Oracle update tuning tips Oracle Database Tips by Donald Burleson

The SQL standard for DML UPDATE statements can be complex and convoluted and there are best practices that can help you to write efficient UPDATE statements.

• Run updates in batch mode
• Use CTAS in lieu of large updates
• Include the SET condition in the WHERE clause
• Simplify the WHERE predicates
• Have a small, separate data cache for high DML tables

Running updates in batch mode

If you can take your application offline during the monthly update, you use many update performance features to speed up the job performance:

• Drop indexes/constraints and rebuild after mass update:  Dropping indexes before a mass update and rebuilding them afterwards can improve update performance significantly. Oracle removes index entries without re-balancing the index tree (a "logical delete"), but this is still time-consuming, especially if you have lots of indexes on the target table.  Also note that you can rebuild the dropped indexes nologging mode.
  
  
• Parallelize the updates:  If you have an SMP server you can run Oracle parallel DML.    You can also manually parallelize the update by breaking them into multiple jobs and submit them simultaneously using dbms_scheduler, cron  or nohup.
  

Use CTAS in lieu of large updates

When you are updating the majority of rows in a table, using Create Table As Select (CTAS) is often more efficient performance than a standard update.  For example, assume that the following update changed 75% of the table rows:

update
   mytab
set
   status = 'new'
where
   status = 'old;

In this case, a parallelized CTAS may perform far faster (Note: Make sure that you have an SMP server before using the parallel degree option):

create table new_mytab NOLOGGING as
select  /*+ full parallel(mytab,35)*/
   decode (status,'new','old',status,
   col2, col3, col4
from mytab;

-- rebuild indexes, triggers and constraints to new_mytab

rename mytab to bkup_mytab;
rename new_mytab to mytab;
 

Include the SET condition in the WHERE clause

This note shows a case where the developer forgot to include the SET condition in the UPDATE WHERE clause, causing high redo waits (log file parallel write waits, log file sync waits).  Simply including the existing state of the SET clause can result in a huger performance improvement for UPDATE statements:

-- zillion row update
UPDATE HISTORY SET FLAG=0 WHERE CLASS='X'

-- hundred row update
UPDATE HISTORY SET FLAG=0 WHERE CLASS='X' AND FLAG!=0

The select for update is not a good locking strategy because there are many things that can go wrong.  Instead of select for update, savvy Oracle developers will adopt alternatives mechanisms like a re-read upon update commit where the transaction re-read the rows and ensure that it has not changed since the original read.

Have a small, separate data cache for high DML tables

As more people adopt 64-bit servers with giant data buffers, we see a delay caused by the database writer process having to scan through giant data buffers seeking dirty blocks.  Many shops are replacing their platter-style disks with solid-state disks, and creating a very small data buffer, just for the updates.  The book Oracle Tuning: The Definitive Reference notes that if you are still using traditional disks, many shops segregate high-updates objects (tables & indexes) into a separate blocksize so that they can have a separate, small data buffer.

"By segregating high activity tables into a separate, smaller data buffer, Oracle has far less RAM frames to scan for dirty block, improving the throughput and also reducing CPU consumption. This is especially important for high update tables with more than 100 row changes per second."

MOSC Note:223299.1 also embraces the importance of multiple blocksizes, listing the multiple buffer regions as among the most important tuning parameters in Oracle9i.

Simplify the WHERE clause predicates

The most common issue with updates is the requirement to have a complex SELECT statement is the where clause to identify the rows to be updated.  The best techniques for simplifying UPDATE where clauses include:

• Predicate pushing
• Rewriting subqueries as outer joins (if possible)
• Oracle SQL subquery unnesting
• Partitioning Pruning
• Avoid IN and NOT IN - It is a good idea to discourage the use of the NOT IN clause (which invokes a sub-query) and to prefer NOT EXISTS (which invokes a correlated sub-query).

Also see my other notes on high performance UPDATES and INSERTS: