How does Optimizer Plan Stability work in PostgreSQL?

In PostgreSQL, optimizer plan stability is a feature that allows the query planner to reuse a previously cached query plan for a SQL statement instead of generating a new plan. This can improve query performance and reduce overhead by avoiding the need to regenerate a new query plan each time the query is executed.

The optimizer plan stability feature works as follows:

  1. When a SQL statement is executed for the first time, the PostgreSQL query planner generates a query plan for that statement based on the current statistics and indexes.
  2. The generated plan is then cached in the query plan cache with a unique query ID.
  3. When the same SQL statement is executed again, the query planner checks the query plan cache to see if there is a cached plan for the query ID.
  4. If a cached plan is found, the query planner uses that plan to execute the SQL statement without generating a new plan.
  5. If there is no cached plan, the query planner generates a new plan and caches it for future use.
  6. The cached plan is kept in the query plan cache until it is evicted due to memory pressure or a change in the schema or statistics.

By reusing previously generated query plans, the optimizer plan stability feature can significantly reduce query overhead and improve query performance, especially for frequently executed SQL statements. However, it is important to note that this feature is not a silver bullet and may not work well for queries with varying parameters or data distributions. Additionally, if there are significant changes in the data or schema, the cached plan may become suboptimal, which can negatively impact performance. Therefore, it is important to monitor the cache hit rate and consider the appropriate cache size and eviction policies to achieve the best performance for the system.

How can we monitor what SQL is executed by an Application in PostgreSQL?

There are several ways to monitor what SQL is executed by an application in PostgreSQL:

  1. Use the PostgreSQL log files: By default, PostgreSQL logs all SQL statements that are executed. The log files contain detailed information about the statements, including the SQL text, execution time, and query plan. By reviewing the log files, you can identify which SQL statements are being executed by an application.
  2. Use the pg_stat_statements extension: This extension is a powerful tool for monitoring SQL statement execution in PostgreSQL. It provides a view that displays statistics about each SQL statement that has been executed, including the execution time, number of times executed, and more. By querying this view, you can identify which SQL statements are being executed by an application and how frequently they are being executed.
  3. Use third-party monitoring tools: There are several third-party tools available that can help monitor SQL statement execution in PostgreSQL. These tools typically provide more advanced features and better visualization than the built-in logging and statistics tools in PostgreSQL.
  4. Use the PostgreSQL audit extension: This extension allows you to log all SQL statements that are executed by a specific user or application. By enabling auditing for the application’s database user, you can capture a detailed log of all SQL statements that are executed by the application.
  5. Use PostgreSQL’s built-in client-side tools: PostgreSQL provides several client-side tools that can help monitor SQL statement execution, such as psql’s \timing command, which displays the execution time for each statement, or the EXPLAIN command, which displays the query plan for a statement. By using these tools, you can identify which SQL statements are being executed by an application and how long they take to execute.

By using one or more of these methods, you can monitor what SQL is executed by an application in PostgreSQL and identify any performance or security issues that may arise.

About Shiv Iyer 443 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.