PostgreSQL Sizing for CPU, Memory, Storage, and Connections

PostgreSQL sizing

PostgreSQL Sizing for CPU, Memory, Storage, and Connections

PostgreSQL sizing is not picking the largest cloud instance you can afford. It is matching CPU, memory, I/O, storage growth, and connection behavior to the workload you actually run.

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Search Topics This Page Covers

  • postgresql sizing
  • pg sizing
  • postgres server sizing
  • postgresql capacity planning
  • postgresql memory sizing

Signals Worth Watching

  • CPU is saturated while the top queries are CPU-heavy
  • Cache hit ratio drops as the working set outgrows memory
  • Temp files and sort spills rise during peak traffic
  • WAL generation grows faster than archive and replica capacity
  • Connection count drives memory pressure before query work begins

Practical PostgreSQL Checks

Read the current memory and connection shape

Memory sizing has to account for shared memory, OS cache, and per-query memory multiplied by concurrency.

SELECT name, setting, unit
FROM pg_settings
WHERE name IN ('max_connections','shared_buffers','work_mem','maintenance_work_mem','effective_cache_size')
ORDER BY name;

Estimate I/O pressure from the workload

A bigger instance will not fix a query pattern that keeps reading unnecessary blocks. Sizing and tuning should be read together.

SELECT query, calls, shared_blks_read, shared_blks_hit, temp_blks_written
FROM pg_stat_statements
ORDER BY shared_blks_read DESC
LIMIT 20;

How MonPG Helps

  • MonPG shows CPU, memory, I/O, connection, temp file, and WAL pressure next to query workload.
  • Historical trends help separate one bad release from steady capacity growth.
  • Cloud-specific pages help teams reason about RDS, Aurora, Azure, Cloud SQL, and self-hosted PostgreSQL differently.

Related PostgreSQL Guides

PostgreSQL Tools

  • pgvector HNSW Index Tuner — Benchmark 48 HNSW configurations against your real pgvector data in 10 minutes. Get the optimal m, ef_construction, and ef_search plus zero-downtime migration SQL.
  • PostgreSQL Plan Autopsy — Paste EXPLAIN ANALYZE output and get an incident-style read of the plan: planner estimate drift, loop explosions, disk spills, buffer pressure, and the evidence SQL to prove the fix.
  • PostgreSQL Index Rollout Simulator — Model a proposed PostgreSQL index as a production rollout: DDL shape, lock level, WAL pressure, write amplification, replica lag risk, validation SQL, and rollback criteria.

Related Topic Hubs

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