SQL query is performing slow in Greenplum database, What to look?

posted Sep 14, 2012, 5:00 PM by Sachchida Ojha   [ updated Sep 14, 2012, 5:00 PM ]
If your SQL query is performing slow, looking at its query plan can help identify problem areas. Here are some things to look for:

1. Is there one operation in the plan that is taking exceptionally long? When looking through the query plan, is there one operation that is consuming the majority of the query processing time? For example, if an index scan is taking longer than expected, perhaps the index is out-of-date and needs to be reindexed. You could also temporarily experiment with the enable_ parameters to see if you can force the planner to choose a different (and potentially better) plan by disabling a particular query plan operator for that query.

2. Are the planner’s estimates close to reality? Run an EXPLAIN ANALYZE and see if the number of rows estimated by the planner is close to the number of rows actually returned by the query operation. If there is a huge discrepancy, you may need to collect more statistics on the relevant columns. 

3. Are selective predicates applied early in the plan? The most selective filters should be applied early in the plan so that less rows move up the plan tree. If the query plan is not doing a good job at estimating the selectivity of a query predicate, you may need to collect more statistics on the relevant columns.  You can also try reordering the WHERE clause of your SQL statement.

4. Is the planner choosing the best join order? When you have a query that joins multiple tables, make sure that the planner is choosing the most selective join order. Joins that eliminate the largest number of rows should be done earlier in the plan so that less rows move up the plan tree. If the plan is not choosing the optimal join order, you can set join_collapse_limit=1 and use explicit JOIN syntax in your SQL statement to force the planner to the specified join order. You can also collect more statistics on the relevant join columns. 

5. Is the planner selectively scanning partitioned tables? If you are using table partitioning, is the planner selectively scanning only the child tables required to satisfy the query predicates? Do scans of the parent tables return 0 rows (they should, since the parent tables should not contain any data). 

6. Is the planner choosing hash aggregate and hash join operations where applicable? Hash operations are typically much faster than other types of joins or aggregations. Row comparison and sorting is done in memory rather than reading/writing from disk. In order for hash operations to be chosen, there has to be sufficient work memory available to hold the number of estimated rows. Try increasing work memory to see if you can get better performance for a given query. If possible run an EXPLAIN ANALYZE for the query, which will show you which plan operations spilled to disk, how much work memory they used, and how much was required to not spill to disk. For example:

Work_mem used: 23430K bytes avg, 23430K bytes max (seg0).
Work_mem wanted: 33649K bytes avg, 33649K bytes max (seg0) to lessen workfile I/O affecting 2 workers.

Note that the bytes wanted message from EXPLAIN ANALYZE is only a hint, based on the amount of data written to work files and is not exact. The minimum work_mem needed could be more or less than the suggested value.


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