Hadoop and rdbms with sqoop
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This document discusses using SQOOP to connect Hadoop and relational databases. It describes four common interoperability scenarios and provides an overview of SQOOP's features. It then focuses on optimizing SQOOP for Oracle databases by discussing how the Quest/Cloudera OraOop extension improves performance by bypassing Oracle parallelism and buffering. The document concludes by recommending best practices for using SQOOP and its extensions.
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Hadoop and rdbms with sqoop
- 1. Exchanging data with the Elephant: Connecting Hadoop and an RDBMS using SQOOPGuy HarrisonDirector, R&D Melbournewww.guyharrison.netGuy.harrison@quest.com@guyharrison
- 4. AgendaRDBMS-Hadoop interoperability scenariosInteroperability optionsCloudera SQOOPExtending SQOOP Quest OraOop extension for Cloudera SQOOPPerformance comparisons Lessons learned and best practices
- 5. Scenario #1: Reference data in RDBMS RDBMSCustomersHDFSProductsWebLogs
- 6. Scenario #2: Hadoop for off-line analyticsRDBMSCustomersHDFSProductsSales History
- 7. Scenario #3: Hadoop for RDBMS archiveRDBMSHDFSSales 2010Sales 2009 Sales 2008 Sales 2008
- 8. Scenario #4: MapReduce results to RDBMS RDBMSHDFSWebLogSummaryWebLogs
- 9. Options for RDBMS inter-opDBInputFormat:Allows database records to be used as mapper inputs.BUT:Not inherently scalable or efficientFor repeated analysis, better to stage in HadoopTedious coding of DBWritable classes for each tableSQOOPOpen source utility provided by ClouderaConfigurable command line interface to copy RDBMS->HDFSSupport for Hive, HbaseGenerates java classes for future M-R tasksExtensible to provide optimized adaptors for specific targetsBi-Directional
- 10. SQOOP Details SQOOP import Divide table into ranges using primary key max/minCreate mappers for each range Mappers write to multiple HDFS nodesCreates text or sequence files Generates Java class for resulting HDFS fileGenerates Hive definition and auto-loads into HIVESQOOP exportRead files in HDFS directory via MapReduceBulk parallel insert into database table
- 11. SQOOP detailsSQOOP features:Compatible with almost any JDBC enabled databaseAuto load into HIVE Hbase support Special handling for database LOBsJob management Cluster configuration (jar file distribution)WHERE clause supportOpen source, and included in Cloudera distributionsSQOOP fast paths & plug insInvokemysqldump, mysqlimport for MySQL jobs Similar fast paths for PostgreSQLExtensibility architecture for 3rd parties (like Quest )Teradata, Netezza, etc.
- 12. Working with Oracle SQOOP approach is generic and applicable to all RDBMSHowever for Oracle, sub-optimal in some respects:Oracle may parallelize and serialize individual mappersOracle optimizer may decline to use index range scansOracle physical storage often deliberately not in primary key order (reverse key indexes, hash partitioning, etc)Primary keys often not be evenly distributedIndex range scans use single block random readsvs.faster multi-block table scansIndex range scans load into Oracle buffer cache Pollutes cache increasing IO for other usersLimited help to SQOOP since rows are only read once Luckily, SQOOP extensibility allows us to add optimizations for specific targets
- 13. Oracle – parallelism AggregateSortScanOracleSALEStableOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle Master(QC)Client (JDBC)Oracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveOracle PQ SlaveSELECT cust_id, SUM (amount_sold)FROM sh.salesGROUP BY cust_idORDER BY 2 DESC
- 14. Oracle – parallelism HDFSOracleSALEStableHadoop MapperHadoop MapperHadoop MapperHadoop Mapper
- 15. Oracle – parallelism HDFSOracleSALEStableHadoop MapperHadoop MapperHadoop MapperHadoop Mapper
- 16. Index range scans Hadoop MapperID > 0 and ID < MAX/2Hadoop MapperID > MAX/2OracleSessionOracleSessionIndex range scanBuffer cacheIndex range scanIndex blockIndex blockIndex blockIndex blockIndex blockIndex blockOracle table
- 17. Ideal architecture HDFSOracleSALEStableOracleSessionHadoop MapperOracleSessionHadoop MapperOracleSessionHadoop MapperOracleSessionHadoop Mapper
- 18. Quest/Cloudera OraOop for SQOOPDesign goals Partition data based on physical storageBy-pass Oracle bufferingBy-pass Oracle parallelismDo not require or use indexesNever read the same data block more than onceSupport esoteric datatypes (eventually) Support RAC clusters Availability:Freely available from www.quest.com/ora-oopPackaged with Cloudera Enterprise Commercial support from Quest/Cloudera within Enterprise distribution
- 20. Oracle overhead
- 21. Extending SQOOP SQOOP lets you concentrate on the RDBMS logic, not the Hadoop plumbing:Extend ManagerFactory (what to handle)Extend ConnManager (DB connection and metadata)For imports:Extend DataDrivenDBInputFormat (gets the data)Data allocation (getSplits())Split serialization (“io.serializations” property)Data access logic (createDBRecordReader(), getSelectQuery())Implement progress (nextKeyValue(), getProgress())Similar procedure for extending exports
- 22. SQOOP/OraOop best practices Use sequence files for LOBs ORSet inline-lob-limit Directly control datanodes for widest destination bandwidthCan’t rely on mapred.max.maps.per.nodeSet number of mappers realistically Disable speculative execution (our default)Leads to duplicate DB reads Set Oracle row fetch size extra high Keeps the mappers streaming to HDFS
- 23. ConclusionRDBMS-Hadoop interoperability is key to Enterprise Hadoop adoptionSQOOP provides a good general purpose tool for transferring data between any JDBC database and HadoopSQOOP extensions can provide optimizations for specific targetsEach RDBMS offers distinct tuning opportunities, so SQOOP extensions offer real value Try out OraOop for SQOOP!