1. Tuning a Very Large Data Warehouse Pichai Bala

2. About Me Working in the IT industry for the past 17 years Working in Oracle since 1993. Working in Data Warehouse and BI since 2003

3. Disclaimer The views expressed in this presentation are mine and does not represent the organization I work for or the organizations I had worked for in the past. Please always test and validate the ideas presented here in a test environment first.

4. A Day in the life of a DBA

5. How about this one?

6. What the chaos mean for the DBA? Free buffer waits enq: KO - fast object checkpoint enq: TM - contention db sequential read CPU time Logical I/O Physical I/O

7. What it means to the End Users? ETL Load/Batch Job Delays Reporting Delays Decision Making Delays Business Analytics Delays Customer Intelligence Delays Planning and Forecasting Delays Key Performance Metrics Delays

8. Data Warehouse is now in Death Bed

9. But, Why?

10. Data Warehouse Vicious Cycle Data gets deployed Gains User Acceptance More Users and More Demands and Needs Existing Data Grows and New Data gets Deployed …and gets into the Death Spiral

12. Possible Causes…. Lack of proper and meaningful maintenance Human errors Poor Design Bad SQLs by developers, users Poor monitoring and scheduling etc..

13. Tuning Strategy Keep it Simple Low Intensity Changes with low impact but with high performance benefits Localized changes No change in logic Easy to understand, test and deploy

14. Reduce Wastage Reduce CPU Reduce Logical IO Reduce Physical IO Reduce UNDO Reduce Direct Path Reads

15. How it can be done? Server Tuning Instance Tuning and Maintenance Database Tuning and Maintenance Table Reorganizations/Redefinitions New Indexes Regular Statistics Collection Views SQL/PLSQL Code Changes Working with other teams Educating/Training the users

16. Instance/Database Tuning SGA Max Size DB Cache Size Shared Pool Large Pool No. of DB Writers Redo Log File Size Typical Init.ORA parameters like QUERY_REWRITE, BITMAP_MERGE_JOIN

17. SQL/PLSQL Tuning Avoid Clutter Use Indexes when appropriate Full Table Scan is not bad Revisit the code Cunning code is not always necessary Work with other teams and business to reduce complexity in code Avoid Hints

18. Query Results can be wrong In 10G use ORDER BY whenever GROUP BY is used Hidden parameter can be enabled with the help of Oracle Support

19. Pillars of the Data Warehouse Partitioning Parallelism Aggregations Compression Materialized Views Read Only Tablespaces Data Archival

20. Partitioning Range Partitioning List Partitioning Range List Partitioning Range Hash Partitioning Hash Partitioning Caveat: Joins beware.

21. Parallelism Tables can be built parallel Parallel Indexes Parallel Hints while loading or querying. Alter table move … parallel (degree 8) …; Alter table split … parallel( degree 4) …; Create table parallel(degree 4)… Sufficient LARGE_POOL helps greatly

22. Aggregations Aggregations and MVs are the soul of any DSS Most BI tools supports Aggregation Awareness Have multiple aggregations Aggregations help users with adhoc queries Daily, Monthly and Yearly Aggregations are very common in most DSS

23. Compression Saves Disk Space by 40 to 50% Reduces Logical IO Reduces Physical IO Reads will be fast DMLs will be slow Compress Table as well as Index Caveat : You cant uncompress after the table is compressed ORA-01735: invalid ALTER TABLE option

24. Materialized View Fast Refresh may be very slow From 10G MV can be parallel MVs can be partitioned MV_CAPABILITY results can be misleading. ALTER MATERIALIZED VIEW parallel (degree 4 ); For MV Fast Refresh to be successful a Complete Refresh should happen before

25. Exchange Partitions Very Useful Dictionary update only Cant Exchange a table with bitmap indexes with a partition Partition exchange has issue with BITMAP indexes with the ora error for mismatch indexes 0RA-14098

26. READONLY Tablespaces Data Warehouse has time variant non- volatile data Say Range Partition on TIME, and making historic tablespaces READONLY helps Database Checkpoint process

27. Data Archival With various regulatory and internal requirements data needs to be retained for 2 to 30 years. Data growth is exponential Archival is needed to start it small and keep it small Saves $$$ in Database licenses and maintenance. Helps the optimizer to get results faster from a smaller set

28. Rolling Partitions If design permits instead of creating new partitions every time the same partition can be reused again and again. Like SUNDAY can be reloaded again on the same partition next Sunday. Rolling Partitions by HOUR or by DAY of the WEEK can be considered Helps Data Retention Strategies too.

29. Case of HUGE UNDO More than 30G of UNDO was getting generated for a 1.5G table Fix the code and fix the problem.

30. Misleading V$lock Blocking locks wont show in v$lock but locks would exist Use x$kgllk or x$kglpn to identify and kill the blocking sessions.

31. Package Invalidations Package gets invalidated but cant recompile itself because of sessions holding them invisibly Coding and deployment standards can help

32. ORA-02049: timeout: distributed transaction waiting for lock Flush the Shared Pool, the failures go away From 10G you can avoid bounces by flushing buffer_cache and shared_pool

33. Again?

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