Still-Executing QueriesĪll the techniques listed above have one thing in common: they produce actionable output only after a query has finished execution. This will cause plans to be logged as JSON format, which can then be visualized in tools like these. # enabling these provide more information, but have a performance cost # logs execution plans of queries that take 10s or more to run These are described in detail in the Postgres documentation. Unlike a certain other DBMS that makes this easy, PostgreSQL presents us with a bunch of similar-looking configuration settings: The simpler alternative is to log slow queries. Knowing the actual value of the parameters for which the query execution was slow can help diagnose slow query issues faster. For example if most rows of a table have the value of an indexed column country as “US”, the planner might decide to do a sequential scan of the entire table for the where clause country = "US", and might decide to use an index scan for country = "UK" since the first where clause is expected to match most rows in the table. One of the things that the Postgres query planner estimates for selecting an execution plan is the number of rows a condition is likely to filter out. Pg_stat_statements does not capture the values of bind parameters passed to queries. This does work reasonably well in practice, but you’ll need a good monitoring infrastructure, or a dedicated service like pgDash. If this average execution time exceeds an upper threshold, you can trigger an alert to take action. (total time at 10.10 AM - total time at 10.00 AM) ÷ (total count at 10.10 AM - total count at 10.00 AM) For these queries, you can compute the average execution time during this interval, using: For example, if have the contents of pg_stat_statements at 10.00 AM and 10.10 AM, you can select those queries which have a higher execution count at 10.10 AM than at 10.00 AM. In order to “catch” slow queries when they happen, you need to periodically fetch the entire contents of the pg_stat_statements view, store it in a timeseries database, and compare the execution counts. For each query, it shows, among other metrics, the total number of times it has been executed, and the total time taken across all executions. The pg_stat_statements extension provides cumulative statistics about every query ever executed by the server. It has however, a couple of limitations when it comes to discovering slow queries. It is invaluable, and is more or less the only way to get statistics on queries without installing custom extensions. Pg_stat_statements is a popular extension that is included in the core PostgreSQL distribution and available by default on nearly all DBaaS providers. Read on to see how to discover queries that take too long to execute, and how to figure out why they are slow. In every deployment, there are always a few queries that run too slow.
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