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Cost Based Optimizer 
in Apache Hive 
Page 1 
Laljo John Pullokkaran 
12/03/2014
Agenda 
Page 2 
• Status 
• Performance Numbers 
• Architecture 
• Future Directions 
• Q&A
© Hortonworks Inc. 2013 
Status 
Page 3 
•Merged in to Apache Hive Trunk 
• Included in Hive 14.0 
• All of Hive Test passes with CBO 
• ~ 3 man years of development effort 
• Contributors: Harish Bhutani, Ashutosh Chauhan, Julian Hyde, 
Sergey Shelukhin, Pengcheng Xiong, Mostafa Moktar, Laljo John 
Pullokkaran
Performance Numbers 
© Hortonworks Inc. 2013 
Page 4 
• Average gains of 2X on TPCDS queries 
• 30X gains with TPCDS query 29
Performance Numbers - Query Break Down 
© Hortonworks Inc. 2013 
Page 5
Performance Numbers – Why? 
© Hortonworks Inc. 2013 
Page 6 
• Reduced Intermediary data set 
• Bushy Joins results in better parallelism 
• Enables more Map Join as opposed to shuffle Join
Architecture - Overview 
© Hortonworks Inc. 2013 
Page 7 
Hive SQL Hive AST Hive QB Tree 
Hive AST Optimized Calcite Plan Calcite Plan 
Hive Operator Tree Optimized Hive OP tree TEZ/MR DAG
Architecture - Optimizations in Calcite 
© Hortonworks Inc. 2013 
Page 8 
• Projection Pruning 
• Partition Pruning 
• Predicate Push Down 
• Transitive Predicate inference 
• Push Down of Semi Joins 
• Join Simplification 
• Predicate Factorization 
• Join Ordering
Architecture - Join Ordering 
© Hortonworks Inc. 2013 
Page 9 
• Requires Column Statistics 
• Cost Model uses NDV based heuristics 
• Cost Model favors bushy join plans 
• Cost Model is agnostic of execution engine
Future Directions 
© Hortonworks Inc. 2013 
Page 10 
• Histogram support 
• Join Algorithm selection 
• Calcite return path

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Hive - Cost Based Optimizer

  • 1. Cost Based Optimizer in Apache Hive Page 1 Laljo John Pullokkaran 12/03/2014
  • 2. Agenda Page 2 • Status • Performance Numbers • Architecture • Future Directions • Q&A
  • 3. © Hortonworks Inc. 2013 Status Page 3 •Merged in to Apache Hive Trunk • Included in Hive 14.0 • All of Hive Test passes with CBO • ~ 3 man years of development effort • Contributors: Harish Bhutani, Ashutosh Chauhan, Julian Hyde, Sergey Shelukhin, Pengcheng Xiong, Mostafa Moktar, Laljo John Pullokkaran
  • 4. Performance Numbers © Hortonworks Inc. 2013 Page 4 • Average gains of 2X on TPCDS queries • 30X gains with TPCDS query 29
  • 5. Performance Numbers - Query Break Down © Hortonworks Inc. 2013 Page 5
  • 6. Performance Numbers – Why? © Hortonworks Inc. 2013 Page 6 • Reduced Intermediary data set • Bushy Joins results in better parallelism • Enables more Map Join as opposed to shuffle Join
  • 7. Architecture - Overview © Hortonworks Inc. 2013 Page 7 Hive SQL Hive AST Hive QB Tree Hive AST Optimized Calcite Plan Calcite Plan Hive Operator Tree Optimized Hive OP tree TEZ/MR DAG
  • 8. Architecture - Optimizations in Calcite © Hortonworks Inc. 2013 Page 8 • Projection Pruning • Partition Pruning • Predicate Push Down • Transitive Predicate inference • Push Down of Semi Joins • Join Simplification • Predicate Factorization • Join Ordering
  • 9. Architecture - Join Ordering © Hortonworks Inc. 2013 Page 9 • Requires Column Statistics • Cost Model uses NDV based heuristics • Cost Model favors bushy join plans • Cost Model is agnostic of execution engine
  • 10. Future Directions © Hortonworks Inc. 2013 Page 10 • Histogram support • Join Algorithm selection • Calcite return path