Postgres Tuning Checklist

work_mem, shared_buffers.

Overview

The Postgres tuning checklist matches key configuration to memory, workload, and connection patterns. The defaults are conservative for a small VM; production hardware needs explicit tuning to match the workload to the available resources.

• work_mem. Memory per sort and hash operation; tuned to query patterns; too low and disk spills, too high and OOM.
• shared_buffers. Postgres-managed cache; roughly 25% of RAM is the rule of thumb; the rest stays with the OS page cache.
• effective_cache_size. Planner hint about total cache; matches OS cache plus shared_buffers; influences index-versus-seq-scan decisions.
• max_connections plus autovacuum. Connection pooling via PgBouncer reduces backend overhead; aggressive autovacuum keeps write-heavy tables from bloating.

The approach

The practical approach is pgtune for the starting point, tune per workload, pool with PgBouncer, aggressive autovacuum on write-heavy tables, document every change. The team’s discipline produces tuned databases that survive the next traffic spike.

• pgtune defaults. Hardware-aware starting point; replaces guessing with a baseline derived from the actual instance.
• work_mem per query pattern. Tune based on observed sort and hash sizes; EXPLAIN ANALYZE tells you what the queries actually need.
• PgBouncer for pooling. Connection pool reduces backend overhead; transaction-level pooling supports the highest concurrency.
• Aggressive autovacuum plus documented changes. Lower thresholds on write-heavy tables; per-cluster rationale committed to the repo for operational reviews.

Why this compounds

Postgres tuning compounds across clusters. Each tuned cluster produces ongoing performance; the team’s database expertise grows; new clusters inherit the patterns from the previous round.

• Better query performance. Tuned memory produces fast queries; the workload runs in cache instead of spilling to disk.
• Better resource use. Right configuration matches hardware; the cluster uses what it has without over-allocating.
• Reduced incident risk. Aggressive autovacuum prevents bloat-driven incidents; the slow degradation never reaches the page.
• Institutional knowledge. Each tuning iteration teaches Postgres internals; the team’s database engineering muscle grows.

Postgres tuning is an operational discipline that pays off across years. Nova AI Ops integrates with database telemetry, surfaces patterns, and supports the team’s database engineering discipline.