07.03.2025 Problem:
The client experienced a problem with query slowness in their PostgreSQL database. Several queries were running slowly, and the application became unresponsive during the issue. The client required assistance in diagnosing and optimizing the queries contributing to the performance issues.
Process:
Step 1 – Initial Investigation
The expert reviewed the PGAWR reports for the timeframe when the application experienced slowness. Several high-impact queries were identified that contributed to database performance issues:
- UPDATE Query: A high-execution-time update query was identified (taking around 2724 seconds).
- DELETE Query: A long-running DELETE query was also identified (taking more than 27000 seconds).
- SELECT Query: High-latency SELECT queries contributed to slowness (with execution times averaging around 100ms).
- Blocking Queries: Long-running queries and autovacuum processes were blocking other transactions.
Step 2 – Analysis and Request for Additional Information
The expert requested the following information from the client to proceed with further analysis:
- EXPLAIN ANALYZE Output: The expert asked the client to run EXPLAIN ANALYZE on the identified slow queries to analyze execution plans.
- Index Usage: The expert requested a query to check for unused or inefficient indexes, specifically on the columns used in the WHERE clause of the problematic tables.
- Blocking Queries: The expert asked the client to run a query to identify any blocking queries and transactions.
- Vacuum Analysis: The expert asked for a vacuum analysis to check for dead tuples and to run VACUUM ANALYZE on the tables involved.
- PostgreSQL Configuration: The expert requested the PostgreSQL configuration file to review and recommend optimizations.
Step 3 – Proposed Solutions
The expert provided recommendations for resolving the query slowness and optimizing the database:
- Indexing Optimization: Create necessary indexes to improve query performance, especially on columns like GROUP_ID, CYCLE_CODE, and SUBSCRIBER_ID.
- Optimize DELETE Queries: Rewrite DELETE queries to use JOINs rather than subqueries, and batch large DELETEs to prevent locking issues.
- Lock Contention Resolution: Identify blocking queries and terminate long-running transactions to clear locks.
- PostgreSQL Configuration Tuning: Adjust PostgreSQL settings such as work_mem, shared_buffers, and checkpoint_timeout to optimize memory and reduce I/O bottlenecks.
- Autovacuum Settings: Increase autovacuum workers to clean up dead tuples more effectively and improve query performance.
Step 4 – Follow-Up and Action Items
The client was requested to take the following actions to assist with resolving the issue:
- Run EXPLAIN ANALYZE: The client was asked to run EXPLAIN ANALYZE on the identified slow queries and provide the output.
- Check Index Usage: The client was instructed to provide index usage details to ensure the right indexes were in place.
- Provide Logs and Configuration: The client was asked to share PostgreSQL logs for the issue timeframe and the PostgreSQL configuration file.
- Run VACUUM ANALYZE: The client was asked to run VACUUM ANALYZE on the affected tables.
Solution:
The expert’s recommendations allowed the client to improve query performance and resolve slowness issues in the PostgreSQL database. The implementation of proper indexing, query optimization, and PostgreSQL configuration adjustments resulted in faster query execution times and reduced locking issues. Additionally, the increased autovacuum workers and vacuum analysis ensured that dead tuples were removed, preventing future slowness.
Conclusion:
This case demonstrated the importance of conducting a thorough investigation to identify the root cause of performance issues. By implementing the expert’s suggestions, the client was able to optimize their PostgreSQL database and prevent future query slowness. The case also highlighted the value of detailed log analysis, indexing, and query optimization in improving overall database performance.