In real life, you usually want to see the full query. The reason for that is to make it easier to display the data on the website. Also, note that I have used substring to make the query shorter.
Finally, I have put the numbers into context and calculated the percentage of total runtime for each type of query. What you see here is how often a certain type of query has been executed, and how many milliseconds of total execution time we have measured. Query | calls | total_time | mean_time | percentage Round((100 * total_exec_time / sum(total_exec_time) OVER ())::numeric, 2) AS percentage
Round(total_exec_time::numeric, 2) AS total_time, Test=# SELECT substring(query, 1, 30) AS query, The most important one is used to find out which operations are the most time-consuming.
To make it easier for our readers to extract as much information as possible from pg_stat_statements, we have compiled a couple of queries that we have found useful over the years. Therefore it makes sense to process this data a bit to extract useful information. The danger here is that people get lost in the sheer volume of information. Stddev_exec_time | double precision | | | Stddev_plan_time | double precision | | | Note that the view has been growing over the years and more and more vital information is added as PostgreSQL is steadily extended:Ĭolumn | Type | Collation | Nullable | Default Here is the definition of the view, as of PostgreSQL 13. Pg_stat_statements provides a ton of useful information. The last step will deploy a view – we will need to inspect the data collected by the pg_stat_statements machinery. Test=# CREATE EXTENSION pg_stat_statements The first thing you have to do is to change shared_preload_libraries in nf: shared_preload_libraries = ‘pg_stat_statements’įinally, the module can be enabled in your desired database: All you have to do to enjoy the full benefit of this exceptionally useful extension, is enable it. Installing pg_stat_statements in PostgreSQLĪs mentioned above, pg_stat_statements comes as part of PostgreSQL. There is one law of nature that has been true for the past 20 years and will most likely still hold true 20 years from now:Īnd slow queries are the main reason for such load.Īrmed with this important, yet highly under-appreciated wisdom, we can use pg_stat_statements to figure out which queries have caused the most load, and tackle those – instead of wasting time on irrelevant guess work. Instead, we have to fall back on some pretty “boring techniques” such as inspecting queries to figure out what is going on in your database. It does not exist and it most likely never will. Sure, parameter tuning does help, but I can assure you, there is no such thing as “speed = on”. Over the years, I have begun to get the impression that for most people “tuning” is limited to adjusting some magical PostgreSQL parameters. It helps you to instantly figure out which queries cause bad performance and how often they are executed. I believe the best and most efficient way to detect performance problems is to make use of pg_stat_statement, which is an excellent extension shipped with PostgreSQL and is used to inspect query statistics in general. Performance tuning also implies that we have to find performance bottlenecks first, isolate slow queries and understand what the system is doing. Performance tuning does not only mean adjusting nf properly, or making sure that your kernel parameters are properly tuned. Performance tuning pg_stat_statement postgresql slow queries
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