8 Rules to Improve Query Performance in PostgreSQL

8-rules-improve-query-performance-postgresql.md

1. Indexing

• Create indexes on frequently filtered columns: Ensure you have indexes on columns used in WHERE, JOIN, and ORDER BY clauses (e.g., deleted_at IS NULL, created_at).
• Use conditional indexes: If you’re often filtering by a condition (e.g., deleted_at IS NULL), create a partial index.
• Use CONCURRENTLY when creating indexes in production: This prevents locking the table during the index creation.
• Remove duplicate indexes: Multiple identical or overlapping indexes slow down writes without providing extra benefits.

2. Parallelism and PostgreSQL Settings

• Enable parallel execution: Make sure PostgreSQL is configured to utilize parallelism for large queries by adjusting parameters like max_parallel_workers_per_gather and max_parallel_workers.
• Use enable_parallel_append for UNION queries: For UNION queries across multiple tables, enabling parallel append can optimize performance.

3. Optimizing Query Structure

• Avoid UNION unless necessary: If deduplication isn’t required, use UNION ALL instead of UNION. UNION adds overhead by trying to remove duplicates.
• Use CASE expressions sparingly: Optimize the logic inside CASE expressions if possible, especially when used in JOIN conditions or large result sets.
• Postpone complex formatting: Functions like pg_size_pretty or similar formatting should be handled after fetching the raw data to avoid slowing down the main query.

4. Reduce Data Scanned

• Limit data with targeted WHERE clauses: Filter data earlier in the query rather than scanning entire tables.
• Optimize date filters: When filtering by date (e.g., created_at), ensure that the date range is used effectively with appropriate indexes.
• Reduce JSON filtering: JSON or text-based filters (e.g., LIKE '%value%') are costly. Try to move this filtering into application logic or preprocess the data.

5. Pre-aggregation and Caching

• Use materialized views for heavy aggregation: If the result set is relatively static, you can use a materialized view to cache the results and refresh it periodically.
• Cache results for frequently executed heavy queries: For queries that count large datasets (e.g., counting rows in huge tables), periodically store these results in a summary table and refresh the table periodically.

6. Analyze and Tune Slow Queries

• Use EXPLAIN (ANALYZE) to understand query execution: This will help you identify which part of the query is slow, whether it’s related to disk I/O, sequential scans, or sorting.
• Look at buffer usage: Using BUFFERS in EXPLAIN can help analyze how much of the query is reading from disk versus memory.
• Batch processing or chunking: For large tables, consider breaking the query into smaller batches or using parallel processing if the data can be divided.

7. Optimize Joins

• Ensure appropriate indexes for JOINs: Index the columns used in JOINs to prevent full table scans.
• Simplify JOINs when possible: Reduce unnecessary complexity in JOINs by ensuring that only relevant columns and filters are used.

8. General PostgreSQL Tuning

• Tune PostgreSQL configuration: Adjust settings like work_mem, maintenance_work_mem, and shared_buffers based on the size of your data and queries.
• Monitor disk I/O: Large scans on unindexed tables can cause disk bottlenecks. Optimize by limiting the size of scanned data or using indexes.