Optimizing Query Performance: Tips for Troubleshooting PostgreSQL Statistics and Cost Estimation

Troubleshooting statistics and cost estimation in PostgreSQL is crucial for understanding and optimizing query performance. PostgreSQL uses statistics to make informed decisions about the best way to execute queries, including which indexes to use and how to join tables. Here are some tips and tricks to help you troubleshoot and improve the accuracy of statistics and cost estimations:

1. Regularly Update Statistics with ANALYZE

PostgreSQL relies on statistical information about tables to plan queries. These statistics can become outdated as the data changes, leading to suboptimal query plans.

  • Update Statistics: Use the ANALYZE command to update statistics for a table, or VACUUM ANALYZE to also reclaim storage and update statistics.
  • Automate with AutoVacuum: Ensure the AutoVacuum daemon is enabled and properly configured to automatically analyze tables. Adjust the AutoVacuum parameters if necessary to ensure frequent and relevant updates.

2. Check for Correlated Columns with Extended Statistics

PostgreSQL’s planner may not accurately estimate the selectivity of queries involving columns that are statistically correlated. Creating extended statistics can help:

After creating them, run ANALYZE to populate the statistics.

3. Adjust the Statistics Target for More Accuracy

The default_statistics_target parameter sets the level of detail of the statistics gathered by ANALYZE. Increasing this value can improve the planner’s estimates at the cost of longer analysis times and more space consumption.

  • Global Setting: Adjust in postgresql.conf
  • Per-Column Setting: For critical columns involved in joins or where conditions, consider setting a higher statistics target individually.

4. Review and Adjust Cost Parameters

PostgreSQL’s planner makes decisions based on cost estimations of different operations like disk I/O, CPU usage, and row fetching. Review and adjust the cost settings in postgresql.conf if the defaults don’t align with your actual hardware performance.

  • seq_page_cost: Cost of a sequential disk page read.
  • random_page_cost: Cost of a non-sequential disk page read.
  • cpu_tuple_cost: Cost of processing each row.

5. Use EXPLAIN to Understand Query Plans

The EXPLAIN and EXPLAIN ANALYZE commands are invaluable for troubleshooting. They show the execution plan of a query and, with EXPLAIN ANALYZE, the actual execution statistics.

  • Look for discrepancies between estimated rows and actual rows returned by each step.
  • Identify steps with high costs or unexpected sequential scans.

6. Consider the Impact of Parameterized Queries

The planner’s estimates can be less accurate for parameterized queries due to generic plan caching. Use PREPARE and EXECUTE with caution and monitor their performance.

7. Monitor and Analyze Slow Queries

Log and review slow queries regularly to spot patterns or frequent operations that may benefit from statistics or cost parameter adjustments.

8. Experiment with Join Algorithms and Set Operations

For complex queries involving multiple joins or set operations, try rewriting the query or forcing a specific join method to see if performance improves.


Effective troubleshooting of statistics and cost estimation in PostgreSQL involves a combination of regularly updating statistics, fine-tuning the database configuration, and deep analysis using the EXPLAIN command. By taking a proactive approach to monitoring and optimization, you can significantly enhance query performance and ensure that your PostgreSQL database runs efficiently.


Fine-Tuning PostgreSQL: Understanding Selectivity and Cardinality Estimations


Tips and tricks to troubleshoot indexes with high maintenance costs in PostgreSQL


Cardinality in PostgreSQL: The Key to Query Performance Optimization


How PostgreSQL Optimizer selects which indexes to use?

How to compute cost of slow query in PostgreSQL?


About Shiv Iyer 444 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.