Cloudera Impala + PostgreSQL
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Hacking Cloudera Impala for running on PostgreSQL cluster as MPP style. Performances under typical sql stmt and concurrence case are verified.
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- 7. SQL on Hadoop • • • • • Google Dremel Apache Drill Cloudera Impala Facebook Presto EMC Greenplum/Pivotal Latency matters Pig Impala/Drill /Pivotal/Presto Map Reduce HDFS Hive
- 8. What’s this • A kind of MPP engine • In memory processing • Small to big join – Broadcast join • Small result size
- 9. Why Cloudera Impala • The team move fast – UDF coming out – Better join strategy on the way • Good code base – Modularize – Easy to add sub classes • Really fast – Llvm code generation • 80s/95s – uv test – Distributed aggregation Tree – In-situ data processing (inside storage)
- 10. Typical Arch. SQL Interface Meta Store Query Planner Query Planner Query Planner Coordinat or Coordinat or Coordinat or Exec Engine Exec Engine Exec Engine
- 11. Our target • A MPP database – Build on PostgreSQL9.1 – Scale well – Speed • A mixed data source MPP query engine – Join two tables in different sources – In fact…
- 12. Hacking… from where • Add, not change – Scan Node type – DB Meta info • Put changes in configuration – Thrift Protocol update • TDBHostInfo • TDBScanNode
- 13. Front end • Meta store update – Link data to the table name – Table location management • Front end – Compute table location
- 14. Back end • Coordinator – pg host • New scan node type – db scan node • Pg scan node • Psql library using cursor
- 15. SQL Plan • select count(distinct id) from table – MR like process HDFS/PG scan Aggr. : group by id Exchange node Aggr. : group by id Aggr. : count(id) Exchange node Aggr.: sum(count(id)
- 16. Env. • Ads impression logs – 150 millions, 100KB/line • 3 servers – – – – 24 cores 32 G mem 2T * 12 HD 100Mbps LAN • Query – Select count(id) from t group by campaign – Select count(distinct id) from t group by campaign – Select * from t where id = ‘xxxxxxxx’
- 17. Performance • Group by speed / core • 20 M /s 700 600 500 400 impala hive 300 pg+impala 200 100 0 1 2 3
- 18. With index
- 19. Codegen on/off • select count(distinct id) from t group by c 100 90 80 70 • select distinct id from t 60 50 en_codegen 40 dis_codegen 30 • 20 select id from t 10 group by id 0 having uv_test count(case when c = '1' then 1 else null end) > 0 and count(case when c= 2' then 1 else null end) > 0 limit 10; distinct duplicated
- 20. Multi-users
- 21. Conclusion • Source quality – Readable – Google C++ style – Robust • MPP solution based on PG – Proved perf. – Easy to scale • Mixed engine usage – HDFS and DB
- 22. What’s next • • • • • Yarn integrating UDF Join with Big table BI roadmap Fail over
- 23. Rerf. • Cloudera Impala online doc. & src • http://files.meetup.com/1727991/Impala%20and %20BigQuery.ppt • http://www.cubrid.org/blog/dev-platform/meetimpala-open-source-real-time-sql-querying-onhadoop/ • http://berlinbuzzwords.de/sites/berlinbuzzwords. de/files/slides/Impala%20tech%20talk.pdf • @datascientist, @dongxicheng, @flyingsk, @zhh
- 24. Thanks! Q&A