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Apache Accumulo User Manual Version 1.5

1. This enables Accumulo to find ZooKeeper Accumulo uses ZooKeeper to coordinate settings between processes and helps finalize TabletServer failure Apache Accumulo User Manual Version 1 5 38 39 lt property gt lt name gt walog lt name gt lt value gt var accumulo walogs lt value gt lt description gt local directory for write ahead logs lt description gt lt property gt Accumulo records all changes to tables to a write ahead log before committing them to the table The walog setting specifies the local directory on each machine to which write ahead logs are written This directory should exist on all machines acting as TabletServers lt property gt lt name gt instance secret lt name gt lt value gt DEFAULT lt value gt lt property gt The instance needs a secret to enable secure communication between servers Configure your secret and make sure that the accumulo site xml file is not readable to other users Some settings can be modified via the Accumulo shell and take effect immediately but some settings require a process restart to take effect See the configuration documentation available on the monitor web pages for details 11 5 4 Deploy Configuration Copy the masters slaves accumulo env sh and if necessary accumulo site xml from the KACCUMULO_ HOME conf directory on the master to all the machines specified in the slaves file 11 6 Initialization Accumulo must be init
2. Related EntityID Weight For example to keep track of employees managers and products the following entity attribute table could be used Note that the weights are not always necessary and are set to 0 when not used RowID Column Family Column Qualifier Value E001 name bob 0 E001 department sales 0 E001 hire_date 20030102 0 E001 units_sold P001 780 E002 name george 0 E002 department sales 0 E002 manager_of E001 0 E002 manager_of E003 0 E003 name harry 0 E003 department accounts_recv 0 E003 hire_date 20000405 0 E003 units_sold P002 566 E003 units_sold P001 232 P001 product_name nike_airs 0 P001 product_type shoe 0 P001 in_stock germany 900 P001 in_stock brazil 200 P002 product_name basic_jacket 0 P002 product_type clothing 0 P002 in_stock usa 3454 P002 in_stock germany 700 Apache Accumulo User Manual Version 1 5 27 39 To allow efficient updating of edge weights an aggregating iterator can be configured to add the value of all mutations applied with the same key These types of tables can easily be created from raw events by simply extracting the entities attributes and relationships from individual events and inserting the keys into Accumulo each with a count of 1 The aggregating iterator will take care of maintaining the edge weights 7 5 Document Partitioned Indexing Using a simple index as described above works well when looking for
3. 10 3 Authorization cios bia rd ee eae eR dir as E ta eG 34 104 User Authorizations isla dnd BA a eee Sd 34 10 5 Secure Authorizations Handling 34 10 6 Query Services Layer lt s ene i ee eR ER OR SR e e A Re EER e eR ee e 35 11 Administration 36 TI Hardware oi a RR E ERE e A Re E RO a 36 Il NSTWOTS ii ia ae 36 11 3 Installation dae e E RE E A A e e 36 11 4 Dependencies e eke a a E A e RA E AE RIE og A eek 36 13 Configurationi 2554 ia irene A a 3 11 5 1 Edit conf accumulo env sh gt s s lt s ee 37 11 5 2 Cluster Specification s ss scs a se e ata a as o a ao e e 37 11 5 3 Accumulo Settings 4 cee bt lana ai ET r amp 37 11 5 4 Deploy Configura 38 TI 6 Inimalizationi oca popa a a e AE a he E Pe Paes A 38 MR A E Lea ANN E r N 38 11 7 1 Starting ACCUMULO ss e sea d ao ae dee a A ar a 38 11 7 2 Stopping Accumulo s ssp oe see ee a e e e R ee ee ee ee 38 118 Monitores dsd liane ho lia 39 T19 Logon eric oa lA i Al 39 WI TORECOVELY s ocio as PO A A E li pa er A oe ee ee e 39 Apache Accumulo User Manual Version 1 5 1 39 Chapter 1 Introduction Apache Accumulo is a highly scalable structured store based on Google s BigTable Accumulo is written in Java and operates over the Hadoop Distributed File System HDFS which is part of the popular Apache Hadoop project Accumulo supports efficient storage and retrieval of structured data including queries for ranges and provides support for using Accumulo t
4. 4 1 Rimning Chent Code sks eae AA AAA 9 4 2 COMMECUNE sio nL a eS SEES OR REE RR DEERE EES GEA Kee Pe ware ee os 9 43 Wnts Data ss A EA be ete es eb be Gabe ae Hs eS Sas eS 10 43 1 BatehWriter lt o 3 aeea es oe Ae ee Ped Bae eee be ee Oe Pew Bete eee a 10 44 Reading Data iii Sear eS SEES PAA RR REE SR ERE ERS GA Kade Rn eos 10 44 1 Scanner capea PE eee EES ee SESE e ELE i 10 44 2 Isolated Scanner asuma a PE Bae Reo Ae Sew E eo eS 11 44 3 BatchScanner i e a AI E a ee a we 11 Apache Accumulo User Manual Version 1 5 iv 5 Development Clients 12 3 1 Mock Accumulo a sss pe i e a eR ee ee e e ea ee 12 52 Mint Accumulo Cluster spire A eH Sed A oe 13 Table Configuration 14 6 1 Locally Groups voice al al a e ee ew A ai 14 6 1 1 Managing Locality Groups via the Shell 14 6 1 2 Managing Locality Groups via the Client API o o 2000000000 14 6 2 Constraints s cos a ir daa laa aa a de Aa 13 6 3 Bloom Filters cia eek od ee eee ee A RA e 15 04 Tterators once 6 o Be eS Be ewe eh Be eS Abe ee Sh 15 6 4 1 Setting Iterators via the Shell ona 16 6 4 2 Setting Iterators Programmatically ee 16 6 4 3 Versioning Iterators and Timestamps 2 a ee 16 643 1 Logical Times sses 6602054 e eo ea bbe bd a 17 64 32 Del teSi2 4 cg os eepe a a a e el 17 O44 Filters cj oe E RES ee ea A e a a 17 64 5 Combiners i A a A e RA 18 63 Block Cache ms a A EP ORE A la 19 6 6 Compact
5. Accumulo includes a client library that is linked to every application The client library contains logic for finding servers managing a particular tablet and communicating with TabletServers to write and retrieve key value pairs 2 4 Data Management Accumulo stores data in tables which are partitioned into tablets Tablets are partitioned on row boundaries so that all of the columns and values for a particular row are found together within the same tablet The Master assigns Tablets to one Tablet Server at a time This enables row level transactions to take place without using distributed locking or some other complicated synchronization mechanism As clients insert and query data and as machines are added and removed from the cluster the Master migrates tablets to ensure they remain available and that the ingest and query load is balanced across the cluster Apache Accumulo User Manual Version 1 5 4 39 Data Distribution Assignment NS h Partitioning rowA col2 rowA col2 1 A rowB col1 4 rowB coll 4 rowC coli 1 rowC coll 1 _ rowC col2 2 rowC col2 2 rowC col3 6 Tablet Servers rowC col3 6 rowF col 4 gt rowF col2 4 rowH col2 5 SORTA ENTRE E i rowH col2 5 00 rowH col3 Tablets 2 5 Tablet Service When a write arrives at a TabletServer it is written to a Write Ahead Log and then inserted into a sorted data structure in memory called a MemTable When the MemTable reaches a certain size t
6. For the purposes of providing parallelism to ingest it is not necessary to create more tablets than there are physical machines within the cluster as the aggregate ingest rate is a function of the number of physical machines Note that the aggregate ingest rate is still subject to the number of machines running ingest clients and the distribution of rowIDs across the table The aggregation ingest rate will be suboptimal if there are many inserts into a small number of rowIDs 8 2 Multiple Ingester Clients Accumulo is capable of scaling to very high rates of ingest which is dependent upon not just the number of TabletServers in operation but also the number of ingest clients This is because a single client while capable of batching mutations and sending them to all TabletServers is ultimately limited by the amount of data that can be processed on a single machine The aggregate ingest rate will scale linearly with the number of clients up to the point at which either the aggregate I O of TabletServers or total network bandwidth capacity is reached In operational settings where high rates of ingest are paramount clusters are often configured to dedicate some number of machines solely to running Ingester Clients The exact ratio of clients to TabletServers necessary for optimum ingestion rates will vary according to the distribution of resources per machine and by data type 8 3 Bulk Ingest Accumulo supports the ability to import files produced by
7. Manual Version 1 5 15 39 conn tableOperations setLocalityGroups mytable localityGroups existing locality groups can be obtained as follows Map lt String Set lt Text gt gt groups conn table0perations getLocalityGroups mytable The assignment of Column Families to Locality Groups can be changed at any time The physical movement of column fam ilies into their new locality groups takes place via the periodic Major Compaction process that takes place continuously in the background Major Compaction can also be scheduled to take place immediately through the shell user myinstance mytable gt compact t mytable 6 2 Constraints Accumulo supports constraints applied on mutations at insert time This can be used to disallow certain inserts according to a user defined policy Any mutation that fails to meet the requirements of the constraint is rejected and sent back to the client Constraints can be enabled by setting a table property as follows user myinstance mytable gt constraint t mytable a com test ExampleConstraint com test AnotherConstraint user myinstance mytable gt constraint 1 com test ExampleConstraint 1 com test AnotherConstraint 2 Currently there are no general purpose constraints provided with the Accumulo distribution New constraints can be created by writing a Java class that implements the org apache accumulo core constraints Constraint interface To deploy a new constraint
8. Specifies the size of the cache for file data blocks tserver cache index size Specifies the size of the cache for file indices To enable the block cache for your table set the following properties table cache block enable Determines whether fil data block cache is enabled table cache index enable Determines whether index cache is enabled The block cache can have a significant effect on alleviating hot spots as well as reducing query latency It is enabled by default for the IMETADATA table 6 6 Compaction As data is written to Accumulo it is buffered in memory The data buffered in memory is eventually written to HDFS on a per tablet basis Files can also be added to tablets directly by bulk import In the background tablet servers run major compactions to merge multiple files into one The tablet server has to decide which tablets to compact and which files within a tablet to compact This decision is made using the compaction ratio which is configurable on a per table basis To configure this ratio modify the following property table compaction major ratio Increasing this ratio will result in more files per tablet and less compaction work More files per tablet means more higher query latency So adjusting this ratio is a trade off between ingest and query performance The ratio defaults to 3 The way the ratio works is that a set of files is compacted into one file if the sum of the sizes of the files in the set
9. a set of rows that are not consecutive whose IDs have been retrieved from a secondary index for example The BatchScanner is configured similarly to the Scanner it can be configured to retrieve a subset of the columns available but rather than passing a single Range BatchScanners accept a set of Ranges It is important to note that the keys returned by a BatchScanner are not in sorted order since the keys streamed are from multiple TabletServers in parallel ArrayList lt Range gt ranges new ArrayList lt Range gt populate list of ranges BatchScanner bscan conn createBatchScanner table auths 10 bscan setRanges ranges bscan fetchFamily attributes for Entry lt Key Value gt entry scan System out printin entry getValue An example of the BatchScanner can be found at accumulo docs examples README batch Apache Accumulo User Manual Version 1 5 12 39 Chapter 5 Development Clients Normally Accumulo consists of lots of moving parts Even a stand alone version of Accumulo requires Hadoop Zookeeper the Accumulo master a tablet server etc If you want to write a unit test that uses Accumulo you need a lot of infrastructure in place before your test can run 5 1 Mock Accumulo Mock Accumulo supplies mock implementations for much of the client API It presently does not enforce users logins permis sions etc It does support Iterators and Combiners Note that MockAcc
10. accumulo core iterators Versioninglterator table table iterator scan vers opt maxVersions 1 6 4 5 Combiners Accumulo allows Combiners to be configured on tables and column families When a Combiner is set it is applied across the values associated with any keys that share rowID column family and column qualifier This is similar to the reduce step in MapReduce which applied some function to all the values associated with a particular key For example if a summing combiner were configured on a table and the following mutations were inserted Row Family Qualifier Timestamp Value rowID1 colfA colgA 20100101 1 rowID1 colfA colgA 20100102 1 The table would reflect only one aggregate value rowID1 colfA colgA 2 Combiners can be enabled for a table using the setiter command in the shell Below is an example root al4 perDayCounts gt setiter t perDayCounts p 10 scan minc majc n daycount class org apache accumulo core iterators user SummingCombiner TypedValueCombiner can interpret Values as a variety of number encodings VLong Long or String before combining gt set SummingCombiner parameter columns lt col fam gt lt col qual gt lt col fam gt lt col qual gt day gt set SummingCombiner parameter type lt VARNUM LONG STRING gt STRING root al4 perDayCounts gt insert foo day 20080101 root al4 perDayCounts gt insert foo day 20080101 root al4 perDayCounts gt insert foo day 200801
11. an external process such as MapReduce into an existing table In some cases it may be faster to load data this way rather than via ingesting through clients using BatchWriters This allows a large Apache Accumulo User Manual Version 1 5 29 39 number of machines to format data the way Accumulo expects The new files can then simply be introduced to Accumulo via a shell command To configure MapReduce to format data in preparation for bulk loading the job should be set to use a range partitioner instead of the default hash partitioner The range partitioner uses the split points of the Accumulo table that will receive the data The split points can be obtained from the shell and used by the MapReduce RangePartitioner Note that this is only useful if the existing table is already split into multiple tablets user myinstance mytable gt getsplits aa ab ac ZX Ly ZZ Run the MapReduce job using the AccumuloFileOutputFormat to create the files to be introduced to Accumulo Once this is complete the files can be added to Accumulo via the shell user myinstance mytable gt importdirectory files_dir failures Note that the paths referenced are directories within the same HDFS instance over which Accumulo is running Accumulo places any files that failed to be added to the second directory specified A complete example of using Bulk Ingest can be found at accumulo docs examples README bulkIngest 8 4 Logical Time for
12. and TabletServer machines Itis also a good idea to run Network Time Protocol NTP within the cluster to ensure nodes clocks don t get too out of sync which can cause problems with automatically timestamped data Apache Accumulo User Manual Version 1 5 37 39 11 5 Configuration Accumulo is configured by editing several Shell and XML files found in SACCUMULO_HOME conf The structure closely resembles Hadoop s configuration files 11 5 1 Edit conf accumulo env sh Accumulo needs to know where to find the software it depends on Edit accumulo env sh and specify the following 1 Enter the location of the installation directory of Accumulo for SACCUMULO_HOME 2 Enter your system s Java home for JAVA_ HOME 3 Enter the location of Hadoop for FSHADOOP_ HOME 4 Choose a location for Accumulo logs and enter it for SACCUMULO_LOG_DIR 5 Enter the location of ZooKeeper for ZOOKEEPER_HOME By default Accumulo TabletServers are set to use 1GB of memory You may change this by altering the value of SACCUMULO _TSERVER_OPTS Note the syntax is that of the Java JVM command line options This value should be less than the physical memory of the machines running TabletServers There are similar options for the master s memory usage and the garbage collector process Reduce these if they exceed the physical RAM of your hardware and increase them within the bounds of the physical RAM if a proces
13. be merged into tablets that are already larger than the given size This can leave isolated small tablets To force small tablets to be merged into larger tablets use the force option root myinstance gt merge t myTable s 100M force Merging away small tablets works on one section at a time If your table contains many sections of small split points or you are attempting to change the split size of the entire table it will be faster to set the split point and merge the entire table root myinstance gt config t myTable s table split threshold 256M root myinstance gt merge t myTable 6 9 Delete Range Consider an indexing scheme that uses date information in each row For example 20110823 15 20 25 013 might be a row that specifies a date and time In some cases we might like to delete rows based on this date say to remove all the data older than the current year Accumulo supports a delete range operation which efficiently removes data between two rows For example root myinstance gt deleterang t myTable s 2010 e 2011 This will delete all rows starting with 2010 and it will stop at any row starting 2011 You can delete any data prior to 2011 with root myinstance gt deleterang t myTable e 2011 force The shell will not allow you to delete an unbounded range no start unless you provide the force option Range deletion is implemented using splits at the given start end posit
14. can be found under accumulo docs examples README mapred Apache Accumulo User Manual Version 1 5 30 39 Chapter 9 Analytics Accumulo supports more advanced data processing than simply keeping keys sorted and performing efficient lookups Analytics can be developed by using MapReduce and Iterators in conjunction with Accumulo tables 9 1 MapReduce Accumulo tables can be used as the source and destination of MapReduce jobs To use an Accumulo table with a MapReduce job specifically with the new Hadoop API as of version 0 20 configure the job parameters to use the AccumuloInputFormat and AccumuloOutputFormat Accumulo specific parameters can be set via these two format classes to do the following e Authenticate and provide user credentials for the input e Restrict the scan to a range of rows e Restrict the input to a subset of available columns 9 1 1 Mapper and Reducer classes To read from an Accumulo table create a Mapper with the following class parameterization and be sure to configure the Accu muloInputFormat class MyMapper extends Mapper lt Key Value WritableComparable Writable gt public void map Key k Value v Context c transform key and value data here To write to an Accumulo table create a Reducer with the following class parameterization and be sure to configure the Accu muloOutputFormat The key emitted from the Reducer identifies the table to which the mutation is sent This allows a single Reduce
15. is larger than the ratio multiplied by the size of the largest file in the set If this is not true for the set of all files in a tablet the largest file is removed from consideration and the remaining files are considered for compaction This is repeated until a compaction is triggered or there are no files left to consider The number of background threads tablet servers use to run major compactions is configurable To configure this modify the following property Apache Accumulo User Manual Version 1 5 20 39 tserver compaction major concurrent max Also the number of threads tablet servers use for minor compactions is configurable To configure this modify the following property tserver compaction minor concurrent max The numbers of minor and major compactions running and queued is visible on the Accumulo monitor page This allows you to see if compactions are backing up and adjustments to the above settings are needed When adjusting the number of threads available for compactions consider the number of cores and other tasks running on the nodes such as maps and reduces If major compactions are not keeping up then the number of files per tablet will grow to a point such that query performance starts to suffer One way to handle this situation is to increase the compaction ratio For example if the compaction ratio were set to 1 then every new file added to a tablet by minor compaction would immediately queue the tablet for majo
16. records that match one of a set of given criteria When looking for records that match more than one criterion simultaneously such as when looking for documents that contain all of the words the and white and house there are several issues First is that the set of all records matching any one of the search terms must be sent to the client which incurs a lot of network traffic The second problem is that the client is responsible for performing set intersection on the sets of records returned to eliminate all but the records matching all search terms The memory of the client may easily be overwhelmed during this operation For these reasons Accumulo includes support for a scheme known as sharded indexing in which these set operations can be performed at the TabletServers and decisions about which records to include in the result set can be made without incurring network traffic This is accomplished via partitioning records into bins that each reside on at most one TabletServer and then creating an index of terms per record within each bin as follows RowID Column Family Column Qualifier Value BinID Term DocID Weight Documents or records are mapped into bins by a user defined ingest application By storing the BinID as the RowID we ensure that all the information for a particular bin is contained in a single tablet and hosted on a single TabletServer since Accumulo never splits rows across tablets Storing the
17. supports storing sets of column families separately on disk to allow clients to efficiently scan over columns that are frequently used together and to avoid scanning over column families that are not requested After a locality group is set Scanner and BatchScanner operations will automatically take advantage of them whenever the fetchColumnFamilies method is used By default tables place all column families into the same default locality group Additional locality groups can be configured anytime via the shell or programmatically as follows 6 1 1 Managing Locality Groups via the Shell usage setgroups lt group gt lt col fam gt lt col fam gt lt group gt lt col fam gt lt col fam gt t lt table gt user myinstance mytable gt setgroups group_one colfl colf2 t mytable user myinstance mytable gt getgroups t mytable 6 1 2 Managing Locality Groups via the Client API Connector conn HashMap lt String Set lt Text gt gt localityGroups new HashMap lt String Set lt Text gt gt HashSet lt Text gt metadataColumns new HashSet lt Text gt metadataColumns add new Text domain metadataColumns add new Text link HashSet lt Text gt contentColumns new HashSet lt Text gt contentColumns add new Text body contentColumns add new Text images localityGroups put metadata metadataColumns localityGroups put content contentColumns Apache Accumulo User
18. system Once user identity is established their credentials can be accessed by the client code and passed to Accumulo outside of the reach of the user Apache Accumulo User Manual Version 1 5 35 39 10 6 Query Services Layer Since the primary method of interaction with Accumulo is through the Java API production environments often call for the implementation of a Query layer This can be done using web services in containers such as Apache Tomcat but is not a requirement The Query Services Layer provides a mechanism for providing a platform on which user facing applications can be built This allows the application designers to isolate potentially complex query logic and enables a convenient point at which to perform essential security functions Several production environments choose to implement authentication at this layer where users identifiers are used to retrieve their access credentials which are then cached within the query layer and presented to Accumulo through the Authorizations mechanism Typically the query services layer sits between Accumulo and user workstations Apache Accumulo User Manual Version 1 5 36 39 Chapter 11 Administration 11 1 Hardware Because we are running essentially two or three systems simultaneously layered across the cluster HDFS Accumulo and MapReduce it is typical for hardware to consist of 4 to 8 cores and 8 to 32 GB RAM This is so each running process can have at least o
19. useful for inspecting tables root myinstance mytable gt scan root myinstance mytable gt insert rowl colf colq valuel insert successful root myinstance mytable gt scan rowl colf colq valuel The value in brackets would be the visibility labels Since none were used this is empty for this row You can use the st option to scan to see the timestamp for the cell too 3 2 Table Maintenance The compact command instructs Accumulo to schedule a compaction of the table during which files are consolidated and deleted entries are removed root myinstance mytable gt compact t mytable 07 16 13 53 201 shell Shell INFO Compaction of table mytable scheduled for 20100707161353EDT The flush command instructs Accumulo to write all entries currently in memory for a given table to disk root myinstance mytable gt flush t mytable 07 16 14 19 351 shell Shell INFO Flush of table mytable initiated 3 3 User Administration The Shell can be used to add remove and grant privileges to users root myinstance mytable gt createuser bob Enter new password for bob xxxxxx x xx x Please confirm new password for bob xxxxxxx xx x root myinstance mytable gt authenticate bob Enter current password for bob xx x xxxxxx Valid root myinstance mytable gt grant System CREATE_TABLE s u bob root myinstance mytable gt user bob Enter current password for bo
20. 03 root al4 perDayCounts gt insert bar day 20080101 root al4 perDayCounts gt insert bar day 20080101 beep Apache Accumulo User Manual Version 1 5 19 39 root al4 perDayCounts gt scan bar day 20080101 2 foo day 20080101 2 foo day 20080103 1 Accumulo includes some useful Combiners out of the box To find these look in the org apache accumulo core iterators user package Additional Combiners can be added by creating a Java class that extends org apache accumulo core iterators Combiner and adding a jar containing that class to Accumulo s lib ext directory An example of a Combiner can be found under accumulo examples simple main java org apache accum ulo examples simple combiner StatsCombiner java 6 5 Block Cache In order to increase throughput of commonly accessed entries Accumulo employs a block cache This block cache buffers data in memory so that it doesn t have to be read off of disk The RFile format that Accumulo prefers is a mix of index blocks and data blocks where the index blocks are used to find the appropriate data blocks Typical queries to Accumulo result in a binary search over several index blocks followed by a linear scan of one or more data blocks The block cache can be configured on a per table basis and all tablets hosted on a tablet server share a single resource pool To configure the size of the tablet server s block cache set the following properties tserver cache data size
21. Apache Accumulo User Manual Version 1 5 Apache Accumulo User Manual Version 1 5 Apache Accumulo User Manual Version 1 5 COLLABORATORS TITLE Apache Accumulo User Manual Version 1 5 ACTION NAME DATE SIGNATURE WRITTEN BY Apache Accumulo May 17 2013 Project Management Committee REVISION HISTORY NUMBER DATE DESCRIPTION NAME Apache Accumulo User Manual Version 1 5 iii Contents 1 Introduction 1 2 Accumulo Design 2 2 1 Data Modell zi pui e Lal bok eg E E E o a 2 2 2 VAfGhitectutte sue ica ek Bs wed ela e Ble ee bee BAe Ged BG eA ols ee ORES LEAS S 2 2 3 COMPODENTS lt a i OR Ew ER e EY AE RA AA a 2 23 1 A EAS s Lie nani bela ee eae gno Ue le bok ee e a 2 2 32 Garbage Collector cura i Rete abe yh entre e ee We ee ele Eyed e i ELE As Ge 3 233 Master E RA 3 294 Chet 3g A AN A 24 Data Management i icy gek saie le Be ee oe re ee dee gene PR pre 3 2 9 Tablet Service ii aan a A E E e Ra 4 2 6 COMPACUONS sui ca e a Ea E e ea A bE a aw e e a 4 2T SPINE esl o ARI ee te e Sie Bec eas od eg Bech Ree aes A Ghee Se Se 4 2 8 Fault Lolerance pi 2 oe a4 tee eee ee bes P44 OHSS ss ebb e Pew ee eae ee 3 3 Accumulo Shell 6 3 1 Basic Administration oa a 6 3 2 Table Maintenance p c oe csc eee e E E RAEE LE a 7 3 3 User Administrationi seas be alee Eee a bea es Deal ea BRE Dee rd bea ee F 4 Writing Accumulo Clients 9
22. Bulk Ingest Logical time is important for bulk imported data for which the client code may be choosing a timestamp At bulk import time the user can choose to enable logical time for the set of files being imported When its enabled Accumulo uses a specialized system iterator to lazily set times in a bulk imported file This mechanism guarantees that times set by unsynchronized multi node applications such as those running on MapReduce will maintain some semblance of causal ordering This mitigates the problem of the time being wrong on the system that created the file for bulk import These times are not set when the file is imported but whenever it is read by scans or compactions At import a time is obtained and always used by the specialized system iterator to set that time The timestamp assigned by accumulo will be the same for every key in the file This could cause problems if the file contains multiple keys that are identical except for the timestamp In this case the sort order of the keys will be undefined This could occur if an insert and an update were in the same bulk import file 8 5 MapReduce Ingest It is possible to efficiently write many mutations to Accumulo in parallel via a MapReduce job In this scenario the MapReduce is written to process data that lives in HDFS and write mutations to Accumulo using the AccumuloOutputFormat See the MapReduce section under Analytics for details An example of using MapReduce
23. F COI2 4 gt Detect Failure rowH col2 5 rowH col3 a Tablets Tablet Servers N rowC col2 D rowC col3 00 Apache Accumulo User Manual Version 1 5 6 39 Chapter 3 Accumulo Shell Accumulo provides a simple shell that can be used to examine the contents and configuration settings of tables insert up date delete values and change configuration settings The shell can be started by the following command SACCUMULO_HOME bin accumulo shell u username The shell will prompt for the corresponding password to the username specified and then display the following prompt Shell Apache Accumulo Interactive Shell version 1 5 instance name instance id myinstance 00000000 0000 0000 0000 000000000000 type help for a list of available commands 3 1 Basic Administration The Accumulo shell can be used to create and delete tables as well as to configure table and instance specific options root myinstance gt tables IMETADATA root myinstance gt createtable mytable root myinstance root myinstance IMETADATA mytable root myinstance root myinstance root myinstance mytable gt mytable gt tables mytable gt createtable testtable testtable gt testtable gt deletetable testtable Apache Accumulo User Manual Version 1 5 7 39 root myinstance gt The Shell can also be used to insert updates and scan tables This is
24. Monitoring The Accumulo Master provides an interface for monitoring the status and health of Accumulo components This interface can be accessed by pointing a web browser to http accumulomaster 50095 status 11 9 Logging Accumulo processes each write to a set of log files By default these are found under SACCUMULO logs 11 10 Recovery In the event of TabletServer failure or error on shutting Accumulo down some mutations may not have been minor compacted to HDFS properly In this case Accumulo will automatically reapply such mutations from the write ahead log either when the tablets from the failed server are reassigned by the Master in the case of a single TabletServer failure or the next time Accumulo starts in the event of failure during shutdown Recovery is performed by asking the loggers to copy their write ahead logs into HDFS As the logs are copied they are also sorted so that tablets can easily find their missing updates The copy sort status of each file is displayed on Accumulo monitor status page Once the recovery is complete any tablets involved should return to an online state Until then those tablets will be unavailable to clients The Accumulo client library is configured to retry failed mutations and in many cases clients will be able to continue processing after the recovery process without throwing an exception
25. Terms as column families serves to enable fast lookups of all the documents within this bin that contain the given term Finally we perform set intersection operations on the TabletServer via a special iterator called the Intersecting Iterator Since documents are partitioned into many bins a search of all documents must search every bin We can use the BatchScanner to scan all bins in parallel The Intersecting Iterator should be enabled on a BatchScanner within user query code as follows Text terms new Text the new Text white new Text house BatchScanner bs conn createBatchScanner table auths 20 IteratorSetting iter new IteratorSetting 20 ii Intersectinglterator class Intersectinglterator setColumnFamilies iter terms bs addScanIterator iter bs setRanges Collections singleton new Range for Entry lt Key Value gt entry bs System out printin entry getKey getColumnQualifier This code effectively has the BatchScanner scan all tablets of a table looking for documents that match all the given terms Because all tablets are being scanned for every query each query is more expensive than other Accumulo scans which typically involve a small number of TabletServers This reduces the number of concurrent queries supported and is subject to what is known as the straggler problem in which every query runs as slow as the slowest server participating Of course fast se
26. ables as input and output for MapReduce jobs Accumulo features automatic load balancing and partitioning data compression and fine grained security labels Apache Accumulo User Manual Version 1 5 2 39 Chapter 2 Accumulo Design 2 1 Data Model Accumulo provides a richer data model than simple key value stores but is not a fully relational database Data is represented as key value pairs where the key and value are comprised of the following elements Key Column Value ROAD Family Qualifier Visibilty ili All elements of the Key and the Value are represented as byte arrays except for Timestamp which is a Long Accumulo sorts keys by element and lexicographically in ascending order Timestamps are sorted in descending order so that later versions of the same Key appear first in a sequential scan Tables consist of a set of sorted key value pairs 2 2 Architecture Accumulo is a distributed data storage and retrieval system and as such consists of several architectural components some of which run on many individual servers Much of the work Accumulo does involves maintaining certain properties of the data such as organization availability and integrity across many commodity class machines 2 3 Components An instance of Accumulo includes many TabletServers one Garbage Collector process one Master server and many Clients 2 3 1 Tablet Server The TabletServer manages some subset of
27. ache Accumulo User Manual Version 1 5 11 39 scan fetchFamily attributes for Entry lt Key Value gt entry scan String row entry getKey getRow Value value entry getValue 4 4 2 Isolated Scanner Accumulo supports the ability to present an isolated view of rows when scanning There are three possible ways that a row could change in accumulo e a mutation applied to a table e iterators executed as part of a minor or major compaction e bulk import of new files Isolation guarantees that either all or none of the changes made by these operations on a row are seen Use the IsolatedScanner to obtain an isolated view of an accumulo table When using the regular scanner it is possible to see a non isolated view of a row For example if a mutation modifies three columns it is possible that you will only see two of those modifications With the isolated scanner either all three of the changes are seen or none The IsolatedScanner buffers rows on the client side so a large row will not crash a tablet server By default rows are buffered in memory but the user can easily supply their own buffer if they wish to buffer to disk when rows are large For an example look at the following examples simple src main java org apache accumulo examples simple isolation InterferenceTest java 4 4 3 BatchScanner For some types of access it is more efficient to retrieve several ranges simultaneously This arises when accessing
28. all the tablets partitions of tables This includes receiving writes from clients persisting writes to a write ahead log sorting new key value pairs in memory periodically flushing sorted key value pairs to new files in HDFS and responding to reads from clients forming a merge sorted view of all keys and values from all the files it has created and the sorted in memory store TabletServers also perform recovery of a tablet that was previously on a server that failed reapplying any writes found in the write ahead log to the tablet Apache Accumulo User Manual Version 1 5 3 39 2 3 2 Garbage Collector Accumulo processes will share files stored in HDFS Periodically the Garbage Collector will identify files that are no longer needed by any process and delete them 2 3 3 Master The Accumulo Master is responsible for detecting and responding to TabletServer failure It tries to balance the load across TabletServer by assigning tablets carefully and instructing TabletServers to unload tablets when necessary The Master ensures all tablets are assigned to one TabletServer each and handles table creation alteration and deletion requests from clients The Master also coordinates startup graceful shutdown and recovery of changes in write ahead logs when Tablet servers fail Multiple masters may be run The masters will choose among themselves a single master and the others will become backups if the master should fail 2 3 4 Client
29. ally split a tablet is based on the size of a tablets files The size threshold at which a tablet splits is configurable per table In addition to automatic splitting a user can manually add split points to a table to create new tablets Manually splitting a new table can parallelize reads and writes giving better initial performance without waiting for automatic splitting As data is deleted from a table tablets may shrink Over time this can lead to small or empty tablets To deal with this merging of tablets was introduced in Accumulo 1 4 This is discussed in more detail later 2 8 Fault Tolerance If a TabletServer fails the Master detects it and automatically reassigns the tablets assigned from the failed server to other servers Any key value pairs that were in memory at the time the TabletServer fails are automatically reapplied from the Write Ahead Log to prevent any loss of data The Master will coordinate the copying of write ahead logs to HDFS so the logs are available to all tablet servers To make recovery efficient the updates within a log are grouped by tablet TabletServers can quickly apply the mutations from the sorted logs that are destined for the tablets they have now been assigned TabletServer failures are noted on the Master s monitor page accessible viahttp master address 50095 monitor Automatic Failure Handling Assignment rowA col2 1 ee rowB coll a rowC coli 1 ae Reassignment Master a row
30. ass name Some iterators take additional parameters from client code as in the following example IteratorSetting iter new IteratorSetting iter addOption myoptionname myoptionvalue scanner addIterator iter Tables support separate Iterator settings to be applied at scan time upon minor compaction and upon major compaction For most uses tables will have identical iterator settings for all three to avoid inconsistent results 6 4 3 Versioning Iterators and Timestamps Accumulo provides the capability to manage versioned data through the use of timestamps within the Key If a timestamp is not specified in the key created by the client then the system will set the timestamp to the current time Two keys with identical rowIDs and columns but different timestamps are considered two versions of the same key If two inserts are made into accumulo with the same rowID column and timestamp then the behavior is non deterministic Timestamps are sorted in descending order so the most recent data comes first Accumulo can be configured to return the top k versions or versions later than a given date The default is to return the one most recent version The version policy can be changed by changing the VersioningIterator options for a table as follows user myinstance mytable gt config t mytable s table iterator scan vers opt maxVersions 3 user myinstance mytable gt config t mytable s table iterator minc vers opt ma
31. b xx x xxxxxx bob myinstance mytable gt userpermissions System permissions System CREATE_TABLE Table permissions METADATA Table READ Table permissions mytable NONE Apache Accumulo User Manual Version 1 5 8 39 bob myinstance mytable gt createtable bobstable bob myinstance bobstable gt bob myinstance bobstable gt user root Enter current password for root xx x xxxxxx x root myinstance bobstable gt revoke System CREATE_TABLE s u bob Apache Accumulo User Manual Version 1 5 9 39 Chapter 4 Writing Accumulo Clients 4 1 Running Client Code There are multiple ways to run Java code that uses Accumulo Below is a list of the different ways to execute client code e using java executable e using the accumulo script e using the tool script In order to run client code written to run against Accumulo you will need to include the jars that Accumulo depends on in your classpath Accumulo client code depends on Hadoop and Zookeeper For Hadoop add the hadoop core jar all of the jars in the Hadoop lib directory and the conf directory to the classpath For Zookeeper 3 3 you only need to add the Zookeeper jar and not what is in the Zookeeper lib directory You can run the following command on a configured Accumulo system to see what its using for its classpath SACCUMULO_HOME bin accumulo classpath Another option for running your code is to put a jar f
32. bset of the users authorizations then an exception will be thrown To prevent users from writing data they can not read add the visibility constraint to a table Use the evc option in the createtable shell command to enable this constraint For existing tables use the following shell command to enable the visibility constraint Ensure the constraint number does not conflict with any existing constraints config t table s table constraint l org apache accumulo core security VisibilityConstraint Any user with the alter table permission can add or remove this constraint This constraint is not applied to bulk imported data if this a concern then disable the bulk import permission 10 5 Secure Authorizations Handling For applications serving many users it is not expected that an accumulo user will be created for each application user In this case an accumulo user with all authorizations needed by any of the applications users must be created To service queries the application should create a scanner with the application user s authorizations These authorizations could be obtained from a trusted 3rd party Often production systems will integrate with Public Key Infrastructure PKI and designate client code within the query layer to negotiate with PKI servers in order to authenticate users and retrieve their authorization tokens credentials This requires users to specify only the information necessary to authenticate themselves to the
33. can fetchFamily attributes for Entry lt Key Value gt entry scan System out printin entry getValue 10 One advantage of the dynamic schema capabilities of Accumulo is that different fields may be indexed into the same physical table However it may be necessary to create different index tables if the terms must be formatted differently in order to maintain proper sort order For example real numbers must be formatted differently than their usual notation in order to be sorted correctly In these cases usually one index per unique data type will suffice 7 4 Entity Attribute and Graph Tables Accumulo is ideal for storing entities and their attributes especially of the attributes are sparse It is often useful to join several datasets together on common entities within the same table This can allow for the representation of graphs including nodes their attributes and connections to other nodes Rather than storing individual events Entity Attribute or Graph tables store aggregate information about the entities involved in the events and the relationships between entities This is often preferrable when single events aren t very useful and when a continuously updated summarization is desired The physical schema for an entity attribute or graph table is as follows RowID Column Family Column Qualifier Value EntityID Attribute Name Attribute Value Weight EntityID Edge Type
34. create a jar file containing the class implementing the new constraint and place it in the lib directory of the Accumulo installation New constraint jars can be added to Accumulo and enabled without restarting but any change to an existing constraint class requires Accumulo to be restarted An example of constraints can be found in accumulo docs examples README constraints with corresponding code under accumulo examples simple main java accumulo examples simple constraints 6 3 Bloom Filters As mutations are applied to an Accumulo table several files are created per tablet If bloom filters are enabled Accumulo will create and load a small data structure into memory to determine whether a file contains a given key before opening the file This can speed up lookups considerably To enable bloom filters enter the following command in the Shell user myinstance gt config t mytable s table bloom enabled true An extensive example of using Bloom Filters can be found at accumulo docs examples README bloom 6 4 Iterators Iterators provide a modular mechanism for adding functionality to be executed by TabletServers when scanning or compacting data This allows users to efficiently summarize filter and aggregate data In fact the built in features of cell level security and column fetching are implemented using Iterators Some useful Iterators are provided with Accumulo and can be found in the org apache accumulo core iterator
35. ese can be specified alone or combined using logical operators Users must have admin privileges admin Apache Accumulo User Manual Version 1 5 34 39 Users must have admin and audit privileges admin audit Users with either admin or audit privileges admin audit Users must have audit and one or both of admin or system admin system amp audit When both and amp operators are used parentheses must be used to specify precedence of the operators 10 3 Authorization When clients attempt to read data from Accumulo any security labels present are examined against the set of authorizations passed by the client code when the Scanner or BatchScanner are created If the authorizations are determined to be insufficient to satisfy the security label the value is suppressed from the set of results sent back to the client Authorizations are specified as a comma separated list of tokens the user possesses user possesses both admin and system level access Authorization auths new Authorization admin system Scanner s connector createScanner table auths 10 4 User Authorizations Each accumulo user has a set of associated security labels To manipulate these in the shell use the setuaths and getauths commands These may also be modified using the java security operations API When a user creates a scanner a set of Authorizations is passed If the authorizations passed to the scanner are not a su
36. for a given key have been seen since new mutations can be inserted at anytime This precludes using the total number of values in the aggregation such as when calculating an average for example 9 2 1 Feature Vectors An interesting use of combining iterators within an Accumulo table is to store feature vectors for use in machine learning algorithms For example many algorithms such as k means clustering support vector machines anomaly detection etc use the concept of a feature vector and the calculation of distance metrics to learn a particular model The columns in an Accumulo table can be used to efficiently store sparse features and their weights to be incrementally updated via the use of an combining iterator 9 3 Statistical Modeling Statistical models that need to be updated by many machines in parallel could be similarly stored within an Accumulo table For example a MapReduce job that is iteratively updating a global statistical model could have each map or reduce worker reference the parts of the model to be read and updated through an embedded Accumulo client Using Accumulo this way enables efficient and fast lookups and updates of small pieces of information in a random access pattern which is complementary to MapReduce s sequential access model Apache Accumulo User Manual Version 1 5 33 39 Chapter 10 Security Accumulo extends the BigTable data model to implement a security mechanism known as cell level securi
37. he TabletServer writes out the sorted key value pairs to a file in HDFS called Indexed Sequential Access Method ISAM file This process is called a minor compaction A new MemTable is then created and the fact of the compaction is recorded in the Write Ahead Log When a request to read data arrives at a TabletServer the TabletServer does a binary search across the MemTable as well as the in memory indexes associated with each ISAM file to find the relevant values If clients are performing a scan several key value pairs are returned to the client in order from the MemTable and the set of ISAM files by performing a merge sort as they are read 2 6 Compactions In order to manage the number of files per tablet periodically the TabletServer performs Major Compactions of files within a tablet in which some set of ISAM files are combined into one file The previous files will eventually be removed by the Garbage Collector This also provides an opportunity to permanently remove deleted key value pairs by omitting key value pairs suppressed by a delete entry when the new file is created 2 7 Splitting When a table is created it has one tablet As the table grows its initial tablet eventually splits into two tablets Its likely that one of these tablets will migrate to another tablet server As the table continues to grow its tablets will continue to split and be migrated Apache Accumulo User Manual Version 1 5 5 39 The decision to automatic
38. hell INFO Compacting table 27 15 03 03 303 shell Shell INFO Compaction of table cic completed for given range root al4 cic gt du ci cic 428 482 573 ci 428 482 612 cic root al4 cic gt Apache Accumulo User Manual Version 1 5 23 39 6 11 Exporting Tables Accumulo supports exporting tables for the purpose of copying tables to another cluster Exporting and importing tables preserves the tables configuration splits and logical time Tables are exported and then copied via the hadoop distcp command To export a table it must be offline and stay offline while discp runs The reason it needs to stay offline is to prevent files from being deleted A table can be cloned and the clone taken offline inorder to avoid losing access to the table See docs examples README export for an example Apac he Accumulo User Manual Version 1 5 24 39 Chapter 7 Table Design 7 1 Basic Table Since Accumulo tables are sorted by row ID each table can be thought of as being indexed by the row ID Lookups performed by row ID can be executed quickly by doing a binary search first across the tablets and then within a tablet Clients should choose a row ID carefully in order to support their desired application A simple rule is to select a unique identifier as the row ID for each entity to be stored and assign all the other attributes to be tracked to be columns under this row ID For example if we have the follo
39. ialized to create the structures it uses internally to locate data across the cluster HDFS is required to be configured and running before Accumulo can be initialized Once HDES is started initialization can be performed by executing SACCUMULO_HOME bin accumulo init This script will prompt for a name for this instance of Accumulo The instance name is used to identify a set of tables and instance specific settings The script will then write some information into HDFS so Accumulo can start properly The initialization script will prompt you to set a root password Once Accumulo is initialized it can be started 11 7 Running 11 7 1 Starting Accumulo Make sure Hadoop is configured on all of the machines in the cluster including access to a shared HDFS instance Make sure HDFS and ZooKeeper are running Make sure ZooKeeper is configured and running on at least one machine in the cluster Start Accumulo using the bin start all sh script To verify that Accumulo is running check the Status page as described under Monitoring In addition the Shell can provide some information about the status of tables via reading the METADATA table 11 7 2 Stopping Accumulo To shutdown cleanly run bin stop all sh and the master will orchestrate the shutdown of all the tablet servers Shutdown waits for all minor compactions to finish so it may take some time for particular configurations Apache Accumulo User Manual Version 1 5 39 39 11 8
40. ile in SACCUMULO_HOME 1lib ext After doing this you can use the accumulo script to execute your code For example if you create a jar containing the class com foo Client and placed that in Lib ext then you could use the command ACCUMULO_HOME bin accumulo com foo Client to execute your code If you are writing map reduce job that access Accumulo then you can use the bin tool sh script to run those jobs See the map reduce example 4 2 Connecting All clients must first identify the Accumulo instance to which they will be communicating Code to do this is as follows String instanceName myinstance String zooServers zooserver one zooserver two Instance inst new ZooKeeperInstance instanceName zooServers Connector conn inst getConnector user passwd Apache Accumulo User Manual Version 1 5 10 39 4 3 Writing Data Data are written to Accumulo by creating Mutation objects that represent all the changes to the columns of a single row The changes are made atomically in the TabletServer Clients then add Mutations to a BatchWriter which submits them to the appropriate TabletServers Mutations can be created thus Text rowID new Text rowl Text colFam new Text myColFam Text colQual new Text myColQual ColumnVisibility colVis new ColumnVisibility public long timestamp System currentTimeMillis Value value new Value myValue getBytes Mutation mutati
41. ion Li e SER ES e E RES i ee ila 19 6 7 Pre splittng tables s cnoe ce wee eee aa ewe I ee ewe ee ae he 20 6 8 Mergingtablets is sees eee OR RN RE EER e Se PO ee ee a eee oo 21 6 9 Delete Range scopas epa SARS BER ENO EE RRO ER ea we ol a eh ae la Zi 6 10 Cloning Tables passe e lie A a 21 6 11 Exporting Tables s pi s ers i i E ROR OR e E e ala 23 Table Design 24 Fil Basie Table do ila ke Sew Bee Gee a a A a ae ee ae de Ae ae eon oe 24 7 2 RowlD Design 2 54 4 cea ea W bee SOP EER a 24 ASS e oe oe eA eG Se 25 7 4 Entity Attribute and Graph Tables 26 7 5 Document Partitioned Indexing 21 High Speed Ingest 28 8 1 Pre Splitting New Tables 00 000 28 2 Multiple Inpester Clients a gcns g eo e ee ie e Ve eee oS OS 28 63 BulkInpest s piu ae A Pee RA eae a i e a A Se A Ln 28 8 4 Logical Time for Bulk Ingest ooa 29 8 3 MapReduce Ingest a 3er pe Me ee E E e 29 Apache Accumulo User Manual Version 1 5 v 9 Analytics 30 9 1 MapReduce ie ed a de ER E GL RR ewe e a a e e e 30 9 1 1 Mapper and Reducer classes il i ee ee A E E E bs 30 9 1 2 AccumuloInputFormat options 2 a ee 31 9 1 3 AccumuloOutputFormat options 3i 92 COMDINGIS 4 4 sitio e Be A e SMe a E AS AA e 31 9 2 1 Feature VectOrS oo o oe don ee e RA EE 32 9 3 Statistical Modeling c p o oc oso rss wR A eR ee ee 32 10 Security 33 10 1 Security Label Expressions Li ee ee E i E ee S 10 2 Security Label Expression Syntax L22200 33
42. ion or if the distribution of the row information is not flat then you would pick different split points Now ingest and query can proceed on 4 nodes which can improve performance 66499 t Apache Accumulo User Manual Version 1 5 21 39 6 8 Merging tablets Over time a table can get very large so large that it has hundreds of thousands of split points Once there are enough tablets to spread a table across the entire cluster additional splits may not improve performance and may create unnecessary bookkeeping The distribution of data may change over time For example if row data contains date information and data is continually added and removed to maintain a window of current information tablets for older rows may be empty Accumulo supports tablet merging which can be used to reduce the number of split points The following command will merge all rows from A to Z into a single tablet root myinstance gt merge t myTable s A e Z If the result of a merge produces a tablet that is larger than the configured split size the tablet may be split by the tablet server Be sure to increase your tablet size prior to any merges if the goal is to have larger tablets root myinstance gt config t myTable s table split threshold 2G In order to merge small tablets you can ask accumulo to merge sections of a table smaller than a given size root myinstance gt merge t myTable s 100M By default small tablets will not
43. ions and will affect the number of splits in the table 6 10 Cloning Tables A new table can be created that points to an existing table s data This is a very quick metadata operation no data is actually copied The cloned table and the source table can change independently after the clone operation One use case for this feature is testing For example to test a new filtering iterator clone the table add the filter to the clone and force a major compaction To perform a test on less data clone a table and then use delete range to efficiently remove a lot of data from the clone Another use Apache Accumulo User Manual Version 1 5 22 39 case is generating a snapshot to guard against human error To create a snapshot clone a table and then disable write permissions on the clone The clone operation will point to the source table s files This is why the flush option is present and is enabled by default in the shell If the flush option is not enabled then any data the source table currently has in memory will not exist in the clone A cloned table copies the configuration of the source table However the permissions of the source table are not copied to the clone After a clone is created only the user that created the clone can read and write to it In the following example we see that data inserted after the clone operation is not visible in the clone root al4 gt createtable peopl root al4 people gt in
44. is configured by default to return the one most recent value associated with a key Lookups can then be done by scanning the Index Table first for occurrences of the desired values in the columns specified which returns a list of row ID from the main table These can then be used to retrieve each matching record in their entirety or a subset of their columns from the Main Table To support efficient lookups of multiple rowIDs from the same table the Accumulo client library provides a BatchScanner Users specify a set of Ranges to the BatchScanner which performs the lookups in multiple threads to multiple servers and returns an Iterator over all the rows retrieved The rows returned are NOT in sorted order as is the case with the basic Scanner interface first we scan the index for IDs of rows matching our query Text term new Text mySearchTerm HashSet lt Text gt matchingRows new HashSet lt Text gt Scanner indexScanner createScanner index auths indexScanner setRange new Range term term we retrieve the matching rowIDs and create a set of ranges for Entry lt Key Value gt entry indexScanner matchingRows add new Text entry getKey getColumnQualifier now we pass the set of rowIDs to the batch scanner to retrieve them Apache Accumulo User Manual Version 1 5 26 39 BatchScanner bscan conn createBatchScanner table auths bscan setRanges matchingRows bs
45. itted keys are removed from disk as part of the regular garbage collection process 6 4 4 Filters When scanning over a set of key value pairs it is possible to apply an arbitrary filtering policy through the use of a Filter Filters are types of iterators that return only key value pairs that satisfy the filter logic Accumulo has a few built in filters that can be configured on any table AgeOff ColumnAgeOff Timestamp NoVis and RegEx More can be added by writing a Java class that extends the org apache accumulo core iterators Filter class The AgeOff filter can be configured to remove data older than a certain date or a fixed amount of time from the present The following example sets a table to delete everything inserted over 30 seconds ago user myinstance gt createtable filtertest user myinstance filtertest gt setiter t filtertest scan minc majc p 10 n gt myfilter ageoff AgeOffFilter removes entries with timestamps more than lt tt1 gt milliseconds old gt set org apache accumulo core iterators user AgeOffFilter parameter negate default false keeps k v that pass accept method true rejects k v that pass accept method gt set org apache accumulo core iterators user AgeOffFilter parameter ttl time to live milliseconds 3000 gt set org apache accumulo core iterators user AgeOffFilter parameter currentTime if set use the given value as the absolute time in milliseconds as the current time of day ser myinstance filterte
46. ne core and 2 4 GB each One core running HDFS can typically keep 2 to 4 disks busy so each machine may typically have as little as 2 x 300GB disks and as much as 4 x 1TB or 2TB disks It is possible to do with less than this such as with lu servers with 2 cores and 4GB each but in this case it is recommended to only run up to two processes per machine i e DataNode and TabletServer or DataNode and MapReduce worker but not all three The constraint here is having enough available heap space for all the processes on a machine 11 2 Network Accumulo communicates via remote procedure calls over TCP IP for both passing data and control messages In addition Accumulo uses HDFS clients to communicate with HDFS To achieve good ingest and query performance sufficient network bandwidth must be available between any two machines 11 3 Installation Choose a directory for the Accumulo installation This directory will be referenced by the environment variable ACCUMULO_ HOME Run the following tar xzf accumulo assemble 1 5 0 bin tar gz unpack to subdirectory mv accumulo assemble 1 5 0 bin ACCUMULO_HOME move to desired location Repeat this step at each machine within the cluster Usually all machines have the same SACCUMULO_ HOME 11 4 Dependencies Accumulo requires HDFS and ZooKeeper to be configured and running before starting Password less SSH should be configured between at least the Accumulo master
47. nputFormat fetchColumns job columns To use a regular expression to match row IDs AccumuloInputFormat setRegex job RegexType ROW 9 1 3 AccumuloOutputFormat options boolean createTables true String defaultTable mytable Accumulo0utputFormat setO0utputInfo job user E passwd getBytes createTables defaultTable Accumulo0utputFormat setZooKeeperInstance job myinstance zooserver one zooserver two Optional Settings Accumulo0utputFormat setMaxLatency job 300 milliseconds Accumulo0utputFormat setMaxMutationBufferSize job 5000000 bytes An example of using MapReduce with Accumulo can be found at accumulo docs examples README mapred 9 2 Combiners Many applications can benefit from the ability to aggregate values across common keys This can be done via Combiner iterators and is similar to the Reduce step in MapReduce This provides the ability to define online incrementally updated analytics without the overhead or latency associated with batch oriented MapReduce jobs Apache Accumulo User Manual Version 1 5 32 39 All that is needed to aggregate values of a table is to identify the fields over which values will be grouped insert mutations with those fields as the key and configure the table with a combining iterator that supports the summarizing operation desired The only restriction on an combining iterator is that the combiner developer should not assume that all values
48. ompactions causes O N 2 work to be done The amount of work done by major compactions is O N logr N where R is the compaction ratio Compactions can be initiated manually for a table To initiate a minor compaction use the flush command in the shell To initiate a major compaction use the compact command in the shell The compact command will compact all tablets in a table to one file Even tablets with one file are compacted This is useful for the case where a major compaction filter is configured for a table In 1 4 the ability to compact a range of a table was added To use this feature specify start and stop rows for the compact command This will only compact tablets that overlap the given row range 6 7 Pre splitting tables Accumulo will balance and distribute tables across servers Before a table gets large it will be maintained as a single tablet on a single server This limits the speed at which data can be added or queried to the speed of a single node To improve performance when the a table is new or small you can add split points and generate new tablets In the shell root myinstance gt createtable newTabl root myinstance gt addsplits t newTable g nt 6699 6699 This will create a new table with 4 tablets The table will be split on the letters g n and t which will work nicely if the row data start with lower case alphabetic characters If your row data includes binary information or numeric informat
49. on new Mutation rowID mutation put colFam colQual colVis timestamp value 4 3 1 BatchWriter The BatchWriter is highly optimized to send Mutations to multiple TabletServers and automatically batches Mutations destined for the same TabletServer to amortize network overhead Care must be taken to avoid changing the contents of any Object passed to the BatchWriter since it keeps objects in memory while batching Mutations are added to a BatchWriter thus long memBuf 1000000L bytes to store before sending a batch long timeout 1000L milliseconds to wait before sending int numThreads 10 BatchWriter writer conn createBatchWriter table memBuf timeout numThreads writer add mutation writer close An example of using the batch writer can be found at accumulo docs examples README batch 4 4 Reading Data Accumulo is optimized to quickly retrieve the value associated with a given key and to efficiently return ranges of consecutive keys and their associated values 4 4 1 Scanner To retrieve data Clients use a Scanner which acts like an Iterator over keys and values Scanners can be configured to start and stop at particular keys and to return a subset of the columns available specify which visibilities we are allowed to s Authorizations auths new Authorizations public Scanner scan conn createScanner table auths scan setRange new Range harry john Ap
50. r compaction So if a tablet has a 200M file and minor compaction writes a 1M file then the major compaction will attempt to merge the 200M and 1M file If the tablet server has lots of tablets trying to do this sort of thing then major compactions will back up and the number of files per tablet will start to grow assuming data is being continuously written Increasing the compaction ratio will alleviate backups by lowering the amount of major compaction work that needs to be done Another option to deal with the files per tablet growing too large is to adjust the following property table file max When a tablet reaches this number of files and needs to flush its in memory data to disk it will choose to do a merging minor compaction A merging minor compaction will merge the tablet s smallest file with the data in memory at minor compaction time Therefore the number of files will not grow beyond this limit This will make minor compactions take longer which will cause ingest performance to decrease This can cause ingest to slow down until major compactions have enough time to catch up When adjusting this property also consider adjusting the compaction ratio Ideally merging minor compactions never need to occur and major compactions will keep up It is possible to configure the file max and compaction ratio such that only merging minor compactions occur and major compactions never occur This should be avoided because doing only merging minor c
51. r to write to more than one table if desired A default table can be configured using the AccumuloOutputFormat in which case the output table name does not have to be passed to the Context object within the Reducer class MyReducer extends Reducer lt WritableComparable Writable Text Mutation gt public void reduce WritableComparable key Iterable lt Text gt values Context c Mutation m create the mutation based on input key and value c write new Text output table m The Text object passed as the output should contain the name of the table to which this mutation should be applied The Text can be null in which case the mutation will be applied to the default table name specified in the AccumuloOutputFormat options Apache Accumulo User Manual Version 1 5 31 39 9 1 2 AccumulolnputFormat options Job job new Job getConf AccumuloInputFormat setInputInfo job user passwd getBytes table new Authorizations AccumuloInputFormat setZooKeeperInstance job myinstance zooserver one zooserver two Optional settings To restrict Accumulo to a set of row ranges ArrayList lt Range gt ranges new ArrayList lt Range gt j populate array list of row ranges AccumuloInputFormat setRanges job ranges To restrict accumulo to a list of columns ArrayList lt Pair lt Text Text gt gt columns new ArrayList lt Pair lt Text Text gt gt populate list of columns AccumuloI
52. rvers will return their results to the client which can display them to the user immediately while they wait for the rest of the results to arrive If the results are unordered this is quite effective as the first results to arrive are as good as any others to the user Apache Accumulo User Manual Version 1 5 28 39 Chapter 8 High Speed Ingest Accumulo is often used as part of a larger data processing and storage system To maximize the performance of a parallel system involving Accumulo the ingestion and query components should be designed to provide enough parallelism and concurrency to avoid creating bottlenecks for users and other systems writing to and reading from Accumulo There are several ways to achieve high ingest performance 8 1 Pre Splitting New Tables New tables consist of a single tablet by default As mutations are applied the table grows and splits into multiple tablets which are balanced by the Master across TabletServers This implies that the aggregate ingest rate will be limited to fewer servers than are available within the cluster until the table has reached the point where there are tablets on every TabletServer Pre splitting a table ensures that there are as many tablets as desired available before ingest begins to take advantage of all the parallelism possible with the cluster hardware Tables can be split anytime by using the shell user myinstance mytable gt addsplits sf local_splitfile t mytable
53. s fails because of insufficient memory Note that you will be specifying the Java heap space in accumulo env sh You should make sure that the total heap space used for the Accumulo tserver and the Hadoop DataNode and TaskTracker is less than the available memory on each slave node in the cluster On large clusters it is recommended that the Accumulo master Hadoop NameNode secondary NameNode and Hadoop JobTracker all be run on separate machines to allow them to use more heap space If you are running these on the same machine on a small cluster likewise make sure their heap space settings fit within the available memory 11 5 2 Cluster Specification On the machine that will serve as the Accumulo master 1 Write the IP address or domain name of the Accumulo Master to the SACCUMULO_HOME conf masters file 2 Write the IP addresses or domain name of the machines that will be TabletServers in SACCUMULO_HOME conf sla ves one per line Note that if using domain names rather than IP addresses DNS must be configured properly for all machines participating in the cluster DNS can be a confusing source of errors 11 5 3 Accumulo Settings Specify appropriate values for the following settings in SACCUMULO_HOME conf accumulo site xml lt property gt lt name gt zookeeper lt name gt lt value gt zooserver one 2181 zooserver two 2181 lt value gt lt description gt list of zookeeper servers lt description gt lt property gt
54. s user package In each case any custom Iterators must be included in Accumulo s classpath typically by including a jar in SACCUMULO_HOME 1ib or SACCUMULO_HOME 1ib ext although the VES classloader allows for classpath manipulation using a variety of schemes including URLs and HDFS URIs Apache Accumulo User Manual Version 1 5 16 39 6 4 1 Setting Iterators via the Shell Iterators can be configured on a table at scan minor compaction and or major compaction scopes If the Iterator implements the OptionDescriber interface the setiter command can be used which will interactively prompt the user to provide values for the given necessary options usage setiter ageoff agg class lt name gt regex regvis vers majc minc n lt itername gt p lt pri gt scan t lt table gt user myinstance mytable gt setiter t mytabl scan p 15 n myiter class com company MyIterator The config command can always be used to manually configure iterators which is useful in cases where the Iterator does not implement the OptionDescriber interface config t mytable s table iterator scan minc majc myiter 15 com company MyIterator config t mytable s table iteartor scan minc majc myiter opt myoptionname gt myoptionvalue 6 4 2 Setting Iterators Programmatically scanner addIterator new IteratorSetting WS ff EMO ey myiter name this iterator com company MyIterator cl
55. sert 890435 name last Doe root al4 people gt insert 890435 name first John root al4 people gt clonetable people test root al4 people gt insert 890436 name first Jane root al4 people gt insert 890436 name last Doe root al4 people gt scan 890435 name first John 890435 name last Doe 890436 name first Jane 890436 name last Doe root al4 people gt table test root al4 test gt scan 890435 name first John 890435 name last Doe root al4 test gt The du command in the shell shows how much space a table is using in HDFS This command can also show how much overlapping space two cloned tables have in HDFS In the example below du shows table ci is using 428M Then ci is cloned to cic and du shows that both tables share 428M After three entries are inserted into cic and its flushed du shows the two tables still share 428M but cic has 226 bytes to itself Finally table cic is compacted and then du shows that each table uses 428M root al4 gt du ci 428 482 573 ci root al4 gt clonetable ci cic root al4 gt du ci cic 428 482 573 ci cic root al4 gt table cic root al4 cic gt insert rl cfl cql vl root al4 cic gt insert rl cfl cq2 v2 root al4 cic gt insert rl cfl cq3 v3 root al4 cic gt flush t cic w 27 15 00 13 908 shell Shell INFO Flush of table cic completed root al4 cic gt du ci cic 428 482 573 ci cic 226 cic A root al4 cic gt compact t cic w 27 15 00 35 871 shell S
56. sorted together for scanning purposes This can be done by appending a random substring at the end of the row com google code_00 com google code_01 com google code_02 com google labs_00 com google mail_00 com google mail_01 It could also be done by adding a string representation of some period of time such as date to the week or month com google code_201003 com google code_201004 com google code_201005 com google labs_201003 com google mail_ 201003 com google mail_ 201004 Appending dates provides the additional capability of restricting a scan to a given date range 7 3 Indexing In order to support lookups via more than one attribute of an entity additional indexes can be built However because Accumulo tables can support any number of columns without specifying them beforehand a single additional index will often suffice for supporting lookups of records in the main table Here the index has as the rowID the Value or Term from the main table the column families are the same and the column qualifier of the index table contains the rowID from the main table RowID Column Family Column Qualifier Value Term Field Name MainRowID Note We store rowIDs in the column qualifier rather than the Value so that we can have more than one rowID associated with a particular term within the index If we stored this in the Value we would only see one of the rows in which the value appears since Accumulo
57. st gt ser myinstance filtertest gt scan ser myinstance filtertest gt insert foo ab c ser myinstance filtertest gt scan foo a b c user myinstance filtertest gt sleep 4 user myinstance filtertest gt scan user myinstance filtertest gt E G G G To see the iterator settings for a table use Apache Accumulo User Manual Version 1 5 18 39 user example filtertest gt config t filtertest f iterator SCOPE NAME VALUE table table iterator majc myfilter 10 org apache accumulo core iterators user AgeOffFilter table table iterator majc myfilter opt ttl 3000 table table iterator majc vers ooooooooo ooo 20 org apache accumulo core iterators Versioninglterator table table iterator majc vers opt maxVersions 1 table table iterator minc myfilter 10 org apache accumulo core iterators user AgeOffFilter table table iterator minc myfilter opt ttl 3000 table table iterator minc vers 006 20 org apache accumulo core iterators Versioninglterator table table iterator minc vers opt maxVersions 1 table table iterator scan myfilter 10 org apache accumulo core iterators user AgeOffFilter table table iterator scan myfilter opt ttl 3000 table table iterator scan versS ooooooooo ooo 20 org apache
58. the client API for unit testing it is often necessary to write more realistic end to end integration tests that take advantage of the entire ecosystem The Mini Accumulo Cluster makes this possible by configuring and starting Zookeeper initializing Accumulo and starting the Master as well as some Tablet Servers It runs against the local filesystem instead of having to start up HDFS To start it up you will need to supply an empty directory and a root password as arguments File tempDirectory JUnit and Guava supply mechanisms for creating temp directories MiniAccumuloCluster accumulo new MiniAccumuloCluster tempDirectory password accumulo start Once we have our mini cluster running we will want to interact with the Accumulo client API Instance instance new ZooKeeperInstance accumulo getInstanceName accumulo getZooKeepers Connector conn instance getConnector root new PasswordToken password Upon completion of our development code we will want to shutdown our MiniAccumuloCluster accumulo stop delete your temporary folder Apache Accumulo User Manual Version 1 5 14 39 Chapter 6 Table Configuration Accumulo tables have a few options that can be configured to alter the default behavior of Accumulo as well as improve perfor mance based on the data stored These include locality groups constraints bloom filters iterators and block cache 6 1 Locality Groups Accumulo
59. ty Every key value pair has its own security label stored under the column visibility element of the key which is used to determine whether a given user meets the security requirements to read the value This enables data of various security levels to be stored within the same row and users of varying degrees of access to query the same table while preserving data confidentiality 10 1 Security Label Expressions When mutations are applied users can specify a security label for each value This is done as the Mutation is created by passing a ColumnVisibility object to the put method Text rowID new Text rowl Text colFam new Text myColFam Text colQual new Text myColQual ColumnVisibility colVis new ColumnVisibility public long timestamp System currentTimeMillis Value value new Value myValue Mutation mutation new Mutation rowID mutation put colFam colQual colVis timestamp value 10 2 Security Label Expression Syntax Security labels consist of a set of user defined tokens that are required to read the value the label is associated with The set of tokens required can be specified using syntax that supports logical AND and OR combinations of tokens as well as nesting groups of tokens together For example suppose within our organization we want to label our data values with security labels defined in terms of user roles We might have tokens such as admin audit system Th
60. umulo holds all data in memory and will not retain any data or settings between runs While normal interaction with the Accumulo client looks like this Instance instance new ZooKeeperInstance Connector conn instance getConnector user passwordToken To interact with the MockAccumulo just replace the ZooKeeperInstance with MockInstance Instance instance new MockInstance In fact you can use the fake option to the Accumulo shell and interact with MockAccumulo bin accumulo shell fake u root p Shell Apache Accumulo Interactive Shell version 1 5 instance name fake instance id mock instance id type help for a list of available commands root fake gt createtable test root fake test gt insert rowl cf cq value root fake test gt insert row2 cf cq value2 root fake test gt insert row3 cf cq value3 root fake test gt scan rowl cf cq value row2 cf cq value2 row3 cf cq value3 root fake test gt scan b row2 e row2 row2 cf cq value2 root fake test gt Apache Accumulo User Manual Version 1 5 13 39 When testing Map Reduce jobs you can also set the Mock Accumulo on the AccumuloInputFormat and AccumuloOutputFormat classes AccumuloInputFormat setMockInstance job mockInstance Accumulo0utputFormat setMockInstance job mockInstance 5 2 Mini Accumulo Cluster While the Mock Accumulo provides a lightweight implementation of
61. wing data in a comma separated file userid age address account balance We might choose to store this data using the userid as the rowID and the rest of the data in column families Muta m pu m pu m pu writ tion m new Mutation new Text userid t new Text age age t new Text address address t new Text balance account_balance er add m We could then retrieve any of the columns for a specific userid by specifying the userid as the range of a scanner and fetching specific columns Rang Scan s se s fe FORN 7 2 r new Range userid userid single row ner s conn createScanner userdata auths tRange r tchColumnFamily new Text age Entry lt Key Value gt entry s System out println entry getValu RowlD Design ORCOS Trin 0 Often it is necessary to transform the rowID in order to have rows ordered in a way that is optimal for anticipated access patterns A good example of this is reversing the order of components of internet domain names in order to group rows of the same parent doma com com com com com in together google code google labs google mail yahoo mail yahoo research Apache Accumulo User Manual Version 1 5 25 39 Some data may result in the creation of very large rows rows with many columns In this case the table designer may wish to split up these rows for better load balancing while keeping them
62. xVersions 3 user myinstance mytable gt config t mytable s table iterator majc vers opt maxVersions 3 Apache Accumulo User Manual Version 1 5 17 39 When a table is created by default its configured to use the Versioninglterator and keep one version A table can be created without the VersioningIterator with the ndi option in the shell Also the Java API has the following method connector tableOperations create String tableName boolean limitVersion 6 4 3 1 Logical Time Accumulo 1 2 introduces the concept of logical time This ensures that timestamps set by accumulo always move forward This helps avoid problems caused by TabletServers that have different time settings The per tablet counter gives unique one up time stamps on a per mutation basis When using time in milliseconds if two things arrive within the same millisecond then both receive the same timestamp When using time in milliseconds accumulo set times will still always move forward and never backwards A table can be configured to use logical timestamps at creation time as follows user myinstance gt createtabl tl logical 6 4 3 2 Deletes Deletes are special keys in accumulo that get sorted along will all the other data When a delete key is inserted accumulo will not show anything that has a timestamp less than or equal to the delete key During major compaction any keys older than a delete key are omitted from the new file created and the om

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