described in The TSVTMode parameter on page 4 16 age 4 16 TSColName optional For base tables that have more than one TimeSeries column specifies the name of the TimeSeries column to be used to create the virtual table The default value for the TSColName parameter is NULL in which case the base table must have only one TimeSeries column Usage Use the TSCreateExpressionVirtualTab procedure to create a virtual table based on a time series that results from an expression that is performed on time series data each time a query such as a SELECT statement is performed You specify the name of the TimeSeries subtype in the virtual table with the subtype parameter The total length of a row in the virtual table non time series and TimeSeries columns combined must not exceed 32 KB If you specify either of the optional parameters you m
471. such as AggregateBy Apply and Union the type of the resulting time series is not necessarily the same as a time series argument These functions require that their return types be cast to particular time series types If a time series returned by one of these functions cannot use the container of the original time series and a container name is not specified the resulting time series is stored in a container associated with the matching TimeSeries subtype and regularity If no matching container exists a new container is created CalendarPatterns table The CalendarPatterns table contains information about calendar patterns The CalendarPatterns table contains two columns a VARCHAR 255 column cp_name and a CalendarPattern column cp_pattern To insert a calendar pattern into the CalendarPatterns table use the INSERT statement Related tasks Defining a calendar on page 3 12 Related reference CalendarPattern data type on page 2 1 CalendarTable table 2 8 The CalendarTable table maintains information about the time series calendars used by the database When you create a calendar you insert a row into the CalendarTable table The CalendarTable table contains seven predefined calendars that you can use instead of creating calendars You can change a calendar by running an UPDATE statement on a row in the CalendarTable table The following table contains the columns in the CalendarTable table
472. t counts the number of elements where the value of the energy column is greater than 128 SELECT CountIf readings energy gt 128 FROM sm expression 11 1 row s retrieved The following statement is equivalent to the previous statement except that the format uses separate arguments for the column name the operator and the comparison value instead of a single expression argument SELECT CountIf readings energy gt 128 FROM sm expression Chapter 7 Time series SQL routines 7 41 11 1 row s retrieved Example Count elements in a BSON column This example is based on the following row type and time series definition The TimeSeries row type contains an INTEGER column that is named v1 and the BSON column contains a field that is also named v1 CREATE ROW TYPE rb timestamp datetime year to fraction 5 data bson v1 int INSERT INTO tj VALUES 1 origin 2011 01 01 00 00 00 00000 calendar ts_ 15min container kontainer threshold 0 regular v1 99 20 If a column and a BSON field have the same name the column takes precedence The following statement counts the number of elements that have a value less than 50 for the v1 INTEGER column SELECT CountIf tsdata v1 lt 50 FROM tj expression 1 1 row s retrieved The following statement counts the number of elements that have a value less than 100 for the v1 field in the BSON data column SELECT CountIf tsdata data vl1
473. t devices Keyboard navigation This product uses standard Microsoft Windows navigation keys Related accessibility information IBM is committed to making our documentation accessible to persons with disabilities Our publications are available in HTML format so that they can be accessed with assistive technology such as screen reader software IBM and accessibility For more information about the IBM commitment to accessibility see the IBM Accessibility Center at http www ibm com able Dotted decimal syntax diagrams The syntax diagrams in our publications are available in dotted decimal format which is an accessible format that is available only if you are using a screen reader In dotted decimal format each syntax element is written on a separate line If two or more syntax elements are always present together or always absent together the elements can appear on the same line because they can be considered as a single compound syntax element Each line starts with a dotted decimal number for example 3 or 3 1 or 3 1 1 To hear these numbers correctly make sure that your screen reader is set to read punctuation All syntax elements that have the same dotted decimal number for example all syntax elements that have the number 3 1 are mutually exclusive alternatives If you hear the lines 3 1 USERID and 3 1 SYSTEMID your syntax can include either USERID or SYSTEMID but not both The dotted decimal numbering level d
474. t in the time series is used e ts_end_time Instead of specifying a DATETIME value you can use this variable and specify the ending time point of the time series in the WHERE clause of the SELECT statement when you query the virtual table If the WHERE clause does not contain the ending timepoint the last timepoint in the time series is used You can test whether the subtype that you create for the results of the expression is valid by running the following statement against your base table with the IBM Informix TimeSeries Data User s Guide expression and subtype parameters that you plan to use in the TSCreateExpressionVirtualTab procedure SELECT expression timeseries subtype FROM BaseTableName If the statement fails you cannot create the virtual table Examples The following examples use a table named smartmeters that contains a column named meter_id and a TimeSeries column named readings The TimeSeries subtype has the columns t and energy Example 1 Find the daily maximum and minimum values The following statement creates a virtual table that is named smartmeters_vti_agg_max_min based on a time series that contains the maximum and minimum energy readings per day EXECUTE PROCEDURE TSCreateExpressionVirtual Tab smartmeters_vti_agg_max_min smartmeters AggregateBy max energy min energy smartmeter_daily readings 0 tworeal_row The following query shows the daily maximum and minimum of the
475. t includes a compression definition for each column in the TimeSeries subtype except the first column For the syntax of the compression parameter and descriptions of the compression types and attributes see TSCreatelrr function on page 7 118 The value of the threshold parameter must be 0 The value of the flags parameter must be 5 Description The equivalent SQL function is TSCreate or TSCreatelrr If you include the hertz or compression parameter you must run the ts_create function within an explicit transaction Returns A pointer to a new time series The user can free this value by calling ts_free Related reference TSCreatelrr function on page 7 118 The ts_free procedure on page 9 26 The ts_open function on page 9 44 The ts_get_threshold function on page 9 33 The ts_get_typeid function on page 9 34 The ts_create_with_metadata function The ts_create_with_metadata function creates a time series with user defined ts_timeseries 9 18 ts_create_with_metadata MI_ CONNECTION conn mi_string calname mi_datetime origin mi_integer threshold mi_integer flags MI_TYPEID typeid mi_integer nelem mi_string container mi_lvarchar metadata MI_TYPEID xmetadata_typeid ts_timeseries ts_create_with_metadata MI_CONNECTION IBM Informix TimeSeries Data User s Guide conn mi_string calname mi_datetime origin mi_integer threshold M
476. t of data in the data mart to specific time intervals for accessing time series data on Performance of queries on virtual tables on page 4 2 Faster queries on time series virtual tables You can run queries in parallel on a virtual table that is fragmented The virtual table must be based on a time series table that is fragmented by expression Include the fragment flag in the TSVTMode parameter when you create the virtual table You can include the flags for the TSVTMode parameter as a set of strings instead of as a number Example of creating a fragmented virtual table on 4 10 The TSVTMode parameter on page 4 16 XiV IBM Informix TimeSeries Data User s Guide Table 3 What s New in IBM Informix TimeSeries Data User s Guide for 12 10 xC2 continued Overview Reference Replicate time series data with all high availability Planning for replication of time series data on page clusters 1 23 You can now replicate time series data with all types of high availability clusters Previously you could replicate time series data only with High Availability Data Replication HDR clusters and not with shared disk secondary and remote stand alone secondary clusters Secondary servers must be read only Order TimeSeries columns in query results TSInstanceTable table on page 2 12 You can include a TimeSeries column in an ORDER BY clause of an SQL query The ORDER BY clause sorts
477. t on colname The time series column to return Description The TSRowNameToList function can only be used on rows with one TimeSeries column You must cast the return variable to match the names and types of the columns being returned exactly Returns A list collection of rows Chapter 7 Time series SQL routines 7 145 Example The query returns a list of rows each containing the ID and high columns select TSRowNameToList d high list row id integer name lvarchar high real not null from daily_stocks d Related reference TSColNameToList function on page 7 91 Q TSRowNumToList function 7 146 The TSRowNumToList function returns a list collection of rows containing one individual column from a time series column plus the non time series columns of a table Null elements are not added to the list Syntax TSRowNumToList ts_row row colnum integer returns list row not null ts_row The time series to act on colnum The number of the time series column to return Description The TSRowNumToList function can only be used on rows with one TimeSeries column The column is specified by its number column numbering starts at 1 with the first column following the time stamp column You must cast the return variable to match the names and types of the columns being returned exactly Returns A list collection of rows Example The query returns a list of rows ea
478. t the TSVTMode parameter to 514 512 2 You can also specify the numeric value as a hexadecimal number e String List the flag names that you want to include separated by one of the following delimiters plus sign pipe or comma For example if you want both null and hidden elements to be displayed in the virtual table set the TSVTMode parameter to scan_hiddent show_nulls Table 4 6 Settings for the TSVTMode parameter Flag name Value Description putelemnodups 0 Default The virtual table has the following behavior Multiple elements for the same timepoint are not allowed Updates to the underlying time series update existing elements for the same timepoint Uses the PutElemNoDups function Null elements are not included in the virtual table The virtual table is not fragmented If the base table has a primary key the primary key is used to find the row to update and updates to the base table do not require accurate values for columns that are not part of the primary key If the base table does not have a primary key all columns in the table except the TimeSeries column are used to identify the row to be updated and updates to the base table require accurate values for every column in the base table other than the TimeSeries column NOT NULL constraints are included in the virtual table for the primary key columns and other columns that have NOT NULL constraints in the base table For update
479. ta between two timepoints in a time series and returns a new time series that contains that data You can extract periods of interest from a large time series and to store or operate on them separately from the large series Syntax Clip ts TimeSeries begin_stamp datetime year to fraction 5 end_stamp datetime year to fraction 5 flag integer default 0 returns TimeSeries Clip ts TimeSeries begin_stamp datetime year to fraction 5 end_offset integer flag integer default 0 returns TimeSeries Clip ts TimeSeries begin_offset integer end_stamp datetime year to fraction 5 flag integer default 0 returns TimeSeries Clip ts TimeSeries Chapter 7 Time series SQL routines 7 31 7 32 begin_offset integer end_offset integer flag integer default 0 returns TimeSeries ts The time series to clip begin_stamp The begin point of the range Can be NULL end_stamp The end point of the range Can be NULL begin_offset The begin offset of the range regular time series only end_offset The end offset of the range regular time series only flag optional The configuration of the resulting time series Each flag value other than 0 reverses one aspect of the default behavior The value of the flag argument is the sum of the flag values that you want to use 0 Default behavior e The origin of the resulting time series is the later of the begin point and the origin of the input time series e Hidd
480. ta types in your TimeSeries subtype 2 Create a time series table that is named tstable_h by running the following SOL statement CREATE TABLE IF NOT EXISTS tstable_h id int not null primary key ts timeseries ts_data_h LOCK MODE ROW 3 Create a container that is named container_h by running the following SQL statement EXECUTE PROCEDURE TSContainerCreate container_h rootdbs ts_data_h 512 512 Chapter 3 Create and manage a time series through SQL 3 5 3 6 You can choose to create the container in a different dbspace than the root dbspace Create a calendar by running the following SQL statement INSERT INTO CalendarTable c_name c_calendar VALUES ts_1sec startdate 2014 01 01 00 00 00 00000 pattern 1 on second You cannot specify a subsecond interval for a calendar however the hertz time series definition overrides the calendar interval Create a hertz time series with a hertz value of 50 by running the following SQL statement in an explicit transaction BEGIN Started transaction INSERT INTO tstable_h VALUES 50 TSCreateIrr ts_1sec 2014 01 01 00 00 00 00000 0 50 0 container_h 1 row s inserted COMMIT The threshold and nelems parameters must be set to 0 Insert five records for the same second into the time series by running the following SQL statements BEGIN Started transaction UPDATE tstable_h SET ts InsElem ts row 2014 01 01 00 00 00 00000 1 201 99
481. table Usage To create a calendar insert the keywords and their values into the CalendarTable table Set the calendar start date and calendar pattern start dates at the logical beginning of an interval For example if the interval size is a day specify a start date time of midnight Calendars can be combined with functions that form the Boolean AND OR and NOT of the calendars The resulting calendars can be stored in the CalendarTable table or used as arguments to other functions You can define both a calendar pattern starting time and a calendar starting time if the calendar and calendar pattern starting times do not coincide The calendar start date and the pattern start date can be one or more intervals apart depending on the calendar pattern length For example if the calendar pattern is 1 on 14 off the pattern length is 15 The calendar start date and the pattern start date can be from 0 to 15 intervals apart Occasionally if you have a regular time series you have elements for which there is no data For example if you have a daily calendar you might not obtain data on holidays These exceptions to your calendar are marked as null elements However you can hide exceptions so that they are not included in calculations or analysis by using the HideElem function Examples The following example inserts a calendar called weekcal into the CalendarTable table INSERT INTO CalendarTable c_name c_calendar VALUES wee
482. tainerWindow_r A row type that contains interval information for the partitions that are in the dormant window To view the starting timestamp and location information about the partitions query the TSContainerUsageDormantWindowVTI table container_size INTEGER The first extent size of the partitions container_grow INTEGER The next extent size of the partitions windowcontrol INTEGER A flag that indicates how active partitions are handled when they are manually or automatically detached e 0 Default Partitions are not automatically destroyed if they cannot fit into the dormant window e 1 Partitions are automatically destroyed if they cannot fit into the dormant window Related concepts TSContainerTable table on page 2 9 Monitor containers on page 3 18 TSContainerUsageActiveWindowVTI Table The TSContainerUsageActiveWindowVTI table is a virtual table that is based on the TSContainerWindowTable table The TSContainerUsageActiveWindowVTI table has one row for each partition in the active window of a rolling window container Table 2 7 The columns in the TSContainerUsageActiveWindowVT table Column name Data type Description name VARCHAR 128 The name of the rolling window container windowinterval VARCHAR 8 The size of the interval of each partition year month week or day activewindowsize INTEGER The number of partitions in the active window d
483. te function The ts_create_with_metadata function The ts_current_offset function The ts_current_timestamp function The ts_datetime_cmp function The ts_del_elem function The ts_elem function i The TS_ELEM_HIDDEN macro The TS_ELEM_NULL macro The ts_elem_to_row function 7 140 7 141 7 141 7 142 7 145 7 146 7 147 7 147 7 149 7 150 7 151 7 152 7 153 7 154 7 156 7 157 7 159 7 159 7 160 7 161 8 1 8 3 8 3 8 4 8 4 8 5 9 1 9 1 9 2 lt 9 2 9 2 lt 9 2 9 3 9 3 s 927 9 9 9 9 9 10 Contents s911 9 11 9 12 s 9 12 9 13 9 14 9 14 9 15 9215 9 16 9217 9 18 lt 9 20 29 21 9 21 lt 9 22 9 22 9 23 9 24 9 24 vii The ts_end_scan procedure The ts_first_elem function The ts_free procedure The ts_free_elem procedure The ts_get_all_cols procedure The ts_get_calname function The ts_get_col_by_name function The ts_get_col_by_number function The ts_get_compressed function The ts_get_containername function The ts_get_flags function The ts_get_hertz function The ts_get_metadata function The ts_get_origin function The ts_get_packed function The ts_get_stamp_fields procedure The ts_get_threshold function The ts_get_ts function The ts_get_typeid function The ts_hide_elem function The ts_index function
484. te load and query a time series that contains JSON data 1 Create a TimeSeries subtype that is named ts_data_j in a database by running the following SQL statement CREATE ROW TYPE ts data_j tstamp datetime year to fraction 5 sensor_data BSON Because the JSON documents are stored in the database as BSON documents the sensor_data column has a BSON data type 3 10 IBM Informix TimeSeries Data User s Guide 10 Create a time series table that is named tstable_j by running the following SQL statement CREATE TABLE IF NOT EXISTS tstable_j id int not null primary key ts timeseries ts_data_j LOCK MODE ROW Create a container that is named container_b in a dbspace by running the following SQL statement EXECUTE PROCEDURE TSContainerCreate container_b dbspacel ts data_j 512 512 Create a time series with a JSON document by running the following SQL statement INSERT INTO tstable_j VALUES 1 origin 2014 01 01 00 00 00 00000 calendar ts_15min container container_b L v1 1 5 v2 20 5 A predefined calendar with 15 minute intervals is specified The JSON document contains two values Create a pipe delimited file in any directory with the name json un1 that contains the time series data to load 1 2014 01 01 00 15 00 00000 v1 2 0 v2 17 4 1 2014 01 01 00 30 00 00000 v1 1 9 v2 20 2 1 2014 01 01 00 45 00 00000 v1 1 8 v2 19 7 Create an external table and load it with time seri
485. ter_data timestamp DATETIME YEAR TO FRACTION 5 data DECIMAL 4 3 s The timestamp column contains the time of the meter reading and the data column contains the reading value 2 Create a time series table named my_ts_data by running the following SQL statement CREATE TABLE IF NOT EXISTS my_ts_data meter_id BIGINT NOT NULL PRIMARY KEY raw_reads TIMESERIES my_meter_data LOCK MODE ROW Related tasks Creating a TimeSeries subtype on page 3 13 Related reference Create the database table on page 3 14 Creating regular empty time series You need to define the properties of the time series for each meter ID by loading the meter IDs into the time series table and creating a regular empty time series for each meter ID You use the meter IDs from the ts_data table in the stores_demo database to populate the meter_id column of your my_ts_data table To create regular empty time series 1 Create an unload file named my_meter_id un that contains the meter IDs from the loc_esi_id column of the ts_data table by running the following SQL statement UNLOAD TO my_meter_id un SELECT loc_esi_id FROM ts_ data 2 Create a temporary table named my_tmp and load the meter IDs into it by running the following SQL statements 3 2 IBM Informix TimeSeries Data User s Guide CREATE TEMP TABLE my_tmp id BIGINT NOT NULL PRIMARY KEY LOAD FROM my_meter_id un INSERT INTO my_tmp You use this table in the next st
486. than once 2 If a dotted decimal number has an asterisk next to it and several items have that dotted decimal number you can use more than one item from the list but you cannot use the items more than once each In the previous example you can write HOST STATE but you cannot write HOST HOST 3 The symbol is equivalent to a loop back line in a railroad syntax diagram Specifies a syntax element that must be included one or more times A dotted decimal number followed by the symbol indicates that this syntax element must be included one or more times For example if you hear the line 6 1 data area you must include at least one data area If you hear the lines 2 2 HOST and 2 STATE you know that you must include HOST STATE or both As for the symbol you can repeat a particular item if it is the only item with that dotted decimal number The symbol like the symbol is equivalent to a loop back line in a railroad syntax diagram Appendix C Accessibility C 3 C 4 IBM Informix TimeSeries Data User s Guide Notices This information was developed for products and services offered in the U S A This material may be available from IBM in other languages However you may be required to own a copy of the product or product version in that language in order to access it IBM may not offer the products services or features discussed in this document in other countries Consult your local IBM representative for informatio
487. that you insert generates two log records one for the inserted element and one for the page header update If this flag is set page header updates are logged per transaction instead of per element which improves performance Can be used only in the following functions BulkLoad InsElem PutElem PutElemNoDups PutNthElem PutTimeSeries Functions that include this flag must be run within a transaction The transaction can include other functions that use this flag The transaction cannot include functions that do not use this flag or other SQL statements The elements that are inserted are not visible by dirty reads until after the transaction commits 7 10 IBM Informix TimeSeries Data User s Guide Abs function Acos function The Abs function returns the absolute value of its argument It is one of the unary arithmetic functions that work on time series The others are Acos Asin Atan Cos Exp Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithmetic functions on page 7 156 The Acos function returns the arc cosine of its argument It is one of the unary arithmetic functions that work on time series The others are Abs Asin Atan Cos Exp Logn Negate Positive Round Sin Sqrt and Tan Related reference Time series routines that run in parallel on page 7 7 Unary arithme
488. the results from the TimeSeries column by the time series instance ID Improvements for time series loader programs TSL_FlushAll function on page 7 128 You have new options for how you flush time series data TSL_Commit function on page 7 124 to disk when you write a loader program You can flush time series elements for all containers to disk in a single TSL_FlushInfo function on page 7 129 transaction or in multiple transactions If you want your client application to control transactions run the TSL_FlushAll function The TSL_FlushAll function flushes time series elements to disk in one transaction If you want the loader program to control the size of your transactions run the TSL_Commit function The TSL_Commit function flushes time series elements to disk in multiple transactions based on the commit interval that you specify You can view the results of the data flushing function by running the TSL_FlushInfo function You can specify that no duplicate elements are allowed when you flush time series data to disk Faster aggregation of an interval of time series data TSRollup function on page 7 142 You can aggregate an interval of time series data faster by including start and end dates in the TSRollup function Previously you selected an interval of time series data with the Clip or similar function and passed the results to the TSRollup function Introduction XV Table 4 What s New in IBM I
489. the same as another column in the TimeSeries data type you must qualify the field name in one of the following ways e colname bson_field_name For example if the BSON column name is b_data and the field name is v1 the column identifier is b_data vl e colnumber bson_field_name For example if the BOON column number is 1 and the field name is v1 the column identifier is 1 v1 You must cast the results of the AggregateRange function on a BSON field to a TimeSeries data type the appropriate type of columns for the result of the expression for example a timestamp column and an INTEGER column The Nth expression returns the value of a column for the specified aggregation period using the following syntax Nth col n col The column identifer n A positive or negative number indicating the position of the TimeSeries row within the aggregation period Positive values of n begin at the first row in the aggregation period therefore Nth col 1 is equivalent to FIRST col Negative values of n begin with the last row in the aggregation period therefore Nth col 1 is equivalent to LAST col If an aggregation period does not have a value for the nth row then the Nth function returns a null value for that period The Nth function is more efficient for positive values of the n argument than for negative values An aggregation time period is denoted by the start date and time of the period The flags argument values The fl
490. the source time series by a fixed offset Chapter 7 Time series SQL routines 7 73 Syntax Lag ts TimeSeries nelems integer returns TimeSeries ts The source time series nelems The number of elements to lag the series by Positive values lag the result behind the argument and negative values lead the result ahead Description Lag shifts only offsets not the source time series Therefore a lag of 2 eliminates the first two elements For example if there is a daily time series Monday to Friday and a one day lag an argument of 1 is imposed then there is no first Monday the first Tuesday is Monday and the next Monday is Friday It would be more typical of a daily time series to lag a full week For example this function allows the user to create a hypothetical time series with closing stock prices for each day moved two days ahead on the calendar Lag is valid only for regular time series Returns A new time series with the same calendar and origin as the source time series but that has its elements assigned to different offsets Example The following query creates a new time series that lags the original time series by three days select Lag stock_data 3 from daily_stocks where stock_name IBM Related reference Time series routines that run in parallel on page 7 7 Logn function The Logn function returns the natural logarithm of a time series The Logn function is one of the unary
491. the caller it is valid until the next element is fetched from the descriptor Related reference GetLastValid function on page 7 58 The ts_previous_valid function on page 9 45 The ts_make_elem function The ts_make_elem function makes an element from an array of values and nulls Each array has one value for each column in the element 9 38 IBM Informix TimeSeries Data User s Guide Syntax ts_tselem ts_make_elem ts_tsdesc tsdesc MI_DATUM values mi_boolean nulls mi_integer off tsdesc The descriptor for a time series returned by ts_open values An array of data to be placed in the element Data that is by value is placed in the array and data that is by reference stores pointers nulls Stores columns in the element that should be NULL off For a regular time series off contains the offset of the element on return For an irregular time series off is set to 1 This argument can be NULL Returns An element and its offset If tsdesc is a descriptor for a regular time series the time stamp column in the element is set to NULL if tsdesc is a descriptor for an irregular time series the time stamp column is set to whatever was in values 0 This element must be freed by the caller using ts_free_elem Related reference The ts_elem_to_row function on page 9 24 The ts_free_elem procedure on page 9 26 The ts_ins_elem function on page 9 36 The ts_elem functio
492. the container definition must exist You can create a control file by unloading the results of a SELECT statement that defines the delete criteria into a file Use the following TimeSeries functions in the SELECT statement to populate the control file with the container names instance IDs and for regular time series the indexes of the end range for reach instance e GetContainerName function e Instanceld function e GetIndex function regular time series only If you intend to delete a large amount of data at one time running multiple TSContainerPurge functions to delete data from different containers might be faster than running a single TSContainerPurge function Returns An LVARCHAR string that describes how many containers were affected how many pages were freed and how many elements were deleted For example containers 4 deleted _pages 2043 deleted_slots 260300 Example 1 Delete regular time series data from multiple containers The following statement creates a control file named regular_purge unl to delete elements from 10 regular time series instances in all containers that store those instances UNLOAD TO regular_purge unl SELECT GetContainerName readings InstanceId readings Get Index readings 2011 10 01 23 45 00 00000 datetime year to fraction 5 varchar 25 FROM sm WHERE meter_id IN metQ met1 met11 met4 met5 met6 met61 met7 met8 met9 The resulting control file has
493. the following contents e 8 7295 13 6911 2 7775 sm0 sm0 sm0 sm1 Chapter 7 Time series SQL routines 7 109 7 110 sm1 9 7199 sm1 14 6815 sm2 3 7679 sm2 10 7103 sm3 7 7391 sm3 12 7007 The 10 time series instances that are specified in the WHERE clause are stored in the four different containers which are listed in the first column The second column lists the ID for each time series instance The third column lists the element index number that corresponds to the timestamp 2011 10 01 23 45 00 00000 The following statement deletes all elements at and before 2011 10 01 23 45 00 00000 for the 10 time series instances EXECUTE FUNCTION TSContainerPurge regular_purge un 1 Any deleted elements that remain are marked as NULL Example 2 Delete elements from a specific container The following statement creates a control file named regular_purge2 un1 to delete elements from all time series instances in the container named sm0 UNLOAD TO regular_purge2 unl SELECT GetContainerName readings InstanceId readings Get Index readings 2011 10 01 23 00 00 00000 datetime year to fraction 5 varchar 25 FROM sm WHERE GetContainerName readings sm0 The resulting control file has entries for a single container sm0 1 7871 sm0 8 7295 sm0 13 6911 Example 3 Deleting irregular time series data The following statement creates a control file named irregular_purge unl to delete elements from
494. the name of the function The data argument is in the row type data element format The WHERE clause specifies which row in the table to update The following example appends an element to a time series by running the PutElem function update daily_stocks set stock_data PutElem stock_data row NULL datetime year to fraction 5 2 3 3 4 5 6 67 stock_bar where stock_name IBM IBM Informix TimeSeries Data User s Guide You can also use more complicated expressions to load a time series for example by including binary arithmetic functions Related concepts Planning for loading time series data on page 1 23 Related reference Time series input function on page 3 24 Binary arithmetic functions on page 7 27 Delete time series data You can delete time series data to remove incorrect data or to remove old data The easiest and fastest way to delete old time series data is to create a rolling window container from which aging data in partitions can be manually or automatically detached or destroyed You can remove the oldest time series data through an end date in one for more containers for multiple time series instances by running the TSContainerPurge function You can delete data from a single time series instance in the following ways Delete a single element by running the DelElem function Delete elements in a time range and free any resulting empty pages by running the DelRange fu
495. the offset for the last element returned by ts_next regular time series The ts_current_offset function on page 9 20 Make elements visible or Make an element invisible The ts_hide_elem function on page 9 34 invisible to a scan Make an element visible The ts_reveal_elem function on page 9 50 Select individual elements Get the element closest to a specified time stamp The ts_closest_elem function on from a time series page 9 13 Get the element associated with a specified time stamp The ts_elem function on page 9 22 Get the element at a specified position The ts_nth_elem function on page 9 43 Get the first element The ts_first_elem function on page 9 25 Get the last element The ts_last_elem function on page 9 37 Find the next element after a specified time stamp The ts_next_valid function on page 9 42 Find the last element before a specified time stamp The ts_previous_valid function Find the last element at or before a specified time stamp The ts_last_valid function on page 9 38 Update a time series Insert an element The ts_ins_elem function on page 9 36 Update an element The ts_upd_elem function on page 9 54 Delete an element The ts_del_elem function on page 9 22 Put an element in a place specified by a time stamp The ts_put_elem function on page 9 46 and The ts_put_elem_no_dups function on page
496. tic functions on page 7 156 AggregateBy function The AggregateBy function aggregates the values in a time series using a new time interval that you specify by providing a calendar This function can be used to convert a time series with a small interval to a time series with a larger interval for instance to produce a weekly time series from a daily time series If you supply the optional start and end DATETIME parameters just that part of the time series is aggregated to the new time interval Syntax AggregateBy agg_express lvarchar cal_name lvarchar ts TimeSeries flags integer default 0 start datetime year to fraction 5 default NULL end datetime year to fraction 5 default NULL returns TimeSeries agge_express A comma separated list of these SQL aggregate operators MIN MAX MEDIAN SUM AVG FIRST LAST or Nth The MIN MAX MEDIAN SUM and AVG expressions can operate only on numeric columns cal_name The name of a calendar that defines the aggregation period ts The time series to be aggregated flags optional Determines how data points in off periods of calendars are handled during aggregation See The flags argument values on page 7 13 start optional The date and time at which to start aggregation Chapter 7 Time series SQL routines 7 11 end optional The date and time at which to end aggregation Description The AggregateBy function converts the input time series to a regular t
497. time series IBM Informix TimeSeries Data User s Guide to mi_integer hash_size MI_CONNECTION conn sec_entry_t hash Value buffers only allocated once MI_DATUM col_data DAILY_COL_COUNT mi_boolean col_is_nul1 DAILY_COL_COUNT char carryover char priority mi_double_precision price yield mi_integer instances_created A count of the number of tsinstancetable entries added Used decide when to update statistics on this table MI_SAVE_SET save_set loader_context_t kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk name init_context purpose Initialize the loader context structure notes kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk static void init_context mi_lvarchar table_name mi_lvarchar calendar_name mi_datetime origin mi_integer threshold mi_boolean regular mi_lvarchar container_name mi_integer nelems loader_context_t context_ptr mi_string buf 256 mi_integer table_name_len mi_get_varlen table_name MI_ROW row NULL MI_DATUM retbuf 0 mi_integer retlen 0 mi_lvarchar typename NULL MI_TYPEID typeid NULL mi_integer err 0 if table_name_len gt IDENTSIZE mi_db_error_raise NULL MI_EXCEPTION The table name is too long memset context_ptr 0 sizeof context_ptr context_ptr gt conn mi_open NULL NULL NULL typename
498. time series cannot be earlier than the origin of the input time series You can also control how hidden elements are handled with the flag argument By default hidden elements from the input time series are not included in the resulting time series You can include hidden element in the resulting time series and specify whether those elements remain hidden or are revealed in the resulting time series Returns A new time series that contains only data from the requested range The new series has the same calendar as the original but it can have a different origin and number of entries Examples The results of the Clip function are slightly different for regular and irregular time series Example 1 Regular time series The following query extracts data from a time series and creates a table that contains the specified stock data for a single week create table week_1_analysis stock_id int stock data TimeSeries stock_bar insert into week_l_analysis select stock_id Clip stock_data 2011 01 03 00 00 00 00000 datetime year to fraction 5 2011 01 07 00 00 00 00000 datetime year to fraction 5 from daily_stocks where stock_name IBM Chapter 7 Time series SQL routines 7 33 7 34 The following query returns the first six entries for a specified stock in a time series select Clip stock_data 0 5 from daily_stocks where stock_name IBM Example 2 Irregular time series An irregular time series has th
499. time series data is in a container rather than in memory or in a row The TS_IS_ IRREGULAR macro The TS_IS_IRREGULAR macro determines whether the given time series is irregular Syntax TS_IS_IRREGULAR ts_timeseries ts ts A pointer to a time series Returns A nonzero value if the given time series is irregular otherwise 0 is returned Related reference The ts_last_elem function The ts_last_elem function returns the last element from a time series Syntax ts_tselem ts_last_elem ts_tsdesc tsdesc mi_integer STATUS mi_integer off tsdesc The descriptor for a time series returned by ts_open STATUS A pointer to a mi_integer value See The ts_hide_elem function on age 9 34 for a description of STATUS off If the time series is regular off is set to the offset of the returned element If the time series is irregular or if the time series is empty off is set to 1 This argument can be passed in as NULL Description This function fills in off with the element s offset if off is not NULL and the time series is regular and it sets STATUS to indicate if the element is NULL or hidden The equivalent SQL function is GetLastElem Returns The last element of the specified time series its offset and whether it is NULL or hidden If the time series is irregular the offset is set to 1 This value must not be freed by the caller The element is overwritten after two calls to fetch element
500. time stamp for the element column_name optional If you specify a column using the column_name argument the GetNextNonNull function returns the next non null element on or after the specified date that has a non null value in the specified column If you do not specify the column_name argument the GetLastNonNull function returns the next non null element on or after the date specified by tstamp It is possible that all the columns except the time stamp could be NULL flags Valid values for the flags parameter are described in falues on page 7 5 lues on page 7 9 The default is 0 Description There are no null elements in an irregular time series Therefore when you use the GetNextNonNull function on an irregular time series always specify a column name If you use the GetNextNonNull function on an irregular time series without specifying a column name the function s effect is equivalent to that of the GetNextValid function Returns A non null element of the time series Related reference Time series routines that run in parallel on page 7 7 GetNextValid function The GetNextValid function returns the nearest entry after a given time stamp Syntax GetNextValid ts TimeSeries tstamp datetime year to fraction 5 flags integer default 0 returns row ts The source time series tstamp The time stamp of the entry Chapter 7 Time series SQL routines 7 61 flags Valid values for the flags argume
501. tines 7 29 Unary arithmetic functions on page 7 15 Minus function on page 7 74 6 Mod function on page 7 75 N Plus function on page 7 7 Pow function on page 7 77 Times function on page 7 86 BulkLoad function 7 30 The BulkLoad function loads data from a client file into an existing time series Syntax BulkLoad ts TimeSeries filename lvarchar flags integer default 0 returns TimeSeries ts The time series in which to load data filename The path and file name of the file to load flags Valid values for the flags argument are described in Falues on page 7 9 lues on page 7 9 The default is 0 Description The file is located on the client and can be an absolute or relative path name Two data formats are supported for the file loaded by BulkLoad e Using type constructors e Using tabs Each line of the client file must have all the data for one element The type constructor format follows the row type convention comma separated columns surrounded by parentheses and preceded by the ROW type constructor The first two lines of a typical file look like this row 2011 01 03 00 00 00 00000 1 1 2 2 row 2011 01 04 00 00 00 00000 10 1 20 2 If you include collections in a column within the row data type use a type constructor SET MULTISET or LIST and curly braces surrounding the collection values A row including a set of rows has t
502. tion on page 9 54 UpdSet function 7 160 The UpdSet function updates a set of existing elements in a time series Syntax UpdSet ts TimeSeries set_ts multiset flags integer default 0 returns TimeSeries s The time series to update set_ts A set of rows that replace existing elements in the given time series ts flags Valid values for the ithe argument are described in The flags argument alues on page 7 9 The default is 0 Description The rows in set_ts must be of the correct type for the time series beginning with a time stamp otherwise an error is raised If the time stamp of any element does not correspond to an element already in the time series an error is raised and the entire update is void IBM Informix TimeSeries Data User s Guide Hidden elements cannot be updated Returns The updated time series Example The following example updates elements in a time series update activity_stocks set activity_data select UpdSet activity_data set_data from activity_load_tab where stock_id 600 where stock_id 600 Related reference InsSet function on page 7 70 PutSet function on page 7 80 UpdElem function on page 7 159 WithinC and WithinR functions The WithinC and WithinR functions perform calendar based queries converting among time units and doing the calendar math to extract periods of interest from a time series value Syntax WithinC ts T
503. tions with the same names but different definitions Enterprise Replication You can replicate time series data with Enterprise Replication You must follow the requirements for setting up Enterprise Replication for TimeSeries data types Related concepts L Replication of TimeSeries data ypes Enterprise Replication Guide igh availability cluster configuration Administrator s Guide Related tasks Creating containers on page 3 15 Planning for accessing time series data You use SQL routines Java classes and methods and C API routines to access and manipulate time series data directly You can also transform time series data into virtual tables that you can query with standard SQL statements You can create a data mart for time series data to query on other dimensions than time Call routines from within SQL statements or from within Java or C applications on either the client or the server computer Use TimeSeries SQL routines Java classes and methods and API routines and API routines that are written in C to perform the following types of operations to access or manipulate time series data e Manipulate individual elements or sets of elements e Perform statistical and arithmetic calculations e Aggregate data e Convert between time stamps and offsets e Extract values for a time interval e Find or delete null elements e Remove older data by deleting a range of elements IBM Informix TimeSeries Data User s G
504. to close the time series Returns A descriptor for the open time series Example See the ts_interp function Appendix A The Interp function example on page A 1 for an example of ts_open Related reference The ts_begin_scan function on page 9 7 The ts_close function on page 9 12 The ts_create function on page 9 17 The ts_create_with_metadata function on page 9 18 The ts_get_typeid function on page 9 34 The ts_previous_valid function The ts_previous_valid function returns the last element preceding the given time stamp Syntax ts_tselem ts_previous_valid ts_tsdesc tsdesc mi_datetime tstamp mi_integer STATUS mi_integer off tsdesc The time series descriptor returned by ts_open tstamp Points to the time stamp that follows the element returned STATUS Points to an mi_integer value that is filled in on return If no element exists before the time stamp or if the time stamp falls before the time series origin STATUS is set to a nonzero value See The ts_hide_elem function on page 9 34 for a description of STATUS off For regular time series off points to an mi_integer value that is filled in on return with the offset of the returned element For irregular time series off is set to 1 This argument can be passed as NULL Q Description The equivalent SQL function is GetPrevious Valid Chapter 9 Time series API routines 9 45
505. to the INSERT statement in the order specified by the function Returns The container name in which to store the time series value Related reference TSContainerPoolRoundRobin function on page 7 107 Create a time series 3 22 There are several ways to create an instance of a time series depending on whether there is existing data to load and if so the format of that data The following table lists the methods for creating and populating a time series Table 3 6 Methods for creating time series Task Function Create an empty time series TSCreate regular time series e TSCreatelrr irregular time series IBM Informix TimeSeries Data User s Guide Table 3 6 Methods for creating time series continued Task Function Create a time series with metadata e TSCreate with the metadata parameter regular time series e TSCreatelIrr with the metadata parameter irregular time series Create and populate a time series TSCreate with the set_ts argument regular time series e TSCreatelrr with the set_ts argument irregular time series e The time series input function e The output of a function Create a hertz time series TSCreatelrr with the hertz parameter e The time series input function Create a compressed time series TSCreatelrr with the compression parameter e The time series input function Related concepts TimeSeries data type on page 2 6 Regular t
506. tterns 1 13 collapsing 5 3 data type for 2 1 Copyright IBM Corp 2006 2014 Calendar patterns continued expanding 5 4 getting 7 48 intersection of two 5 1 interval options 2 1 reversing intervals for 5 4 specification for 2 1 start date for 2 4 system table for 2 8 union of two 5 5 Calendar routines 6 1 CalendarPattern data type 2 1 CalendarPatterns system table defined 2 8 Calendars 1 13 applying new to time series 7 24 calibrated search using 7 161 data type for 2 4 getting 7 49 intersection of time series from 7 71 intervals determining number of between time stamps 6 2 intervals determining number of between timestamps 9 9 lagging 7 74 names of getting 9 27 predefined 3 13 relative search using 7 161 returned time series and 2 8 specifying 2 4 3 24 start date for 2 4 system table for 2 8 timestamp getting after intervals 6 4 9 11 timestamps getting ina range 6 3 9 10 9 11 union of two 6 5 CalendarTable system table defined 2 8 Calibrated search type 7 161 CallIndex function 6 2 CalPattStartDate function 5 2 CalRange function 6 3 CalStamp function 6 4 CalStartDate function 6 5 Change Data Capture and time series 1 24 CLASSPATH variable 8 3 Clip function 7 31 ClipCount function 7 35 ClipGetCount function 7 37 Clipping a time series 7 18 7 31 7 35 Closing a time series 9 12 Collapse function 5 3 Collapsing a calendar pattern 5 3 Columns data getting 9 28 ID number getting 9 14 9 15 number of in
507. tual table in the same way as you destroy any other database table When you drop a virtual table the underlying base table is unaffected Trace functions 4 26 Trace functions are available to help you debug your work with virtual tables Restriction You should not use these trace functions unless you are working with an IBM Informix Technical Support or Engineering professional The functions are TSSetTraceFile Allows you to specify a file to which the trace information is appended TSSetTraceLevel Sets the level of tracing to perform in effect turns tracing either on or off The TSSetTraceFile function The TSSetTraceFile function specifies a file to which trace information is appended Syntax TSSetTraceFile traceFileName lvarchar returns integer traceFileName The full path and name of the file to which trace information is appended Description The file you specify using TSSetTraceFile overrides any current trace file The file is located on the server computer The default trace file is tmp session_number trc TSSetTraceFile calls the mi_set_trace_file DataBlade API function For more information about mi_set_trace_file see the IBM Informix DataBlade API Programmer s Guide Returns Returns 0 on success 1 on failure Example The following example sets the file tmp test1 trc to receive trace information execute function TSSetTraceFile tmp testl trc IBM Informix TimeSeries Data User
508. turn isNull is set to indicate whether the column is NULL Related reference The ts_datetime_cmp function on page 9 21 The ts_get_col_by_number function The ts_get_col_by_number function 9 28 The ts_get_col_by_number function pulls the individual pieces of data from an element The column 0 zero is always the time stamp Syntax MI_DATUM ts_get_col_by_number ts_tsdesc tsdesc ts_tselem tselem mi_integer colnumber mi_boolean isNull mi_integer off tsdesc A pointer returned by ts_open tselem An element to get column data from IBM Informix TimeSeries Data User s Guide colnumber The column number Column numbers start at 0 which represents the time stamp isNull A pointer to a null indicator off For a regular time series off is the offset of the element in the time series For an irregular time series off is ignored Returns The data in the specified column If the type of the column is by reference a pointer is returned If the type is by value the data itself is returned The caller cannot free this value On return isNull is set to indicate whether the column is NULL Example See the ts_interp function Appendix A The Interp function example on page A 1 for an example of ts_get_col_by_number Related reference The ts_datetime_cmp function on page 9 21 The ts_get_col_by_name function on page 9 28 The ts_get_compressed functi
509. type of quantization with a compression size of 2 bytes a lower bound of 1 and an upper bound of 100 The second INTEGER column has a compression type of linear boxcar and a maximum deviation of 20 The third INTEGER column has a compression type of linear swing door and a maximum deviation of 20 6 Insert five records for the same element into the time series by running the following SQL statements BEGIN Started transaction UPDATE tstable_c SET ts InsElem ts row 2014 01 01 00 00 00 00000 1 201 99991 ts_data_c WHERE id 50 1 row s updated UPDATE tstable_c SET ts InsElem ts row 2014 01 01 00 00 01 00000 2 202 99992 ts_data_c WHERE id 50 1 row s updated UPDATE tstable_c SET ts InsElem ts row 2014 01 01 00 00 02 00000 3 203 99993 ts_data_c WHERE id 50 1 row s updated UPDATE tstable_c SET ts InsElem ts row 2014 01 01 00 00 03 00000 4 204 99994 ts_data_c WHERE id 50 1 row s updated UPDATE tstable_c SET ts InsElem ts row 2014 01 01 00 00 04 00000 5 205 99995 ts_data_c WHERE id 50 1 row s updated COMMIT Data committed You must insert records in chronological order 7 Create a virtual table that is named vt_tstable_c that is based on the compressed time series by running the following SQL statement EXECUTE PROCEDURE TSCreateVirtualTab vt_tstable_c tstable_c 4096 ts 8 Query the virtual table to view the compressed data by running th
510. u manually destroy partitions Multiple dbspaces for multiple containers The following illustration shows multiple containers in multiple dbspaces The time series data is distributed by the primary key value Data for meters 1 100 for all dates Other tables Containers dbs2 Data for meters 201 300 for all dates Other tables dbs1 Container2 Data for meters 101 200 for all dates Other tab dbs3 Container4 les Data for meters 301 400 for all dates Other tables Figure 1 4 Architecture of dbspaces for multiple containers 1 16 Each container stores the data for all dates for a specific set of meter IDs Each dbspace stores a container and other tables If the containers were created IBM Informix TimeSeries Data User s Guide automatically each dbspace contains the table fragment that stores the same primary key values as the container Dbspaces for a rolling window container When you create a rolling window container you specify a time interval by which to store the data and the list of dbspaces in which to store the data A rolling window container stores time series data by the specified time interval in separate partitions Partitions are stored in the specified dbspaces in round robin order The container dbspace stores the information about what data is in which partition The following illustration shows a container that stores data in four dbspaces The t
511. u do not include an ORDER BY clause the results are grouped in batches of up to 128 rows per primary key value Within each batch the results are ordered by timestamp The batches for each primary key value are ordered chronologically but interspersed with batches for different primary key values For example suppose that the results of a query include 396 values for the primary key value meter1 347 values for meter2 and 280 values for meter3 The results might be ordered in the following way 128 rows of meterl 128 rows of meter2 128 rows of meter3 128 rows of meterl 128 rows of meter3 128 rows of meterl 128 rows of meter3 12 rows of meterl 24 rows of meter3 91 rows of meter2 Update values that are not in the primary key The following statement creates a virtual table named smartmeters_vti_nn_nk_nn with the TSVTMode parameter set to 32 EXECUTE PROCEDURE TSCreateVirtualTab smartmeters vti_nn_nk_nn smartmeters origin 2011 01 01 00 00 00 00000 calendar ts_15min regular threshold 20 container 32 The following statement inserts a new row into the virtual table and a new element in the time series in the base table Chapter 4 Virtual tables for time series data 4 21 4 22 INSERT INTO smartmeters_vti_nn_nk_nn acct_no meter_id t energy temperature owner address VALUES 6546 234 2011 01 01 00 45 00 00000 datetime year to fraction 5 3234 12 00 Jim NULL 1 row s inserted The va
512. uage support Time series data has limited support for non default locales Datetime data The DATETIME data type used in the TimeSeries subtype must be in the default U S format yyyy mo dd hh mm ss fffff IBM Informix TimeSeries Data User s Guide yyyy Year expressed in digits mo Month of year expressed in digits dd Day of month expressed in digits hh Hour of day expressed in digits mm Minute of hour expressed in digits ss Seconds of minute in digits M Fraction of a second in digits Character data Character I O is not GLS compliant You can convert time series data only to character strings that are in the default U S locale You can use the BulkLoad function only on character data that is in the default U S locale However the following character strings can use any locale and can contain multibyte characters e Character fields in a TimeSeries data type e Column names e Table names e Calendar names e Calendar pattern names e Container names Numeric data Floating point data must use the default U S format e The ASCII period is the decimal separator e The ASCII plus and minus signs must be used Decimal and money data types are GLS compliant except that the ASCII plus and minus signs must be used Sample smart meter data If you want to practice querying time series data before you define and load your time series you can use the sample data in the stores_demo dat
513. uide Create virtual tables to view and query time series data by using standard SQL statements You can display the results of TimeSeries SQL functions on time series data in virtual tables If you want to query time series data on dimensions other than time such as customer or location or run analytic functions you can create data marts with Informix Warehouse Accelerator Informix Warehouse Accelerator can efficiently run queries on the typically large amounts of time series data You can control the time range of time series values that you load into the accelerator You can output time series data in XML format to display in applications Related concepts Chapter 4 Virtual tables for time series data on page 4 1 L Data marts for time series data Informix Warehouse Accelerator Guide Related reference Chapter 7 Time series SQL routines on page 7 1 Chapter 8 Time series Java class library on page 8 1 D Chapter 9 Time series API routines on page 9 1 TSToXML function on page 7 154 Hardware and software requirements Before you create a time series ensure that you have the required hardware and software a supported operating system and that you understand the restrictions for SQL statements and data replication The Informix TimeSeries solution might not be supported on all platforms supported by Informix database servers See the system requirements for the Informix TimeSeries so
514. unction The TSL_PutRow function loads a row of time series data Syntax TSL_PutRow handle lvarchar primary_key lvarchar row ROW returns integer handle The table and column name combination returned by the TSL_Attach or the TSL_Init function primary_key A primary key value Can be a composite of multiple values separated by a pipe symbol For example if a primary key in the source table consists of two columns id1 and id2 a primary key value where id1 is 5 and id2 is 2 is represented as 5 2 row A ROW data type with values that are compatible with the TimeSeries column in the handle 7 137 Chapter 7 Time series SQL routines Usage Use the TSL_PutRow function to load a row of time series data as part of a loader program You must run the TSL_PutRow function in the context of a loader session that was initialized by the TSL_Init function You can run the TSL_PutRow function multiple times in the same session The data is stored in the database server until you run the TSL_Flush function to write the data to disk Returns e An integer that indicates the number of records that were inserted e An exception if no records were inserted Examples This example runs in the context of an initialized loader session The following statement loads a row of data from a ROW data type that is cast to the TimeSeries data type named readings EXECUTE FUNCTION TSL_PutRow meter readings 5 row datetime 2011 01 0
515. unction Returns The result of the decay function Example The following example computes the decay create function ESA18 a smallfloat returns smallfloat return 18 a TSDecay 18 a a 82 end function Related reference TSRunningAvg function on page 7 14 TSRunningSum function on page 7 151 TSL_Attach function The TSL_Attach function opens a database session for loading data Syntax TSL_Attach table_name varchar 128 column_name varchar 128 reject_file varchar 255 default NULL returns lvarchar Chapter 7 Time series SQL routines 7 123 TSL_Attach table_name varchar 128 column_name varchar 128 returns lvarchar table_name The name of the time series table Must not contain uppercase letters Must match the table name supplied to the TSL_Init function to initialize the loader session column_name The name of the TimeSeries column Must not contain uppercase letters Must match the column name supplied to the TSL_Init function to initialize the loader session reject_file optional The path and file name for storing records that were not applied For example records with an incorrect number of fields or a formatting error are not applied By default only the number of rejected records is recorded Overwrites the reject file specified in the TSL_Init function Usage Use the TSL_Attach function to open an additional database session to load time series data in p
516. uraged to send your comments about IBM Informix user documentation Use one of the following methods e Send email to docinf us ibm com e In the Informix information center which is available online at http www ibm com software data sw library open the topic that you want to comment on Click the feedback link at the bottom of the page complete the form and submit your feedback e Add comments to topics directly in the information center and read comments that were added by other users Share information about the product documentation participate in discussions with other users rate topics and more Feedback from all methods is monitored by the team that maintains the user documentation The feedback methods are reserved for reporting errors and omissions in the documentation For immediate help with a technical problem contact IBM Technical Support at http www ibm com planetwide We appreciate your suggestions Introduction XX xxii IBM Informix TimeSeries Data User s Guide Chapter 1 Informix TimeSeries solution Database administrators and applications developers use the Informix TimeSeries solution to store and analyze time series data A time series is a set of time stamped data Types of time series data vary enormously for example electricity usage that is collected from smart meters stock price and trading volumes ECG recordings seismograms and network performance records The types of querie
517. urns A row type containing the time stamp and the data from the time series at that time stamp The type of the row is the same as the time series subtype Example The following query retrieves the stock data of two stocks for a particular day select GetElem stock_data 2011 01 04 00 00 00 00000 from daily_stocks where stock_name IBM or stock_name HWP Related reference GetLastValid function on page 7 58 GetNextValid function on page 7 61 IBM Informix TimeSeries Data User s Guide GetNthElem function on page 7 62 GetPreviousValid function on page 7 65 InsElem function on page 7 69 PutElem function on page 7 77 Transpose function on page 7 86 The ts_elem function on page 9 2 GetIndex function on page 7 55 GetStamp function on page 7 66 UpdElem function on page 7 159 The ts_first_elem function on page 9 25 GetFirstElem function 2 The GetFirstElem function returns the first element in a time series Syntax GetFirstElem ts TimeSeries flags integer default 0 returns row ts The source time series flags Valid values for the flags argument are described in falues on page 7 9 lues on page 7 9 The default is 0 Description The API equivalent of this function is ts_first_elem Returns A row type containing the first element of the time series or NULL if there are n
518. ust include them in the order that is shown in the syntax but you can use either one without the other For example you can specify the TSCo Name parameter without including the TSVTMode parameter The virtual table is read only You cannot run INSERT UPDATE or DELETE statements on a virtual table that is based on an expression When you query the virtual table the WHERE clause in the SELECT statement cannot have any predicates that are based on the columns in the virtual table that are derived from the resulting TimeSeries subtype In the expression you can use time series SQL routines and other SQL statements to manipulate the data for example the AggregateBy function and the Apply function You can use the following variables in the expression e ts_column_name If the base table has multiple TimeSeries columns instead of specifying the name of the TimeSeries column in the expression you can use the ts_column_name variable to substitute the value of the TScolName parameter in the TSCreateExpressionVirtualTab procedure Because the column name is a variable you can use the same expression for each of the TimeSeries columns in the table e ts_begin_time Instead of specifying a DATETIME value you can use this variable and specify the beginning time point of the time series in the WHERE clause of the SELECT statement when you query the virtual table If the WHERE clause does not contain the beginning timepoint the first timepoin
519. ut into the time series See Creating al time series with metadata on page 3 23 for more information about metadata set_rows A set of row type values used to populate the time series The type of these rows must be the same as the subtype of the time series Description If TSCreate is called with a metadata argument then the metadata is saved in the time series Returns A regular time series that is empty or populated with the given set and optionally contains user defined metadata Example The following example creates an empty time series using TSCreate insert into daily_stocks values 901 IBM TSCreate daycal 2011 01 03 00 00 00 00000 20 0 0 NULL The following example creates a populated regular time series using TSCreate select TSCreate daycal 2011 01 05 00 00 00 00000 20 0 NULL set_data TimeSeries stock_trade from activity_load_tab where stock_id 600 Related tasks Creating a time series with metadata on page 3 23 Related reference Create a time series on page 3 22 GetCalendar function on page 7 48 Chapter 7 Time series SQL routines 7 117 GetInterval function on page 7 55 The ts_get_metadata function on page 9 31 The ts_update_metadata function on page 9 54 TSCreatelrr function 7 118 The TSCreatelrr function creates an empty irregular time series or an irregular time series that is populate
520. utomatically and change the extent sizes of partitions To take advantage of rolling windows set the sizes of the active and dormant windows to positive integers If you set the size of the active window to 0 the active window size is unlimited and you must manually detach partitions into the dormant window If you set the size of the dormant window to 0 you must manually destroy partitions regardless of whether you enable the automatic destroying of partitions Set the active window size to the same or larger than the time range of data that you plan to load If the range of data is larger than the active window size the load might fail For example suppose that you are storing meter data and choose an interval of one week and an active window size of four weeks When you add a month of data for the first meter ID five partitions are created because a month is usually longer than four weeks The partition that stores the data for the first week of the month is moved to the dormant window When you attempt to insert a month of data for the next meter ID the insert fails because some of the data does not fit into the active window In general set the size of the dormant window larger than the size of the active window The dormant window provides a staging area for data that you no longer need but that you do not yet want to delete If you enable the automatic destroying of partitions test your system under realistic conditions before you
521. values from the specified column name of the time series plus non time series columns TSRowNameToList function on page 7 145 Return a list of values from the specified column number of the time series plus non time series columns TSRowNumToList function on page 7 146 7 4 IBM Informix TimeSeries Data User s Guide Table 7 1 Time series SQL routines by task type continued Task type Description Used within the Apply Sum SMALLFLOAT or DOUBLE PRECISION values TSAddPrevious function on page function to perform 7 90 statistical calculations on a time series Compute the decay function TSDecay function on page 7 122 Compute a running average over a specified number of values TSRunningAveg function on page 7 147 Compute a running correlation between two time series over a specified number of values TSRunningCor function on page 7 149 Compute a running median over a specified number of values TSRunningMed function on page 7 150 Compute a running sum over a specified number of values TSRunningSum function on page 7 151 Compute a running variance over a specified number of values TSRunningVar function on page 7 152 Compare SMALLFLOAT or DOUBLE PRECISION values TSCmp function on page Return a previously saved value TSPrevious function on page 7 141 Chapter 7 Time series SQL routines 7 5 Table 7 1 Time series SQL routines by task type
522. ve any number of columns in addition to the Secid and series columns Copyright IBM Corp 2006 2014 B 1 B 2 Each line of the data file has the following format Secid year mon day carryover spread pricing_bmk_id price yield priority For example 25000006 2010 1 7 m 2 12 2 2000000000 22 2 6 You can run the TSIncLoad procedure with an SQL statement like execute procedure TSIncLoad corporates data_file_name cal_name 2010 1 1 20 ae container name 1 include lt ctype h gt include lt fcnt1 h gt include lt stdio h gt include lt stdlib h gt include lt string h gt include datetime h include mi h include tseries h define DAY_INFO_TYPE_NAME day_info define DAILY_COL_COUNT 7 typedef struct mi_integer fd mi_unsigned_integer flags define LDBUF_LAST_CHAR_EOL 0x1 mi_integer buf_index mi_integer buf_len mi_integer line_no mi_lvarchar file_name mi_string data 2048 FILE BUF define STREAM EOF 1 typedef struct sec_entry_s mi_integer sec_id ts_tsdesc tsdesc int in_row Indicates whether the time series is stored in row struct sec_entry_s next sec_entry_t typedef struct mi_lvarchar table_name MI_TYPEID ts_typeid The type id of timeseries day_info mi_string calendar_name mi_datetime origin mi_integer threshold mi_boolean regular mi_string container_name mi_integer nelems For created
523. ve window to the dormant window execute function TSContainerManage readings container detach active partitions before 2012 01 08 The following message describes the result detach succeeded 2 partitions moved The partitions are now distributed between the active and the dormant windows in the following way e Partitions that are in the active window IBM Informix TimeSeries Data User s Guide P8 elements for 2012 01 08 P9 elements for 2012 01 09 P10 elements for 2012 01 10 e Partitions that are in the dormant window P1 elements for 2012 01 01 P2 elements for 2012 01 02 P3 elements for 2012 01 03 P4 elements for 2012 01 04 P5 elements for 2012 01 05 P6 elements for 2012 01 06 P7 elements for 2012 01 07 Partitions P6 and P7 moved into the dormant window Example 2 Attach partitions The following example moves a partition from the dormant window to the active window execute function TSContainerManage readings container attach dormant partitions after 2012 01 06 The following message describes the result attach succeeded 1 partition moved The partitions are now distributed between the active and the dormant windows in the following way e Partitions that are in the active window P7 elements for 2012 01 07 P8 elements for 2012 01 08 P9 elements for 2012 01 09 P10 elements for 2012 01 10 e Partitions that are in the dormant window
524. w AS tab mr The statements returns a table named tab that contains one column named mr The mr column is an unnamed row type that has the same fields as the TimeSeries subtype named smartmeter_row The output has a field for time and a field for energy t energy 2011 01 01 00 00 00 00000 29 2011 01 01 00 15 00 00000 18 2011 01 01 00 30 00 00000 13 2011 01 01 00 45 00 00000 26 2011 01 01 01 00 00 00000 21 2011 01 01 01 15 00 00000 15 2011 01 01 01 30 00 00000 20 2011 01 01 01 45 00 00000 24 2011 01 01 02 00 00 00000 30 2011 01 01 02 15 00 00000 30 2011 01 01 02 30 00 00000 29 2011 01 01 02 45 00 00000 32 2011 01 01 03 00 00 00000 29 Example 5 Display specific data in a table with multiple columns The following statement uses the statement from the previous example inside a table expression in the FROM clause SELECT FROM SELECT mr t mr energy mr temperature FROM TABLE transpose SELECT readings FROM smartmeters WHERE meter_id 13243 smartmeter_row AS tab mr AS sm t energy temp WHERE temp lt 10 The statement returns the following data in the form of a table named sm that contains three columns t energy temp 2011 01 01 00 00 00 00000 29 13 0000000000 2011 01 01 00 30 00 00000 13 18 0000000000 2011 01 01 01 00 00 00000 21 13 0000000000 2011 01 01 01 15 00 00000 15 11 0000000000 2011 01 01 03 15 00 00000 22 19 0000000000 2011 01 01 03 45 00 00000 28 14 0000000000 2011 01 01 04 00 00 00000 19
525. were created during the session are removed Run the TSL_SessionClose function to close each database session before running the TSL_Shutdown procedure Example EXECUTE PROCEDURE TSL_Shutdown ts_data raw_reads Related tasks Writing a loader program on page 3 31 TSPrevious function The TSPrevious function records the supplied argument and returns the last argument it was passed Chapter 7 Time series SQL routines 7 141 Syntax TSPrevious value int returns int TSPrevious value smallfloat returns smallfloat TSPrevious value double precision returns double precision value The value to save Description Use the TSPrevious function within the Apply function TSPrevious function is useful in comparing a value in a time series with the value immediately preceding it The TSPrevious function can take parameters that are columns of a time series Use the same parameter format that the Apply function accepts Returns The value previously saved The first time TSPrevious is called it returns NULL Example See the example for the TSCmp function on page 7 90 Related reference Apply function on page 7 18 TSAddPrevious function on page 7 90 TSCmp function on page 7 90 TSDecay function on page 7 122 TSRunningAve function on page 7 147 TSRunningSum function on page 7 151 TSRollup function 7 142 The TSRollup function aggregates time series values b
526. xpr gt gt D ogical_operator ts expr expr_col lt expr_value q gt lt NOT lt L IS NULL nor The time series to count An expression to filter elements by comparing element values to a number or string You can combine multiple expressions with the AND or the OR operator and use parentheses to nest multiple expressions Use the following arguments within an expression expr_col The name of the column within a TimeSeries data type If the column is of type BSON the expr_col must be the name of a field in one or more BSON documents in the BSON column If the BSON field name is the same as another column in the TimeSeries data type you must precede the field name with the column name and a dot bson_column_name bson_field_name expr_value The value that is used in the comparison Can be either a number a string or NULL logical_operator The AND or the OR operator begin_stamp optional The begin point of the range Can be NULL By default begin_stamp is the beginning of the time series end_stamp optional col The end point of the range Can be NULL By default end_stamp is the end of the time series The name of the column within a TimeSeries data type Can be prefixed with the words IS NULL OR Must be surrounded by quotation marks If the column is of type BSON the col must be the name of a field in one or more BSON documents in the
527. xpression instead of the subtype from the base table For example the table ts_data contains a TimeSeries column called raw_reads that contains a row type with tstamp and value columns The following table displays IBM Informix TimeSeries Data User s Guide part of the ts_data table The actual time stamp values are shown for clarity although the time stamp values are calculated instead of stored in regular time series Table 4 1 Data in a table with a TimeSeries column loc_esi_id measure_unit direction raw_reads 4727354321000111 KWH P 2010 11 10 00 00 00 00000 0 092 2010 11 10 00 15 00 00000 0 084 4727354321046021 KWH P 2010 11 10 00 00 00 00000 0 041 2010 11 10 00 15 00 00000 0 041 4727354321090954 KWH P 2010 11 10 00 00 00 00000 0 026 2010 11 10 00 15 00 00000 0 035 The virtual table that is based on the ts_data table converts the raw_reads column elements into individual columns The rows are ordered by timestamp starting with the earliest timestamp The following table displays part of the virtual table that is based on the ts_data table Table 4 2 Data in a virtual table based on a time series loc_esi_id measure_unit direction tstamp value 4727354321000111 KWH P 2010 11 10 00 00 00 00000 0 092 4727354321000111 KWH P 2010 11 10 00 15 00 00000 0 084 4727354321046021 KWH 2010 11 10 00 00 00 00000 0 041 4727354321046021 K
528. y time for multiple rows in the table Syntax TSRol lup ts TimeSeries agg_express lvarchar RETURNS TimeSeries agg_express A comma separated list of the following elements in any order SQL aggregate operator AVG COUNT MIN MAX SUM or the FIRST and LAST operators The FIRST operator returns a time series that contains the first element that was entered into the database for each timestamp The LAST operator returns the last element that is entered for each timestamp You can include multiple operators Each operator requires an argument that is the column of the input time series which is specified by one of the following column identifiers IBM Informix TimeSeries Data User s Guide colname The colname is the name of the column to aggregate in the TimeSeries data type For example if the column name is high the column identifier is high colnumber The colnumber is the position of the column to aggregate in the TimeSeries data type For example if the column number is 1 the column identifier is 1 bson_field_name The bson_field_name is the name of a field in at least one BOON document in the BSON column in the TimeSeries data type For example if the field name is v1 the column identifier is v1 If the BSON field name is the same as another column in the TimeSeries data type you must qualify the field name in one of the following ways e colname bson_field_name For example if the BSON column name is
529. your metadata data type For information on creating support functions see the IBM Informix User Defined Routines and Data Types Developer s Guide 3 Run the TSCreate or TSCreatelIrr function with the metadata argument Chapter 3 Create and manage a time series through SQL 3 23 3 24 After you have created a time series with metadata you can add change remove and retrieve the metadata You can also retrieve the name of your metadata type Related reference TSCreate function on page 7 116 TSCreatelrr function on page 7 118 UpdMetaData function on page 7 159 GetMetaData function on page 7 59 Time series input function You can use the time series input function to create a time series with the INSERT statement Use the following syntax for the time series input function gt gt INSERT INTO table_name VALUES column_value gt gt cal enda euler Me eal container container_name L_ metadata metadata_value metatype metadata _type origin origin A Peo eee compress A comp definition p irregular hertz hertz irregular threshold threshold regular P gt lt data_value NULL times tamp Ldatafile datafile_name Tab
530. ys The ts_get_all_cols procedure on page 9 27 Create and perform Compare two time stamps The ts_datetime_cmp function on page 9 21 calculations with time stamps Get fields from a time stamp The ts_get_stamp_fields procedure on page 9 32 Create a time stamp The ts_make_stamp function on page 9 40 Calculate the number of intervals between two time stamps ts_tstamp_difference function on page 9 51 Subtract N intervals from a time stamp The ts_tstamp_minus function on page Add N intervals to a time stamp The ts_tstamp_plus function on page 9 53 Get information about Find the number of a column The ts_col_id function on page 9 14 element data Return the number of columns contained in each element The ts_col_cnt function on page 9 14 Get type information for a column specified by number The ts_colinfo_number function on page 9 15 Get type information for a column specified by name The ts_colinfo_name function on page 9 15 Determine whether an element is hidden The TS_ELEM_HIDDEN macro on page 9 23 Determine whether an element is NULL The TS_ELEM_ NULL macro on page 9 24 Chapter 9 Time series API routines 9 5 Table 9 1 Time series API routines sorted by task continued Task type Description Get information about a time series Get information about a calendar Get the name of a calendar associated w
531. ze dormant_windowsize 2 active_windowsize The maximum number of partitions in the active window If you do not intend to set a limit to the number of partitions estimate the maximum number of partitions you expect container_name_length The length of the container name in bytes dbspace_name_length The length of the dbspace name for the container partition in bytes dormant_windowsize The maximum number of partitions in the dormant window If you do not intend to set a limit to the number of partitions estimate the maximum number of partitions you expect A rolling window container has multiple window partitions You can allocate multiple dbspaces for window partitions Use the following formula to estimate the number of partitions in each dbspace Approximate number of partitions in each dbspace CEIL active_windowsize dormant_windowsize number_dbspaces 1 active_windowsize The maximum number of partitions in the active window If you do not intend to set a limit to the number of partitions estimate the maximum number of partitions you expect dormant_windowsize The maximum number of partitions in the dormant window If you do not intend to set a limit to the number of partitions estimate the maximum number of partitions you expect number_dbspaces The number of dbspaces that are allocated for the window partitions IBM Informix TimeSeries Data User s Guide Related tasks Related reference Plannin

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