Exploring Alternatives to SQL Server Query Store in PostgreSQL

PostgreSQL Query Performance Alternatives

In PostgreSQL, the pg_stat_statements extension serves as the closest alternative to SQL Server’s Query Store. Specifically, it captures critical query performance metrics, such as execution counts, total time, and query text. As a result, this extension helps you monitor database activity and identify slow-running queries for optimization. Moreover, pg_stat_statements integrates seamlessly with other PostgreSQL features, further enhancing its ability to deliver valuable insights into query behavior.

Although PostgreSQL does not include a feature identical to Query Store, you can achieve similar functionality through alternative tools. For instance, combining pg_stat_statements with advanced logging and monitoring tools provides robust query performance analysis. Additionally, tools like pgBadger or PMM offer detailed query analytics and historical performance trends. Moreover, integrating these tools with pg_stat_statements ensures comprehensive monitoring, making performance tuning and troubleshooting significantly more efficient.

pg_stat_statements

The pg_stat_statements module is included with PostgreSQL and provides a means to track execution statistics of all SQL statements executed by the server, not just queries. This tracks how many times a statement was executed and the total time spent in the database for those executions.

To use pg_stat_statements, you need to:

  1. Enable the extension in your database:
  2. Add it to the shared_preload_libraries in your postgresql.conf file to ensure it’s loaded at server start:

    After changing the configuration, you’ll need to restart your PostgreSQL server.
  3. Query the pg_stat_statements view to analyze query performance:

Additional Monitoring and Logging

For a more comprehensive solution akin to SQL Server’s Query Store, consider integrating pg_stat_statements with other PostgreSQL features and third-party tools:

  • Logging:
    PostgreSQL allows you to configure its extensive logging capabilities to log long-running queries or all queries as needed. You can analyze these logs using tools like pgBadger for detailed insights.
  • Extensions and External Tools:
    Consider using extensions like pg_qualstats, pg_stat_kcache, and auto_explain for deeper insights into query execution and performance issues. External monitoring tools like Prometheus with Grafana, or commercial platforms like pganalyze, provide powerful interfaces for visualizing and analyzing PostgreSQL performance data over time.
  • Custom Views and Functions:
    You can create custom views or functions to aggregate and report performance data tailored to your specific needs. Combine data from pg_stat_statementswith other PostgreSQL statistics to gain deeper insights into database performance.

PostgreSQL requires more setup and integration than SQL Server’s Query Store but provides greater flexibility and powerful monitoring options. Use the pg_stat_statements extension as the foundation of your query performance analysis strategy. Combine it with additional tools and best practices to build a comprehensive solution for monitoring and optimization.

About Shiv Iyer 497 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.