Radim Marek: Reading Buffer Statistics in EXPLAIN Output

Why It Matters

Understanding buffer metrics lets DBAs pinpoint disk I/O, memory spills, and catalog reads, enabling targeted performance improvements and more efficient resource allocation.

Key Takeaways

• EXPLAIN ANALYZE now includes buffers by default (PG18)
• Hit ratio reveals cache effectiveness per query
• Temp buffers indicate work_mem spills to disk
• Planning buffers expose catalog I/O during plan creation
• pg_stat_statements aggregates buffer metrics across workload

Pulse Analysis

PostgreSQL 18’s decision to embed buffer statistics directly in EXPLAIN ANALYZE marks a significant usability boost for database professionals. Previously, analysts had to remember the BUFFERS keyword; now every plan instantly shows shared, local and temporary buffer activity. This change encourages routine inspection of I/O patterns, turning a once‑optional diagnostic into a default part of query analysis and helping teams catch performance regressions early.

Interpreting the numbers requires a clear mental model. Shared buffers track reads and hits on the main buffer pool, with the hit‑ratio (hits / (hits + reads)) indicating how well data stays cached. Low ratios on frequently accessed OLTP queries signal insufficient shared_buffers or cache pressure. Local buffers surface when temporary tables are used, while temp read/write counters expose work_mem spills during sorts or hashes. By correlating these metrics with execution time, engineers can decide whether to increase shared_buffers, raise work_mem, or add indexes to eliminate costly sorts.

Beyond single‑query analysis, the article highlights the power of aggregated views like pg_stat_statements, which summarize buffer usage across the entire workload. Monitoring planning buffers also reveals catalog cache health, a hidden source of latency in heavily partitioned schemas. Armed with these insights, teams can implement concrete actions—tuning shared_buffers, adjusting bgwriter settings, resizing temp_buffers, or refining query plans—to reduce disk I/O, improve cache hit rates, and ultimately deliver faster, more reliable PostgreSQL services.