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Best practices for Query Store

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APPLIES TO: Azure Database for PostgreSQL - Single Server

Important

Azure Database for PostgreSQL - Single Server is on the retirement path. We strongly recommend that you upgrade to Azure Database for PostgreSQL - Flexible Server. For more information about migrating to Azure Database for PostgreSQL - Flexible Server, see What's happening to Azure Database for PostgreSQL Single Server?.

This article outlines best practices for using Query Store in Azure Database for PostgreSQL.

Set the optimal query capture mode

Let Query Store capture the data that matters to you.

pg_qs.query_capture_mode Scenario
All Analyze your workload thoroughly in terms of all queries and their execution frequencies and other statistics. Identify new queries in your workload. Detect if ad hoc queries are used to identify opportunities for user or auto parameterization. All comes with an increased resource consumption cost.
Top Focus your attention on top queries - those issued by clients.
None You've already captured a query set and time window that you want to investigate and you want to eliminate the distractions that other queries may introduce. None is suitable for testing and bench-marking environments. None should be used with caution as you might miss the opportunity to track and optimize important new queries. You can't recover data on those past time windows.

Query Store also includes a store for wait statistics. There is an additional capture mode query that governs wait statistics: pgms_wait_sampling.query_capture_mode can be set to none or all.

Note

pg_qs.query_capture_mode supersedes pgms_wait_sampling.query_capture_mode. If pg_qs.query_capture_mode is none, the pgms_wait_sampling.query_capture_mode setting has no effect.

Keep the data you need

The pg_qs.retention_period_in_days parameter specifies in days the data retention period for Query Store. Older query and statistics data is deleted. By default, Query Store is configured to retain the data for 7 days. Avoid keeping historical data you do not plan to use. Increase the value if you need to keep data longer.

Set the frequency of wait stats sampling

The pgms_wait_sampling.history_period parameter specifies how often (in milliseconds) wait events are sampled. The shorter the period, the more frequent the sampling. More information is retrieved, but that comes with the cost of greater resource consumption. Increase this period if the server is under load or you don't need the granularity

Get quick insights into Query Store

You can use Query Performance Insight in the Azure portal to get quick insights into the data in Query Store. The visualizations surface the longest running queries and longest wait events over time.

Next steps