Tuesday, October 6, 2009

What to do when your query is too slow?

First of all, you have to know why it is slow. What is the real cause of your problem. If the reason why is not known, suggesting to rewrite the query, or hinting the query, suggesting parallellization et cetera is not very productive. Once in a while you may get lucky. But even then you have to realize that if your problem seems "solved", but you don't know why, nothing guarantees you that the problem won't come back tomorrow. So the first step should always be to investigate the root cause.

The tools at your disposal are, among more:
- dbms_profiler
- explain plan
- SQL*Trace / tkprof
- statspack

Use dbms_profiler if you want to know where time is being spent in PL/SQL code. Statspack is a must if you are a dba and want to know what is going on in your entire database. For a single query or a small process, explain plan and SQL*Trace and tkprof are your tools.

explain plan

in SQL*Plus you have to type:
explain plan for ;
select * from table(dbms_xplan.display);

When you get error messages or a message complaining about an old version of plan_table, make sure you run the script utlxplan.sql.

The output you get here basically shows you what the cost based optimizer expects. It gives you an idea on why the cost based optimizer chooses an access path.


For this you have to type in SQL*Plus:
- alter session set sql_trace true;
- disconnect (this step is important, because it ensures all cursors get closed, and "row source operation" is generated)
- identify your trace file in the server directory as specified in the parameter user_dump_dest
- on your operating system: tkprof a.txt sys=no sort=prsela exeela fchela

The file a.txt will now give you valuable information on what has actually happened. No predictions but the truth.

By comparing the output from explain plan with the output from tkprof, you are able to identify the possible problem areas.

So before rushing into possible solutions, always post the output of explain plan and tkprof with your question and don't forget to post them between the tags [pre] and [/pre] for readability.

PS: I am fully aware that this text is only a tiny fraction of what can be done, and that other people may choose different tools and actions, but the above gives you a very reasonable start at solving your performance problem.

Oracle bitmap indexes are very different from standard b-tree indexes. In bitmap structures, a two-dimensional array is created with one column for every row in the table being indexed. Each column represents a distinct value within the bitmapped index. This two-dimensional array represents each value within the index multiplied by the number of rows in the table. At row retrieval time, Oracle decompresses the bitmap into the RAM data buffers so it can be rapidly scanned for matching values. These matching values are delivered to Oracle in the form of a Row-ID list, and these Row-ID values may directly access the required information.

The real benefit of bitmapped indexing occurs when one table includes multiple bitmapped indexes. Each individual column may have low cardinality. The creation of multiple bitmapped indexes provides a very powerful method for rapidly answering difficult SQL queries.

For example, assume there is a motor vehicle database with numerous low-cardinality columns such as car_color, car_make, car_model, and car_year. Each column contains less than 100 distinct values by themselves, and a b-tree index would be fairly useless in a database of 20 million vehicles. However, combining these indexes together in a query can provide blistering response times a lot faster than the traditional method of reading each one of the 20 million rows in the base table. For example, assume we wanted to find old blue Toyota Corollas manufactured in 1981:

color = ‘blue’
make = ‘toyota’
year = 1981;

Oracle uses a specialized optimizer method called a bitmapped index merge to service this query. In a bitmapped index merge, each Row-ID, or RID, list is built independently by using the bitmaps, and a special merge routine is used in order to compare the RID lists and find the intersecting values.

Using this methodology, Oracle can provide sub-second response time when working against multiple low-cardinality columns:

Oracle Bitmap indexes are a very powerful Oracle feature, but they can be tricky!
You will want a bitmap index when:
1 - Table column is low cardinality - As a ROUGH guide, consider a bitmap for any index with less than 100 distinct values

select region, count(*) from sales group by region;
2 - The table has LOW DML - You must have low insert./update/delete activity. Updating bitmapped indexes take a lot of resources, and bitmapped indexes are best for largely read-only tables and tables that are batch updated nightly.
3 - Multiple columns - Your SQL queries reference multiple, low cardinality values in there where clause. Oracle cost-based SQL optimizer (CBO) will scream when you have bitmap indexes on .
Troubleshooting Oracle bitmap indexes:
Some of the most common problems when implementing bitmap indexes include:
1. Small table - The CBO may force a full-table scan if your table is small!
2. Bad stats - Make sure you always analyze the bitmap with dbms_stats right after creation:
ON index_demo (gender);

exec dbms_stats.gather_index_stats(OWNNAME=>'SCOTT', INDNAME=>'EMP_BITMAP_IDX');
3. Test with a hint - To force the use of your new bitmap index, just use a Oracle INDEX hint:
select /*+ index(emp emp_bitmap_idx) */
emp, dept
emp.deptno = dept.deptno;

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