Database

pg_stat_statements: Query Performance Monitoring

pg_stat_statements is a database extension that exposes a view, of the same name, to track statistics about SQL statements executed on the database. The following table shows some of the available statistics and metadata:

Column NameColumn TypeDescription
useridoid (references pg_authid.oid)OID of user who executed the statement
dbidoid (references pg_database.oid)OID of database in which the statement was executed
toplevelboolTrue if the query was executed as a top-level statement (always true if pg_stat_statements.track is set to top)
queryidbigintHash code to identify identical normalized queries.
querytextText of a representative statement
plansbigintNumber of times the statement was planned (if pg_stat_statements.track_planning is enabled, otherwise zero)
total_plan_timedouble precisionTotal time spent planning the statement, in milliseconds (if pg_stat_statements.track_planning is enabled, otherwise zero)
min_plan_timedouble precisionMinimum time spent planning the statement, in milliseconds (if pg_stat_statements.track_planning is enabled, otherwise zero)

A full list of statistics is available in the pg_stat_statements docs.

For more information on query optimization, check out the query performance guide.

Enable the extension

Inspecting activity

A common use for pg_stat_statements is to track down expensive or slow queries. The pg_stat_statements view contains a row for each executed query with statistics inlined. For example, you can leverage the statistics to identify frequently executed and slow queries against a given table.


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select
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calls,
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mean_exec_time,
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max_exec_time,
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total_exec_time,
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stddev_exec_time,
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query
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from
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pg_stat_statements
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where
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calls > 50 -- at least 50 calls
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and mean_exec_time > 2.0 -- averaging at least 2ms/call
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and total_exec_time > 60000 -- at least one minute total server time spent
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and query ilike '%user_in_organization%' -- filter to queries that touch the user_in_organization table
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order by
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calls desc

From the results, we can make an informed decision about which queries to optimize or index.

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