![]() ![]() 09:45:39.023 UTC LOG: duration: 0.107 ms statement: UPDATE pgbench_tellers SET tbalance = tbalance + -2716 WHERE tid = 1794 09:45:39.023 UTC LOG: duration: 15.824 ms statement: UPDATE pgbench_accounts SET abalance = abalance + -3726 WHERE aid = 5290358 09:45:39.022 UTC LOG: duration: 16.450 ms statement: UPDATE pgbench_branches SET bbalance = bbalance + 1359 WHERE bid = 195 09:45:39.022 UTC LOG: duration: 0.082 ms statement: UPDATE pgbench_tellers SET tbalance = tbalance + 3063 WHERE tid = 3244 09:45:39.022 UTC LOG: duration: 0.065 ms statement: SELECT abalance FROM pgbench_accounts WHERE aid = 16318529 09:45:39.022 UTC LOG: duration: 0.107 ms statement: SELECT abalance FROM pgbench_accounts WHERE aid = 11782597 What you’ll see in the log are entries as below: 09:45:39.022 UTC LOG: duration: 0.145 ms statement: SELECT abalance FROM pgbench_accounts WHERE aid = 29817899 For our purposes let’s stick to the database level logging. The logs will include all of the traffic coming to PostgreSQL system tables, making it more noisy. If you do not see any logs, you may want to enable logging_collector = on as well. This enables logging of all queries across all of the databases in your PostgreSQL. To PostgreSQL configuration and then reload config: pgbench=# SELECT pg_reload_conf() It is also possible to enable this globally by adding: log_min_duration_statement = 0 First, you can enable it on a single database: pgbench=# ALTER DATABASE pgbench SET log_min_duration_statement=0 Īfter this all new connections to ‘pgbench’ database will be logged into PostgreSQL log. There are a couple of ways you can do it. Generally speaking, the most typical way of identifying performance problems with PostgreSQL is to collect slow queries. In this blog we’d like to talk about how you can identify problems with slow queries in PostgreSQL. OLTP is one of the common use cases for PostgreSQL therefore you want your queries to run as smooth as possible. ![]() Slow queries mean that the application feels unresponsive and slow and this results in bad conversion rates, unhappy users, and all sets of problems. ![]() When working with OLTP (OnLine Transaction Processing) databases, query performance is paramount as it directly impacts the user experience. ![]()
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