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IBM Informix TimeSeries Data User's Guide

1. Minus function on page 7 66 Mod function on page 7 66 Plus function on page 7 68 Pow function on page 7 68 Times function on page 7 79 BulkLoad function 7 24 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 parameter are described later in this topic 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 this format row timestamp set row value value row value value value The tab format is to separate the values
2. 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 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
3. Create a custom type map on page 8 The ts_upd_elem function on page 9 52 UpdMetaData function 7 120 The UpdMetaData function updates the user defined metadata in the specified time series Syntax create function UpdMetaData ts TimeSeries metadata TimeSeriesMeta returns TimeSeries ts The time series for which to update metadata metadata The metadata to be added to the time series Can be NULL Description 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 IBM Informix TimeSeries Data User s Guide Related tasks Creating a time series with metadata on page 3 13 Related reference GetMetaData function on page 7 50 GetMetaTypeName function on page 7 50 TSCreate function on page 7 97 TSCreatelrr function on page 7 99 The ts_create_with_metadata function on page 9 1 The ts_get_metadata function on page 9 28 The ts_update_metadata function on page 9 52 UpdSet function The UpdSet function updates a set of existing elements in a time series Syntax UpdSet ts Ti
4. The ts_begin_scan function on page 9 6 The ts_close function on page 9 12 The ts_create function on page 9 16 The ts_create_with_metadata function on page 9 17 The ts_get_typeid function on page 9 31 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 31 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 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 Chapter 9 Time series API routines 9 43 9 44 For ir
5. 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 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
6. 2011 01 05 00 00 00 00000 datetime year to fraction 5 The following query returns a list of the two time stamps following 2011 01 03 in the calendar daycal execute function CalRange daycal 2011 01 03 00 00 00 00000 2 Related reference The ts_cal_stamp function on page 9 11 The CalStamp function 6 4 The CalStamp function returns the time stamp at a given number of calendar intervals after a given time stamp Syntax CalStamp cal_name lvarchar tstamp datetime year to fraction 5 num_stamps integer 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 representing the given offset Example The following example returns the time stamp that is two intervals after 2011 01 03 execute function CalStamp daycal 2011 01 03 00 00 00 00000 2 IBM Informix TimeSeries Data User s Guide Related reference The ts_cal_range_index function on page 9 10 The ts_cal_stamp function on page 9 11 The CalStartDate function The CalStartDate function takes a calendar name and returns a DATETIME value containing the start date of that calendar Syntax CalStartDate cal_name lvarchar returns datetime year to fraction 5 cal_name
7. IfmxTmeSeries com informix timeseries lfmxCalendar Key gt Implements IfmxResultSetMetaData mj Figure 8 6 TimeSeries class diagram Get data from the database The JDBC 2 0 specification describes the procedure for retrieving and sending user defined types to and from a database This is achieved by using a type map system To enable your Java program to retrieve or send Calendar CalendarPattern and TimeSeries data to or from an IBM Informix database you must create a custom type map as described in Create a custom type map After you have created the type map you can select time series data and manipulate the data as shown in The IfmxTimeSeries object on page 8 7 Note that the time series must be stored in containers Also if you clip a time series the resultant time series is read only Create a custom type map To define mappings between Java classes and user defined SQL data types use a type map For the Informix JDBC Driver to determine which class to call to handle extended data types such as TimeSeries your application must define an entry in the type map You can do this in two ways e Create an entry in the type map of the current database connection the entry is valid only for the current connection e Create a map independently and use the map in the getObject method call to extract the TimeSeries type from the result set in your application This can be a better method if you have ma
8. Returns The container name in which to store the time series value Related tasks Configuring additional container pools on page 3 9 Related reference TSContainerPoolRoundRobin function on page 7 89 Create a time series 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 Chapter 3 Create and manage a time series 3 11 3 12 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 options for creating and populating a time series Task Function Create an empty time series e TSCreate regular time series e TSCreatelrr irregular time series Create an empty time series with metadata e TSCreate with the metadata argument regular time series TSCreatelrr with the metadata argument irregular time series Create and populate a time series TSCreate with the set_ts argument regular time series e TSCreatelIrr with the set_ts argument irregular time series e The implicit input function e The output of a function Create and populate a time series with e TSCreate with the set_ts and metadata metadata arguments regular time series e TSCreatelIrr with the set_ts and metadata arguments irregular time series Populate an existing time series BulkLo
9. 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 Examples Example 1 Move a container into a container pool The following statement moves a container named ctn_1 into a container pool 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 Related tasks TSContainerTotalPages function The TSContainerTotalPages function returns the total number of pages allocated to the specified container or in all containers Syntax TSContainerTotalPages container_name varchar 128 1 returns integer 7 94 IBM Informix TimeSeries Data User s Guide container_name The name of an existing container Can be NULL Description Use the TSContainerTotalPages function to view the size of a container If you specify NULL as the container name information about all containers for the database is returned Returns The number of pages that are allocated to the specified container o
10. from daily_stocks where stock_name IBM Chapter 7 Time series SQL routines 7 49 Related reference Clip function on page 7 26 ClipCount function on page 7 29 GetElem function on page 7 44 GetLastElem function on page 7 47 GetNextValid function on page 7 52 GetPreviousValid function on page 7 56 GetMetaData function The GetMetaData function returns the user defined metadata from the given time series Syntax create function GetMetaData ts TimeSeries returns TimeSeriesMeta S 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 Creating a time series with metadata on page 3 13 1 oE S 8 oO ola T a a g e Ss ran 3 oO eh 5 A fan jo 5 TSCreate function on page 7 97 ny 9 77 WN Q oO o S oO T z ia a 5 a y 5 O 5 gel o ej oO D O j N 0 _with_ function on page 9 17 ro a lt c o w o B o 5 Fa ct 5 o 5 ro o ag oO N z wn Q o o 2 D z gt 3 Q o O o D o Pa The ts_get_metadata function on page 9 28 2 function on page 9 52 z ie
11. ts 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 Related reference GetMetaTypeName function on page 7 50 The ts_get_metadata function on page 9 28 The ts_upd_elem function The ts_upd_elem function updates an element in an existing time series at a given timepoint 9 52 IBM Informix TimeSeries Data User s Guide Syntax ts_timeseries ts_upd_elem ts_tsdesc tsdesc ts_tselem tselem mi_datetime tstamp tsdesc A descriptor of the time series to be updated returned by ts_open 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 20 The ts_ins_elem function on page 9 3 The ts_put_elem function on page 9 4 The ts_put_elem_no_dups function on page 9 45 UpdElem function on page 7 119
12. 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 the 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 2011 01 01 00 00 00 00000 energy 33070 temperature 13 0000000000 owner John IBM Informix TimeSeries Data User s Guide 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 Example 2 Setting the TSVTMode parameter to 32 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 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
13. Creating a time series ith metadata on page 3 13 for more information about metadata Can be NULL metadata_typeid The type ID of the metadata Can be NULL if the metadata argument is NULL 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 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 TSCreate function on page 7 97 The ts_get_metadata function on page 9 28 The ts_get_typeid function on page 9 31 The ts_update_metadata function on page 9 52 The ts_current_offset function The ts_current_offset function returns the offset for the last element returned by ts_next 9 18 IBM Informix TimeSeries Data User s Guide Syntax mi_integer ts_current_offset ts_tscan tscan tscan The scan descriptor returned by ts_begin_scan 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 Relate
14. 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 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 00000000060 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 concepts CalendarPattern data type on page 2 1 Related reference Clip function on page 7 26 IBM Informix TimeSeries Data User s Guide Chapter 8 Time series Java class library The time series Java class library enables you to access and manipulate TimeSeries type data from within Java applications or applets The time serie
15. 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 output 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 See the IBM Accessibility Center at http www ibm com able for more information about the IBM commitment to accessibility 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
16. 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 Chapter 7 Time series SQL routines 7 107 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 00 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 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 fi
17. 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 IBM Informix TimeSeries Data User s Guide Related reference The ts_free procedure on page 9 24 The ts_put_elem function on page 9 44 The ts_put_elem_no_dups function on page 9 45 The ts_put_last_elem function on page 9 46 The ts_put_nth_elem function on page 9 46 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 ts 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 pointer to an MI_TYPEID structure that contains the type ID of the specified time series You must no
18. 7 91 7 93 7 94 7195 7 96 7 97 gt 7 99 7 101 7 102 7 103 7 104 7 105 7 106 7 107 7 108 7 110 7 111 7 112 7 113 7 114 7 116 7 117 7 119 7 120 7 121 7 122 The IfmxCalendarPattern class The IfmxCalendar class The IfmxTimeSeries class The IfmxTimeSeries class m thi d Problem solving Tracing with the Java dag library Chapter 9 Time series API routines Differences in using functions on the server and on the dient API data structures The ts_timeseries structure The ts_tscan structure The ts_tsdesc structure The ts_tselem structure API routines 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_create function The ts_create_with_metadata fonction The ts_current_offset function The ts_current_timestamp function The ts_datetime_cmp function The ts_del_elem function The ts_elem function The TS_ELEM_HIDDEN macro The TS_ELEM_NULL macro The ts_elem_to_row function The ts_end_scan procedure The ts_first_elem function The ts_f
19. Informix Product Family Informix Version 11 70 IBM Informix TimeSeries Data User s Guide all Informix Product Family Informix Version 11 70 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 3567 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 2012 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 stale What s new in TimeSeries data for Informix Version 11 70 Example code conventions Additional documentation Compliance with industry siandards Syntax diagrams z How to read a command ine syntax diagram Keywords and punctuation Identifiers and names How to provide documentation feedback Chapter 1 Info
20. 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 Chapter 7 Time series SQL routines 7 15 Related reference AgeregateBy function on page 7 6 7 AgeregateRange function on page 7 9 Clip function on page 7 2 n Q 5 Q o 5 2 g 5 Fy o 5 o 5 go og oO Ra N o ClipGetCount function on page 7 30 Intersect function on page 7 6 TSAddPrevious function on page 7 8 TSCmp function on pa
21. Nor ts The time series to count expr 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 expr_value The value 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 The end point of the range Can be NULL By default end_stamp is the end of the time series col 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 op An operator Can be lt lt gt or gt Must be surrounded by quotation marks value The value 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
22. The value 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 Example 3 Setting the TSVTMode parameter to 64 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 Chapter 4 Virtual tables for time series data 4 15 4 16 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
23. either on or off in a calendar pattern is 2035 Internal calculations take longer to perform if you use a long calendar pattern Usage The information inside the curly brackets 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 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 a daily calendar pattern with hourly intervals must contain exactly 24 intervals and so on When the calendar pattern begins is specified by the calendar pattern start date 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 would be 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 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 exa
24. g for loading time series data on Delete time series data You can delete time series data to remove incorrect data or to remove old data You can delete data from a single time series instance in the following ways e Delete a single element by running the DelElem function e Delete elements in a time range and free any resulting empty pages by running the DelRange function e Free any empty pages in a time series instance by running the NullCleanup function 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 Related reference Chapter 3 Create and manage a time series 3 19 3 20 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
25. 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 structure 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_
26. on page 9 2 77 ys O lle all all amp amp olle ala3 l2 Ho ofS 5 Qaiais WB WS We WS o IO BIA Ho 5 O J la SIEIS oo G gel Q zi O B Ea 2 5 5 go fo oa lo N O The ts_elem function on page 9 20 DelRange function 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 fma S The time series to act on begin_stamp The begin point of the range Chapter 7 Time series SQL routines 7 37 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 5 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
27. on page 9 43 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 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_ELEM_HIDDEN macro on page 9 21 The ts_elem function on page 9 20 The ts_first_elem function on page 9 23 The ts_hide_elem function on page 9 31 The ts_last_elem function on page 9 34 The ts_next function on page 9 39 The ts_next_valid function on page 9 40 The ts_nth_elem function on page 9 41 The ts_previous_valid function on page 9 43 The ts_elem_to_row function The ts_elem_to_row function converts a time series element into a new row 9 22 IBM Informix TimeSeries Data User s Guide Syntax MI_ROW ts_elem_to_row ts_tsdesc tsdesc ts_tselem elem mi_integer off tsdesc The descriptor for a time series returned by ts_open elem A
28. 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 met 0001 origin 2006 01 01 00 00 00 00000 calendar smartmeter regular threshold 0 container TSContainerPoolRoundRobin readings 33070 13 00 100
29. 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 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
30. yyyy mo dd hh mm ss fffff 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 Sif 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 IBM Informix TimeSeries Data User s Guide 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 database 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 numb
31. Exp 7 41 Logn 7 65 Negate 7 66 Positive 7 68 Round 7 77 Sin 7 78 Sqrt 7 78 Tan 7 78 Union function 7 117 Union of time series 7 117 X 8 IBM Informix TimeSeries Data User s Guide UPDATE statement 3 17 UPDATE STATISTICS statement 4 19 updateAsciiStream method 8 13 updateBigDecimal method 8 13 updateBinaryStream method 8 13 updateBoolean method 8 13 updateByte method 8 13 updateBytes method 8 13 updateCharacterStream method 8 13 updateDate method 8 13 updateDouble method 8 13 updateFloat method 8 13 updateInt method 8 13 updateLong method 8 13 updateNull method 8 13 updateObject method 8 13 updateRow method 8 13 updateShort method 8 13 updateString method 8 13 updateTime method 8 13 updateTimestamp method 8 13 Updating element 9 44 9 45 element in a time series 9 52 metadata in a time series 9 52 Updating a set of elements 7 121 Updating an element 7 119 UpdElem function 7 119 UpdMetaData function 7 120 UpdSet function 7 121 V Version TimeSeries Java class 8 8 Virtual table 4 1 Virtual table interface 4 6 Virtual tables creating with expressions 4 8 display of data 4 3 structure 4 2 VirtuallableName parameter 4 4 Visual disabilities reading syntax diagrams C 1 W wasNull method 8 13 WithinC function 7 122 WithinR function 7 122 writeSQL method 8 8 8 9 8 13 Writing TimeSeries data to database Java 8 7 Printed in USA SC27 3567 03 p ny S Jasf Qq s n g w XIWOJUY INI OLE UOISI3
32. 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 The argument are described in The flags argument alues on page 7 5 The default is 0 7 68 IBM Informix TimeSeries Data User s Guide 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 w
33. TSColNameToList function TSColNumToList function TSContainerCreate procedure TSContainerDestroy procedure TSContainerNElems function TSContainerPctUsed function TSContainerPoolRoundRobin hinGion TSContainerPurge function TSContainerSetPool procedure TSContainerTotalPages function TSContainerTotalUsed function TSContainerUsage function TSCreate function TSCreatelrr function TSDecay function TSPrevious function TSRollup function TSRowNameToList fanton TSRowNumTolList function TSRowToList function TSRunningAvg function TSRunningCor function TSRunningMed function TSRunningSum function TSRunningVar 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 System requirements for Java programs Install the time series Java files Sample programs Time series Java classes The IfmxCalendarPattern dass The IfmxCalendar class The IfmxTimeSeries class Get data from the database Create a custom type map The IfmxTimeSeries object Write TimeSeries data back to the database Obtain the time series Java class version vi IBM Informix TimeSeries Data User s Guide LTS fap 7 76 7 77 7 77 7 77 7 78 7 78 7 78 i 7 79 7 79 7 79 7 82 7 83 7 84 7 85 7 86 lt 7 87 7 88 7 89 7 89
34. The AndOp function on page 6 1 The 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 given calendar IBM Informix TimeSeries Data User s Guide Example 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 The CalStartDate function on page 6 5 The ts_cal_pattstartdate function on page 9 8 The Collapse function The Collapse function collapses the given calendar pattern into destination units which must have a larger interval unit than that of the given calendar pattern Syntax Collapse cal_patt CalendarPattern interval lvarchar 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 cale
35. 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 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 Chapter 7 Time series SQL routines 7 91 7 92 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 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 seri
36. 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 Chapter 7 Time series SQL routines 7 111 Related reference Apply function on page 7 11 TSAddPrevious function on page 7 82 TSCmp function on page 7 83 TSDecay function on page 7 101 TSPrevious function on page 7 102 TSRunningAvg function on page 7 10 N TSRunningCor function on page 7 108 TSRunningMed function on page 7 110 TSRunningVar function TSRunningVar function 7 112 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 irregular 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 sec
37. data type 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 routines on page 6 1 Time series data storage Time series data is stored in a container unless the data remains small enough to fit in a single row of a table Time series containers are created automatically when they are needed 1 8 IBM Informix TimeSeries Data User s Guide A container exists in a dbspace which is a logical grouping of physical storage chunks When a time series is stored in a container the data is stored contiguously and is retrieved with a minimum number of disk reads 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 customers The time series data for a particular source is stored on pages in time stamp order Database dbspaces dbspaces1 container1 container 1 other tables data for data for data for customer1 customer2 customer3 Data storage Multiple dbspaces Data storage in dbspace1 Data pages in container1 on disk in the database Figu
38. double x Updates the current time series element with the given double object updateDouble void updateDouble java lang String Updates the current time series element columnName double x with the given double object updateFloat void updateFloat int columnIndex float x Updates the current time series element with the given float object updateFloat void updateFloat java lang String Updates the current time series element columnName float x with the given float object updateInt void updateInt int columnIndex int x Updates the current time series element with the given int object updateInt void updateInt java lang String columnIndex Updates the current time series element int x with the given int object updateLong void updateLong int columnIndex long x Updates the current time series element with the given long object Chapter 8 Time series Java class library 8 15 Table 8 2 Public methods continued Method Signature Description updateLong void updateLong java lang String Updates the current time series element columnName long x with the given long object updateNull void updateNull int columnIndex Updates the current time series element with a null value updateNull void updateNull java lang String Updates the current time series element columnName with a null value updateRow void updateRow Connection conn Updates the underlying time series with the contents of
39. 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_name 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 Da
40. 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 S Ue gt ctnr_daily 1 Any name can be used for the corporates table The corporates table can have any number of columns in addition to the Secid and series columns Copyright IBM Corp 2006 2012 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
41. 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 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 cont
42. on page 9 41 The ts_put_nth_elem function on page 9 46 The ts_time function on page 9 49 The ts_ins_elem function 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 Chapter 9 Time series API routines 9 33 9 34 Related reference The ts_free procedure on page 9 24 The ts_put_elem function on page 9 44 The ts_upd_elem function on page 9 52 The ts_put_elem_no_dups function on page 9 4 5 The TS_IS_INCONTAINER macro 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 Returns This function returns nonzero if the time series data is in a container rather than in memory or in a row The TS_IS_
43. 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 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 th
44. timeseries stock_trade from activity_load_tab Related reference TSCreate function on page 7 97 TSCreatelrr function on page 7 99 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 your metadata data
45. tsdesc tsdesc The time series descriptor returned by ts_open IBM Informix TimeSeries Data User s Guide Description The equivalent SQL function is GetNelems Returns The number of elements in the time series Related reference ClipGetCount function on page 7 30 GetNelems function on page 7 51 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 Example See the ts_interp function Appendix A The Interp function example on page 4 1 for an example of ts_next Chapter 9 Time series API routines 9 3
46. 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 IBM Informix TimeSeries Data User s Guide Related tasks Configuring additional container pools on page 3 9 Related reference User defined container pool policy on page 3 10 TSContainerPurge function The TSContainerPurge function deletes time series data through a specified timestamp from one or more containers Syntax TSContainerPurge control_file lvarchar 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 instance An instance is a row in a table that includes a TimeSeries column end_range
47. 12 1 12 1 13 1 13 1 13 1 14 1 14 1 15 1 15 22 5 2 8 Create a time series column Creating a TimeSeries subtype Create the database table Managing containers Monitoring time series containers Configuring additional container pools User defined container pool policy Create a time series Creating a time series with the TSCreate o or TSCreatelrr fonction n Create a time series with its input function Create a time series with the output of a function Load data into an existing time series Loading data with the IBM Informix TimeSeries Plug i in for Data Studio Loading data from a file into a virtual table wie ual Load data with BulkLoad s Load small amounts of data with functions Delete time series data Chapter 4 Virtual tables for time series data The structure of virtual tables The display of data in virtual tables The insertion of data through virtual tables Creating a time series virtual table TSCreateVirtualTab procedure Example of creating a virtual table TSCreateExpressionVirtualTab procedure The TSVTMode parameter Drop a virtual table Manage performance Trace functions The TSSetTraceFile funeti ri TSSetTraceLevel function Chapter 5 Calendar pattern routines The AndOp function The CalPattStartDate function The Collapse function The Expand function The NotOp function The OrOp function Chapter 6 Calendar routines T
48. 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 OrOp cl c_calendar c2 c_calendar from CalendarTable cl CalendarTable c2 where cl c_name daycal and c2 c_name hourcal 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 The OrOp function on page 5 5 The 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
49. 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 ApplyOpToTsSet function on page 7 19 Binary arithmetic functions on page 7 21 ApplyCalendar function 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 given time series from which specific timepoints will be projected cal_name The name of the calendar to apply Chapter 7 Time series SQL routines 7 17 7 18 flags Valid values for the flags argument are described later in this topic 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 The flags argument values When opening the source time series your setting of the flags argument is combined using the AND operator with the TSOPEN_READ_HIDDEN value The returned time series is opened with your setting of the flags argumen
50. 24 7 31 7 72 7 73 7 79 9 6 TS_SCAN_SKIP_BEGIN flag 7 5 7 17 7 24 7 72 7 73 7 79 9 6 TS_SCAN_SKIP_END flag 7 5 7 17 7 24 7 72 7 73 7 79 9 6 ts_time function 9 12 9 49 9 50 9 51 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 52 ts_update_metadata function 9 52 TS_VTI_DEBUG trace class 4 20 TSAddPrevious function 7 82 TSCmp function 7 83 TSColName parameter 4 4 TSContainerCreate procedure 7 86 TSContainerDestroy procedure 7 87 TSContainerNElems 7 88 TSContainerPctUsed 7 89 TSContainerPurge function 7 91 TSContainerTable system table 2 9 TSContainerTotalPages 7 94 TSContainerTotalUsed 7 95 TSContainerUsage 7 96 TSCreate function 7 97 TSCreateExpressionVirtualTab 4 4 4 8 TSCreatelrr function 7 99 TSCreateVirtualTab procedure 4 4 TSDecay function 7 101 TSInstanceTable system table 2 8 TSPrevious function 7 102 TSRollup function 7 103 TSRowNameToList function 7 104 TSRowNumfToList function 7 105 TSRowToList function 7 106 TSRunningAvg function 7 84 7 85 7 107 TSRunningCor function 7 108 TSRunningMed function 7 110 TSRunningSum function 7 111 TSRunningVar function 7 112 TSSetToList function 7 113 TSSetTraceFile function 4 19 TSSetTraceLevel function 4 19 4 20 TSToXML function 7 114 TSVTMode parameter 4 11 Type map 8 5 U Unary arithmetic functions Abs 7 6 Acos 7 6 Asin 7 20 Atan 7 21 Cos 7 32 description 7 116
51. 3 14 InsElem function 3 18 7 60 INSERT statement 3 14 Inserting element 7 60 7 68 7 70 9 33 9 36 9 44 9 45 element at an offset 7 71 9 46 element at end of a time series 9 46 elements set of 7 61 7 72 time series into another time series 7 73 insertRow method 8 13 InsSet function 3 18 7 61 Instance ID getting for a time series 7 62 Instanceld function 7 62 Intersect function 7 62 Intersection calendar patterns of 5 1 calendars of 6 1 time series of 7 62 Interval calendar pattern for 1 8 2 1 getting for a time series 7 47 number of between time stamps determining 9 8 Irregular time series 1 7 creating with metadata 7 99 creating with TSCreatelrr 7 99 determining if 9 34 specifying 3 14 isAfterLast method 8 13 isBeforeFirst method 8 13 isFirst method 8 13 isHidden method 8 12 isLast method 8 13 IsRegular function 7 64 isRegular method 8 12 J jar file 8 2 Java2 8 2 Java class library 8 1 Java Developers Kit 8 2 JavaSoft website 8 2 JDBC 8 1 JDBC 2 0 specification 8 1 L Lag function 7 65 Lagging creating new time series 7 65 last method 8 13 LessThan operator 1 13 load command 3 17 Loading data 3 16 from a file 3 17 7 24 time series 1 11 using virtual tables 3 17 Loading time series data 3 1 Local time converting to 9 29 Logn function 7 65 X 4 IBM Informix TimeSeries Data User s Guide M Mapping API functions to SQL functions 9 3 Metadata adding to a time series 7 120 9 52 c
52. 6 5 The ts_cal_pattstartdate function on page 9 8 The ts_close function The ts_close procedure closes the associated time series 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 A 1 for an example of ts_close Related reference The ts_open function on page 9 41 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 IBM Informix TimeSeries Data User s Guide 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 fla
53. Federal Information Processing Standard 173 as referenced by the document Content Standard for Geospatial Metadata Federal Geographic Data Committee June 8 1994 FGDC Metadata Standard IBM Informix TimeSeries Data User s Guide 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 Poe eee gt gt ee eh Se gt Statement continues on next line i Sob Soe eee ee Statement continues from previous line Pee aas oo ae sao gt lt Statement ends SELEG Ni enerireaiersts SELECT Required item WA ESE EE DEES TO Optional item LOCAL LOCAL i ALL PEE ESA Aliseuocte Pace pala item with H lt DISTINCT lt nly one item must be oes UNIQUE i present i UNIQUE E E jess Optional items with choice FOR UPDATE FOR UPDATE are shown below the main FOR READ ONLY line one of which you might FOR READ ONLY specify The values below the main NEXT NEXT fo aes ee eee ee ERE TORSE line are optional one of PRIOR which you might specify If PRIOR PREVIOUS you do not specify an item PREVIOUS the value above the
54. 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 GetCalendar function on page 7 41 GetInterval function on page 7 46 GetOrigin function on page 7 55 TSCreate function on page 7 97 TSCreatelrr function on page 7 9 GetContainerName function The GetContainerName function returns the name of the container for the given time series Syntax GetContainerName ts TimeSeries returns lIvarchar S The time series from which to obtain the container name fa Chapter 7 Time series SQL routines 7 43 Description The API equivalent of this function is ts_get_containername Returns The name 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 select GetContainerName activity data from activity_stocks where stock_id 600 Related reference The ts_get_containername function on page 9 27 GetElem function 7 44 Th
55. 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_get_flags function on page 9 27 The ts_last_elem function The ts_last_elem Q 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 IBM Informix TimeSeries Data User s Guide STATUS A pointer to a mi_integer value See The ts_hide_elem function on age 9 3 age 9 31 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 fe
56. 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 Chapter 7 Time series SQL routines 7 41 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 GetClosestElem function GetInterval function on page 7 4 GetOrigin function on page 7 5 TSCreate function on page 7 9 GetCalendarName function TSCreatelrr function on page 7 9 GetCalendarName function The GetCalendarName function returns the name of the calendar used by the given time series Syntax GetCalendarName ts TimeSeries returns lvarchar 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 GetCalendar function on page 7 41 GetClosestElem function 7 42 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
57. 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 low real final real vol real l 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 ints broker int buyer int seller int Related concepts TimeSeries data type on page 2 5 TimeSeries data type technical overview on page 1 5 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 can create the table in a dbspace that uses non default page size 3 6 IBM Informix TimeSeries Data User s Guide You cannot use delimited identifiers for table or column names The 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_name Examples The following example creates a table called daily_stocks that contains a tim
58. 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 IBM Informix TimeSeries Data User s Guide Qa mp 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 vertical 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
59. TS_SCAN_SKIP_HIDDEN 4 Used by ts_begin_scan to tell ts_next not to return hidden elements 7 80 IBM Informix TimeSeries Data User s Guide Returns Multiple rows containing a time stamp and the other columns of the time series elements Examples 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 Example 4 Display specific data as multiple fields within a single column The following statement selects the time and energy readings from a time ser
60. The Transpose function converts time series data for processing in a tabular format Syntax Transpose 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 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 Chapter 7 Time series SQL routines 7 79 flags Determines how a scan should work on the returned set query A string containing 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 returned by the query string version of the Transpose function col_name If col_name is not NULL only the column specified with this parameter will be 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
61. 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 Related concepts Time series data storage on page 1 8 E 5 R for data storage on page 1 10 Related reference TSContainerCreate procedure on page 7 86 TSContainerDestroy procedure on page 7 87 TSContainerSetPool procedure on page 7 93 TSContainerPurge function on page 7 91 Monitoring time series containers You can view information about the size and capacity of time series containers If you monitor the containers over a period of time you can predict how quickly containers fill and how much data fits into each container To view specific information about a container run one of the following functions specifying the container name The TSContainerTotalPages function returns the number of pages allocated to the container The TSContainerTotalUsed function returns the number of pages containing time series data The TSContainerPctUsed function returns what percent of the container is full IBM Informix TimeSeries Data User s Guide e The TSContainerNElems function returns the number of time series data elements stored in the container If you specify NULL instead of a container name th
62. The name of the calendar Description The equivalent API function is ts_cal_startdate Returns The start date of the given calendar Example The following example returns the start dates of all the calendars in the CalendarTable table select c_name CalStartDate c_name from CalendarTable Related reference The CalPattStartDate function on page 5 2 The ts_cal_startdate function on page 9 11 The 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 If the two calendars have different sizes of interval units the resultant calendar has the smaller of the two intervals Chapter 6 Calendar routines 6 5 6 6 Returns A calendar that is the result of two others combined with the OR operator Example The following OrOp function returns the union of an hourly calendar with a daily calendar having 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
63. The ts_elem function on page 9 20 rs The ts_last_elem function on page 9 3 z he 3 o F 3 oy oO O Q Q X function on page 9 The ts_make_elem_with_buf function on page 9 37 The ts_next function on page 9 39 Chapter 9 Time series API routines 9 53 9 54 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 daily 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
64. TimeSeries subtype and regularity If no matching container exists a new container is created Load data into an existing time series 3 16 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 Loading data with the IBM Informix TimeSeries Plug in for Data Studio Use the IBM Informix TimeSeries Plug in for Data Studio to load data from a file into a time series You must have the following prerequisites before you load data 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 e Primary key values in your table and a time series defined for each row If 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 e A file of time based data that you want to load into the database e Connectivity information for the Informix database server that contains the time series table The TimeSeries plug in includes a cheat sheet that provides detailed instructions for loading data To create load job for time series data in Data Studio Choose Help gt Cheat Sheets expand the TimeSeries Data category choose Load time series data and click OK Follow the instructions in the cheat
65. a connection to an IBM Informix database or database server using a property list see the IBM Informix JDBC Driver Programmer s Guide 8 18 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 raise 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 appli
66. calendar pattern Syntax NotOp cal_patt CalendarPattern returns CalendarPattern cal_patt The calendar pattern to convert IBM Informix TimeSeries Data User s Guide Returns The inverted calendar pattern 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 workweek_day expression 1 on 5 off 1 on day The OrOp function The OrOp function returns the union of the two calendar patterns Syntax OrOp 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 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 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_d
67. 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 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 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 Chapter 2 Data types and system tables 2 7 Table 2 4 The CalendarTable table continued Column name Data type Description c_id SERIAL Internal The serial number of the calendar Related concepts Calendar data type on page 2 3 Related tasks Defining a calendar on page 3 5 Related reference Predefined calendars on page 3 5 TSInstanceTable table 2 8 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
68. class diagram shows the time series and JDBC 2 0 interfaces that the class implements SQLData is a JDBC interface and IfmxCalendarUDT is a time series interface described in The IfmxCalendar class on page 8 9 SQLData lfmxCalendarPatternUDT java sql com informix timeseries IfmxCalendar com informix timeseries GregorianCalendar IfmxCalendarPattern com informix timeseries Key gt Interface implements IfxCalendar com informix timeseries Bend Figure 8 5 Calendar class diagram The IfmxTimeSeries class The IfmxTimeSeries class represents the TimeSeries SQL type in Java The IfmxTimeSeries class is used to read and write TimeSeries types to and from a database This class is based on the JDBC ResultSet interface and can be thought of as a set of rows Each individual row has metadata associated with it that provides information such as the column name type and size 8 4 IBM Informix TimeSeries Data User s Guide The IfmxTimeSeries class contains most of the time series methods The class also implements the methods in the ResultSet interface The class diagram shows the time series and JDBC 2 0 interfaces that the class implements SQLData and ResultSet_are JDBC interfaces and IfmxTimeSeriesUDT is a time series interface described in The IfmxTimeSeries class on page 8 11 SQLData ResultSet IfmxTimeSeriesUDT java sql java sql com informix timeseries
69. col The name or number of a column within a TimeSeries row 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 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 Jan 10 2011 i
70. column and do not appear in the TSInstanceTable table Table 2 5 The columns in the TSInstanceTable table Column name Data type Description id SERIAL The serial number of the time series This is the primary key for the table You can use the Instanceld function to return this number see Instanceld function on page 7 62 cal_id INTEGER The identification of the CalendarTable row for the time series flags SMALLINT Stores various flags for the time series including one that indicates whether the time series is regular or irregular 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 and users do not modify it directly nor should they normally be required to view it Rows in this table are automatically inserted or deleted when large time series are created or destroyed IBM Informix TimeSeries Data User s Guide Related reference TSContainerCreate procedure on page 7 86 TSContainerSetPool procedure on page 7 93 TSContainerDestroy procedure on page 7 87 TSContainerTable table The TSContainerTable table has one row for each container Table 2 6 The columns in the TSContainerTable
71. current time series element as a byte value getConcurrency int getConcurrency Gets the concurrency type for this IfmxTimeSeries object getDate java sql Date getDate int columnIndex Gets the value in the current time series element as a java sql date value getDate java sql Date getDate java lang String Gets the value in the current time series columnName element as a java sql date value getDouble double getDouble int columnIndex Gets the value in the current time series element as a double value getDouble double getDouble java lang String Gets the value in the current time series columnName element as a double value getFloat float getFloat int columnIndex Gets the value in the current time series element as a float value etFloat float getFloat java lang String columnName Gets the value in the current time series 8 8 J 8 8 element as a float value getInt int getInt int columnIndex Gets the value in the current time series element as an int value getInt int getInt java lang String columnName Gets the value in the current time series element as an int value Chapter 8 Time series Java class library 8 13 Table 8 2 Public methods continued Method Signature Description getLong int getLong int columnIndex Gets the value in the current time series element as a long value getLong int getLong java lang String columnName Gets the value in the current
72. date and time at which to start aggregation end optional The date and time at which to end aggregation Chapter 7 Time series SQL routines 7 9 7 10 Description The AggegateRange function converts the input section of a time series to a row of aggregate values The agg_express expressions operate on a column of the input time series specified as colname or colnumber for example high or 1 An error is raised if the MIN MAX MEDIAN SUM or AVG expression is used on a non numeric column The Nth expression returns the value of a column for the specified aggregation period using the following syntax Nth col n col The name or number of a column within a TimeSeries row 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 flags argument determines how data points in the off periods of c
73. 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 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 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 put into the time series See Creating al for more information about metadata multiset_rows A multiset of rows used to populate the time series The type of these rows must be the same as the subtype of the time series Description If TSCreatelrr is called with the metadata argument then metadata is saved in the time series See Creating a time series with the TSCreate or TSCreatelrr function on page for a description of how to use this function IBM Informix TimeSeries Data User s Guide The following example creates an empty irregular time series using TSCreatelIrr select TSCreateIrr daycal 2011 01 05 00 00 00 00000 20 0 NULL set_data TimeSeries stock_trade from activity_load_tab where stock_id 600 The following example creates a populated irregular time seri
74. example high or 1 select stock_name Apply TSRunningMed high 10 stock_data TimeSeries stock_bar TimeSeries one_real from daily_stocks IBM Informix TimeSeries Data User s Guide Related reference TSRunningCor function on page 7 108 Apply function on page 7 11 TSRunningAvg function on page 7 107 TSRunningSum function TSRunningVar function on page 7 112 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 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 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
75. file collection application Create a virtual table and load data that is in standard relational format Use the BulkLoad function The file must be specifically formatted according to the BulkLoad function requirements Alter or add one or more Use the InsElem function to insert an element or the PutElem elements to edit incorrect function to update an element Use the InsSet function to insert data or insert missing multiple elements or the PutSet function to update multiple values elements Create a virtual table and use a standard SQL INSERT statement You can add or update elements Chapter 1 Informix TimeSeries solution 1 11 Related concepts Chapter 4 Virtual tables for time series data on page 4 1 Related tasks Loading data from a file into a virtual table on page 3 17 Related reference Load data with BulkLoad on page 3 17 Load small amounts of data with functions on page 3 18 Planning for accessing time series data You use SQL functions Java classes and methods and C API routines to access and manipulate time series data 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 to perform the following types of operations to access or manipulate time series data Manipulate individual elements or sets of elements Perform statistical and arithmetic calculati
76. 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 Chapter 7 Time series SQL routines 7 67 UPDATE ts_data SET meter_data Nul1Cleanup meter_data NULL 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 19 Plus function The Plus function adds two time series together It is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Mod Pow and Times Related reference Binary arithmetic functions on page 7 21 Positive function ao The Positive function returns the argument It is bound to the unary operator It 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 Unary arithmetic functions on page 7 116 Pow function The Pow function raises the first argument to the power of the second It is one of the binary arithmetic functions that work on time series The others are Atan Divide Minus Mod Plus and Times Related reference Binary arithmetic functions on page 7 21 PutElem function The PutElem function adds an element to a time series at the timepoint indicated in the supplied row type
77. function on page 7 5 The ts_elem function on page 9 20 GetIndex function on page 7 46 GetStamp function on page 7 5 6 UpdElem function on page 7 11 The ts_first_elem function on page 9 23 GetFirstElem function The GetFirstElem function returns the first element in a time series Syntax GetFirstElem 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 5 lues on page 7 5 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 no 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 Chapter 7 Time series SQL routines 7 45 select GetFirstElem activity_data from activity_stocks where stock_id 600 Related reference GetLastElem function on page 7 47 The ts_first_elem function on page 9 23 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 Descripti
78. in The flags argument falues on page 7 5 lues on page 7 5 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 GetNextValid function 7 52 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 IBM Informix TimeSeries Data User s Guide S The source time series tstamp The time stamp of the entry flags Valid values for the flags argument are described in The flags argument falues on page 7 5 lues on page 7 5 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
79. in a place specified by a time stamp ts_put_elem ts_put_elem_no_dups Append an element ts_put_last_elem regular only Put an element in a place specified by an offset ts_put_nth_elem regular only Modify metadata Update metadata ts_update_metadata Convert between an index and a time stamp Convert time stamp to index ts_index regular only Convert index to time stamp ts_time regular only Transform an element Create an element from an array of values and nulls ts_make_elem ts_make_elem_rowdesc ts_make_elem_with_buf Convert an MI_ROW value to an element ts_row_to_elem Convert an element to an MI_ROW value ts_elem_to_row Free memory from a time series element created by ts_make_elem or ts_row_to_elem ts_free_elem Extract column data from an element Get a column from an element by name Get a column from an element by number ts_get_col_by_name ts_get_col_by_number Pull columns from an element into values and nulls arrays ts_get_all_cols 9 4 IBM Informix TimeSeries Data User s Guide Task type Description Routine name Create and perform calculations with time stamps Compare two time stamps ts_datetime_cmp Get fields from a time stamp ts_get_stamp_fields Create a time stamp ts_make_stamp Get information about element data Calculate the number o
80. in must match the subtype of the time series Hidden elements cannot be updated The flags argument values The value of the flags argument is the sum of the desired flag values from the following table Flag Value Meaning TSOPEN_RDWRITE 0 Default Indicates that the time series can be read and written to IBM Informix TimeSeries Data User s Guide Flag Value Meaning TSOPEN_READ_HIDDEN 1 Indicates that hidden elements should be treated as if they are not hidden TSOPEN_WRITE_HIDDEN 2 Allows hidden elements to be written to without first revealing them The element remains hidden afterward TSOPEN_WRITE_AND_HIDE 4 Causes any elements written to a time series also to be marked as hidden TSWRITE_AND_REVEAL 8 Reveals any hidden element written to TSOPEN_NO_NULLS 32 TS_PUTELEM_NO_DUPS 64 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 Determines whether the PutSet function adds elements using the PutElem function default or the PutElemNodups function see PutElem function on page 7 68 and PutElemNoDups function on page 7 70 If this flag is set the PutSet function uses PutElemNoDups Returns A modified time series that includes the new values E
81. is one of the binary arithmetic functions that work on time series The others are Atan2 Minus Mod Plus Pow and Times w o 2 er a e a Binary arithmetic functions on page 7 21 ElemlsHidden function The ElemIsHidden function determines if an element is hidden Syntax ElemIsHidden ts TimeSeries offset integer returns Boolean Chapter 7 Time series SQL routines 7 39 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 element is hidden and FALSE if it is not Related reference ElemIsNull function FindHidden function on page 7 41 ElemlsNull function The ElemIsNull function determines if an element contains no data Syntax ElemIsNul1 ts TimeSeries offset integer returns Boolean ElemIsNul1 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 h
82. 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 mapped 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 a
83. not be freed by the caller it is valid until the next element is fetched from the descriptor Related reference GetLastValid function on page 7 49 The ts_previous_valid function on page 9 43 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 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 IBM Informix TimeSeries Data User s Guide Related reference The ts_elem_to_row function on page 9 22 The ts_free_elem procedure on page 9 24 The ts_ins_elem function on page 9 33 The ts
84. null value The virtual table now contains two 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 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 already 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
85. of PutElemNoDups is ts_put_elem_no_dups Returns A modified time series that includes the new values IBM Informix TimeSeries Data User s Guide Example The following example updates a time 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 11 Related reference PutElem function on page 7 68 The ts_put_elem_no_dups function on page 9 45 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 The default is 6 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
86. of the last time stamp in the time series Example 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 GetLastValid function on page 7 49 The ts_next_valid function on page 9 40 GetNthElem function The GetNthElem function extracts the entry at a particular offset or position in a time series Syntax GetNthElem ts TimeSeries N integer flags integer default 0 returns row cH S The source time series Chapter 7 Time series SQL routines 7 53 7 54 N The offset or position of an entry in the time series This value cannot be less than 0 flags Valid values for the flags argument are described in The flags argument falues on page 7 5 lues on page 7 5 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 al
87. 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 The following table lists the properties of a time series Chapter 1 Informix TimeSeries solution 1 9 1 10 Table 1 6 Properties of a time series Time series property Description How to define Timepoint size How long a timepoint lasts Define a calendar pattern When timepoints are valid The times when elements can Define a calendar pattern be accepted 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 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 Related tasks Defining a calendar on page 3 5 Related reference Create a time series column on page 3 6 Create a time series on page 3 11 Planning for data storage Time series data is stored in containers within dbsp
88. 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 defined by the Microsoft OLE DB standards You can override that default by setting an Informix environment variable or registry entry such as DBDATE 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 1
89. 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 xviii 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 queries 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 Moni
90. reference The ts_datetime_cmp function on page 9 19 The ts_get_col_by_number function The ts_get_col_by_number function 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 IBM Informix TimeSeries Data User s Guide tselem An element to get column data from 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 19 The ts_get_containername function The ts_get_containername function gets the container name of the given time series Syntax mi_string ts_get_containernam
91. 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 given 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 combined by the AND operator Copyright IBM Corp 2006 2012 5 1 Example The first AndOp statement returns the intersection of two daily calendar patterns and the second AndOp statement returns the 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
92. run the TSCreateVirtualTab procedure To create a virtual table based on the results of an expression that is performed on a time series run the TSCreateExpressionVirtualTab procedure Related reference TSCreateVirtualTab procedure TSCreateExpressionVirtualTab procedure on page 4 8 TSCreateVirtualTab procedure 4 4 The TSCreateVirtualTab procedure creates a virtual table based on a table containing a TimeSeries column Syntax TSCreateVirtualTab VirtualTableName varchar BaseTableName lvarchar NewTimeSeries 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 NewTimeSeries optional The definition of the new time series to create IBM Informix TimeSeries Data User s Guide TSVTMode optional Sets the virtual table mode as described in The TSVTMode parameter on page 4 11 age 4 11 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 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 virt
93. 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 if any of the inputs are irregular Related reference ApplyBinaryTsOp function on page 7 16 Binary arithmetic functions on page 7 21 ApplyUnaryTsOp function The ApplyUnaryTsOp function applies a unary arithmetic function to a time series Chapter 7 Time series SQL routines 7 19 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 7 116 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 high NULL datetime year to fraction 5 NULL datetime year to fraction 5 stock_da
94. 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 denotes 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 2012 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 dec
95. 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 client 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 API data structures 9 2 These topics describe the data structures used by time series API routines 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 v
96. 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 the 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 es
97. specify null values for columns that have NOT NULL constraints The following examples illustrate some of the settings for the TSVTMode parameter 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 created in the examples have 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 Example 1 Setting the TSVTMode parameter to 0 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
98. 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 4 18 IBM Informix TimeSeries Data User s Guide 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 The insertion of data through virtual tables on page 4 3 Related reference PutElemNoDups function on page 7 71 PutElem function on page 7 68 Drop a virtual table You use the DROP statement to destroy a virtual table in the same way as you destroy any other database table When you drop a virtual table the underlying base table is una
99. that represents the number of elements that fit the criteria Examples The examples are based on the following time series Chapter 7 Time series SQL routines 7 33 7 34 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 smQ tsspaceO sm_row 0 0 Routine executed CREATE ROW TYPE sm_row 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 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 9 753 8 0 9 0 123 0 NULL 0 NULL 0 2 400 3 1 row s inserted Example 1 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 2 1 row s retrieved Two elements contain null values for the energy column Example 2 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
100. the current row using the connection set by the setConnection method The setConnection method is described in updateShort void updateShort int columnIndex short x Updates the current time series element with the given short object updateShort void updateShort java lang String Updates the current time series element columnName short x with the given short object updateString void updateString int columnIndex Updates the current time series element java lang String x with the given String object updateString void updateString java lang String Updates the current time series element columnName java lang String x with the given String object updateTime void updateTime int columnIndex Updates the current time series element java sql Time x with the given Time object updateTime void updateTime java lang String Updates the current time series element columnName java sql Time x with the given Time object updateTimestamp void updateTimestamp int columnIndex Updates the current time series element java sql Timestamp x with the given Timestamp object updateTimestamp void updateTimestamp java lang String Updates the current time series element columnName java sql Timestamp x with the given Timestamp object wasNull boolean wasNull Checks whether the current element is NULL writeSQL void writeSQL java sql Output stream Called automatically by the JDBC driver when a setObject method is called to insert an IfmxTimeSeries object
101. the elem argument is NULL a new time series element is returned Chapter 9 Time series API routines 9 37 9 38 Related reference The ts_put_last_elem function on page 9 46 The ts_upd_elem function on page 9 52 The ts_make_stamp function 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 The ts_get_stamp_fields procedure on page 9 29 The ts_nelems function The ts_nelems function returns the number of elements in the time series Syntax mi_integer ts_nelems ts_tsdesc
102. 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 Chapter 9 Time series API routines 9 47 9 48 Related reference PutTimeSeries function on page 7 73 The ts_put_elem function on page 9 44 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_reveal_elem ts_tsdesc xtsdesc 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 58 The ts_hide_elem function o
103. the values of columns in an existing row that are not part of the primary key To prevent updating a non primary key column set it to NULL in the INSERT statement e Update the value of all the columns in an existing row that are not part of the primary key Update columns that allow null values to NULL The TSVTMode parameter on page 4 11 Container name and TimeSeries subtype names extended to 128 bytes The maximum length of container names and TimeSeries subtype names is 128 bytes Previously the maximum length of the names was 18 bytes TSContainerCreate procedure on page 7 86 Creating a TimeSeries subtype on page 3 6 Time series tables and containers can use non default page sizes You can now store time series tables and containers in dbspaces that use non default page sizes Previously all time series tables and containers had to be stored in dbspaces with the default page size TSContainerCreate procedure on page 7 8 Create the database table on page 3 6 Faster deleting of time series data When you delete time series data the performance is faster than in previous releases You can delete large amounts of time series data in less time xii IBM Informix TimeSeries Data User s Guide Table 3 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC3 continued Overview Reference Informix TimeSeries Plug in for OAT The IBM Informi
104. 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 dl 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 Apply function on page 7 11 Union function on page 7 117 IsRegular function 7 64 The IsRegular function tells whether a given 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 IBM Informix TimeSeries Data User s Guide Lag function select stock_id from activity_stocks where not IsRegular activity_data Related reference The ts_get_flags function on page 9 27 The Lag function creates a new regular time series in which the data values lag the source time series by a fixed offset 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 element
105. time series columns from the base table If the TSVTMode parameter includes the value 2 null time series elements appear as null values in the virtual table Hidden elements also appear as null values If the TSVTMode parameter does not include the value 2 null time series elements do not appear in the virtual table IBM Informix TimeSeries Data User s Guide If the TSVTMode parameter includes the value 512 hidden time series elements appear 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 The following query shows that the inst_vt0 table does not contain the hidden element for 2011
106. 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 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 representing electricity usage Time series table The following columns e 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 Metadata No metadata Amount of storage space Approximately 1 MB 8640 timepoints for each of the 28 rows Copyright IBM Corp 2006 2012 3 1 Table 3 1 Time series properties used in this example continued Time series property Definition 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 usa
107. to a ts_typeinfo structure This structure is defined as follows IBM Informix TimeSeries Data User s Guide 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 name of the column ts_typeinfo Related reference 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 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_
108. 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 elements 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 should work on the returned set The value of flags is the sum of the desired flag values from the following table Flag Value Meaning TS_SCAN_HIDDEN 512 Return hidden elements marked by HideElem see HideElem function on page 7 58 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
109. 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 IBM Informix TimeSeries Data User s Guide N The offset indicating where the element to add should be placed Offsets start at 0 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 32 The ts_elem function on page 9 20 The ts_get_ts function on page 9 30 The ts_make_elem function on page 9 36 The ts_next function on page 9 39 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_tsdesc 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
110. 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 already 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
111. 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 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 Chapter 3 Create and manage a time series 3 13 3 14 Related reference Create a time series with its input function You can use the time series input function to create a time series with the INSERT statement The syntax for using INSERT to create a time series and insert data is insert into table_name values coll_value col2_value parameter_input_string s The parameter_input_string value contains the time series information All data types have an associated input function that is automatically invoked when ASCII data is inserted into the column In the case of the TimeSeries data type the input has several pieces of data 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 and the initial time series data The format for the parameter input string is paramname value paramname value data_element The values are specific to the parameters and each has a different format The following table indicates the value associated with each paramete
112. used to convert a regular time series to an irregular one Important Converting an irregular time series to regular often requires aggregation information which can be provided using 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 The flags argument values When the source time series opens your setting of the flags argument is combined using the AND operator with the TSOPEN_READ_HIDDEN value The returned time series is opened with your setting of the flags argument combined using the AND operator with TSOPEN_WRITE_AND_HIDE TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_HIDDEN The value of flags is the sum of the desired flag values from the following table Flag Value Meaning TSOPEN_RDWRITE 0 Default Hidden elements are read from the source time series as NULL TSOPEN_READ_HIDDEN 1 Indicates that the elements in the source time series that are hidden are read as if they are not hidden TSOPEN_WRITE_HIDDEN 2 Elements that were hidden in the source time series are hidden in the resulting time series TSOPEN_WRITE_AND_HIDE 4 Causes all elements written to a resulting time series also to be marked as hidden TSWRITE_AND_REVEAL 8 Reveals all elements written to the resulting time series 7 74 IBM Informix TimeSeries Data User s Gui
113. 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 table 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 on the effect of the NewTimeSeries parameter see TSCreateVirtualTab procedure on page 4 4 Table 4 6 TSVTMode parameter settings that affect which columns are updated in the base table Columns to update TSVTMode parameter setting columns Update only the TimeSeries column You must specify valid 0 but not necessarily accurate values for non primary key Update only the TimeSeries column You can specify NULL 16 as the values for non primary key columns NULL constraints to null values key columns You can set columns that do not have NOT Update the TimeSeries column and all other non primary 32 key columns that do not have null values in the INSERT statement Update the TimeSeries column and all other non primary 64 64 16 Chapter 4 Virtual tables for time series data 4 13 4 14 Table 4 6 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 32 16 key columns that have NOT NULL constraints You can
114. 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 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 IBM Informix TimeSeries Data User s Guide The ts_col_name parameter is replaced by the column name specified by the TSCreateExpressionVirtualTab procedure in this case readings The ts_begin_time and ts_end_time parameters are replaced when the virtual table is queried 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
115. 000 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 of January 2011 The second argument is a SUM operator that sums the daily electricity usage by zipcode The resulting table contains a row for each zipcode Each row has a time series that contains the sum of the electricity used by customers who live in that zipcode for each day in January 2011 Related reference AgeregateBy function on page 7 6 TSRowNametToList function 7 104 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 IBM Informix TimeSeries Data User s Guide ts_row The time series to act 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 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
116. 0000 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 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 51 The ts_tstamp_minus function 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 as described next result The resulting date IBM Informix TimeSeries Data User s Guide 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 Returns The time stamp at the specified
117. 01 01 03 45 00 00000 28 14 0000000000 2011 01 01 04 00 00 00000 19 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 4 GetNthElem function on page 7 53 TSColNameToList function on page 7 8 TSColNumToList function on page 7 8 TSRowNameToList function on page 7 10 Ol Ol TSRowNumToList function on page 7 10 m aD TSRowToList function on page 7 10 TSSetToList function on page 7 113 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 um n Syntax TSAddPrevious current_value smallfloat returns smallfloat TSAddPrevious current_value double precision returns double precision current_value The current value 7 82 IBM Informix TimeSeries Data User s Guide 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
118. 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 Chapter 4 Virtual tables for time series data 4 17 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 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
119. 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 4 The TSVTMode parameter The TSVTMode parameter configures the behavior and display of the virtual table You use the TSVTMode parameter with the TSCreateVirtualTab procedure to control e How data is updated in the base table when you perform an insert in the virtual table e Whether NULL time series elements appear in a virtual table 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 NULL values can be used in the INSERT statement for columns other than the primary key columns e Whether hidden time series elements appear 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 You use the TSVTMode parameter with the TSCreateExpressionVirtualTab procedure to control e Whether NULL time series elements appear in a virtual table e Whether hidden time series elements
120. 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 2012 A 1 A 2 This example interpolates between values to fill in null elements It assumes that all columns are of 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 fParamP
121. 252 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 2012 ix What s new in TimeSeries data for Informix Version 11 70 This publication includes information about new features and changes in existing functions For a complete list of what s new in this release see the release notes or the information center at http publib boulder ibm com infocenter idshel topic com ibm po doc new_features htm p v117 Table 1 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC5 Overview Reference Count the time series elements that match expression criteria You can count the number of elements in a time series that match the criteria of a simple arithmetic expression by running the CountIf function For example you can count the number of null elements Countlf function on page 7 32 Remove old time series data from containers You can remove the oldest time series data through an end date in one or more containers for multiple time series instances by r
122. 3 Q fed Q o es TSCreatelrr function The TSCreatelrr function creates an empty irregular time series or an irregular time series populated with the given multiset of data The new time series can also have user defined metadata attached to it Syntax TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar multiset_rows multiset returns TimeSeries with handlesnulls Chapter 7 Time series SQL routines 7 99 7 100 TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name lvarchar metadata TimeSeriesMeta returns TimeSeries with handlesnulls TSCreateIrr cal_name lvarchar origin datetime year to fraction 5 threshold integer zero integer nelems integer container_name 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 This 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
123. 41 F findColumn method 8 13 first method 8 13 Flags argument 7 5 getting for a time series 9 27 TS_CREATE IRR 9 16 9 17 TS_SCAN_EXACT_START 7 5 7 17 7 24 7 72 7 73 7 79 9 6 TS_SCAN_HIDDEN 7 5 7 17 7 24 7 31 7 72 7 73 7 79 9 6 TS_SCAN_SKIP_BEGIN 7 5 7 17 7 24 7 72 7 73 7 79 9 6 TS_SCAN_SKIP_END 7 5 7 17 7 24 7 72 7 73 7 79 9 6 Freeing memory for a time series 9 24 Freeing memory for a time series element 9 24 Function output creating time series with 3 16 G getArray method 8 13 getAsciiStream method 8 13 getBigDecimal method 8 13 getBinaryStream method 8 13 getBlob method 8 13 getBoolean method 8 13 getByte method 8 13 getBytes method 8 13 GetCalendar function 7 41 getCalendar method 8 12 GetCalendarName function 7 42 getCharacterStream method 8 13 getClob method 8 13 getConcurrency method 8 13 GetContainerName function 7 43 getContainerName method 8 12 getCursorName method 8 13 getDate method 8 13 getDouble method 8 13 GetElem function 7 44 getFetchDirection method 8 13 getFetchSize method 8 13 GetFirstElem function 7 45 getFloat method 8 13 GetIndex function 7 46 getInt method 8 7 8 13 GetInterval function 7 47 getInterval method 8 8 getIntervalStr method 8 8 GetLastElem function 7 47 7 48 7 52 GetLastValid function 7 49 getLong method 8 13 GetMetaData function 7 50 getMetaData method 8 13 GetMetaTypeName function 7 50 getName method 8 9 GetNelems function 7 51 getNelems m
124. 5 95 2000000 t2 97 95 5 96 3000000 905 FNM t1 49 25 47 75 48 2500000 t2 48 75 48 48 25 3000000 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 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 you use a query like this select stock_id Clip stock_data tl t2 from daily_stocks Create the virtual table The following statement uses the TSCreateVirtualTab function to create a virtual table called daily_stocks_no_ts based on daily_stocks execute procedure TSCreateVirtualTab daily_stocks_no_ts daily_stocks 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 virtual table daily_stocks_no_ts looks lik
125. 7 yntax ClipCount ts TimeSeries begin_stamp datetime year to fraction 5 num_stamps integer flags integer default 0 returns TimeSeries S 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 flags optional A flag specifying how to determine the resulting time series origin and whether to copy hidden elements to the resulting time series See The flags argument values on page 7 30 Chapter 7 Time series SQL routines 7 29 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 stamp as the begin point of the range If the begin point is NULL the origin of the time series is used The flags argument values The flags argument is an optional parameter that works in the same way as the flags argument of the Clip function See The flags argument values on page 7 2 for a description Returns A new time series containing 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 Example T
126. 70 xC3 continued Overview Reference Time series data in the stores_demo database You can use the new time series tables in the stores_demo database to experiment with time series data by running SQL queries and time series routines The stores_demo database has three new tables that contain smart meter time series data Sample smart meter data on page 1 15 Predefined calendars for time series You can use one of the seven predefined calendars when you create a time series instead of creating your own calendar The calendars start at the beginning of 2011 The calendar patterns have interval durations of one minute 15 minutes 30 minutes one hour one day one week and one month Predefined calendars on page 3 5 Run the Transpose function in table expressions You can use the Transpose function in table expressions to return time series data in a tabular format that is easy to read Transpose function on page 7 7 Easier time series data updates from virtual tables When you update time series data from a virtual table by default only the primary key is used to find the row to update You do not need to provide accurate values for columns that are not part of the primary key Previously all columns except the TimeSeries subtype column were used by the virtual table to identify the row to update When you create virtual tables you can configure the update behavior e Update
127. 9 9 40 Related reference The TS_ELEM_NULL macro on page 9 22 The ts_put_elem function on page 9 44 The ts_put_elem_no_dups function on page 9 45 The ts_put_last_elem function on page 9 46 The ts_put_nth_elem function on page 9 46 The ts_upd_elem function on page 9 52 The ts_next_valid function 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 31 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 following 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_ne
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129. 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 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 Administration IBM OpenAdmin Tool for Informix Application 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 serie
130. 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 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 m
131. IME YEAR TO FRACTION 5 value DECIMAL 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 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 5 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 5 Related tasks Creating a TimeSeries subtype on page 3 6 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 reg
132. It reverses the effect of HideRange Syntax RevealRange 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 Returns The modified time series IBM Informix TimeSeries Data User s Guide Related reference HideRange function on page 7 59 Round function The Round function rounds a time series to the nearest whole number It 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 Tan Related reference Unary arithmetic functions on page 7 116 SetContainerName function 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 t
133. L See The ts_hide_elem function on page 9 31 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 GetNthElem function on page 7 53 The ts_elem function on page 9 20 The TS_ELEM_HIDDEN macro on page 9 21 The TS_ELEM_NULL macro on page 9 22 The ts_index function on page 9 32 The ts_last_elem function on page 9 3 4 The ts_open function The ts_open function opens a time series Chapter 9 Time series API routines 9 41 9 42 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 argument 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 re
134. L 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 or ORDER BY clauses e FRAGMENT BY clause e PRIMARY KEY clause Chapter 1 Informix TimeSeries solution 1 13 1 14 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 Replication of time series data You have limited options for replicating time series data You can replicate time series data only between a High Availability Data Replication HDR primary server and a read only secondary server Because some time series calendar and container information is kept in memory you must stop replication before you can drop and then recreate your calendar or container definitions with the same names but different definitions You cannot replicate time series data with the following types of servers and utilities e HDR secondary servers that allow updates e Remote stand alone secondary servers e Shared disk secondary servers e Enterprise Replication e Change Data Capture API Time series global language 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
135. List function on page 7 105 TSSetToList function on page 7 113 TSRowToList function on page 7 106 m TSColINumToList function 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 ts 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 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 Chapter 7 Time series SQL routines 7 85 Related reference TSColNameToList function on page 7 84 Transpose function on page 7 79 TSRowNameToList function on page 7 104 TSRowNumToList function on page 7 105 TSSetToList function on page 7 113 TSRowToList function on page 7 106 TSContainerCreate procedure 7 86 The TSContainerCreate procedure creates a new container with the specified name for the specified TimeSeri
136. NULL pages 2029 1 row s retrieved TSContainerUsage function 7 96 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 returns integer bigint integer container_name The name of an existing container Can be NULL Description Use the TSContainerUsage function to monitor how full the specified container is If you specify NULL as the container name information about all containers for the database is returned You can use the information from this function to determine how quickly your containers are filling and whether you need to allocate additional storage space Returns The number of pages containing time series data in the pages column the number of elements in the slots column and the number of pages allocated to the container in the total column for the specified container or for all containers IBM Informix TimeSeries Data User s Guide Example The following statement returns the information for the container 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 using 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 additional time series elements T
137. Related reference TSColNameToList function on page 7 84 TSColINumToList function on page 7 85 Transpose function on page 7 79 TSRowNumToList function TSRow ToList function on page 7 106 TSSetToList function on page 7 113 TSRowNumtfToList function 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 Chapter 7 Time series SQL routines 7 105 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 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 TSRowNameToList function on page 7 104 TSSetToList function on page 7 113 TSRowToList function 7 106 The TSRowToList func
138. SContainerCreate procedure 2 Add each container to the container pool by using the TSContainerSetPool 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 Chapter 3 Create and manage a time series 3 9 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 The following statements create three containers for the TimeSeries subtype smartmeter_row EXECUTE PROCEDURE TSContainerCreate ctn_sm0 tsspace0 smartmeter_row 0 0 EXECUTE PROCEDURE TSContainerCreate ctn_sm1 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 argume
139. Series Data User s Guide 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 The value previously saved The first time TSPrevious is called it returns NULL Example See the example for the TSCmp function on page 7 83 Related reference Apply function on page 7 11 TSAddPrevious function on page 7 8 TSCmp function on page 7 83 TSDecay function on page 7 101 TSRunningAve function on page 7 107 TSRunningSum function on page 7 111 TSRollup function The TSRollup function aggregates time series values by time for multiple rows in the table Syntax TSRollup ts TimeSeries agg_express lvarchar RETURNS TimeSeries agg_ express A comma separated list of the SOL aggregate operators 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 entered for each timestamp Each operator requires an argument that is the name of the column in a Tim
140. Table 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 will not be replaced by the predefined calendar The following table contains the properties of predefined calendars Table 3 2 Predefined calendars Calendar name Interval duration Start date and time ts_1min Once a minute 2011 01 01 00 00 00 00000 ts_15min Once every 15 minutes 2011 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 Chapter 3 Create and manage a time series Table 3 2 Predefined calendars continued Calendar name Interval duration Start date and time ts_1month Once a month 2011 01 01 00 00 00 00000 Related concepts CalendarTable table on page 2 7 Create a time series column To create a time series column Related concepts for creating a time series on 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
141. Time series SQL routines by task type continued Task type Description Routine name Perform an arithmetic Add two time series together Plus eae Subtract one time series from another Minus Multiply one time series by another Times Divide one time series by another Divide Raise the first argument to the power of the second Pow Get the absolute value Abs Exponentiate the time series Exp Get the natural logarithm of a time series Logn Get the modulus or remainder of a division of one time Mod series by another Negate a time series Negate Return the argument and the argument is bound to the Positive unary operator Round the time series to the nearest whole number Round Get the square root of the time series Sqrt Get the cosine of the time series Cos Get the sine of the time series Sin Get the tangent of the time series Tan Get the arc cosine of the time series Acos Get the arc sine of the time series Asin Get the arc tangent of the time series Atan Get the arc tangent for two time series Atan2 Perform an arithmetic Apply a binary function to a pair of time series or to a ApplyBinaryTsOp operation on one or time series and a compatible row type or number ea Apply a unary function to a time series ApplyUnaryTsOp Apply another function to a set of time series ApplyOpToTsSet Aggregate time series Aggregate values in a time series from a single row Aggregat
142. _SCAN_NO_NULLS 32 0x20 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 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 page A 1 for an example of the ts_begin_scan function Chapter 9 Time series API routines 9 7 Related reference HideElem function on page 7 58 The ts_current_offset function on page 9 18 The ts_current_timestamp function on page 9 19 The ts_end_scan procedure on page 9 23 The ts_next function on page 9 39 The ts_open function on page 9 41 The ts_first_elem function on page 9 23 The ts_cal_index function The ts_cal_index 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 b
143. _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 xresult void xvalues 2 mi_boolean nulls 2 mi_real high low mi_real ns mi_integer len MI_CONNECTION conn mi_integer rc 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 IBM Informix TimeSeries Data User s Guide 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
144. _elem function on page 9 20 The ts_make_elem_with_buf function The ts_put_elem function on page 9 44 The ts_put_elem_no_dups function on page 9 45 The ts_put_last_elem function on page 9 46 The ts_put_nth_elem function on page 9 4 D The ts_row_to_elem function on page 9 48 The ts_upd_elem function on page 9 52 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 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
145. _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 begin_stamp The begin point of the range See Clip function on page 7 26 for more detail about range specifications Chapter 7 Time series SQL routines 7 11 7 12 end_stamp The end point of the range See Clip function on page 7 26 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
146. _offset function 9 18 ts_current_timestamp function 9 19 ts_datetime_cmp function 9 19 ts_del_elem function 9 20 ts_elem function 9 20 TS_ELEM_HIDDEN macro 9 21 TS_ELEM_NULL macro 9 22 ts_elem_to_row 9 22 ts_end_scan procedure 9 23 ts_first_elem function 9 23 ts_free procedure 9 24 ts_free_elem procedure 9 24 ts_get_all_cols procedure 9 25 ts_get_calname function 9 25 ts_get_col_by_name function 9 26 ts_get_col_by_number function 9 26 ts_get_containername function 9 27 ts_get_flags function 9 27 ts_get_metadata function 9 28 ts_get_origin function 9 28 ts_get_stamp_fields procedure 9 29 ts_get_threshold function 9 30 ts_get_ts function 9 30 ts_get_typeid function 9 31 ts_hide_elem function 9 31 ts_index function 9 32 ts_ins_elem function 9 33 TS_IS_INCONTAINER macro 9 34 TS_IS TRREGULAR macro 9 34 ts_last_elem function 9 34 ts_last_valid function 9 35 ts_make_elem function 9 36 ts_make_elem_with_buf function 9 37 ts_make_stamp function 9 38 ts_nelems function 9 38 ts_next function 9 39 ts_next_valid function 9 40 ts_nth_elem function 9 41 ts_open function 9 41 ts_previous_valid function 9 43 ts_put_elem function 9 44 ts_put_elem_no_dups function 9 45 ts_put_last_elem function 9 46 ts_put_nth_elem function 9 46 ts_put_ts function 9 47 ts_reveal_elem function 9 48 ts_row to_elem function 9 48 TS_SCAN_EXACT_END flag 9 6 TS_SCAN_EXACT_START flag 7 5 7 17 7 24 7 72 7 73 7 79 9 6 Index X 7 TS_SCAN_HIDDEN flag 7 5 7 17 7
147. _stocks set activity_data PutTimeSeries activity_data calendar daycal irregular TimeSeries stock_trade where stock_id 600 Related reference n AgeregateBy function on page 7 6 PutSet function on page 7 72 The ts_put_ts function on page 9 47 SetOrigin function on page 7 77 RevealElem function The RevealElem function makes an element at a given time stamp available for a scan It reverses the effect of HideElem Chapter 7 Time series SQL routines 7 75 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 set_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 HideElem function on page 7 58 The ts_reveal_elem function on page 9 48 RevealRange function 7 76 The RevealRange function makes hidden elements in a specified date range visible
148. a TimeSeries stock_bar_union from daily_stocks sl daily_stocks s2 where sl stock_name IBM and s2 stock_name HWP Related reference Apply function on page 7 11 Intersect function on page 7 62 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 falues on page 7 5 lues on page 7 5 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 Chapter 7 Time series SQL routines 7 119 update activity_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 reference DelElem function on page 7 3 GetElem function on page 7 4 InsElem function on page 7 6 PutElem function on page 7 6 UpdSet function on page 7 12
149. a sam dat where stock_name IBM Load small amounts of data with functions You can load individual elements or sets of elements by using TimeSeries 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 given set into a time series IBM Informix TimeSeries Data User s Guide 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 the name of the function The data argument is in the row type data element format see Create a time series with its fa uitinction oni page 1 The WHERE clause specifies which row in the table to update The following example appends an element to a time series using PutElem 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 You_can also use more complicated expressions to load a time series For examples see Binary arithmetic functions on page 7 21 Related concepts
150. a 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 5 Time stamps must be unique multiple entries in a single TimeSeries cannot have the same time stamp The following illustration shows 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 tstamp DATETIME YEAR TO FRACTION 5 value DECIMAL location_id reads 1000111 TimeSeries meter_data 1046021 TimeSeries meter_data 1090954 TimeSeries meter_data Figure 1 2 TimeSeries data type architecture 1 6 IBM Informix TimeSeries Data User s Guide 2010 11 10 00 00 00 00000 0 092 2010 11 10 00 15 00 00000 0 082 2010 11 10 00 30 00 00000 0 090 Time series data for ID 1046021 tstamp DATETIME YEAR TO FRACTION 5 value DECIMAL 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 DATET
151. able 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 based on an expression that is performed on a time series table You can create a virtual table 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 Copyright IBM Corp 2006 2012 4 1 Related concepts Planning for accessing time series data on page 1 12 Planning for loading time series data on page 1 11 Related tasks Loading data from a file into a virtual table on page 3 17 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 expression 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 part of the ts_data table The actual time stamp values are shown for clarity although the time s
152. aces 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 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 additional containers that store data in different dbspaces Estimate the amount of storage space you need by using the following formula space primary_key index_entry time_series_columns x elements x 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 time 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 IBM Informix TimeSeries Data User s Guide 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 table_rows The number of rows in the time series table time_series_columns The s
153. achine 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 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
154. ad e Other functions such as PutElem Related concepts TimeSeries data type on page 2 5 Regular time series on page 1 7 Irregular time series on page 1 7 for creating a time series on Creating a time series with the TSCreate or TSCreatelrr function You can create an empty time series or insert data simultaneously The TSCreate and TSCreatelrr functions create a time series based on the calendar name the origin time stamp the threshold the flags the number of elements and the container name To create a time series Run the TSCreate function for regular time series or TSCreatelrr function for irregular time series If you want to insert data into your time series when you create it include the data in the set_data argument Examples Example 1 Create an empty time series The following example uses the TSCreate function to create an empty time series IBM Informix TimeSeries Data User s Guide insert into daily_stocks values 901 IBM TSCreate daycal 2011 01 03 00 00 00 00000 20 0 0 NULL Example 2 Create a time series with data For example suppose a table called activity_load_tab has a column called set_data of type SET stock_trade The following statement creates a time series and inserts it into the activity_stocks table 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
155. ad and written to TSOPEN_READ_HIDDEN Indicates that hidden elements should be treated as if they are not hidden 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 The TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_HIDDEN IBM Informix TimeSeries Data User s Guide Description Almost all other functions depend on this function being called first Use ts_close 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
156. alendar pattern object writeSQL public void writeSQL SQLOutput stream Called automatically by the Informix JDBC Driver when a setObject method is called to insert an IfmxCalendarPattern object into a prepared statement to be sent to the database server The IfmxCalendar class The IfmxCalendar class implements the IfmxCalendarUDT TimeSeries interface An interface provides an abstract description of the methods and any constants belonging to a class The IfmxCalendarUDT interface specifies the standard constants that any calendar class might be required to use The interface is intended for use only by programmers who want to develop calendar classes Chapter 8 Time series Java class library 8 9 You create an IfmxCalendar object by e Selecting a calendar from the database e Selecting a time series from the database e Instantiating a new object to be inserted into the database The IfmxCalendar class defines the following constructors for these situations IfmxCalendar IfmxCalendar String calName TimeStamp calStart TimeStamp patStart String pattern throws SQLexception IfmxCalendar String calName TimeStamp calStart TimeStamp patStart IfmxCalendarPattern cPat throws SQLexception When you select a calendar from the database the getObject method is used to extract the calendar from the result set The IBM Informix JDBC Driver instantiates a new IfmxCalendar object using the first constructor IfmxCalendar It cr
157. alendars 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 2 Indicates that the scan should run with the TS_SCAN_HIDDEN flag set hidden elements are returned 4 Indicates that the scan should run with the TS_SCAN_SKIP_HIDDEN flag set hidden elements are not returned Returns A single element row Example 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 IBM Informix TimeSeries Data User s Guide Related reference AgeregateBy function on page 7 6 Apply function Apply function The Apply function queries one or more time series and applies a user specified SQL expression or function to the selected time 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
158. alent 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 7 117 Returns A new time series with the results of evaluating the expression on every selected element from the source time series Chapter 7 Time series SQL routines 7 13 7 14 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 year 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
159. alue 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 holds 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 st
160. an move the time series Java class and documentation files to a different location You must modify your CLASSPATH variable to include the location of these files The Java class and documentation files are in the following directories e INFORMIXDIR extend TimeSeries version java lib contains the jar files IBM Informix TimeSeries Data User s Guide There are two jar files IfmxTimeSeries jar and IfmxTimeSeries g jar The g file is a debug version that supports tracing only use this version if you are troubleshooting a problem it was compiled using the g option of the javac command See Problem solving on page 8 17 for more information about tracing e INFORMIXDIR extend TimeSeries version java doc contains the JavaDoc files You must do one of the following e Modify your CLASSPATH variable to point to these files CLASSPATH INFORMIXDIR extend TimeSeries version java lib IfmxTimeSeries jar CLASSPATH export CLASSPATH e Copy these files to an appropriate location and include this in your CLASSPATH variable CLASSPATH new_location IfmxTimeSeries jar CLASSPATH export CLASSPATH e Copy the files to a new location where you undo the jar file and modify your CLASSPATH variable to point to the new location CLASSPATH new_location CLASSPATH export CLASSPATH In this case because the jar file has been undone the CLASSPATH variable is only required to point to where the jar file has been extracted to Samp
161. and Create the database table on page 3 6 The schema for these examples is in the INFORMIXDIR TimeSeries version examples directory Related concepts for accessing time series data on Time series SQL routines sorted by task Time series SQL routines are sorted into logical areas based on the type of task Table 7 1 Time series SQL routines by task type Task type Description Routine name Get information from Get the origin GetOrigin a time series Get the interval GetInterval Get the calendar GetCalendar Get the calendar name GetCalendarName Get the container name GetContainerName Get user defined metadata GetMetaData Get the metadata type GetMetaTypeName Determine whether a time series is regular IsRegular Get the instance ID if the time series is stored in a InstanceId container Convert between a Return the offset given the time stamp GetIndex e andian Return the time stamp given the offset GetStamp Copyright IBM Corp 2006 2012 Table 7 1 Time series SQL routines by task type continued Task type Description Routine name Count the number of Return the number of elements GetNelems clement Get the number of elements between two time stamps ClipGetCount Get the number of elements that match the criteria of an Countlf arithmetic expression Select individual Get the element associated with the sp
162. appear 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 Chapter 4 Virtual tables for time series data 4 11 The default value of the TSVTMode parameter sets the 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 For example if you set the TSVTMode parameter to 514 512 2 both null and hidden elements appear in the virtual table You can specify values for the TSVTMode parameter as either decimal numbers as shown in the table or as hexadecimal numbers Table 4 5 Settings for the TSVTMode parameter Flag Value Description TS_VTI_PUT_ELEM_NO_DUPS 0 Default The virtual table has the following behavior e 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 e Null elements are not included in the virtual table e 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 ide
163. as since been deleted returns FALSE if the element contains data or is hidden Related reference ElemIsHidden function on page 7 39 FindHidden function on page 7 41 Exp function The Exp function exponentiates the time series 7 40 IBM Informix TimeSeries Data User s Guide 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 Unary arithmetic functions on page 7 116 FindHidden function The FindHidden function scans a time series and returns all 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 39 ElemIsNull function on page 7 40 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
164. at 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 agg_express A comma separated list of these SOL aggregate operators MIN MAX MEDIAN SUM AVG FIRST LAST or Nth 7 6 IBM Informix TimeSeries Data User s Guide 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 8 start optional The date and time at which to start aggregation end optional The date and time at which to end aggregation Description The AggregateBy function converts the input time series to a regular time series with a calendar given by the cal_name argument The agg_express expressions operate on a column of the input time series specified as colname or colnumber for example high or 1 The resulting time series has a time stamp column plus one column for each expression in the list An error is raised if the MIN MAX MEDIAN SUM or AVG expression is used on a non numeric column The Nth expression returns the value of a column for the specified aggregation period using the following syntax Nth col n
165. 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 IBM Informix TimeSeries Data User s Guide 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 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_dat
166. ate 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 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 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 e
167. ax 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 CREATE 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 TimeSeries data type technical overview on page 1 5 Related tasks Creating a TimeSeries subtype on page 3 6 Related reference Create the database table on page 3 6 Create a time series on page 3 11 Time series return types When a routine returns a time series calendar information is preserved and if possible threshold and container information is preserved 2 6 IBM Informix TimeSeries Data User s Guide Some functions that retu
168. ay cp_pattern 1 off 4 on 2 off day Chapter 5 Calendar pattern routines 5 5 5 6 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 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 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 OrOp 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 The 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 ea
169. 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 IBM Informix TimeSeries Data User s Guide 2011 01 03 00 00 00 00000 1 1 2 2 2011 01 04 00 00 00 00000 10 1 20 2 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 PutElem function on page 7 68 The flags argument values The value of the flags argument is the sum of the desired flag values from the following table Flag Value Meaning TSOPEN_RDWRITE 0 Default Indicates that the time series can be read and written to TSOPEN_READ_HIDDEN 1 Indicates that hidden elements should be treated as if they are not hidden TSOPEN_WRITE_HIDDEN 2 Allows hidden elements to be written to without first revealing them The element rema
170. c ge Q o es oO 3 Q fed C fo 2 GetMetaTypeName function 7 50 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 lvarchar fma S The time series to retrieve the metadata from IBM Informix TimeSeries Data User s Guide 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 GetMetaData function on page 7 50 TSCreate function on page 7 97 TSCreatelrr function on page 7 99 UpdMetaData function on page 7 120 The ts_create_with_metadata function on page 9 1 The ts_get_metadata function on page 9 28 The ts_update_metadata function on page 9 52 GetNelems function The GetNelems function returns the number of elements stored 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_
171. cation 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 Since 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 12 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 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 2012 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
172. ch 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 8 Calendar data type on page 2 3 The AndOp function The AndOp function returns the intersection of the two calendars Syntax AndOp 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 combined with the AND operator Copyright IBM Corp 2006 2012 6 1 Example The following AndOp statement returns the intersection of an hourly calendar with a daily calendar having 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 sel
173. d 1 Related reference The ts_cal_range function on page 9 9 z a Q e S 5 ag 5 Q D Ss function on page 9 10 z D Q D WD e D 3 gel a function on page 9 11 GetIndex function on page 7 4 D D ee 3 5 5 5 o y Ol NI The 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 fraction 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 given time stamp The equivalent API function is ts_cal_range Returns A list of time stamps Chapter 6 Calendar routines 6 3 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
174. d 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 specified an error is raised Example 2 Create an irregular time series The following example shows an INSERT statement for an irregular time series created in the table activity_stocks insert into activity_stocks values 600 irregular container ctnr_stock origin 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 Create a time series with the output of a function Many functions return a time series Chapter 3 Create and manage a time series 3 15 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
175. d 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 IBM may not offer the products services or features discussed in this document in other countries Consult your local IBM representative for information 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 t
176. d function computes the modulus or remainder of a division of one time series by another It is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Plus Pow and Times Related reference Binary arithmetic functions on page 7 21 Negate function The Negate function negates a time series It 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 Unary arithmetic functions on page 7 116 NullCleanup 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 7 66 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 flags argument are describe
177. d in The flags argument Falnes on page 75 lues on page 7 5 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 pages 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
178. d reference 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 element This value cannot be freed by the user with mi_free Related reference The ts_begin_scan function on page 9 6 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 Chapter 9 Time series API routines 9 19 9 20 Related reference The ts_get_all_cols procedure on page 9 25 The ts_get_col_by_name function on page 9 26 The ts_get_col_by_number function on page 9 26 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 ret
179. d tasks Monitoring time series containers on page 3 8 TSContainerPctUsed function The TSContainerPctUsed function returns the percentage of space used in the specified container or in all containers Syntax TSContainerPctUsed container_name varchar 128 1 returns decimal container_name The name of an existing container Can be NULL Description Use the TSContainerPctUsed function to view the percentage of used space in a container If you specify NULL as the container name information about all containers for the database is returned Returns The percentage of used space in the specified container or in all containers 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 tasks Monitoring time series containers on page 3 8 TSContainerPoolRoundRobin function The TSContainerPoolRoundRobin function provides a round robin policy for inserting time series data into containers in the specified container pool Syntax TSContainerPoo RoundRobin table_name lvarchar column_name lvarchar Chapter 7 Time series SQL routines 7 89 7 90 subtype lvarchar irregular integer
180. database The data is not duplicated At any given moment data visible in the virtual table is the same as the content in the base table You cannot create an index on a time series virtual table The performance of queries on virtual tables versus using TimeSeries functions is similar in most cases 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 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 period of 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 quite 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 t
181. de Flag Value Meaning TSOPEN_NO_NULLS 32 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 TS_PUTELEM_NO_DUPS 64 Determines whether the PutTimeSeries function adds elements using the PutElem function or the PutElemNoDups function see PutElem function on page 7 68 and PutElemNoDups function on page 7 70 If this flag is set PutTimeSeries uses PutElemNoDups 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 The TSOPEN_WRITE_ HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_ HIDDEN Returns A version of the second time series into which the first time series has been 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
182. de 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 returns 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 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
183. e corresponding scan or time series descriptor is closed IBM Informix TimeSeries Data User s Guide Syntax void ts_free_elem ts_tsdesc tsdesc ts_tselem elem 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 22 The ts_make_elem function on page 9 36 The ts_make_elem_with_buf function on page 9 3 7 The ts_row_to_elem function on page 9 48 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 corresponding column is by reference or by value The values in the valu
184. e 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 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 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 5 Managing 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 additional containers and move them between container pools To create a container run the TSContainerCreate procedure To delete a container run the TSContainerDestroy procedure 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 To view container infor
185. e DataBlade module nor do you need to unload and load time series data during migration New editions and product names IBM Informix Dynamic Server editions were withdrawn and new Informix editions are available Some products were also renamed The publications in the Informix library pertain to the following products e IBM Informix database server formerly known as IBM Informix Dynamic Server IDS e IBM OpenAdmin Tool OAT for Informix formerly known as OpenAdmin Tool for Informix Dynamic Server IDS e IBM Informix SQL Warehousing Tool formerly known as Informix Warehouse Feature For more information about the Informix product family go tolhttp www ibm com software data informix Introduction Xiii 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 at the start of each statement and a semicolon or other appropriate delimiter at the end of the statement If you are us
186. e 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 alues on page 7 5 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 Returns 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 IBM Informix TimeSeries Data User s Guide 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 Clip function on page 7 26 GetNextValid function on page 7 52 GetPreviousValid
187. e 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 page 4 4 Loading the time series data You use the TimeSeries plug in to load the data from the my_meter_data un 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 Chapter 3 Create and manage a time series 3 3 3 4 2 From the main menu choose Help gt Cheat Sheets expand the TimeSeries Data category choose Load time series data and click OK 3 Open the TimeSeries perspective 4 Create a project area named my_test 5 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 Integer 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 6 Create the Informix table definition and define the columns o
188. e functions return information about all containers in the database To view the number of elements the number of pages used and the number of pages allocated run the TSContainerUsage function Example The following statement returns the number of pages containing time series data in the pages column the number of elements in the slots column and the number of pages allocated in the total column for the container named raw_container EXECUTE FUNCTION TSContainerUsage raw_container pages slots total 1999 241881 2119 1 row s retrieved Because 1999 of the total 2119 pages are used the container is close to being full Related concepts Time series data storage on page 1 8 Related reference TSContainerUsage function on page 7 9 TSContainerTotalUsed function on page 7 95 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 rs 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 policy on page 3 10 To create a container pool and store data into containers by using a container pool policy 1 Create containers by running the T
189. e into the virtual table See the IBM Informix Administrator s Guide for information about Informix load file formats and load utilities Related concepts for loading time series data on Chapter 4 Virtual tables for time series data on page 4 1 Related reference Load data with BulkLoad You can load data into an existing time series with the BulkLoad function This 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 containing the row type The filename parameter is the name of the data file The WHERE clause specifies which row in the table to update Related concepts Planning for loading time series data on page 1 11 Data file formats for BulkLoad Two data formats are supported for the file loaded by BulkLoad e Using type constructors Chapter 3 Create and manage a time series 3 17 3 18 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 ty
190. e of the binary arithmetic functions that work on time series The others are Divide Minus Mod Plus Pow and Times Related reference 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 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 Chapter 7 Time series SQL routines 7 21 7 22 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 the in
191. e or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you Licensees of this program who wish to have information about it for the purpose of enabling i the exchange of information between independently created programs and other programs including this one and ii the mutual use of the information which has been exchanged should contact IBM Corporation J46A G4 555 Bailey Avenue San Jose CA 95141 1003 USA Such information may be available subject to appropriate terms and conditions including in some 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 produc
192. e 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 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 IBM Informix TimeSeries Data User s Guide TimeSeries cr and does not discard the fractional parts of t
193. e 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 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 keywo
194. e this Chapter 4 Virtual tables for time series data 4 7 Table 4 4 The daily_stocks_no_ts virtual table stock_id stock_name timestamp high low final vol 900 AA01 t1 7 25 6 75 7 1000000 900 AAO1 t2 7 5 6 875 7 125 1500000 901 IBM t1 97 94 25 95 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 now looks like this select stock_id final from daily_stocks_no_ts And the query to obtain price and volume within a specific time range looks like this 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 a new 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 TSCreateExpressionVirtualTab procedure The TSCreateExpression VirtualTab procedure creates a virtual table based on the results of an expression that was pe
195. e 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 7 113 Related reference TSColNameToList function on page 7 84 TSColNumToList function on page 7 85 TSRowNameToList function on page 7 104 TSRowNumToList function on page 7 105 Transpose function on page 7 79 TSRowToList function on page 7 106 TSToXML function 7 114 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 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
196. e time stamp at a given number of intervals after a given time stamp ts_cal_stamp The following functions are used only with regular time series e ts_current_offset e ts _index e ts nth _elem e ts_put_last_elem e ts_put_nth_elem e ts_time Some of the API routines are much the same as SQL routines The mapping is shown in the following table Chapter 9 Time series API routines 9 5 API routine SQL routine ts_cal_index CalIndex ts_cal_range CalRange ts_cal_stamp CalStamp ts_create ts_create_with_metadata 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 tsdesc mi_integer flags mi_datetime begin_stamp mi_datetime end_stamp tsde
197. e trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries Java and all Java based trademarks and logos are trademarks or registered trademarks of Oracle and or its affiliates Linux is a registered trademark of Linus Torvalds in the United States other countries or both Microsoft Windows and Windows NT are trademarks of Microsoft Corporation in the United States other countries or both UNIX is 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 Notices D 3 D 4 IBM Informix TimeSeries Data User s Guide Index A Abs function 7 6 absolute method 8 13 Absolute value determining 7 6 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 6 Adding previous values to current 7 82 Adding two time series 7 68 afterLast method 8 13 AggregateBy function 7 6 7 9 Aggregating time series values 7 6 ALTER TYPE statement 1 13 AndOp function 5 1 6 1 Applets 8 1 Apply function 7 11 virtual tables 4 6 ApplyBinaryTsOp function 7 16 ApplyCalendar function 7 17 Applying a calendar to a time series 7 17 Applying an expression to a time series 7 11 ApplyOpToTsSet function 7 19 ApplyUnaryTsOp function 7 20 Arc cosine determining 7 6 Arc sine determi
198. e ts_timeseries ts 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 43 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 xts ts The source time series Chapter 9 Time series API routines 9 27 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 whether 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 64 The TS_IS_TIRREGULAR macro on page 9 34 The ts_get_metadata function The ts_get_metadata function returns the user defined metadata and its type ID from the specified time series Syntax mi_lvarchar ts_get_metadata ts_timeseries ts MI_TYPEID xxmetadata_typeid ts 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 a
199. eBy values Aggregate values in a time series from a single row over a AggregateRange specified time range Aggregate time series values across multiple rows TSRollup Create a time series Create a time series that lags the source time series by a Lag regular only that lags given offset Reset the origin Reset the origin SetOrigin Manage containers Create a container TSContainerCreate Delete a container TSContainerDestroy Set the container name SetContainerName Specify the container pool for inserting data into a time TSContainerPoolRoundRobin series Add a container into a container pool or remove a TSContainerSetPool container from a container pool Delete elements through a specified timestamp from one TSContainerPurge or more containers 7 4 IBM Informix TimeSeries Data User s Guide Table 7 1 Time series SQL routines by task type continued Task type Description Routine name Monitor containers Return the number of elements in one or all containers TSContainerNElems Return the percentage of space used in one or all containers TSContainerPctUsed Return the total number of pages allocated to one or all TSContainerTotalPages containers Return the number of pages used by one or all containers TSContainerTotalUsed Return the number of elements the number of pages TSContainerUsage used and the total number of pages allocated for one or all containers The following rout
200. eSeries data type prefixed by a 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 Chapter 7 Time series SQL routines 7 103 Returns A TimeSeries data type that is the result of the expression or expressions Examples The following examples show how to use the TSRollup function Example 1 Sum of all electricity usage in a zipcode 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 zipcode 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 2 Sum of daily electricity usage by zipcode Suppose that you have a table named ts_table that contains a user ID the zipcode 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 zipcode SELECT zipcode TSRol 1up AggregateBy SUM value callday ts_col 0 2011 01 01 00 00 00 00000 2011 01 31 23 45 00 00 00
201. eVirtualTab procedure or the TSCreateExpressionVirtualTab procedure Related concepts The TSVTMode parameter on page 4 11 Related reference TSCreateVirtualTab procedure on page 4 4 TSCreateExpressionVirtualTab procedure on page 4 8 The insertion of data through virtual tables 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 an additional 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 new 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 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 TimeSeri
202. eates an empty object with no variables initialized When you create a calendar on the client to insert into the database use the second or third constructor to instantiate the object These constructors initialize the new object by parsing the input string and using the given arguments A calendar contains an embedded calendar pattern The first constructor instantiates an IfmxCalendarPattern object using the specified calendar pattern string The second constructor copies the calendar pattern from the IfmxCalendarPattern object that you specify The IfmxCalendar class provides the following methods Method Signature Description getName public String getName throws SQLException Returns the calendar name getOffset public int getOffset throws SQLException Returns the calendar offset getStartDate public Timestamp getStartDate throws Returns the calendar start date SQLException getPatStartDate public Timestamp getPatStartDate throws Returns the start date of the calendar SQLException pattern associated with the calendar getPattern public IfmxCalendarPattern getPattern throws Returns the calendar pattern associated SQLException with the calendar getSQLTypeName public String getSQLTypeName throws Returns the SQL type name for the object SQLException in this case calendar getStartDate public Timestamp getStartDate throws Returns the calendar start date SQLException getTimestamp public Timesta
203. ecified time stamp GetElem elements Get the element at or before a time stamp GetLastValid Get the element after a time stamp GetNextValid Get the element before a time stamp GetPrevious Valid Get the element at a specified position GetNthElem Get the first element GetFirstElem Get the last element GetLastElem Get the last non null element GetLastNonNull Get the next non null element GetNextNonNull Modify elements ora Add or update a single element PutElem serok elements Add or update a single element PutElemNoDups Add or update a single element at a given offset PutNthElem regular only Add or update an entire set PutSet Insert an element InsElem Insert a set InsSet Update an element UpdElem Update a set UpdSet Put every element of one time series into another time PutTimeSeries series Delete elements Delete an element at the specified timepoint DelElem Delete all elements in a time series instance for a specified DelClip time range Delete all elements and free space in a time series instance DelRange for a specified time range in any part of a time series Delete all elements and free space in a time series instance DelTrim for a specified time range at the end of a time series Free empty pages in a specified time range or throughout NullCleanup the time series instance Delete elements through a specified timestamp from one TSContainerPurge or more containers for one or more time series instances Mod
204. ect 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 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 off 9 on 15 off 9 on 15 off 9 on 31 off hour Related reference The AndOp function on page 5 1 The OrOp function on page 6 5 The Callndex function 6 2 The CalIndex function returns the number of valid intervals in a calendar between two given time stamps Syntax Cal Index 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 Returns The number of valid intervals in the given calendar between the two time stamps IBM Informix TimeSeries Data User s Guide 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 tabi
205. egin_stamp mi_datetime end_stamp conn A valid DataBlade 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 9 The ts_cal_range_index function on page 9 10 The ts_cal_stamp function on page 9 11 The ts_index function on page 9 32 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 9 8 IBM Informix TimeSeries Data User s Guide 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 Returns An mi_datetime pointer that points to the start date of a calendar pattern You must free this value after use Related reference The CalPattStartDate function on page 5 2 The ts_cal_star
206. egular 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 Chapter 7 Time series SQL routines 7 35 end_stamp The end point of the range flags Valid values for the flags argument are described in The flags argument falues on page 75 lues on page 7 5 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 elements 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 Related reference Clip functio
207. elated reference The CalIndex function on page 6 2 GetNthElem function on page 7 53 The ts_time function on page 9 49 GetThreshold function The GetThreshold function returns the threshold associated with the specified time series Chapter 7 Time series SQL routines 7 57 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 The ts_get_threshold function on page 9 30 HideElem function 7 58 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 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 5 The default is 0 Description After an element is hidden reading that element returns NULL and wri
208. en 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 14 for how to achieve this The following examples show valid expressions for Apply to apply Assume two argument time series 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 IBM Informix TimeSeries Data User s Guide e O high 1 high 2 0 low 1 1ow 2 0 1 1 1 2 0 2 1 2 2 e 0 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
209. ence Apply function on page 7 11 Clip function on page 7 26 ClipCount function on page 7 29 GetIndex function on page 7 46 Cos function 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 Unary arithmetic functions on page 7 116 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 end_stamp datetime year returns integer CountIf ts TimeSeries col lvarchar op lvarchar value lvarchar begin_stamp datetime year end_stamp datetime year returns integer CountIf ts TimeSeries col lvarchar op lvarchar value decimal begin_stamp datetime year end_stamp datetime year returns integer Syntax of expr 7 32 IBM Informix TimeSeries Data User s Guide to to to to to to fraction 5 fraction 5 fraction 5 fraction 5 fraction 5 fraction 5 default default default default default default null null null null null null i ogical_operator gt gt n expr_col lt expr_value 7 gt lt NOT lt L IS NULL
210. endar and IfmxCalendarPattern classes are described in the final topics of this section Sample programs on page 8 3 points to complete sample programs that demonstrate how to retrieve and update time series data Write TimeSeries data back to the database You can write a time series back to the database by using the PreparedStatement setObject method pStmt setObject 1 ts Chapter 8 Time series Java class library 8 7 In this example pStmt is a valid PreparedStatement object ts is a valid IfmxTimeSeries object and the TimeSeries type is the first argument in the prepared statement Similarly the following example writes a Calendar object to the database pStmt setObject 1 c In this example pStmt is a valid PreparedStatement object c is a valid IfmxCalendar object and the Calendar type is the first argument in the prepared statement And the following example writes a CalendarPattern object to the database pStmt setObject 1 cp Where pStmt is a valid PreparedStatement object cp is a valid IfmxCalendarPattern object and the CalendarPattern type is the first argument in the prepared statement Writing data back to the database requires the IBM Informix JDBC Driver to use a type map similarly to the way described for retrieving data from the database in Create a custom type map on page 8 5 Obtain the time series Java class version There are two ways to retrieve the version stamp for the t
211. endar 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 time series origin Example 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 Chapter 7 Time series SQL routines 7 55 Related reference GetCalendar function on page 7 41 GetInterval function on page 7 46 GetClosestElem function on page 7 42 TSCreate function on page 7 97 SetOrigin function on page 7 77 TSCreatelrr function on page 7 99 The ts_get_origin function on page 9 28 GetPreviousValid function 7 56 The GetPreviousValid 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 5 lues on page 7 5 The default is 0 Description The equivalent API function is ts_previous_valid Returns A row containing the last element before the given time stamp The type of the row is the same as the time series subtype If the time stamp
212. environment variables Environment variable Description TRACE Traces general information from IBM Informix JDBC Driver Can be set to one of the following levels 0 Tracing not enabled This is the default value 1 Traces the entry and exit points of methods 2 Same as Level 1 except generic error messages are also traced 3 Same as Level 2 except data variables are also traced TRACEFILE Specifies the full path name of the operating system file on the client computer to which the TRACE messages are written Chapter 8 Time series Java class library 8 17 Environment variable Description PROTOCOLTRACE Traces the SQLI protocol messages sent between your Java program and the IBM Informix database server Can be set to the following levels 0 Protocol tracing not enabled This is the default value 1 Traces message IDs 2 Same as Level 1 except the data in the message packets is also traced PROTOCOLTRACEFILE Specifies the full path name of the operating system file on the client computer to which the PROTOCOLTRACE messages are written The following example of a database URL specifies the highest level of protocol tracing and sets tracing output to the operating system file tmp trace out String url jdbc informix sqli 123 45 67 89 1533 informixserver myserver user rdtest password test PROTOCOLTRACE 2 PROTOCOLTRACEFILE tmp trace out For more information about establishing
213. 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 3 Count elements in a specific time range IBM Informix TimeSeries Data User s Guide 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 4 Count elements greater than a value The following statement 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 11 1 row s retrieved DelClip function 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 r
214. er defined metadata Example The following example creates an empty time series using TSCreate IBM Informix TimeSeries Data User s Guide 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 the TSCreate or TSCreatelrr function on page 3 12 Creating a time series with metadata on page 3 13 n m page 7 4 page 7 46 page 7 5 4 page 7 50 page 7 5 page 7 68 PutSet function on page 7 72 O I ie SiS S SIEIS E SIS S 2 Oils fa amp 2 have S 2 PE lal S c JAHE ajo Ve 215 e 5 5 Blo g 5 S Q oO Q Z T o eS O oO Z o oO i g 5 2 5 o 5 n Q oO id o 09 5 bs c 5 e ch 5 O 5 g J c S z oO 3 a Q ct 5 o 5 GetClosestElem function on page 7 42 TSCreatelrr function UpdMetaData function on page 7 12 function on page 9 1 m Di function on page 9 1 _get_ function on page 9 2 function on page 9 52 e e e X IIS 18 eqs S B Ie E a 5 Z o I gt D Sls oO o en o gt o pamm ANS NI z v G ge Q o A
215. eries 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 based 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 In
216. erlycal ts 1 IBM Informix TimeSeries Data User s Guide Returns The aggregated time series which is always regular if you are aggregating to a new time interval Example 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 Related reference TSRollup function on page 7 103 AggregateRange function The AggregateRange function produces an aggregate over each element for a time range specified by start and end DATETIME parameters Syntax AggregateRange agg_express lvarchar ts TimeSeries flags integer default 0 start datetime year to fraction 5 default NULL end datetime year to fraction 5 default NULL returns row agg_express A comma separated list of these SQL aggregate operators MIN MAX MEDIAN SUM AVG FIRST LAST or Nth ts The time series to be aggregated flags optional See The flags argument values on page 7 10 You cannot use a flags argument value of 1 with this function start optional The
217. ern also indicates the time unit in which the interval is measured for example second minute hour day or month The interval size specified in the calendar pattern is not necessarily the same as the size of the timepoint For example you can create a calendar pattern that specifies an interval length of minute and specifies that the pattern has one minute valid and 14 minutes invalid The resulting timepoint is 15 minutes long Suppose you want to collect data once a day Monday through Friday The following table illustrates when data collection is valid or on and invalid or off The calendar pattern has an interval of a day has a calendar start date on a Sunday and specifies one day off five days on and one day off Table 1 5 When data collection is on or off Sunday Monday Tuesday Wednesday Thursday Friday Saturday Off On On On On On Off 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
218. ern start date The pattern length is 168 hours or one week Related concepts CalendarPattern data type on page 2 1 Related tasks Defining a calendar on page 3 5 Related reference Chapter 6 Calendar routines on page 6 1 HideElem function on page 7 58 TimeSeries data type 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 gt gt CREATE ROW TYPE subtype_name gt gt timestamp_field DATETIME YEAR TO FRACTION 5 gt gt _field_name data_type NOT NULL Chapter 2 Data types and system tables 2 5 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 the 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 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 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 Synt
219. ers and location references ts_data_location Contains spatial location information ts_data Contains location references and smart meter time series data Related concepts L Command line syntax DB Access Guide Related reference L The stores_demo Database Map SQL Reference Setting up stock data examples 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 1 inux 2 Run the examples_setup sql command from the INFORMIXDIR extend TimeSeries version examples directory make f Makefile MY_DATABASE dbname The dbname is the name of a database Chapter 1 Informix TimeSeries solution 1 15 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 1 16 IBM Informix TimeSeries Data User s Guide Chapter 2 Data types and system tables Specialized data types and sys
220. es object inContainer public boolean inContainer throws Returns TRUE if the IfmxTimeSeries object SQLException is in a container FALSE otherwise isHidden boolean isHidden Returns TRUE if the current element is hidden isNull public boolean isNull columnIndex int throws The isNull method tests if the value on the SQLException specified column of the selected TimeSeries f f row is NULL and returns TRUE if it is public boolean isNull columnName String otherwise FALSE You can specify the throws SQLException column either by number columnIndex or by name columnName isRegular public boolean isRegular throws Returns TRUE if the IfmxTimeSeries object SQLException represents a regular time series FALSE otherwise setConnection public void setConnection connection Conn Sets the connection to be used to update throws SQLException time series elements Must be a valid connection with a correct type map 8 12 The following methods are not currently supported e getNelems e hideElem Public methods inherited from the JDBC ResultSet interface The IfmxTimeSeries class contains public methods inherited from the JDBC ResultSet interface IBM Informix TimeSeries Data User s Guide The following table shows the supported signatures for the public methods Other signatures for these methods are not supported Table 8 2 Public methods Method Signature Description absolute boolean absolute int row Moves the time ser
221. es 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 using 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 Examples Example 1 Delete regular time series data from multiple containers The following statement creates a control file named regular_purge un1 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 metO metl met11 met4 met5 met6 met61 met7 met8 met9 The resulting control file has the following contents sm0 1 7871 sm0 8 7295 sm0 13 6911 sm1 a 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 sec
222. es 1 9 PutElem function 3 18 7 68 PutElemNoDups function 7 70 PutNthElem function 7 71 PutSet function 3 18 7 72 PutTimeSeries function 7 73 R Raising one time series to the power of another 7 68 readSQL method 8 6 8 8 8 9 8 13 refreshRow method 8 13 Regular time series 1 7 creating with metadata 7 97 creating with TSCreate 7 97 determining if 7 64 specifying 3 14 Regularity 1 4 relative method 8 13 Relative search type 7 122 Replicating time series data 1 14 ResultSet interface 8 4 8 7 inherited methods 8 13 Retrieving time series data Java 8 7 RevealElem function 7 75 7 76 Revealing a hidden element 7 75 7 76 9 48 Round function 7 77 Rounding a time series to a whole number 7 77 Routines API ts_begin_scan 9 6 ts_cal_index 9 8 ts_cal_pattstartdate 9 8 ts_cal_range 9 9 ts_cal_range_index 9 10 ts_cal_stamp 9 11 ts_close 9 12 ts_col_cnt 9 13 ts_col_id 9 14 ts_colinfo_name 9 14 ts_colinfo_number 9 15 ts_copy 9 16 ts_create 9 16 ts_create_with_metadata 9 17 ts_current_offset 9 18 ts_current_timestamp 9 19 ts_datetime_cmp 9 19 ts_del_elem 9 20 ts_elem 9 20 TS_ELEM_HIDDEN 9 21 TS_ELEM_NULL 9 22 ts_elem_to_row 9 22 ts_end_scan 9 23 ts_first_elem 9 23 ts_free 9 24 ts_free_elem 9 24 ts_get_all_cols 9 25 ts_get_calname 9 25 ts_get_col_by_name 9 26 ts_get_col_by_number 9 26 ts_get_containername 9 27 ts_get_flags 9 27 ts_get_metadata 9 28 ts_get_origin 9 28 ts_get_stamp_fields 9 29 ts_get_threshold 9 30 ts_g
223. es 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 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 IBM Informix TimeSeries Data User s Guide Returns Returns 0 on success 1 on failure Example The foll
224. es array must not be freed by the caller Related reference The ts_datetime_cmp function on page 9 19 The ts_col_cnt function on page 9 13 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 Chapter 9 Time series API routines 9 25 9 26 ts The source time series 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 return isNull is set to indicate whether the column is NULL Related
225. es 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 Chapter 4 Virtual tables for time series data 4 3 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 control data insertion by setting the NewTimeSeries parameter and the TSVTMode parameter in the TSCreateVirtualTab procedure Related concepts The TSVTMode parameter on page 4 11 Related reference TSCreateVirtualTab procedure Creating a time series virtual table You can create a virtual table 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 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 based on a table that contains a TimeSeries column
226. es subtype Only users with update privileges on the TSContainerTable table can run this procedure 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 will hold the container ts_type The name of the TimeSeries subtype that will be placed in the container This argument must be the name of an existing row type that begins with a time stamp container_size The initial size of the container in KB If this argument is 0 or less a default size of 16 KB is used If this parameter is positive it must be at least four pages The maximum size of a container 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 increments by which the container grows in KB If this argument is 0 or less a default size of 16 KB is used If this parameter is positive it must be at least four pages Description 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 As a result of the TSContainerCreate procedure t
227. es using TSCreatelIrr 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 Related tasks Creating a time series with the TSCreate or TSCreatelrr function on page 3 12 Creating a time series with metadata on page 3 13 Related reference GetMetaData function on page 7 50 GetMetaTypeName function on page 7 50 TSCreate function on page 7 97 page 7 41 Q oO T Q go Q D H es fan 2 ie p e 5 O 5 page 7 4 6 Q oO cy Q 5 Dn oO n az oO 3 We 5 Q 5 Q 5 N 5 gej D ej oO A 7 Q io a 5 oO lt inn 5 Q T 5 4 page 7 5 Q Oo oO O H i 5 ey x 5 llo 5 Ol e 5 gel o ag oO N N 77 0 get function on page 9 2 gs Q Q S 5 a 5 F a 5 a Dn Q oO on 3 oO o gt o K o A lee z ne c ge E D a oO 3 Q is Qa D l 2 function on page 9 52 TSDecay function 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 retur
228. ese flags can be used in any combination except the following four 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 and TSOPEN_WRITE_AND_HIDE and TSOPEN_WRITE_AND_REVEAL The TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_HIDDEN Abs 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 Unary arithmetic functions on page 7 116 Acos function 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 Unary arithmetic functions on page 7 116 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 th
229. et_ts 9 30 ts_get_typeid 9 31 Routines continued API continued ts_hide_elem 9 31 ts_index 9 32 ts_ins_elem 9 33 TS_IS INCONTAINER 9 34 TS_IS IRREGULAR 9 34 ts_last_elem 9 34 ts_last_valid 9 35 ts_make_elem 9 36 ts_make_elem_with_buf 9 37 ts_make_stamp 9 38 ts_nelems 9 38 ts_next 9 39 ts_next_valid 9 40 ts_nth_elem 9 41 ts_open 9 41 ts_previous_valid 9 43 ts_put_elem 9 44 ts_put_elem_no_dups 9 45 ts_put_last_elem 9 46 ts_put_nth_elem 9 46 ts_put_ts 9 47 ts_reveal_elem 9 48 ts_row_to_elem 9 48 ts_time 9 12 9 49 9 50 9 51 ts_upd_elem 9 52 ts_update_metadata 9 52 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 Expand 5 4 NotOp 5 4 OrOp 5 5 SQL time series Abs 7 6 Acos 7 6 AgegregateBy 7 6 7 9 Apply 7 11 ApplyBinaryTsOp 7 16 ApplyCalendar 7 17 ApplyOpToTsSet 7 19 ApplyUnaryTsOp 7 20 Asin 7 20 Atan 7 21 Atan2 7 21 BulkLoad 3 17 7 24 Clip 7 26 ClipCount 7 29 ClipGetCount 7 31 Cos 7 32 Countlf 7 32 DelClip 7 35 DelElem 7 36 DelRange 7 37 DelTrim 7 38 Divide 7 39 Exp 7 41 GetCalendar 7 41 Index X 5 Routines continued SQL time series continued GetCalendarName 7 42 GetContainerName 7 43 GetElem 7 44 GetFirstElem 7 45 GetIndex 7 46 GetInterval 7 47 GetLastElem 7 47 7 48 7 52 GetLastValid 7 49 GetMetaData 7 50 GetMetaTypeName 7 50 GetNelems 7 51 GetNex
230. ethod 8 12 GetNextValid function 7 52 GetNthElem function 7 53 getNumberOfElements method 8 12 getObject method 8 5 8 6 8 8 8 9 8 11 8 13 getOffset method 8 9 8 12 getOffsetFromTimestamp method 8 9 GetOrigin function 7 55 getOrigin method 8 12 getPatStartDate method 8 9 getPattern method 8 9 GetPreviousValid function 7 56 getRef method 8 13 getRow method 8 13 getShort method 8 13 getSQLTypeName method 8 8 8 9 8 13 GetStamp function 7 57 getStartDate method 8 9 getStatement method 8 13 getString method 8 13 GetThreshold function 7 58 getTime method 8 13 getTimestamp method 8 7 8 13 getTimestampFromOffset method 8 9 getTSMetaData method 8 13 getType method 8 13 getUnicodeStream method 8 13 getVersion method 8 8 getWarnings method 8 13 GMT converting to 9 38 H Hardware requirements 1 13 HDR and time series 1 14 Hidden elements 4 3 HideElem function 7 58 hideElem method 8 12 Hiding an element 7 58 9 31 IfmxCalendar class 8 3 8 9 methods 8 9 IfmxCalendarPattern class 8 3 8 8 methods 8 8 IfmxCalendarPatternUDT interface 8 3 8 8 IfmxCalendarUDT interface 8 4 IfmxTimeSeries class 8 3 8 4 IfmxTimeSeries class methods 8 11 8 12 IfmxTimeSeries object 8 7 IfmxTimeSeriesUDT interface 8 4 8 11 inContainer method 8 12 Index X 3 Indexes base tables 4 19 industry standards xiv Informix JDBC Driver 8 1 Informix TimeSeries DataBlade module system tables 2 9 Input function creating time series with
231. etime 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 this 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 value has the beginning date of the clip and the value of the first original value Because the time series is irregular a value persists until the next element Therefore the value of 26 46 is still valid on 2006 01 01 page 7 1 r Se fe S 2 Oo a FIZ a ot a a m 5 jay Q O O o 5 2 aa z 5 Qa ct 5 ClipGetCount function on page 7 30 4 page 7 44 page 7 49 4 page 7 5 WithinC and WithinR functions on page 7 122 AIA Q DIE n Io SJB lg mS Bile Bq I gt ollo 5 o 5 A Q Z gt ss oO 3 a G sh 5 o 5 w function on page 7 3 page 7 38 page 7 77 J oO D Q WS O Ol All a AE SE 5i 5 J2 SHS o 2 5 io os 5 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
232. ext_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 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
233. f intervals between two time stamps Subtract N intervals from a time stamp ts_tstamp_difference ts_tstamp_minus Add N intervals to a time stamp Find the number of a column ts_tstamp_plus ts_col_id Return the number of columns contained in each element ts_col_cnt Get type information for a column specified by number Get type information for a column specified by name ts_colinfo_number ts_colinfo_name Determine if an element is hidden TS_ELEM_HIDDEN Determine if an element is NULL TS_ELEM_NULL Get information about a time series Get the name of a calendar associated with a time series ts_get_calname Return the number of elements in a time series ts_nelems Return the flags associated with the time series ts_get_flags Get the name of the container ts_get_containername Determine if the time series is in a container TS_IS_INCONTAINER Get the origin of the time series ts_get_origin Get the metadata associated with the time series ts_get_metadata Determine if the time series is irregular TS_IS_IRREGULAR Get information about a calendar Return the number of valid intervals between two time stamps ts_cal_index Return all valid timepoints between two time stamps ts_cal_range Return a specified number of time stamps starting at a given time stamp ts_cal_range_index Return th
234. f 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 7 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 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 tblmap 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 u
235. f the time series Hidden elements cannot be updated The equivalent SQL function is PutElem Returns The original time series with the element added IBM Informix TimeSeries Data User s Guide Related reference PutElem function on page 7 68 The ts_del_elem function on page 9 20 The ts_get_ts function on page 9 30 The ts_ins_elem function on page 9 33 The ts_make_elem function on page 9 36 The ts_elem function on page 9 20 The ts_next function on page 9 39 The ts_put_elem_no_dups function The ts_put_last_elem function on page 9 46 The ts_upd_elem function on page 9 52 The ts_put_ts function on page 9 47 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 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 t
236. ffected Manage performance You can enhance the performance of your virtual tables by performing the following tasks 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 consisting of all key columns Run UPDATE STATISTICS on the base table and on its key columns update statistics high for table daily_stocks update statistics high for table daily_stocks stock_id You should run UPDATE STATISTICS after any load or delete operation you might want to make these commands part of your routine database maintenance Trace functions 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 Chapter 4 Virtual tables for time series data 4 19 4 20 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 varchar returns integer traceFileName The full path and name of the file to which trace information is appended Description The file you specify using TSSetTraceFile overrid
237. ffset you specify for the Clip function and the input time series origin Clip ts begin end 3 Returns A new time series containing 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 Example 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 containing a given stock s 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 The following query displays the first six entries for a given 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 the 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 IBM Informix TimeSeries Data User s Guide 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 dat
238. final TSPrevious final vol 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 from daily_stocks where stock_name IBM Related reference Apply function on page 7 11 TSAddPrevious function on page 7 82 TSDecay function on page 7 101 TSPrevious function on page 7 10 TSRunningAve function on page 7 107 TSRunningSum function on page 7 11 TSCoINameToList function 7 84 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 Syntax TSColNameToList ts TimeSeries colname lvarchar returns list s The time series to act on 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 IBM Informix TimeSeries Data User s Guide select from table select TSColNameToList stock_data high list real not null from daily_stocks Related reference Transpose function on page 7 79 TSColNumToList function TSRowNameToList function on page 7 10 TSRowNumTo
239. formix 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 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
240. 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 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 put into the time series See Creating al time series with metadata on page 3 13 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 See Creating a time series with the TSCreate or TSCreatelrr function on page for a description of how to use this function Returns A regular time series that is empty or populated with the given set and optionally contains us
241. ge 7 8 TSDecay function on page 7 101 TSPrevious function on page 7 102 TSRunningAvg function n TSRunningSum function Union function on page 7 117 Binary arithmetic functions on page 7 21 SetOrigin function on page 7 77 TSRunningCor function on page 7 108 TSRunningMed function on page 7 110 TSRunningVar function on page 7 112 Unary arithmetic functions on page 7 116 ApplyBinaryTsOp function 7 16 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 varchar 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 21 for a description of the co
242. ge 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_meter_data timestamp DATETIME YEAR TO FRACTION 5 data DECIMAL 4 3 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 6 Related reference 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
243. gin of the input time series In this case timepoints before the origin of the time series are set to NULL When you clip a range that contains hidden elements by default hidden elements are not copied to the resulting time series The flags argument allows you to include hidden elements in the result You can also mark the elements as hidden or revealed in the resulting time series To extract data into a new time series include hidden elements in that time range and keep the elements hidden in the resulting time series set the flags argument to 2 Clip ts begin end 2 Chapter 7 Time series SQL routines 7 27 7 28 In this example ts is the time series you are clipping and begin and end are the timepoints marking the range to extract To extract data into a new time series include hidden elements in that time range and reveal the hidden elements in the resulting time series set the flags argument to 6 Clip ts begin end 6 In this example ts is the time series you are clipping and begin and end are the timepoints marking the range to extract You can use the flags argument for handling hidden elements in conjunction with the option for determining the origin of the resulting time series For example if you set the flags argument to 3 the Clip function includes hidden elements in the resulting time series the elements are marked as hidden and the origin of the resulting time series is the earlier of the begin_o
244. gs 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 of the returned element will be returned in the off parameter if off is not NULL 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 otherwi
245. han in the row Default is 20 An in row time series should not be larger than 1500 bytes If a parameter is not present in the input string its default value is used 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 value value value timestamp 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 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 Example 1 Create a regular time series Following example shows an INSERT statement for a regular time series 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 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 use
246. he 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 IBM Informix TimeSeries Data User s Guide Returns A non null element of the time series GetLastValid function 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 values on page 7 5 lues on page 7 5 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 select GetLastValid stock_data 2011 01 08 00 00 00 00000
247. he AndOp function The CallIndex function The CalRange function The CalStamp function The CalStartDate function The OrOp function Chapter 7 Time series SQL routines Time series SQL routines sorted by task The flags argument values Abs function Acos function AggregateBy Function AggregateRange function Apply function ApplyBinaryTsOp function ApplyCalendar function ApplyOpToTsSet function iv IBM Informix TimeSeries Data User s Guide 3 6 3 6 3 8 39 3 10 oll 3 12 3 14 3 15 3 16 3 16 3 17 3 17 3 18 3 19 4 2 4 3 4 3 4 4 4 4 4 6 4 11 4 19 4 19 4 19 4 20 4 20 5 1 5 1 5 2 5 3 5 4 5 5 6 1 6 1 6 2 6 4 6 5 x 6 5 7 1 7 1 7 6 7 6 7 6 o a LD 7 11 7 16 7 17 gt 7 19 ApplyUnaryTsOp function Asin function Atan function Atan2 function ox Binary arithmetic functions BulkLoad function Clip function ClipCount function ClipGetCount function Cos function Countlf function DelClip function DelElem function DelRange function DelTrim function Divide function A ElemIsHidden function ElemIsNull function Exp function FindHidden function GetCalendar function GetCalendarName function GetClosestElem function GetContainerName function GetElem function GetFirstElem function GetIndex function GetInterval function GetLastElem functi
248. he IBM Intellectual Property Department in your country or send inquiries in writing to Intellectual Property Licensing Legal and Intellectual Property Law IBM Japan Ltd 1623 14 Shimotsuruma Yamato shi Kanagawa 242 8502 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 MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE Some states do not allow disclaimer of express or implied warranties in certain transactions therefore this statement may not apply to you This information could include technical inaccuracies or typographical errors Changes are periodically made to the information herein these changes will be incorporated in new editions of the publication IBM may make improvements and or changes in the product s and or the program s described in this publication at any time without notice Any references in this information to non IBM websites are provided for convenience only and do not in any manner serve as an endorsement of those websites The materials at those websites are not part of the materials for this IBM product and use of those websites is at your own risk Copyright IBM Corp 2006 2012 D 1 D 2 IBM may us
249. he 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 TSContainerCreate procedure on page 7 86 SetOrigin function The SetOrigin function moves the origin of a time series back in time Chapter 7 Time series SQL routines 7 77 Syntax SetOrigin ts TimeSeries origin datetime year to fraction 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 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 11 Clip function on page 7 26 GetOrigin function on page 7 55 PutTimeSeries function on page 7 73 Sin function The Sin function returns the sine of its argument It is one of the unary arithmetic functions that work
250. he current time series java lang String columnName element as a Timestamp object getTSMetaData java sql ResultSetMetaData getTSMetaData Retrieves an IfmxResultSetMetaData object that contains the number types and properties of the time series elements in this IfmxTimeSeries object getType int getType Retrieves the type of this IfmxTimeSeries object insertRow void insertRow Connection conn Inserts the current row using the connection set by the setConnection method to update the underlying time series stored in the database The setConnection method is described in Time series methods on page 8 12 isAfterLast boolean isAfterLast Determines whether the time series cursor is after the last element isBeforeFirst boolean isBeforeFirst Determines whether the time series cursor is before the first element isFirst boolean isFirst Determines whether the time series cursor is on the first element in this time series 8 14 IBM Informix TimeSeries Data User s Guide Table 8 2 Public methods continued Method Signature Description isLast boolean isLast Determines whether the current element is the last element in this time series last boolean last Moves the time series cursor to the last element in the time series moveToCurrentRow void moveToCurrentRow Moves the time series cursor to the remembered position in this IfmxTimeSeries object usually the curre
251. he database server creates the container when the first time series is inserted into that container Both regular and IBM Informix TimeSeries Data User s Guide irregular time series are stored in containers when they exceed a specified size which is specified when the time series is created You can create multiple containers in the same dbspace A row is inserted in the TSContainerTable table Example The following example creates a new container called new_cont in the space rootdbs for the time series type stock_bar execute procedure TSContainerCreate new_cont rootdbs stock_bar 0 0 Related concepts TSInstanceTable table on page 2 8 TSContainerTable table on page 2 9 Related tasks Managing containers on page 3 7 Configuring additional container Related reference SetContainerName function on page 7 77 TSContainerDestroy procedure TSContainerDestroy procedure 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 Destroying a container is permitted only when 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 ca
252. he default container pool run the following query SELECT NAME FROM TSContainerTable WHERE pool autopool Related reference TSContainerCreate procedure on page 7 86 TSContainerDestroy procedure on page 7 87 Chapter 2 Data types and system tables 2 9 2 10 IBM Informix TimeSeries Data User s Guide Chapter 3 Create and manage a time series 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 To create and load a time series 1 Create a calendar or choose a predefined calendar 2 Create a time series column 3 Optional Create additional containers and container pools 4 Create the time series 5 Load data into the time series Example Create and load a time series This example shows how to create a TimeSeries data type create a time series table create a time series by using the TSCreate procedure and load data into the time series by using 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
253. he expression The virtual table is based on the specified TimeSeries data type and the other columns from the base table IBM Informix TimeSeries Data User s Guide 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 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 TSCreat
254. he 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 Apply function on page 7 11 Clip function on page 7 26 9 ClipCount function on page 7 2 ClipGetCount function GetElem function on page 7 44 GetLastValid function on page 7 49 GetNthElem function on page 7 53 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 7 30 IBM Informix TimeSeries Data User s Guide 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 ts 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 a
255. he following statement returns the information for all containers EXECUTE FUNCTION TSContainerUsage NULL pages slots total 2029 241907 2169 1 row s retrieved The containers have only 140 pages of available space 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 returns TimeSeries with lvarchar 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 handlesnulls 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 Chapter 7 Time series SQL routines 7 97 7 98 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 origin The origin of the time series This is the first valid date
256. he 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 Chapter 9 Time series API routines 9 45 9 46 Related reference The ts_elem function on page 9 20 The ts_make_elem function on page 9 36 The ts_next function on page 9 39 The ts_put_last_elem function The ts_upd_elem function on page 9 52 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 Related reference The ts_put_elem function on page 9 44 The ts_put_elem_no_dups function on page 9 45 The ts_elem function on page 9 2 The ts_get_ts function on page 9 30 The ts_make_elem function on page 9 36 The ts_make_elem_with_buf function on page 9 37 7 function on page 9 39 D ao 5 Pad 5 The ts_put_nth_elem function The
257. he 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 Chapter 7 Time series SQL routines 7 23 Related reference Unary arithmetic functions on page 7 116 Divide function on page 7 39
258. he 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 Accessibility features for IBM Informix products Accessibility features Keyboard navigation Related accessibility information IBM and accessibility Dotted decimal syntax diagrams Notices Trademarks Index viii IBM Informix TimeSeries Data User s Guide 9535 9 36 9 37 9 38 9 38 9 39 9 40 9 41 9 41 9 43 9 44 9 45 9 46 9 46 9 47 9 48 9 48 9 49 9 49 9 50 9 51 lt 9 52 9 52 B 1 C 1 C 1 C 1 C 1 C 1 C 1 D 1 D 3 X 1 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
259. here 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 Chapter 7 Time series SQL routines 7 69 Related concepts Related reference GetNthElem function on page 7 53 TSCreate function on page 7 97 The ts_put_elem function on page 9 44 PutNthElem function on page 7 71 UpdElem function on page 7 119 PutElemNoDups function 7 70 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 falues on page 75 lues on page 7 5 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
260. i_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 Chapter 7 Time series SQL routines 7 115 Related concepts Planning for accessing time series data on page 1 12 Unary arithmetic functions 7 116 The standard unary functions Abs Acos Asin Atan Cos Exp Logn Negate Positive Round Sin Sqrt and Tan are extended
261. ies IfmxTimeSeries java sql Timestamp startdate IfmxCalendar cal String containerName int threshold String sqlTypeName Connection conn throws SQLexception When you select a time series from the database the getObject method is used to extract the time series from the result set The IBM Informix JDBC Driver instantiates a new IfmxTimeSeries object using the first constructor IfmxTimeSeries It creates an empty object with no variables initialized When you create a time series on the client to be inserted into the database use the second constructor to instantiate the object This constructor initializes the new object by parsing the input string The following information is required e A start date e A calendar the calendar must exist in the database e A container name the container must exist in the database e A threshold e A row type name the row type must exist in the database e A valid connection The connection must include an entry for the time series type in its type map see Create a custom type map on page 83 e Whether the time series is regular or irregular The IfmxTimeSeries class provides methods for manipulating time series The IfmxTimeSeries class also provides methods that it inherits from the JDBC ResultSet interface These methods are listed in Public methods inherited from the DBC ResultSet interface on page 8 12 see your JDBC documentation for more information about these
262. ies SELECT mr t mr energy FROM TABLE transpose SELECT readings FROM smartmeters WHERE meter_id 13243 smartmeter_row 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 Chapter 7 Time series SQL routines 7 81 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 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
263. ies cursor to the row indicated by row afterLast void afterLast Moves the time series cursor to the end of the IfmxTimeSeries object just after the last element beforeFirst void beforeFirst Moves the time series cursor to the beginning of the IfmxTimeSeries object just before the first element deleteRow void deleteRow Connection conn throws Deletes the current row from the time SQLException series and uses the specified connection to delete the time series in the underlying database findColumn int findColumn java lang String columnName Retrieves the column index for J 8 8 columnName first boolean first Moves the time series cursor to the first element in the IfmxTimeSeries object getBigDecimal java math BigDecimal getBigDecimal int Gets the value in the current time series columnIndex element as a bigDecimal value getBigDecimal java math BigDecimal getBigDecimal Gets the value in the current time series java lang String columnName element as a bigDecimal value getBoolean boolean getBoolean int columnIndex Gets the value in the current time series element as a boolean value getBoolean boolean getBoolean java lang String Gets the value in the current time series columnName element as a boolean value getByte byte getByte int columnIndex Gets the value in the current time series element as a byte value getByte byte getByte java lang String columnName Gets the value in the
264. ify metadata Update user defined metadata UpdMetaData Make elements visible Make an element invisible HideElem or mivisible toa scan Make a range of elements invisible HideRange Make an element visible RevealElem Make a range of elements visible RevealRange Check for null or Determine if an element is hidden ElemIsHidden idden clemente Determine if an element is null ElemIsNull 7 2 IBM Informix TimeSeries Data User s Guide Table 7 1 Time series SQL routines by task type continued Task type Description Routine name Extract and use part Extract a period between two time stamps or Apply of a time series corresponding to a set of values and run an expression or function on every entry Extract data between two timepoints Clip Clip some elements ClipCount Output values in XML format TStoXML Extract a period that includes the specified time WithinC Extract a period starting or ending at the specified time WithinR Apply a new calendar Apply a calendar ApplyCalendar to a time series Create and load a Load data from a client file BulkLoad He series Create a regular empty time series a regular populated TSCreate time series or a regular time series with metadata Create an irregular empty time series an irregular TSCreatelIrr populated time series or an irregular time series with metadata Find the intersection Build the intersection of multiple time serie
265. ify null values for columns that are not part of the primary key and do not have NOT NULL constraints 4 12 IBM Informix TimeSeries Data User s Guide Table 4 5 Settings for the TSVTMode parameter continued Flag Value Description TS_VTIUPDATE_NONKEY_NOT_NULLS 32 TS_VTIUPDATE_NONKEY_INCLUDE_NULLS 64 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 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 using 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 TS_VTI_SCAN_HIDDEN 512 Hidden elements appear in the virtual table TS_VTI_SCAN_DISCREET 1024 When selecting 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
266. imal 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 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 following 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 on
267. ime series Java classes e From the command line Execute the following to return the version stamp java com informix timeseries Version e From within an application Use any of the following three methods to return the version stamp String version version IfmxTimeSeries getVersion version IfmxCalendar getVersion version IfmxCalendarPattern getVersion The getVersion method is a static method therefore you are not required to create a time series object to retrieve the version stamp The version returned from the three classes is always the same The IfmxCalendarPattern class 8 8 The IfmxCalendarPattern class implements the IfmxCalendarPatternUDT TimeSeries interface An interface provides an abstract description of the methods and any constants belonging to a class The IfmxCalendarPatternUDT interface specifies the standard constants that any calendar pattern class might have to use The interface is intended for use only by programmers who want to develop calendar pattern classes You can create an IfmxCalendarPattern object by e Selecting a calendar pattern from the database e Selecting a calendar from the database e Selecting a time series from the database IBM Informix TimeSeries Data User s Guide e Instantiating a new object to be inserted into the database The IfmxCalendarPattern class defines the following constructors for these situations IfmxCalendarPattern IfmxCalendarPattern String pa
268. ines are only used with regular time series Lag e PutNthElem e TSCreate The TSCreatelrr function is used only with irregular time series The flags argument values Many of the time series SQL functions provide a flags argument to determine how the function handles null values and hidden elements These values are described here Some functions have specific settings of the flags argument that are relevant only to that function In these cases the flags argument values are documented with the function The value of the flags argument is the sum of the desired flag values from the following table Flag Value Meaning TSOPEN_RDWRITE 0 Default Indicates that the time series can be read and written to TSOPEN_READ_HIDDEN 1 Indicates that hidden elements should be treated as if they are not hidden TSOPEN_WRITE_HIDDEN 2 Allows hidden elements to be written to without first revealing them The element remains hidden afterward TSOPEN_WRITE_AND_HIDE 4 Causes any elements written to a time series also to be marked as hidden TSWRITE_AND_REVEAL 8 Reveals any hidden element written to TSOPEN_NO_NULLS 32 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 Chapter 7 Time series SQL routines 7 5 Th
269. ing 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 being discussed For detailed directions on using SQL statements for a particular application 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 or install the product documentation from the Quick Start CD that is shipped with Informix products To get the most current information see the Informix information centers at ibm com You can access the information centers and other Informix technical information such as technotes white papers and IBM Redbooks publications online at http www ibm com software data sw Compliance with industry standards xiv 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 The IBM Informix Geodetic DataBlade Module supports a subset of the data types from the Spatial Data Transfer Standard SDTS
270. ins hidden afterward TSOPEN_WRITE_AND_HIDE 4 Causes any elements written to a time series also to be marked as hidden TSWRITE_AND_REVEAL 8 Reveals any hidden element written to TSOPEN_NO_NULLS 32 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 instead an element is returned that has each column set to NULL TS_PUTELEM_NO_DUPS 64 Determines whether the BulkLoad function adds elements using the PutElem function default or the PutElemNodups function and PutElemNoDups function on page 7 70 If this flag is set the BulkLoad function uses PutElemNoDups These flags can be used in any combination except the following four e TSOPEN_WRITE_HIDDEN and TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_HIDDEN and TSOPEN_WRITE_AND_REVEAL Chapter 7 Time series SQL routines 7 25 e TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE e TSOPEN_WRITE_HIDDEN and TSOPEN_WRITE_AND_HIDE and TSOPEN_WRITE_AND_REVEAL The TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_HIDDEN 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 Clip function The Cl
271. 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 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 Chapter 9 Time series API routines 9 29 9 30 Related reference The ts_make_stamp function on page 9 38 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 57 The ts_create function on page 9 16 The ts_get_ts function The ts_get_ts function returns a pointer to the time series associated with the given time series descriptor Syntax ts_timeseries
272. integer N ts_desc The time series descriptor returned by ts_open for the source time series N 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 GetStamp function on page 7 57 The ts_cal_range function on page 9 9 The ts_cal_range_index function on page 9 10 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 mi_datetime xdatel mi_datetime date2 mi_integer interval date1 The first date Chapter 9 Time series API routines 9 49 9 50 date2 The date to subtract from the first date interval The interval as described next 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 0
273. 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 IBM Informix TimeSeries Data User s Guide 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 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 form 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 col
274. into a prepared statement to be sent to the database server The following methods are not currently supported e cancelRow Updates e clearWarnings e close e getArray e getAsciiStream e getBytes e getFetchSize e getRef e getUnicodeStream e getWarnings e updateBigDecimal 8 16 IBM Informix TimeSeries Data User s Guide e updateBytes e updateObject e refreshRow e rowDeleted e rowlnserted e rowUpdated e setFetchDirection e setFetchSize e updateAsciiStream e updateBinaryStream e updateCharacterStream Problem solving These topics contain suggestions to help solve problems if they should occur while you are using the time series Java class library If a user defined error is returned turn on JDBC tracing PROTOCOLTRACE The output file should contain the exact error message See your JDBC documentation for information about how to turn on JDBC tracing Tracing with the Java class library To turn on tracing with the time series Java class library change your CLASSPATH variable to point to the debug version of the library AAO1TimeSeries g jar and add the information described in the following to the database URL that you use at connection time To turn on tracing specify the environment variables TRACE TRACEFILE PROTOCOLTRACE and PROTOCOLTRACEFILE in the connection property list when you establish a connection to an IBM Informix database or database server The following table describes the tracing
275. 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 step 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 valu
276. ion_number tre file 4 20 setConnection method 8 12 SetContainerName function 7 77 setFetchDirection method 8 13 setFetchSize method 8 13 setObject method 8 7 8 8 8 9 SetOrigin function 7 77 setup class file 8 2 Shortcut keys keyboard C 1 Sin function 7 78 Sine determining 7 78 Software requirements 1 13 SQL statements ALTER TYPE 1 13 CREATE ROW TYPE 3 6 CREATE TABLE 3 6 INSERT 3 14 restrictions for time series 1 13 SELECT DISTINCT 1 13 UPDATE 3 17 virtual tables 4 1 SQLData interface 8 3 8 4 Sqrt function 7 78 Square root determining 7 78 standards xiv Start date calendar of 2 3 calendar pattern of 2 3 Storage for time series 1 9 Subtracting one time series from another 7 66 Sum running 7 111 Syntax diagrams reading in a screen reader C 1 System tables CalendarPatterns 2 7 CalendarTable 2 7 TSContainerTable 2 9 TSInstanceTable 2 8 T Table 2 9 Tables virtual 4 1 4 6 Tabular form converting time series data to 7 79 Tan function 7 78 Tangent determining 7 78 Threshold for containers specifying 3 14 time series examples directory 1 15 Time series 1 9 accessing 1 12 calendar pattern routines 5 1 calendar routines 6 1 concepts 1 4 creating 3 1 data types 2 7 decisions 1 9 deleting data 3 19 deleting elements 7 91 example of creating and loading 3 1 hardware and software requirements 1 13 loading data 1 11 loading methods 3 16 loading with the plug in 3 16 overview 1 1 planning 1 9 properties 1 9 sol
277. ip function extracts data between two timepoints in a time series and returns a new time series containing that data This allows you to 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 flags integer default 0 returns TimeSeries Clip ts TimeSeries begin_stamp datetime year to fraction 5 end_offset integer flags integer default 0 returns TimeSeries Clip ts TimeSeries begin_offset integer end_stamp datetime year to fraction 5 flags integer default 0 returns TimeSeries Clip ts TimeSeries begin_offset integer end_offset integer flags 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 7 26 IBM Informix TimeSeries Data User s Guide begin_offset The begin offset of the range regular time series only end_offset The end offset of the range regular time series only flags optional A flag specifying how to determine the resulting time series origin_and whether to copy hidden elements to the resulting time series See The flags argument values 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 a
278. 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 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 IBM Informix TimeSeries Data User s Guide Related reference GetElem function on page 7 44 GetLastElem function on page 7 47 GetLastValid function on page 7 49 GetNextValid function on page 7 52 GetNthElem function on page 7 53 The ts_previous_valid function on page 9 43 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 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 R
279. ize of the data types of the columns in the TimeSeries data type For regular time series do not include the size of the time stamp column For irregular time series include the size of the time stamp column The CHAR data type requires an additional 4 bytes when it is included in a TimeSeries data type The equation is a guideline The amount of required space can be affected by other factors such as the small amount of overhead 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 is very small The maximum number of elements allowed on a data page is 254 If the row size of your time series data is very small the page might contain the maximum number of elements but have additional space especially if you are not using a 2 K page size Related concepts Time series data storage on page 1 8 Related tasks Planning 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 7 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
280. l 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 IBM Informix TimeSeries Data User s Guide Related reference 9 GetNextValid function on page 7 52 n GetPreviousValid function n PutElem function on page 7 68 Transpose function on page 7 79 The ts_nth_elem function on page 9 41 GetStamp function on page 7 57 GetOrigin function The GetOrigin function returns the origin of the time series Syntax GetOrigin ts TimeSeries returns datetime year to fraction 5 ts 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 cal
281. l_stamp MI_CONNECTION conn mi_string cal_name mi_datetime tstamp mi_integer offset 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 before 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 CalIndex function on page 6 2 The CalRange function on page 6 3 The CalStamp function on page 6 4 The ts_cal_index function on page 9 8 The ts_cal_range_index function on page 9 10 The ts_cal_range function on page 9 9 The ts_cal_startdate function The ts_cal_startdate function returns the start date of a calendar Chapter 9 Time series API routines 9 11 9 12 Syntax mi_datetime 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 The CalStartDate function on page
282. le If the WHERE clause does not contain the beginning timepoint the first timepoint 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 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 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 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
283. le programs Sample programs are included with the database server To access the sample programs undo the file IfmxTimeSeries jar using the command jar xvf IfmxTimeSeries jar This expands the jar file into a directory structure the examples are located in com informix docExamples The examples include the SQL scripts setup sql and clean sql to set up data for the examples and clean it up afterward Time series Java classes The three time series Java classes represent each of the time series data types CalendarPattern Calendar and TimeSeries The IfmxCalendarPattern class The IfmxCalendarPattern class represents a calendar pattern in Java It has methods that allow you to read and write calendar patterns to and from a database The class diagram shows the time series and JDBC 2 0 interfaces that the class implements SQLData is a JDBC interface and IfmxCalendarPatternUDT is a time series interface described in The IfmxCalendarPattern class on page 8 8 Chapter 8 Time series Java class library 8 3 SQLData IfmxCalendarPatternUDT java sql com informix timeseries lfmxCalendarPattern Key com informix timeseries 5 Implements lfmxCalPatValues os Extends Figure 8 4 CalendarPattern class diagram The IfmxCalendar class The IfmxCalendar class represents Calendar types in Java It contains methods related to calendar functions and to reading and writing calendars to and from a database The
284. lements are still formed from incomplete windows The Apply function never looks at data before the specified start date Chapter 7 Time series SQL routines 7 109 Related reference N TSRunningMed function 7 110 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 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 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
285. line is used by default FA eae eee ees Optional items Several items y are allowed a comma must PeSpsect Sues oS 822 precede each repetition index_name index_name L table name table_name gt Table Reference gt gt Table Reference gt lt Reference to a syntax segment Table Reference view _ table synonym Table Reference view table synonym s Syntax segment Introduction XV xvi 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 n d device D database gt arr S gt t table gt gt lt 1 Setting the Run Mode S server T target Notes 1 See page Z 1 This diagram has a segment 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 PE La To see how to const
286. 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 from daily stocks where stock_name IBM Related reference TSCmp function The TSCmp function compares two values Syntax TSCmp valuel smallfloat value smallfloat returns int TSCmp valuel double precision value 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 Chapter 7 Time series SQL routines 7 83 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
287. mation query the TSContainerTable table or view the container in the IBM OpenAdmin Tool OAT for Informix Chapter 3 Create and manage a time series 3 7 3 8 To delete data from one or more containers run the TSContainerPurge function Examples 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 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_sml 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 named 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
288. me 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 2 4 IBM Informix TimeSeries Data User s Guide Examples The following example inserts a calendar called weekcal into the CalendarTable table INSERT INTO CalendarTable c_name c_calendar VALUES weekcal startdate 2011 01 02 00 00 00 00000 pattstart 2011 01 02 00 00 00 00000 pattname workweek_day This calendar and its pattern starts on 2011 01 02 and it uses a pattern named workweek_day The following example creates an hourly calendar with the specified pattern 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 patt
289. meSeries set_ts multiset flags integer default 0 returns TimeSeries ts 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 flags argument are described in falues on page 75 lues on page 7 5 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 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 Chapter 7 Time series SQL routines 7 121 Related reference N WithinC and WithinR functions 7 122 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 TimeSeries ts tamp 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 lva
290. mestamps getting ina range 6 3 9 9 9 10 union of two 6 5 CalendarTable system table defined 2 7 Calibrated search type 7 122 CalIndex function 6 2 CalPattStartDate function 5 2 CalRange function 6 3 CalStamp function 6 4 CalStartDate function 6 5 cancelRowUpdates method 8 13 Change Data Capture and time series 1 14 CLASSPATH variable 8 2 clearWarnings method 8 13 Clip function 7 26 clip method 8 12 ClipCount function 7 29 ClipGetCount function 7 31 Clipping a time series 7 11 7 26 7 29 close method 8 13 Closing a time series 9 12 Collapse function 5 3 Collapsing a calendar pattern 5 3 Columns data getting 9 26 Columns continued ID number getting 9 14 9 15 number of in a time series getting 9 13 numbering with Java 8 7 TimeSeries type 3 6 type information getting for 9 14 Comparing two time stamps 9 19 Comparing two values 7 83 compliance with standards xiv Constructors IfmxCalendar 8 9 IfmxCalendarPattern 8 8 IfmxTimeSeries 8 11 Container pool default 3 7 round robin order 3 9 user defined policy 3 9 Container pools 1 9 creating 3 7 user defined policy 3 10 Containers 1 9 creating 3 7 7 86 dbspace residing in 1 9 destroying 7 87 determining implicitly 3 16 instance ID of a time series in a getting 7 62 monitor 3 8 moving 3 7 name of getting 7 43 9 27 name setting 7 77 specifying 3 14 system table for 2 9 time series determining if itis ina 9 34 TSContainerNElems 7 88 TSContainerPctUsed 7 89 TSCon
291. methods Chapter 8 Time series Java class library 8 11 The IlfmxTimeSeries class methods The IfmxTimeSeries class provides methods for manipulating time series The IfmxTimeSeries class also provides methods that it inherits from the JDBC ResultSet interface These methods are listed in Public methods inherited from the about these methods Time series methods DBC ResultSet interface see your JDBC documentation for more information The time series methods in the IfmxTimeSeries class are for manipulating time series Table 8 1 Time series methods Method Signature Description cli ublic IfmxTimeSeries clip java sql Timestam Returns a new IfmxTimeSeries object P P Py q P J start java sql Timestamp end throws containing the elements between the start SQLException and end points etCalendar ublic IfmxCalendar getCalendar throws Returns the IfmxCalendar object 8 P 8 J SQLException associated with the IfmxTimeSeries object etContainerName ublic String getContainerName throws Returns the container name associated 8 P 885 SQLException with the IfmxTimeSeries object Returns NULL if no container name is set getNumberOf Elements int getNumberOfElements Gets the number of elements in this IfmxTimeSeries object getOffset int getOffset Returns the offset for the IfmxTimeSeries object getOrigin public int getOrigin throws SQLException Returns the origin for the IfmxTimeSeri
292. mmands exactly as shown in the syntax diagrams 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 additional syntax diagrams When a variable appears in a syntax diagram an example or text it 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 encouraged 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 fill out the form and submit your feedback Introduction xvii 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
293. mp getTimestampFromOffset Returns the time stamp for a given offset FromOffset throws SQLException getOffsetFrom public int getOffsetFromTimestamp throws Returns the offset for a given time stamp Timestamp SQLException readSQL public void readSQL throws SQLException Called automatically by the IBM Informix JDBC Driver to initialize an IfmxCalendar object from a binary result stream toString public String toString Returns the string representation of an IfmxCalendar object 8 10 IBM Informix TimeSeries Data User s Guide Method Signature Description writeSQL public void writeSQL throws SQLException Called automatically by the Informix JDBC Driver when a setObject method is called to insert an IfmxCalendar object into a prepared statement to be sent to the database server The IfmxTimeSeries class The IfmxTimeSeries class implements the IfmxTimeSeriesUDT TimeSeries interface An interface provides an abstract description of the methods and any constants belonging to a class The IfmxTimeSeriesUDT interface specifies the standard constants that any calendar class might require The interface is intended for use only by programmers who want to develop calendar classes You create an IfmxTimeSeries object by e Selecting a time series from the database e Instantiating a new object to be inserted into the database The IfmxTimeSeries class defines the following constructors for these situations IfmxTimeSer
294. mpatibility requirements IBM Informix TimeSeries Data User s Guide 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 21 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 a with more recision to avoid this problem See Binary arithmetic functions on page 7 21 for more information 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 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_data 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
295. mples have a calendar pattern length of seven days or one week Note Make sure that your calendar pattern length is correct or your time series data might not match your requirements For example the following pattern looks like it repeats every week but the pattern repeats every six days because the intervals add up to only six days 1 off 4 on 1 off You can manage exceptions to your calendar pattern by hiding elements for which there is no data by using the HideElem function IBM Informix TimeSeries Data User s Guid The calendar pattern is stored in the CalendarPatterns table and can be used or reused in several calendars 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 boundaries that surround the given starting timepoint Examples The following statement creates a pattern named hour that has a timepoint every ho
296. n on page 7 26 DelElem function DelTrim function on page 7 38 HideElem function on page 7 5 InsSet function on page 7 61 PutSet function on page 7 7 co UpdSet function on page 7 121 DelElem function 7 36 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 co S The time series to act on tstamp The time stamp of the element to be deleted IBM Informix TimeSeries Data User s Guide flags Valid values for the flags parameter are described in The flags argument falues on page 7 5 lues on page 7 5 The default is 0 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 Example The following example deletes an element from a time series instance update activity_stocks 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 19 w mu er a ley 5 a g f L Q z function on page 7 35 DelTrim function on page 7 3 GetElem function on page 7 44 7 7 3 a page 7 6 page 7 6 del function
297. n aggregate the values for all the meters and sum the values for specific time intervals to get a single total for each interval The resulting time series represents the total energy consumption for each time interval for that energy concentrator TSRollup function on page 7 103 X IBM Informix TimeSeries Data User s Guide Table 2 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC4 continued Overview Reference Delete a range of elements and free empty pages from a time series You can delete elements in a time series from a specified time range and free any resulting empty pages by using the DelRange function The DelRange function is similar to the DelTrim function however unlike the DelTrim function the DelRange function frees pages in any part of the range of deleted elements You can free empty pages that have only null elements from a time series for a specified time range or throughout the time series by using the NullCleanup function DelRange function on page 7 37 NullCleanup function on page 7 66 Table 3 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC3 Overview Reference Time series storage management Time series data that is too large to fit into a row ina table is stored in time series containers You do not need to create a container before you insert data into a time series or specify a container name when you insert da
298. n 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 cH s 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 Chapter 7 Time series SQL routines 7 117 7 118 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 calendar 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 ba
299. n execute this procedure Example The following example destroys the container ctnr_stock execute procedure TSContainerDestroy ctnr_stock Chapter 7 Time series SQL routines 7 87 Related concepts TSInstanceTable table on page 2 8 TSContainerTable table on page 2 9 Related tasks Managing containers on page 3 7 Related reference TSContainerCreate procedure on page 7 86 TSContainerNElems function 7 88 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 returns bigint container_name The name of an existing container Can be NULL Description Use the TSContainerNElems function to view the number of elements stored in a container If you specify NULL as the container name information about all containers for the database is returned Returns The number of elements stored in the specified container or in all containers 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 TSContainerNE1 ems NULL elements 241907 1 row s retrieved IBM Informix TimeSeries Data User s Guide Relate
300. n irregular time series deleted elements are not counted For a regular time series 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 26 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 AT nction on page 7 58 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 Chapter 7 Time series SQL routines 7 31 Related refer
301. n page 9 31 RevealElem function on page 7 75 The ts_row_to_elem function 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 IBM Informix TimeSeries Data User s Guide Returns An element and its offset If the time series is regular column 0 the time stamp column of the element is NULL The element must be freed by the caller using the ts_free_elem procedure Related reference The ts_elem_to_row function on page 9 22 The ts_free_elem procedure on page 9 24 The ts_make_elem function on page 9 36 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 tsdesc mi_
302. 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 Chapter 7 Time series SQL routines 7 51 Related reference ClipGetCount function on page 7 30 GetIndex function on page 7 46 GetNthElem function on page 7 53 GetStamp function on page 7 57 The ts_nelems function on page 9 38 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 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
303. nd 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 If it is a time stamp the Clip function uses the time series calendar to find the offset that corresponds to the time stamp If there is no entry in the time series exactly at the requested time stamp Clip uses the calendar s time stamp that immediately follows the given 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 permitted for the begin and end points Data at the beginning and ending offsets is included in the resulting time series The flags argument values The flags argument is an optional parameter that determines e The resulting time series origin e Whether to copy hidden elements to the resulting time series and if the elements should be hidden or revealed in the new time series If the flags argument is not set has the default value of 0 then the origin of the resulting time series is the later of the begin_range argument and the origin of the input time series If the flag is set to 1 then the origin of the resulting time series is set to the earlier of the begin_offset or the begin_range argument and the ori
304. nd 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 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 direction 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 Chapter 7 Time series SQL routines 7 123 7 124
305. ndar 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 Chapter 5 Calendar pattern routines 5 3 Related reference The Expand function The Expand function The Expand function expands the given calendar pattern into the destination units which must have a smaller interval unit than that of the given 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 The Collapse function on page 5 3 The NotOp function 5 4 The NotOp function turns all on intervals off and all off intervals on in the given
306. ng 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 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 IBM Informix TimeSeries Data User s Guide Related reference The CalIndex function on page 6 2 The CalRange function on page 6 3 4 The CalStamp function on page 6 The ts_cal_index function on page 9 8 The ts_cal_range function on page 9 9 The ts_cal_stamp function The ts_time function on page 9 49 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 Syntax mi_datetime ts_ca
307. ning 7 20 Arc tangent determining 7 21 Arithmetic functions binary 7 21 unary 7 116 Asin function 7 20 Atan function 7 21 Atan2 function 7 21 autopool container pool 3 7 Average computing running 7 107 BaseTableName parameter 4 4 beforeFirst method 8 7 8 13 Binary arithmetic functions Atan2 7 21 description of 7 21 Divide 7 39 Minus 7 66 Mod 7 66 Plus 7 68 Pow 7 68 Times 7 79 BulkLoad function 3 17 7 24 C Calendar 1 4 Calendar data type 2 3 8 4 Calendar pattern 1 4 Calendar pattern routines 5 1 Calendar patterns 1 8 Copyright IBM Corp 2006 2012 Calendar patterns continued collapsing 5 3 data type for 2 1 expanding 5 4 getting 7 41 intersection of two 5 1 interval options 2 1 Java representation 8 3 reversing intervals for 5 4 specification for 2 1 start date for 2 3 system table for 2 7 union of two 5 5 Calendar routines 6 1 CalendarPattern data type 2 1 8 3 CalendarPatterns system table defined 2 7 Calendars 1 8 applying new to time series 7 17 built in 3 5 calibrated search using 7 122 data type for 2 3 getting 7 42 intersection of time series from 7 62 intervals determining number of between time stamps 6 2 intervals determining number of between timestamps 9 8 Java representation 8 4 lagging 7 65 names of getting 9 25 relative search using 7 122 returned time series and 2 7 specifying 2 3 3 14 start date for 2 3 system table for 2 7 timestamp getting after intervals 6 4 9 11 ti
308. ns double precision Chapter 7 Time series SQL routines 7 101 current_value The current datum v in the sum shown next 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 function 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 N TSPrevious function 7 102 The TSPrevious function records the supplied argument and returns the last argument it was passed Syntax TSPrevious value int returns int TSPrevious value smallfloat IBM Informix Time
309. nt INSERT INTO smartmeters meter_id rawreadings VALUES met 0001 origin 2006 01 01 00 00 00 00000 calendar smartmeter regular threshold 0 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 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 3 10 IBM Informix TimeSeries Data User s Guide Syntax PolicyName table_name lvarchar column_name lvarchar subty
310. nt element moveTolInsertRow void moveToInsertRow Moves the time series cursor to the insert row next boolean next Initially moves the time series cursor to the first element in the time series Subsequent calls move the cursor to the second element then the third and so on previous boolean previous Moves the time series cursor to the previous row readSQL void readSQL java sql InputStream stream Populates the IfmxTimeSeries object with java lang String type data read from the given binary input stream relative boolean relative int row Moves the time series cursor the number of rows specified by row updateBoolean void updateBoolean int columnIndex boolean Updates the current time series element x with the given boolean object updateBoolean void updateBoolean java lang String Updates the current time series element columnName boolean x with the given boolean object updateByte void updateByte int columnIndex byte x Updates the current time series element with the given byte object updateByte void updateByte java lang String Updates the current time series element columnName byte x with the given byte object updateDate void updateDate int columnIndex Updates the current time series element java sql Date x with the given date object updateDate void updateDate java lang String Updates the current time series element columnName java sql Date x with the given date object updateDouble void updateDouble int columnIndex
311. nt 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 IBM Informix TimeSeries Data User s Guide Related reference DelElem function on page 7 36 GetElem function on page 7 44 TInsSet function 7 PutElem function on page 7 68 The ts_ins_elem function on page 9 33 UpdElem function on page 7 119 InsSet function The InsSet function inserts every element of a given set into a time series Syntax InsSet ts TimeSeries multiset_rows multiset flags integer default 0 returns TimeSeries ts 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 fanes on page 7 5 lues on page 7 5 The default is 0 Description 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 precisi
312. ntify 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 e For updates to existing rows only the TimeSeries column can be updated e Hidden elements are not included in the virtual table e When selecting 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 TS_VTI_PUT_ELEM 1 Multiple elements for the same timepoint are allowed Updates to the underlying time series insert elements even if elements already exist for the timepoints Uses the PutElem function TS_VTISHOW_NULLS 2 Null elements appear in the virtual table Hidden elements appear as null elements unless the value 512 is also set TS_VTI_DISABLE_NOT_NULL_CONSTRAINT 16 For existing rows you can specify NULL values for columns that are not part 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 spec
313. number of intervals before the start date Related reference The ts_tstamp_difference function on page 9 49 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 mi_integer mi_integer mi_datetime startdate xstartdate cnt interval result The date to start from cnt interval The number of intervals to add to the start date The interval as described next result Description The resulting date 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 TS_WEEK TS_MONTH TS_YEAR 9 51 Chapter 9 Time series API routines 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 after the start date Related reference The ts_tstamp_difference function on page 9 4 The ts_tstamp_minus function on page 9 50 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
314. ny time series types Chapter 8 Time series Java class library 8 5 8 6 Related reference UpdElem function on page 7 119 Create an entry in the database connection The following example makes an entry in the type map of a connection for handling TimeSeries data java util Map customTypeMap customlypeMap conn getTypeMap customTypeMap put timeseries stock_bar Class forName com informix timeseries IfmxTimeSeries In this example conn is a valid database connection and timeseries stock_bar is the TimeSeries type When a TimeSeries type is extracted from the database the IBM Informix JDBC Driver searches the type map for an entry for this data type in this case timeseries stock_bar If a type map entry exists an object of the appropriate class is instantiated IfmxTimeSeries in this example and the readSQL method of that object is executed The readSQL method extracts the time series data from the database result set There must be an entry in the type map for every TimeSeries type that your program uses For example you would also require an entry for the timeseries stock_trade TimeSeries type if your Java application accessed that type as well java util Map customTypeMap customlypeMap conn getTypeMap customTypeMap put timeseries stock_trade Class forName com informix timeseries IfmxTimeSeries You must also add entries for the Calendar and CalendarPattern data types if your pr
315. ny 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 user defined type to be useful The value returned can be freed by the caller with mi_var_free Related reference GetMetaData function on page 7 50 GetMetaTypeName function on page 7 50 UpdMetaData function on page 7 120 TSCreate function on page 7 97 TSCreatelrr function on page 7 99 The ts_create_with_metadata function on page 9 17 The ts_get_metadata function The ts_update_metadata function on page 9 52 The ts_get_origin function The ts_get_origin function returns the origin of the given time series 9 28 IBM Informix TimeSeries Data User s Guide Syntax mi_datetime ts_get_origin ts_timeseries ts ts The source time series Description The equivalent SQL function is GetOrigin Returns The origin of the given time series This value must be freed by the caller using mi_free Related reference 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 CONNECTION xconn mi_datetime dt mi_integer xyear mi_integer month mi_integer day mi_integer hour mi_integer minute mi_
316. of the IBM time series Chapter 7 Time series SQL routines 7 71 update daily_stocks set stock_data PutNthElem stock_data row NULL datetime year to fraction 5 355 309 341 999 stock_bar 0 where stock_name IBM Related reference PutElem function on page 7 68 PutSet function 7 72 The PutSet function updates a time series with the supplied multiset of row type values 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 flags argument are described later in this section 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 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 a 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 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 passed
317. 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 19 DelTrim function 7 38 The DelTrim function deletes all elements in the specified time range in a time series instance including the delimiting timepoints The DelTrim function 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 begin_stamp The begin point of the range end_stamp The end point of the range IBM Informix TimeSeries Data User s Guide flag
318. ogram selects those types from the database For these types you must have one entry for the CalendarPattern type and one entry for the Calendar type as shown next For CalendarPattern data java util Map customTypeMap customlypeMap conn getTypeMap customTypeMap put calendarpattern Class forName com informix timeseries IfmxCalendarPattern For Calendar data java util Map customTypeMap customTypeMap conn getTypeMap customTypeMap put calendar Class forName com informix timeseries IfmxCalendar Create a map independently The following example shows how to create a type map independently of your database connection For example you could use just one type map that is global to the application Map typemap new Map typeMap add timeseries test Class forName com informix timeseries IfmxTimeSeries IBM Informix TimeSeries Data User s Guide You can use the type map in your getObject method call ts IfmxTimeSeries rSet getObject 1 typeMap Instead of using the type map associated with the connection the IBM Informix JDBC Driver uses the given type map This example assumes that the time series column is the first column in the result set The IfmxTimeSeries object Your Java program can use a SELECT statement to retrieve TimeSeries data as in String sqlCmd SELECT ts FROM test WHERE id 1 PreparedStatement pStmt conn prepareStatement sq1Cmd ResultSet rSet
319. oid 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 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 s
320. on 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 ClipGetCount function on page 7 30 The Callndex function on page 6 2 The CalRange function on page 6 3 GetElem function on page 7 44 GetNelems function on page 7 51 GetNthElem function on page 7 53 GetStamp function on page 7 57 The ts_index function on page 9 32 Getinterval function The GetInterval function returns the interval used by a time series 7 46 IBM Informix TimeSeries Data User s Guide Syntax GetInterval ts TimeSeries returns lIvarchar S The source time series 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 ho
321. on GetLastNonNull function GetLastValid function GetMetaData function GetMetaTypeName function GetNelems function GetNextNonNull function GetNextValid function GetNthElem function GetOrigin function GetPreviousValid function GetStamp function GetThreshold function HideElem function HideRange function InsElem function 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 lt 7 19 7 20 7 21 7 21 7 21 7 24 7 26 7 29 7 30 7 32 x 1732 1 35 7 36 a LOL 7 38 x 7 39 7 39 7 40 7 40 7 41 7 41 7 42 7 42 7 43 7 44 7 45 7 46 7 46 7 47 7 48 7 49 7 50 7 50 270k 1392 7 52 7 53 7399 7 56 7 57 z 7 57 7 58 7 59 7 60 7 61 7 62 7 62 7 64 7 65 7 65 7 66 7 66 7 66 7 66 7 68 7 68 7 68 7 68 7 70 7 71 7 72 Contents Vv PutTimeSeries function RevealElem function RevealRange function Round function i SetContainerName fonction SetOrigin function Sin function Sqrt function Tan function Times function TimeSeriesRelease f ncton Transpose function TSAddPrevious function TSCmp function
322. on page 9 30 The ts_get_typeid function on page 9 31 The ts_create_with_metadata function The ts_create_with_metadata function creates a time series with user defined metadata attached Syntax 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 xmetadata_typeid 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 Chapter 9 Time series API routines 9 17 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 Must be 0 for regular time series and TS_CREATE_IRR for irregular 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 See
323. on time series The others are Abs Acos Asin Atan Cos Exp Logn Negate Positive Round and Tan Related reference Unary arithmetic functions on page 7 116 Sqrt function The Sqrt function returns the square root of its argument It 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 Related reference Unary arithmetic functions on page 7 116 Tan function The Tan function returns the tangent of its argument It 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 7 78 IBM Informix TimeSeries Data User s Guide Related reference Unary arithmetic functions on page 7 116 Times function The Times function multiplies one time series by another It is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Minus Mod Plus and Pow Related reference 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
324. on 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 given 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 Chapter 7 Time series SQL routines 7 61 Related reference N 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 integer ts The source time series Description The instance ID is used as an index in the container It can also be used to perform a lookup in 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 case 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 Intersect function 7 62 The Intersect function performs an
325. ond 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 unl 1 IBM Informix TimeSeries Data User s Guide 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 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 e
326. ond output is derived from the first two input times and so on Null elements in the input also result in shortened windows The TSRunningVar 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 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 IBM Informix TimeSeries Data User s Guide 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 esse function on page 7 11 K TSRunningSum function on page 7 111 TSSetToList function The TSSetToList function takes a TimeSeries column and returns a list collection of rows containing all the elements in th
327. ons Aggregate data Convert between time stamps and offsets Extract values for a time interval Find or delete null elements Remove older data by deleting a range of elements The Java classes and methods and API routines perform many of the same tasks that the SQL routines do for time series data You can use Java classes and methods in applications written in Java You can use the API routines in applications written in C 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 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 Related reference Chapter 8 Time series Java class library on page 8 1 Hardware and software requirements 1 12 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 IBM Informix TimeSeries Data User s Guide The Informix TimeSeries solution might not be supported on all platforms supported by Informix database servers See the system requirements for the Informix TimeSeries solution at https www ibm com support docview wss rs 630 amp uid swg27020937 Installing the IBM Informix TimeSerie
328. or 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 arbitrarily 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 standard SQL statements on virtual tables to select and insert data When you use a calendar and calendar pattern to specify when time series elements are valid you prevent the storage of null elements for times when data cannot be valid For example if you want to track stock data you would define your calendar to accept elements for timepoints only during trading hours Also you can easily find which elements are missing for valid timepoints by querying for null elements If you do have missing elements in many cases the missing elements do not take up space on disk You can also aggregate information by selecting data and changing the calendar for the results of the query 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 use a daily calendar to aggregate the 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 dat
329. or 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 Related concepts The insertion of data through virtual tables on page 4 3 Related tasks Creating a time series virtual table on page 4 4 Loading data from a file into a virtual table on page 3 17 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 IBM Informix TimeSeries Data User s Guide create row type stock_bar timestamp datetime year to fraction 5 high real low real final real vol real s create table daily stocks stock_id int stock_name lvarchar stock_data TimeSeries stock_bar Ja The daily_stocks base table contains the following data stock_id stock_name stock_data 900 AA01 t1 7 25 6 75 7 1000000 t2 7 5 6 875 7 125 1500000 901 IBM t1 97 94 2
330. owing example turns tracing on execute function TSSetTraceLevel TS_VTI_DEBUG 1001 Chapter 4 Virtual tables for time series data 4 21 4 22 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 pattern 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 Calendar on page 1 8 CalendarPattern data type on page 2 1 The 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
331. 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 executing 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 s 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 in ts beforeFirst while ts next Vw 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 the time stamp column as column 1 All the available methods of the IfmxTimeSeries IfmxCal
332. page 9 31 ms The ts_last_elem function on page 9 3 The ts_nth_elem function on page 9 4 The TS_ELEM_NULL macro on page 9 The ts_first_elem function on page 9 2 N Qo The ts_ins_elem function on page 9 33 The ts_make_elem function on page 9 3 D The ts_put_elem function on page 9 4 The ts_put_elem_no_dups function on page 9 45 The ts_put_last_elem function on page 9 46 The ts_put_nth_elem function on page 9 46 The ts_upd_elem function on page 9 5 The TS_ELEM HIDDEN macro N The TS_ELEM_HIDDEN macro determines whether the STATUS indicator returned 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 Chapter 9 Time series API routines 9 21 Returns A nonzero value if the element associated with the STATUS argument was previously hidden by the ts_hide_elem function Related reference The ts_elem function on page 9 20 The TS_ELEM_ NULL macro The ts_first_elem function on page 9 23 The ts_hide_elem function on page 9 31 The ts_last_elem function on page 9 34 The ts_next function on page 9 39 The ts_next_valid function on page 9 40 The ts_nth_elem function on page 9 41 The ts_previous_valid function
333. pe lvarchar irregular integer user_data lvarchar returns lvarchar PolicyName table_name lvarchar column_name lvarchar 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 to the INSERT statement in the order specified by the function
334. pical 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 2011 01 03 00 00 00 00000 1 1 2 2 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 This 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_dat
335. puts 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 resulting tim
336. r 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 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 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
337. r Table 3 3 Parameters for inserting data into a time series Parameter name Required Value calendar Yes Name of the calendar to use There is no default name container No Name of the container to use The default is no container the time series must fit in the database row or never be assigned to a table If the time series exceeds the threshold size you must set a container datafile No Name of the input file to use The format is the same as for the BulkLoad function If the data file is present no bracketed data is permitted Default is NULL irregular Yes for No value just the string irregular This parameter irregular must be included for an irregular time series but cannot be included for a regular time series metadata No The metadata to be added to the time series Can be NULL If metadata is supplied then the metadata type must also be supplied metatype No The data type of the metadata origin No Time stamp of the origin of the time series The default origin is the calendar start date IBM Informix TimeSeries Data User s Guide Table 3 3 Parameters for inserting data into a time series continued Parameter name Required Value regular No No value just the string regular This parameter is optional for a regular time series but cannot be included for an irregular time series threshold No Number of elements above which data is placed in a container rather t
338. r that are allocated to all containers Example The following statement returns the number of pages 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 2169 1 row s retrieved TSContainerTotalUsed function The TSContainerTotalUsed function returns the total number of pages containing time series data in the specified container or in all containers Syntax TSContainerTotalUsed container_name varchar 128 1 returns integer container_name The name of an existing container Can be NULL Description Use the TSContainerTotalUsed function to view the amount of data in a container If you specify NULL as the container name information about all containers for the database is returned Chapter 7 Time series SQL routines 7 95 Returns The number of pages containing time series data in the specified container or in all containers Example The following statement returns the number of pages used by time series data in the container named mult_container EXECUTE FUNCTION TSContainerTotalUsed mult_container pages 30 1 row s retrieved The following statement returns the number of pages used by time series data in all containers EXECUTE FUNCTION TSContainerTotalUsed
339. rameter are described in The flags argument alues on page 7 5 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 6 If the time stamp is not a valid timepoint in the time series an error is raised Chapter 7 Time series SQL routines 7 59 Returns The modified time series Related reference HideElem function on page 7 58 RevealRange function on page 7 76 InsElem function 7 60 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 5 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 is 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 stateme
340. rchar 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 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 of Within queries calibrated WithinC and relative WithinR IBM Informix TimeSeries Data User s Guide 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
341. rd 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 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 an
342. re 1 3 Architecture of a container in a database When you insert data into a time series and you do not specify a container name 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 store time series data in a different dbspace than the table is stored in by creating containers and then referencing them when you insert data into the time series You can specify a container pool in which to store time series data by using the TSContainerPoolRoundRobin function or create your own container pool policy function Related concepts Planning for data storage on page 1 10 Related tasks Managing containers on page 3 7 Monitoring time series containers on page 3 8 Getting started with the Informix TimeSeries solution Before you can create a time series decide on the properties
343. reating a time series with 7 97 7 99 9 17 creating for a time series 3 13 getting from a time series 7 50 9 28 getting the type name of 7 50 getting type ID from a time series 9 28 using distinct type TimeSeriesMeta 3 13 mi_set_trace_file API routine virtual tables 4 20 mi_set_trace_level API routine virtual tables 4 20 Minus function 7 66 Mod function 7 66 Modulus computing of division of two time series 7 66 moveToCurrentRow method 8 13 moveToInsertRow method 8 13 Multiplying one time series by another 7 79 N Natural logarithm determining 7 65 Negate function 7 66 Negating a time series 7 66 next method 8 7 8 13 NotOp function 5 4 Null elements 4 3 NullCleanup function 7 66 O Offsets 1 7 converting to time stamp 9 49 determining 9 18 element getting for 9 41 inserting an element at 7 71 9 46 timestamp getting for 7 46 7 57 9 32 onpload utility 3 17 OpenAdmin Tool for Informix 1 3 Opening a time series 9 41 Operators LessThan 1 13 Optim Developer Studio TimeSeries plug in 3 16 ORDER BY clause virtual tables 4 6 Origin 1 4 Origin of a time series changing 7 77 getting 7 55 9 28 specifying 3 14 OrOp function 5 5 6 5 Output of a function creating time series with 3 16 P Patterns 1 8 Performance virtual tables 4 19 planning 1 9 pload utility 3 17 Plus function 7 68 Positive function 7 68 Pow function 7 68 PreparedStatement object 8 7 previous method 8 13 Properties of time seri
344. ree 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_containername function The ts_get_flags function The ts_get_metadata function The ts_get_origin 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 The ts_ins_elem function i The TS_IS_INCONTAINER macro The TS_IS_IRREGULAR macro The ts_last_elem function 8 8 8 9 8 11 8 12 8 17 8 17 9 1 9 1 9 2 9 2 9 2 9 2 9 3 9 3 9 6 9 8 9 8 9 9 9 10 Contents 9 11 9 11 9 12 s 9 12 9 13 9 14 9 14 9 15 9 16 9 16 9 17 9 18 9 19 9 19 9 20 9 20 9 21 9 22 9 22 9 23 9 23 9 24 9 24 9 25 9 25 9 26 9 26 s 9 27 927 9 28 9 28 9 29 9 30 lt 9 30 9 31 9 31 9 32 9 33 9 34 9 34 9 34 vii The ts_last_valid function The ts_make_elem function The ts_make_elem_with_buf function 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 f nchion The ts_put_elem function The ts_put_elem_no_dups function T
345. ree all memory associated with a time series ts_free created with ts_copy or ts_create Copy all elements of one time series into another ts_put_ts Chapter 9 Time series API routines 9 3 Task type Description Routine name Scan a time series Start a scan ts_begin_scan Retrieve the next element from a scan ts_net End a scan ts_end_scan Find the time stamp of the last element retrieved from a scan Return the offset for the last element returned by ts_next ts_current_timestamp ts_current_offset regular only Make elements visible or invisible to a scan Make an element invisible ts_hide_elem Make an element visible ts_reveal_elem Select individual elements from a time series Get the element closest to a given time stamp ts_closest_elem Get the element associated with a given time stamp ts_elem Get the element at a specified position ts_nth_elem Get the first element ts_first_elem Get the last element ts_last_elem Find the next element after a given time stamp ts_next_valid Find the last element before a given time stamp ts_previous_valid Find the last element at or before a given time stamp ts_last_valid Update a time series Insert an element ts_ins_elem Update an element ts_upd_elem Delete an element ts_del_elem Put an element
346. regular 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 GetPreviousValid function on page 7 56 The TS_ELEM_NULL macro on page 9 22 The ts_put_elem function 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 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 o
347. rformed on a table containing a TimeSeries column The resulting virtual table is read only Syntax TSCreateExpressionVirtualTab VirtualTableName lvarchar BaseTableName lvarchar expression lvarchar subtype lvarchar TSVTMode integer default 0 TSColName lvarchar default NULL 4 8 IBM Informix TimeSeries Data User s Guide 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 subtype The name of the TimeSeries subtype for the values that are the results of the expression TSVTMode optional Sets the virtual table mode as described in The TSVTMode parameter on 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 ei
348. ries Syntax ts_timeseries ts_create MI_CONNECTION conn mi_string calname mi_datetime origin mi_integer threshold mi_integer flags MI_TYPEID typeid mi_integer nelem mi_string container conn A valid DataBlade API connection calname The name of the calendar origin The time series origin IBM Informix TimeSeries Data User s Guide 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 Must be 0 for regular time series and TS_CREATE_IRR for irregular 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 Can be NULL if the time series can fit in a row or is not going to be assigned to a table Description 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 TSCreate function on page 7 97 The ts_free procedure on page 9 24 The ts_open function on page 9 41 The ts_get_threshold function
349. rim function 7 38 Directory 1 15 Disabilities visual reading syntax diagrams C 1 Disability C 1 Divide function 7 39 Dividing one time series by another 7 39 Documentation files Java 8 2 Dotted decimal format of syntax diagrams C 1 DROP statement virtual tables 4 19 E Element 1 4 Elements columns in getting number of 9 13 converting to a row 9 22 data from one column in getting 9 26 deleting 7 36 9 20 deleting from a clip 7 35 7 38 deleting from a range 7 37 deleting null 7 66 first in a time series getting 7 45 9 23 freeing memory for 9 24 getting 7 44 9 20 9 25 hidden determining if 9 21 hidden revealing 7 75 7 76 9 48 hiding 7 58 9 31 inserting 7 60 7 68 7 70 9 33 9 36 9 44 9 45 inserting a set of 7 61 7 72 inserting at an offset 7 71 9 46 inserting at end of a time series 9 46 last valid getting 7 49 last getting 7 47 9 34 next valid getting 7 52 Elements continued next getting 9 39 null determining if 9 22 number in time series clip getting 7 31 number of getting 7 51 9 38 offset getting for an 7 53 9 18 9 41 timestamp getting for an 9 35 timestamp getting last before 7 56 9 43 timestamp getting nearest to an 9 40 updating 7 119 9 44 9 45 9 52 updating a set of 7 121 Enterprise Replication and time series 1 14 Examples directory 1 15 stock data 1 15 virtual tables 4 6 Exp function 7 41 Expand function 5 4 Expanding a calendar pattern 5 4 Exponentiating a time series 7
350. rmix TimeSeries solution Informix TimeSeries solution architecture Time series concepts TimeSeries data type technical overview Regular time series Irregular time series Calendar Time series data storage A P Getting started with the Informix TimeGenies solution Planning for creating a time series Planning for data storage Planning for loading time series data Planning for accessing time series data Hardware and software requirements Installing the IBM Informix TimeSeries Plug in for Data Studio Database requirements for time series data KR SQL restrictions for time series data Replication of time series data Time series global language support Sample smart meter data Setting up stock data examples Chapter 2 Data types and easel tables CalendarPattern data type Calendar data type TimeSeries data type Time series return types CalendarPatterns table CalendarTable table TSInstanceTable table TSContainerTable table Chapter 3 Create and manage a time series Example Create and load a time series Creating a TimeSeries data type and table Creating regular empty time series Creating the data load file Loading the time series data Accessing time series data through a virtual table Defining a calendar Predefined calendars Copyright IBM Corp 2006 2012 ix ix ix Xiv Xiv Xiv XV Xvi xvii xvii xvii 1 7 s a ee 1 10 1 11 1
351. rn 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 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 concepts CalendarPattern data type on page 2 1 Related tasks CalendarTable table 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
352. rst_stocks The query returns the following result 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 Apply function on page 7 11 TSAddPrevious function on page 7 8 E y WN j SZE k U a Le 3 oO oO oO 5 A N Qo function on page 7 8 TSDecay function on page 7 101 N TSPrevious function on page 7 10 TSRunningSum function on page 7 11 ingCor function TSRunningMed function on page 7 11 77 E WN i n E WN a 5 S o R a e 5 O 5 ge o ee oO T m m N TSRunningCor function 7 108 The TSRunningCor function computes the running correlation of two time series over a running window The TSRunningCor function returns NULL if the variance of either input is zero or NULL over the window Syntax TSRunningCor valuel double precision value2 double precision num_values integer returns double precision IBM Informix TimeSeries Data User s Guide 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 calcul
353. ruct 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 Type onpladm create job and then the name of the job 2 Optionally type p and then the name of the project 3 Type 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 IBM Informix TimeSeries Data User s Guide 4 Optionally you can choose 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 follow the Setting the Run Mode segment diagram to type f optionally type d p or a and then optionally type l or u 5 Follow the diagram to the terminator Keywords and punctuation Keywords are words reserved for statements and all commands except system level commands When a keyword appears in a syntax diagram it 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 co
354. ructure 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_tselem structure API routines This section contains e Time series API routines by task type e The correspondence between API and SQL routines e Individual routine reference pages 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 Task type Description Routine name Open and close a time series Open a time series ts_open Close a time series ts_close Return a pointer to the time series associated with ts_get_ts the given time series descriptor Create and copy a time series Create a time series ts_create Create a time series with metadata ts_create_with_metadata Copy a time series ts_copy F
355. ry_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 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 ro
356. s 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 Logn function The Logn function returns the natural logarithm of a time series It is one of the unary arithmetic functions that work on time series The others are Abs Acos Asin Atan Cos Exp Negate Positive Round Sin Sqrt and Tan Chapter 7 Time series SQL routines 7 65 Related reference Unary arithmetic functions on page 7 116 Minus function Mod function The Minus function subtracts one time series from another It is one of the binary arithmetic functions that work on time series The others are Atan2 Divide Mod Plus Pow and Times Related reference Binary arithmetic functions on page 7 21 The Mo
357. s Java class library uses the JDBC 2 0 specification for supporting User Defined Data Types UDTs in Java When you execute a Java application that uses TimeSeries data it uses 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 connecting to a database You can also use the TimeSeries Java classes in Java applets and servlets as shown in the following figures Copyright IBM Corp 2006 2012 8 1 Web server Browser JDBC driver Figure 8 2 Runtime architecture for a Java applet Web server HTTP servlet subclass fo Request ___ JDBC driver Response Figure 8 3 Runtime architecture for a Java servlet Related concepts Planning for accessing time series data on page 1 12 System requirements for Java programs Java program must use e Java Developers Kit JDK Version 1 2 2 also known as Java 2 or later e IBM Informix JDBC Driver Version 2 20 or later See your IBM Informix JDBC Driver Programmer s Guide for information about using the Informix JDBC Driver Install the time series Java files 8 2 You c
358. s 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 version 2 2 1 or later must be installed and running 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 o 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 Database requirements for time series data The database in which you implement the Informix TimeSeries solution must conform to requirements 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 SQ
359. s Valid values for the flags argument are described in The flags argument falues on page 7 5 lues on page 7 5 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 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 DelClip function on page 7 35 DelElem function on page 7 36 oy function on page 7 2 hk page 7 5 page 7 61 PutSet function on page 7 7 77 TIC aJe oO m oO B zA JE 5 O a fe 53 is 5 ie IE 5 Nn D oO leg 5 Q Q g a 5 5 e ge Q ee io or 5 p 5 5 ge o og oO N p N m Divide function The Divide function divides one time series by another It
360. s and Intersect or union of time series optionally clip the result Build the union of multiple time series and optionally clip Union the result Iterator functions Convert time series data to tabular form Transpose Aggregate functions Return a list collection of rows containing all elements in TSSetToList a time series Return a list of one column in the time series TSColNameToList Return a list of one column in the time series TSColNumToList Return a list that contains the columns of the time series TSRowToList plus non time series columns Return a list that contains one column of the time series TSRowNameToList plus non time series columns Return a list that contains one column of the time series TSRowNumfToList plus non time series columns Used within the Sum SMALLFLOAT or DOUBLE PRECISION values TSAddPrevious Apply ia to Compute the decay function TSDecay perform statistical calculations on a time Compute a running average over a specified number of TSRunningAvg series values Compute a running correlation between two time series TsRunningCor over a specified number of values Compute a running median over a specified number of TsRunningMed values Compute a running sum over a specified number of TSRunningSum values Compute a running variance over a specified number of TSRunningVar values Compare SMALLFLOAT or DOUBLE PRECISION values TSCmp Return a previously saved value TSPrevious Chapter 7 Time series SQL routines 7 3 Table 7 1
361. s as implemented by the Informix TimeSeries solution contains information about how the data is stored in the table column and additional 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 time stamp For example a value of 1 01 for the time stamp 2011 1 1 00 45 00 00000 is an element for customer 1001 Timepoint The time period for a single element for example 15 minutes In some industries a timepoint is referred to as an interval Origin The element in the time series that has the earliest time stamp 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 Calendar A set of valid timepoints in a time series as specified by the calendar pattern Calendar pattern The length of the timepoint and which timepoints are valid For example if you collect electricity usage information every 15 minutes the calendar 1 4 IBM Informix TimeSeries Data User s Guide 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 f
362. sc Returned by ts_open flags Determines how 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 9 6 IBM Informix TimeSeries Data User s Guide 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
363. se 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 Chapter 9 Time series API routines 9 13 9 14 Returns The number of columns Related reference The ts_get_all_cols procedure on page 9 25 The ts_col_id function The ts_col_id function 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 Returns The number of the column associated with colname Related reference The ts_colinfo_name function The ts_colinfo_number function on page 9 15 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
364. sheet to perform the remaining steps You can reuse the file format definition the table definition the mapping definition and the connection profile that you created in subsequent load jobs If you change your table definition you must update the corresponding mapping as well IBM Informix TimeSeries Data User s Guide Related concepts Planning for loading time series data on page 1 11 Related tasks Installing the IBM Informix TimeSeries Plug in for Data Studio on page 1 13 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 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 Put your input data in a single file Format the data according to the standard IBM Informix load file format Pon Use any of the Informix load utilities pload onpload dbload or the load command in DB Access to load the fil
365. 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 returns 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 equiv
366. sing 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 IBM Informix TimeSeries Data User s Guide Related reference TSCreateVirtualTab procedure on page 4 4 Defining a calendar 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 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 TSCreateIrr function You can change a predefined calendar to meet your needs by updating the row for the calendar in the CalendarTable table Related concepts for creating a time series on Predefined calendars You can use predefined calendars when you create a time series Predefined calendars are stored in rows in the Calendar
367. stocks table select GetLastElem stock_data from daily stocks Related reference GetElem function on page 7 4 GetFirstElem function on page 7 4 GetLastValid function on page 7 4 GetNthElem function on page 7 5 PutElem function on page 7 6 The ts_last_elem function on page 9 34 GetPreviousValid function on page 7 56 GetLastNonNull function 7 48 The GetLastNonNull function returns the last non null element on or before the date you specify Syntax GetLastNonNul1 ts TimeSeries tstamp datetime year to fraction 5 column_name l varchar 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 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 flags argument are described in The flags argument Falnes on page 7 5 lues on page 7 5 The default is 0 Description There are no null elements in an irregular time series Therefore when you use t
368. stributing 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 as 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 Trademarks IBM the IBM logo and ibm com are trademarks or registered trademarks of International Business Machines Corp registered in many jurisdictions worldwide Other product and service names might be trademarks of IBM or other companies A current list of IBM trademarks is available on the web at Copyright and trademark information at http www ibm com legal copytrade shtml Adobe the Adobe logo and PostScript are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and or other countries Intel Itanium and Pentium ar
369. 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 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
370. t throws SQLException When you select a calendar pattern from the database the getObject method is used to extract the calendar pattern from the result set The IBM Informix JDBC Driver instantiates a new IfmxCalendarPattern object using the first constructor IfmxCalendarPattern It creates an empty object with no variables initialized When you create a calendar pattern on the client to insert into the database use the second constructor to instantiate the object This constructor initializes the new object by parsing the input string The format of the string is pattern specification interval for example 5 on 2 off day The IfmxCalendarPattern class provides the following methods Method Signature Description getInterval public byte getInterval throws SQLException Returns the calendar pattern interval etIntervalStr ublic String getIntervalStr throws Returns the string representation of the 8 P 88 8 rep SQLException calendar pattern interval Valid values are e Second e Minute e Hour e Day e Week e Month e Year getSQLTypeName public String getSQLTypeName Returns the SQL type name for the object in this case calendar pattern readSQL public void readSQL SQLInput stream String Called automatically by the IBM Informix type JDBC Driver to initialize an IfmxCalendarPattern object from the binary result set stream toString public String toString Returns the String representation of this c
371. t at the given time stamp an error is raised Related reference HideElem function on page 7 58 The ts_elem function on page 9 20 The TS ELEM HIDDEN macro on page 9 21 4 page 9 22 The ts_reveal_elem function on page 9 48 7 YS WN mi E mi Z Z a Et B o Q s 5 The ts_previous_valid function on page 9 43 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 x 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 IBM Informix TimeSeries Data User s Guide 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 Related reference GetIndex function on page 7 46 The ts_cal_index function on page 9 8 The ts_nth_elem function
372. t combined using the AND operator with TSOPEN_WRITE_AND_HIDE TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_HIDDEN The value of flags is the sum of the desired flag values from the following table Flag Value Meaning TSOPEN_RDWRITE 0 Default Indicates that the time series can be read and written to TSOPEN_READ_HIDDEN 1 Indicates that hidden elements should be treated as if they are not hidden TSOPEN_WRITE_HIDDEN 2 Allows hidden elements to be written to without first revealing them The element remains hidden afterward TSOPEN_WRITE_AND_HIDE 4 Causes any elements written to a time series also to be marked as hidden TSWRITE_AND_REVEAL 8 Reveals any hidden element that is written to TSOPEN_NO_NULLS 32 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 These flags can be used in any combination except the following four 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 The TSOPEN_WRITE_HIDDEN TSOPEN_WRITE_AND_REVEAL and TSOPEN_WRITE_AND_HIDE flags cannot be used with TSOPEN_READ_HIDDEN IBM Informix TimeSeries Data User s Gui
373. t free this value after use Related reference The ts_copy function on page 9 16 The ts_create function on page 9 16 The ts_create_with_metadata function on page 9 17 The ts_open function on page 9 41 The ts_hide_elem function 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 Chapter 9 Time series API routines 9 31 9 32 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 The equivalent SQL function is HideElem Returns The modified time series If there is no elemen
374. t is the aggregation of all input values from 00 00 Jan 10 2011 to 23 59 59 99999 Jan 16 2011 Chapter 7 Time series SQL routines 7 7 7 8 Normally AggregateBy 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 should run with
375. tValid 7 52 GetNthElem 7 53 GetOrigin 7 55 GetPreviousValid 7 56 GetStamp 7 57 GetThreshold 7 58 HideElem 7 58 InsElem 3 18 7 60 InsSet 3 18 7 61 Instanceld 7 62 Intersect 7 62 IsRegular 7 64 Lag 7 65 Logn 7 65 Minus 7 66 Mod 7 66 Negate 7 66 NullCleanup 7 66 Plus 7 68 Positive 7 68 Pow 7 68 PutElem 3 18 7 68 PutElemNoDups_ 7 70 PutNthElem 7 71 PutSet 3 18 7 72 PutTimeSeries 7 73 RevealElem 7 75 7 76 Round 7 77 SetContainerName 7 77 SetOrigin 7 77 Sin 7 78 Sqrt 7 78 Tan 7 78 Times 7 79 TimeSeriesRelease 7 79 Transpose 7 79 TSAddPrevious 7 82 TSCmp 7 83 TSContainerCreate 7 86 TSContainerDestroy 7 87 TSContainerPurge 7 91 TSCreate 7 97 TSCreatelIrr 7 99 TSDecay 7 101 TSPrevious 7 102 TSRollup 7 103 TSRunningAvg 7 84 7 85 7 107 TSRunningCor 7 108 TSRunningMed 7 110 TSRunningSum 7 111 TSRunningVar 7 112 TSSetToList 7 113 TSToXML 7 114 Union 7 117 X 6 IBM Informix TimeSeries Data User s Guide Routines continued SQL time series continued UpdElem 7 119 UpdMetaData 7 120 UpdSet 7 121 WithinC 7 122 WithinR 7 122 Row converting to an element 9 48 rowDeleted method 8 13 rowlnserted method 8 13 rowUpdated method 8 13 RSS and time series 1 14 Running average computing 7 107 Running sum computing 7 111 S Scanning beginning for a time series 9 6 ending for a time series 9 23 Screen reader reading syntax diagrams C 1 SDS and time series 1 14 SELECT DISTINCT statement 1 13 Servlets 8 1 sess
376. ta 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 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 Unary arithmetic functions on page 7 116 Asin 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 7 20 IBM Informix TimeSeries Data User s Guide Related reference Unary arithmetic functions on page 7 116 Atan function 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 Unary arithmetic functions on page 7 116 Atan2 function The Atan2 function returns the arc tangent of corresponding elements from two time series It is on
377. ta into a time series Containers for TimeSeries subtypes are created automatically in the same dbspaces in which the table that contains the subtype is stored You can also create custom pools of containers specific to your needs Time series data storage on page 1 8 Managing containers on page 3 Monitor time series containers You can monitor the containers that store time series data to obtain the following information for a specific container or for all containers in the database e The number of allocated pages e The number of pages containing time series data e The percentage of used space The number of time series data elements Monitoring time series containers on page 3 8 View the results of a time series expression in a virtual table You can create a virtual table based on the results of a time series expression such as the AggregateBy function Previously you needed to save the results of the expression in an intermediate table and create a virtual table based on the intermediate table TSCreateExpressionVirtualTab procedure on page 4 8 Output time series data in XML format You can produce an XML representation of a time series by using the new TSToXML function You can use the XML data to send time series information to other applications TSTOXML function on page 7 114 Introduction Xi Table 3 What s New in IBM Informix TimeSeries Data User s Guide for 11
378. ta 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 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 v
379. table Column name Date type Description name VARCHAR 128 1 The name of the container of the time series This is the primary key 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 time series subtype partitionDesc tsPartitionDesec_t The description of the partition that is the container flags INTEGER Stores flags to indicate e If the container is empty and always was empty e If the time series is regular or irregular pool VARCHAR 128 1 The name of the container pool to which the DEFAULT NULL container belongs NULL indicates that the container does not belong to a container pool The default container pool is named autopool The TSContainerTable table is managed by the database server and users do not modify it directly nor should they normally be required to view it 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 For more information see TSContainerCreate procedure and TSContainerDestroy procedure on page 7 87 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 t
380. tainerTotalPages 7 94 TSContainerTotalUsed 7 95 TSContainerUsage 7 96 with Java 8 5 8 11 Converting element to a row 9 22 row to element 9 48 time series data to tabular form 7 79 Copying one time series into another 9 47 time series 9 16 Cos function 7 32 Cosine determining 7 32 Countlf function 7 32 Counting elements returned by an expression 7 32 CREATE ROW TYPE statement 3 6 CREATE TABLE statement 3 6 Creating irregular time series 7 99 regular time series 7 97 table for time series 3 6 time series 3 12 3 16 9 16 9 17 time series from function output 3 16 time series subtype 3 6 time series with input function 3 14 time series with metadata 3 13 virtual tables 4 4 D Data file formats 3 17 X 2 IBM Informix TimeSeries Data User s Guide Data continued loading from a file 7 24 loading into a time series with BulkLoad 3 17 Data structures ts_timeseries 9 2 ts_tscan 9 2 ts_tsdesc 9 2 ts_tselem 9 3 Data Studio 1 3 TimeSeries plug in 3 16 Data types Calendar 2 3 CalendarPattern 2 1 DATETIME 3 6 restrictions for time series 3 6 TimeSeriesMeta 3 13 Database requirements 1 13 DATETIME data type 3 6 dbload utility 3 17 dbspace time series container in 1 9 Decay computing 7 101 DelClip function 7 35 DelElem function 7 36 deleteRow method 8 13 Deleting element 7 36 9 20 elements in a clip 7 35 7 38 elements in a range 7 37 null elements 7 66 Deleting time series data 3 19 7 91 DelRange function 7 37 DelT
381. tamp 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 P 2010 11 10 00 00 00 00000 0 041 4727354321046021 KWH P 2010 11 10 00 15 00 00000 0 041 4727354321090954 KWH P 2010 11 10 00 00 00 00000 0 026 4727354321090954 KWH P 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 t
382. tatistics 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_entry 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 ent
383. tch elements with this tsdesc time series descriptor Related reference The ts_elem function on page 9 20 The ts_upd_elem function on page 9 52 The ts_last_valid function The ts_last_valid function extracts the entry for a particular timepoint 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 onl page 9 31 age 9 31 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 Chapter 9 Time series API routines 9 35 9 36 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 e The element at the timepoint is NULL and the time series is regular e The timepoint is before the origin e The time series is irregular and there are no elements at or before the given time stamp This element must
384. tdate function on page 9 11 The ts_cal_range function The ts_cal_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 Chapter 9 Time series API routines 9 9 9 10 Returns A list of time stamps Related reference The Callndex function on page 6 2 The CalRange function on page 6 3 The CalStamp function on page 6 4 The ts_cal_index function on page 9 8 The ts_cal_range_index function The ts_time function on page 9 49 The ts_cal_stamp function on page 9 11 The ts_cal_range_index function The ts_cal_range_index function returns a list containi
385. te i eh a a i gt lt pattname pattern_name Table 2 2 Calendar data type parameter values Value Data type Description start_date DATETIME YEAR TO Calendar start date 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 pattern_date DATETIME YEAR TO Calendar pattern start date 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 calendar_pattern CalendarPattern Calendar pattern to use pattern_name VARCHAR Name of calendar pattern to use from CalendarPatterns table Usage To create a calendar insert the keywords and their values into the CalendarTable table 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 ti
386. tem tables handle time series data The data types for time series data are e CalendarPattern e Calendar e TimeSeries The system tables for time series data are e CalendarPatterns e CalendarTable e TSInstanceTable e TSContainerTable These system tables are in the sysmaster database 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 TSInstanceTable contains information about all time series Related concepts 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 HFT interval gt lt Copyright IBM Corp 2006 2012 2 1 2 2 Table 2 1 CalendarPattern data type parameter values Value Description interval One of the following interval names Second e Minute e Hour e Day e Week e Month e Year num_intervals The number of interval units that 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
387. that does not the existing row have any time series data For example add the first meter reading for a specific meter Chapter 4 Virtual tables for time series data 4 5 4 6 Table 4 3 Behavior of updates to the base table continued Need to use the NewTimeSeries Goal Result parameter Add an additional time series If the timepoint is not the same as No element to an existing time an existing element the new series For example add a new_ element is inserted to the time meter reading for a meter that series If the timepoint is the same has previous readings as an existing element the existing element is updated with the new value If the TSVTMode parameter includes the value 1 multiple elements for the same timepoint can coexist therefore the new element is inserted and the existing element is also retained Add a new row For example A new row is inserted into the Yes add a 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 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 f
388. 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 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 Chapter 1 Informix TimeSeries solution 1 7 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 returns a null value Related reference Create a time series on page 3 11 Calendar Every time series is associated with a calendar A calendar defines a set of valid times for elements in a time series A calendar determines when and how often entries are accepted Each calendar has a calendar pattern of timepoints that are either valid or invalid with the beginning of the calendar pattern specified by the calendar pattern start date Data is recorded during valid intervals but not during invalid intervals The calendar patt
389. the TS_SCAN_HIDDEN flag set hidden elements are returned 4 Indicates that the scan should run 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 defined as startdate 2010 1 1 00 00 00 00000 pattstart 2010 1 1 00 00 00 00000 pattern 1 on 2 off month 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 AggregateBy max high quart
390. 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 Chapter 7 Time series SQL routines 7 63 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 Returns The time series that results from the intersection Example The following example returns the intersection of two time series select Intersect dl 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
391. ther of the optional parameters you must include them in the order shown in the syntax but you can use either one without the other For example you can specify the TSColName 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 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 Chapter 4 Virtual tables for time series data 4 9 4 10 clause of the SELECT statement when you query the virtual tab
392. time series element as a long value getMetaData java sql ResultSetMetaData getMetaData Retrieves a ResultSetMetaData object that contains the number types and properties of the IfmxTimeSeries object elements getObject java lang Object getObject int columnIndex Gets the value in the current time series element as an Object getRow int getRow Retrieves the number of the current time series element getShort short getShort int columnIndex Gets the value in the current time series element as a short value getShort short getShort java lang String columnName Gets the value in the current time series element as a short value getSQLTypeName java lang String getSQLTypeName Returns the SQL type name used by the database for the data type getString java lang String getString int columnIndex Gets the value in the current time series element as a String value getString java lang String getString java lang String Gets the value in the current time series columnName element as a String value getTime java sql Time getTime int columnIndex Gets the value in the current time series element as a java sql Time value getTime java sql Time getTime java lang String Gets the value in the current time series columnName element as a java sql Time value getTimestamp java sql Timestamp getTimestamp int Gets the value in the current time series columnIndex element as a Timestamp object getTimestamp java sql Timestamp getTimestamp Gets the value in t
393. 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 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 24 The ts_make_elem function on page 9 36 The ts_make_elem_with_buf function on page 9 37 The ts_row_to_elem function on page 9 48 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 6 The ts_first_elem function The ts_first_elem function returns the first element in the time series Syntax ts_tselem
394. ting it results in an error message It is however possible to use ts_begin_scan to read hidden elements The API equivalent to this function is ts_hide_elem If the time stamp is not a valid timepoint in the time series an error is raised IBM Informix TimeSeries Data User s Guide Returns The modified time series Example The following example hides the element at 2011 01 03 in the time series for IBM select HideElem stock_data 2011 01 03 00 00 00 00000 from daily_stocks where stock_name IBM n n 1 g DelClip function on page 7 3 DelElem function on page 7 3 DelTrim function on page 7 3 RevealElem function on page 7 75 JARE Rlotol amp elsis o Qalafasa a BIETE ma 5 CE E l l 5 20e AEA cia So S 2 Sse oe ag olo gt pN 2S rg do gt ag la N l on The ts_begin_scan function on page 9 The ts_hide_elem function on page 9 31 The ts_reveal_elem function on page 9 48 HideRange function HideRange function The HideRange function marks as invisible a range of elements between a starting time stamp and an ending time stamp Syntax HideRange ts TimeSeries start datetime year to fraction 5 end datetime year to fraction 5 flags integer default 0 returns TimeSeries co S The time series to act on start The starting time stamp end The ending time stamp flags Valid values for the flags pa
395. tion 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 IBM Informix TimeSeries Data User s Guide low real final real vol real not null from daily_stocks d Related reference TSRowNameToList function on page 7 104 TSRowNumToList function on page 7 105 Transpose function on page 7 79 TSColNameToList function on page 7 84 TSColNumToList function on page 7 85 TSSetToList function on page 7 113 TSRunningAvg function The TSRunningAvg function computes a running average over SMALLFLOAT or DOUBLE PRECISION values Syntax TSRunningAvg value double precision num_values integer returns double precision TSRunningAvg value real
396. to operate on time series Syntax Function ts TimeSeries returns TimeSeries ts 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 IBM Informix TimeSeries Data User s Guide Related reference Abs function on page 7 6 Acos function on page 7 6 n Sqrt function on page 7 78 Tan function on page 7 78 Apply function on page 7 11 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 Unio
397. tor 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 The 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 2012 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 qu
398. tr 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 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
399. ts 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 Dealer 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 This information contains sample application programs in source language which illustrate programming techniques on various operating platforms You may copy IBM Informix TimeSeries Data User s Guide modify and distribute these sample programs in any form without payment to IBM for the purposes of developing using marketing or di
400. 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 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
401. ts_first_elem ts_tsdesc tsdesc mi_integer STATUS tsdesc The time series descriptor returned by ts_open Chapter 9 Time series API routines 9 23 9 24 STATUS A pointer to an mi_integer value See The ts_hide_elem function on page 9 31 age 9 31 for an explanation of the STATUS argument The TS _ ELEM HIDDEN macro on page 9 21 The TS _ ELEM NULL macro on page 9 22 ion page 7 44 The ts_begin_scan function on page 9 6 Q oO Q Ez oO 3 ct 5 o 5 S The ts_elem function on page 9 2 The ts_next function on page 9 39 The ts_next_valid function on page 9 40 The ts_free procedure Syntax void ts_free ts_timeseries ts cb S The source time series Related reference The ts_copy function on page 9 16 The ts_create function on page 9 16 The ts_create_with_metadata function on page 9 17 The ts_get_ts function on page 9 30 The ts_ins_elem function on page 9 33 The ts_free_elem procedure 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_previous_valid those elements are overwritten with subsequent calls or freed when th
402. typeparameter internal parameter mi_string ti_typename 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 Chapter 9 Time series API routines 9 15 9 16 Related reference The ts_col_id function on page 9 14 The ts_colinfo_name function on page 9 14 The ts_copy function 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 The ts_free procedure on page 9 24 The ts_get_typeid function on page 9 31 The ts_create function The ts_create function creates a time se
403. ual 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 shown in the syntax but you can use any one of them without using the others For example you can specify the TSColName parameter without 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
404. ular 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 elements 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 11 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
405. umns If any of the input time series is irregular the resulting time series is irregular For the purposes of Intersect 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 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 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
406. unning the TSContainerPurge function You can then reuse the space for new data TSContainerPurge function on page 7 91 New operators for aggregating across time series values You can return the first or last elements entered into the database for each timepoint by using the FIRST or LAST operators in the TSRollup function TSRollup function on page 7 103 Table 2 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC4 Overview Reference IBM Informix TimeSeries Plug in for Data Studio You can easily load data from an input file into an Informix table with a TimeSeries column by using IBM Informix TimeSeries Plug in for Data Studio You can also use the plug in with IBM Optim Developer Studio Installing the IBM Informix TimeSeries Plug in for Data Studio on page 1 13 Loading data with the IBM Informix TimeSeries Plug in for Data Studio on page 3 16 Example Create and load a time series on page 3 1 Aggregate time series data across multiple rows You can use a single TimeSeries function TSRollup to aggregate time series values by time for multiple rows in the table and return a time series that contains the results Previously you could aggregate time series values only for each row individually For example if you have a table that contains information about energy consumption for the meters attached to a specific energy concentrator you ca
407. ur INSERT INTO CalendarPatterns VALUES hour 1 on hour The following statement creates a pattern 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 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 n Calendar data type CalendarPatterns table on page 2 7 Related tasks Defining a calendar on page 3 5 Related reference WithinC and WithinR functions on page 7 122 HideElem function on page 7 58 Chapter 5 Calendar pattern routines on page 5 1 GetInterval function on page 7 46 Calendar data type 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 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 The input format for the Calendar data type is a quoted text string Chapter 2 Data types and system tables 2 3 p gt startdate start_date E gt __pattstart pattern_date __pattstart pattern_da
408. urned 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 36 The ts_ins_elem function on page 9 33 The ts_put_elem function on page 9 44 The ts_upd_elem function on page 9 52 The ts_elem function 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 31 for an explanation of the isNull argument IBM Informix TimeSeries Data User s Guide Q 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 GetElem function on page 7 44 oF es oO 3 2 g 5 iO ch 5 o 5 a 4 page 7 3 The TS_ELEM_HIDDEN macro The ts_hide_elem function on
409. ution architecture 1 3 SQL restrictions for 1 13 Time series functions TSContainerNElems 7 88 TSContainerPctUsed 7 89 TSContainerTotalPages 7 94 TSContainerTotalUsed 7 95 TSContainerUsage 7 96 TSCreateExpressionVirtualTab 4 8 Time Series Java class version 8 8 Timepoint 1 4 Timepointes arbitrary 1 7 Times function 7 79 TimeSeries database requirements 1 13 replicating 1 14 TimeSeries data type 1 5 3 1 Java representation 8 4 TimeSeries plug in 1 3 3 1 3 16 TimeSeriesMeta distinct type 3 13 TimeSeriesRelease function 7 79 Timestamps calendar getting froma 9 11 comparing 9 19 current getting 9 19 getting after intervals 6 4 GMT converting to 9 38 local time converting to 9 29 offset associated with 1 7 offset converting from 9 49 offset getting for 7 57 offset getting from 9 32 range getting from a calendar 9 9 9 10 returning set of valid in range 6 3 toString method 8 8 8 9 traceFileName parameter 4 20 traceLevelSpec parameter 4 20 Tracing virtual tables 4 19 Transpose function 7 79 ts_begin_scan function 9 6 ts_cal_index function 9 8 ts_cal_pattstartdate function 9 8 ts_cal_range function 9 9 ts_cal_range_index function 9 10 ts_cal_stamp function 9 11 ts_close procedure 9 12 ts_col_cnt function 9 13 ts_col_id function 9 14 ts_colinfo_name function 9 14 ts_colinfo_number function 9 15 ts_copy function 9 16 ts_create function 9 16 TS_CREATE_IRR flag 9 16 9 17 ts_create_with_metadata function 9 17 ts_current
410. w 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 concepts CalendarPattern data type on page 2 1 Related reference GetCalendar function on page 7 41 GetClosestElem function on page 7 42 GetOrigin function on page 7 55 TSCreate function on page 7 97 TSCreatelrr function on page 7 99 GetLastElem function 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 The flags argument falies on page 7 5 lues on page 7 5 The default is 0 Description The API equivalent of this function is ts_last_elem Returns A row type value containing 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 Chapter 7 Time series SQL routines 7 47 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
411. w 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_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 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
412. x TimeSeries Plug in for OpenAdmin Tool OAT provides a graphical interface for reviewing and administering the TimeSeries data type provided by the Informix TimeSeries extension A time series is a set of data recorded as it varies over time With the TimeSeries plug in you can monitor the database objects related to your time series e Review the TimeSeries subtypes containers and calendars that are used for the time series data in a database e Review the tables and indexes that contain TimeSeries subtypes e Review the columns and virtual tables for tables that contain TimeSeries subtypes e Monitor the percentage of the space that is used in the containers and in the dbspaces for the containers You can also create and drop containers Table 4 What s New in IBM Informix TimeSeries Data User s Guide for 11 70 xC1 Overview Reference Time series data types and functions are built in and automatically registered You can use the data types and functions of the TimeSeries extension which was formerly known as the TimeSeries DataBlade module without performing some of the previously required prerequisites tasks such as installing or registering the TimeSeries extension If you are using a previous version of the IBM Informix TimeSeries DataBlade Module when you install Informix 11 70 the TimeSeries extension is installed and registered automatically You do not need to perform any actions to upgrade th
413. xample 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 DelClip function on page 7 35 7 DelTrim function on page 7 38 n InsSet function on page 7 61 7 PutElem function on page 7 68 TSCreate function UpdSet function on page 7 121 PutTimeSeries function on page 7 97 PutTimeSeries function The PutTimeSeries function puts every element of the first time series into the second time series Chapter 7 Time series SQL routines 7 73 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 flags argument are described later in this topic Description If both time series contain data at the same timepoint the rule of PutElem is followed see PutElem function on page 7 68 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 specified by the first argument must be later than or equal to the origin of the time series specified by the second argument This function can be
414. xample 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 19 Related tasks Managing containers on page 3 7 Related reference GetIndex function on page 7 46 GetContainerName function on page 7 43 Instanceld function on page 7 62 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 Chapter 7 Time series SQL routines 7 93 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 created automatically are in the container pool named autopool by default If 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
415. xt rather than using ts_first_elem and ts_next_valid The equivalent SQL function is GetNextValid 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 IBM Informix TimeSeries Data User s Guide 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 31 for an explanation of STATUS Related reference GetNextValid function on page 7 5 The TS_ELEM_HIDDEN macro on page 9 21 The TS_ELEM_NULL macro on page 9 22 The ts_first_elem function on page 9 23 GetLastValid function on page 7 49 The ts_next function on page 9 39 N The ts_previous_valid function on page 9 43 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 x tsdesc mi_integer N mi_integer STATUS tsdesc The descriptor returned by 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 NUL

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