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Exploring Postgres Performance: A Deep Dive into pg_stat_statements


pg_stat_statements is a built-in PostgreSQL extension that keeps track of SQL statements executed by the database server. It records details about query execution counts, total execution time and I/O related info. By capturing this information, database administrators can identify bottlenecks and optimize queries for better overall performance. In this blog, we’ll take a  look at how pg_stat_statements module can be utilized to enhance the performance of your PostgreSQL database.  

Enable pg_stat_statements for PostgreSQL

pg_stat_statements is included in with standard Postgres, but might not be automatically enabled. Run the following command as superuser:

create extension pg_stat_statements;

The module must be loaded by adding pg_stat_statements to shared_preload_libraries in postgresql.conf, because it requires additional shared memory.

Now add pg_stat_statment to shared_preload_libraries parameter and restart postgresql server:

shared_preload_libraries = 'pg_stat_statements'

To verify if the extension is working correctly, we can run the following command:

SELECT * from pg_stat_statements;

In order to reset the contents of pg_stat_statements view, run the following function:

SELECT pg_stat_statements_reset();

Note: Only admin users and roles with the pg_read_all_stats privilege are permitted to view the SQL text and queryid of queries inside the pg_stat_statement view.

Configuration Parameters

The following parameters can be modified inside postgresql.conf.

ColumnDatatypeDefault ValueDescription
pg_stat_statements.maxinteger5000Maximum number of statements tracked by the module. Information about the least-executed statements is discarded when the threshold is reached.
pg_stat_statements.trackenumtopControls which statements the module tracks. Valid values are top (track statements issued directly by clients), all (track top-level and nested statements), and none (disable statement statistics collection).
pg_stat_statements.track_utilitybooleanonEnables tracking of utility commands.
pg_stat_statements.track_planningbooleanoffControls whether planning operations and duration are tracked by the module. Enabling this parameter may incur a noticeable performance penalty.
pg_stat_statements.savebooleanonSpecifies whether to save statement statistics across server shutdowns.

Key columns

The following table provides a description of the key columns in the pg_stat_statements view.

useridoidOID of user who executed the statement
dbidoidOID of database in which the statement was executed
queryidbigintInternal hash code, computed from the statement’s parse tree
querytextText of a representative statement
callsbigintNumber of times executed
total_exec_timedouble precisionTotal time spent in the statement, in milliseconds
min_exec_timedouble precisionMinimum time spent in the statement, in milliseconds
max_exec_timedouble precisionMaximum time spent in the statement, in milliseconds
mean_exec_timedouble precisionMean time spent in the statement, in milliseconds
stddev_exec_time double precisionIt shows the execution time deviation for this statement. 
rowsbigintTotal number of rows retrieved or affected by the statement
shared_blks_hitbigintThe amount of data read from the shared buffer cache
shared_blks_readbigintThe amount of data read from the disk
shared_blks_dirtiedbigintThe amount of data written, as the number of blocks touched.
shared_blks_writtenbigintThe amount of data that had to be written to the disk synchronously because of cache pressure 
blk_read_timedouble precisionTotal time the statement spent reading blocks, in milliseconds (if track_io_timing is enabled, otherwise zero)
blk_write_timedouble precisionTotal time the statement spent writing blocks, in milliseconds (if track_io_timing is enabled, otherwise zero)
wal_records bigintTotal number of WAL records generated by the statement
wal_fpi bigintTotal number of WAL full page images generated by the statement
wal_bytes numericTotal amount of WAL generated by the statement in bytes

local_blks_hit, local_blks_read, local_blks_dirtied and local_blks_written shows data activity on temporary tables and indexes.

temp_blk_read and temp_blk_written refer to temporary files (as in files used e.g. for external sorts, hashes, Materialize plan nodes, and similar cases).

Find Long Running Queries

The average total duration of each query is one of the simplest ways to identify slow Postgres queries that require your attention. Since the data is cumulative, this is not a time-weighted average, but it still aids in framing an appropriate context for where to begin.

  userid :: regrole,
  mean_exec_time / 1000 as mean_exec_time_secs,
  max_exec_time / 1000 as max_exec_time_secs,
  min_exec_time / 1000 as min_exec_time_secs,
order by
  mean_exec_time DESC;

 userid | dbid | mean_exec_time_secs | max_exec_time_secs | min_exec_time_secs | stddev_exec_time | calls | rows | query
Postgres  | 16402 | 11.19041728359315 | 71.79477529 | 0.00580726  | 1628.6522214021536 | 10945 | 83922491 | SELECT min("account_move_line".id) AS id, count("a
Postgres  | 16402 | 8.441143214310346 | 8.662522655 | 8.060434353 | 133.49689719844346 | 29 | 292120 | SELECT max("account_move_line".id) AS id, count("a
Postgres  | 16402 | 6.374394403519453 | 9.831685138 | 6.120206754 | 226.79499038949734 | 797 | 797 | DELETE FROM account_move WHERE id IN (23750178)
Postgres  | 16402 | 6.331272233807015 | 6.623289872 | 6.142401752 | 100.66318493612958 | 57 | 57 | SELECT min(".id) AS id, count("a

The first query has been called 10945 times and the average execution time is around 11 seconds. We can also evaluate stddev_exec_time column which defines how widely the time each query execution takes compared to the overall mean. If the value is small, then all queries take a similar amount of time to execute. If the value is too large, this indicates that the execution time of the query is variable and we need to check I/O consumption of that query.

We can calculate coefficient of variation by this formula:

coeff_of_variance = stddev_exec_time/mean_exec_time

If this number is near to 0, this means every time the statement is executed, it takes roughly the same amount of time to complete or vice versa.

Find I/O Intensive Queries

If PostgreSQL is reading data from disk, it indicates that other operations and data are taking precedence and “pushing” the data your query needs back out to disk each time. To run the following query, make sure track_io_timing GUC is enabled inside postgresql.conf

mean_exec_time / 1000 as mean_exec_time_secs, 
shared_blks_hit /(shared_blks_hit + shared_blks_read):: NUMERIC * 100 as hit_ratio, 
(blk_read_time + blk_write_time)/calls as average_io_time_ms, 
shared_blks_hit > 0 
(blk_read_time + blk_write_time)/calls DESC;

 mean_exec_time_secs | calls | rows | shared_blks_hit | shared_blks_read | hit_ratio | average_io_time_ms | query
47.15276140405882 | 17 | 15063  | 102154092 | 16124529 | 86.36733429619542148700 | 1987.1274318235292 | SELECT account_move_line.account_id as groupby where a=$1
15.67629799178571 | 28 | 84     | 91257431  | 17777969 | 83.69523200721967361100 | 1268.3345261785714 | SELECT account_move_line.account_id as id where = $1 left
8.260415101000001 | 61 | 61     | 702231111 | 23702045 | 96.73495489163192320100 | 776.7368886229513  | SELECT min("account_move_line".id) AS id, count("account_move_line".id) AS
6.331272233807015 | 57 | 57     | 129728949 | 22799900 | 85.05207365722664045000 | 760.4856407017546  | SELECT min("account_move_line".id) AS id, count("account_move_line".id) AS
8.441143214310346 | 29 | 292120 | 333631668 | 11586119 | 96.64382328017182961700 | 750.1903752413795  | SELECT min("account_move_line".id) AS id, count("account_move_line".id) AS

By analyzing first query, we can see that roughly 14 % of the time, PostgreSQL has to retrieve data from disk to satisfy the query. We can also check which query spent the most time reading from disk on average using average_io_time_ms column. Look out for missing indexes and partition tables which can help to avoid large sequential scans.

Find Temporary I/O Intensive Queries

mean_exec_time / 1000 as mean_exec_time_secs, 
order by 
temp_blks_read + temp_blks_written / calls DESC;

userid | dbid | mean_exec_time_secs | calls | rows | temp_blks_read | temp_blks_written | query
postgres | 16402 | 26.774439807683848 | 5201 | 2685972 | 1509041060 | 1520966260 | SELECT account_move_line.accou
odoouser | 16402 | 11.193412404985823 | 8527 | 84698343 | 255130928 | 255343779 | SELECT min("account_move_line".id) AS id, count("a
odoouser | 16402 | 7.2981765913771435 | 17622 | 17622 | 251619630 | 258266960 | SELECT min("account_move_line".id) AS id, count("a
odoouser | 16402 | 91.93665896475237 | 525 | 43722 | 243110227 | 459557199 | SELECT max("account_move_line".id) AS id, count("a
odoouser | 16402 | 27.09240567426149 | 784 | 414980 | 230130830 | 231604980 | SELECT account_move_line.accou

If you are looking for good performance, it makes sense to consider temporary I/O as a potential factor. temp_blks_read and temp_blks_written are the important parameters which are populated in case of external sorts, hashes, Materialize plan node operations.  For higher temp_* values, try optimizing work_mem parameter and also check for missing indexes.

temp_blks_read_time and temp_blks_read_time columns are now added in PostgreSQL 15.


PostgreSQL users can benefit greatly from the use of pg_stat_statement. It offers thorough analysis of query that may be applied to boost database and application performance. For instance, an expensive query which is rarely executed might not be worth the effort to tune right now. Instead, a moderately slow query which is frequently called will probably a better candidate for optimization.  If you want to find out more about performance, we have tons of other useful tips for you. You can check out our post about PostgreSQL Memory Allocation Best Practices  and  Understand PostgreSQL Parallel Query

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