Tuning a Very Large Data Warehouse

Pichai Bala

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

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.

A Day in the life of a DBA

How about this one?

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

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

Data Warehouse is now in Death Bed

But, Why?

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

Data Growth

Performance

Possible Causes….

• Lack of proper and meaningful maintenance

• Human errors

• Poor Design

• Bad SQLs by developers, users

• Poor monitoring and scheduling etc..

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

Reduce Wastage

• Reduce CPU

• Reduce Logical IO

• Reduce Physical IO

• Reduce UNDO

• Reduce Direct Path Reads

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

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

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

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

Pillars of the Data Warehouse

• Partitioning

• Parallelism

• Aggregations

• Compression

• Materialized Views

• Read Only Tablespaces

• Data Archival

Partitioning

• Range Partitioning

• List Partitioning

• Range List Partitioning

• Range Hash Partitioning

• Hash Partitioning

Caveat: Joins beware.

Parallelism

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

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

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 can’t uncompress after the table is

compressed ORA-01735: invalid ALTER TABLE option

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 <mv_name> parallel (degree 4 );

For MV Fast Refresh to be successful a Complete Refresh should happen before

Exchange Partitions

• Very Useful

• Dictionary update only

• Can’t 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

READONLY Tablespaces

• Data Warehouse has time variant non-volatile data

• Say Range Partition on TIME, and making historic tablespaces READONLY helps Database Checkpoint process

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

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.

Case of HUGE UNDO

• More than 30G of UNDO was getting generated for a 1.5G table

Fix the code and fix the problem.

Misleading V$lock

• Blocking locks won’t show in v$lock but locks would exist

• Use x$kgllk or x$kglpn to identify and kill the blocking sessions.

Package Invalidations

• Package gets invalidated but can’t recompile itself because of sessions holding them invisibly

• Coding and deployment standards can help

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

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