PostgreSQL sizing
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.
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
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;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;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.
Hands-on setup, integration, and troubleshooting guides from the MonPG product docs.
Run the MonPG collector in Docker so credentials never leave your network.
Deploy the MonPG collector as a Deployment with a Helm chart.
Run the collector as a systemd service on Ubuntu / Debian / RHEL / Rocky.
Drop the collector binary on a host without Docker / systemd / Kubernetes.
Run the collector with no internet access — push metrics over your VPN to a MonPG bastion.
Token errors, network failures, SSL issues, missing metrics — diagnostic walkthrough.
The baseline PostgreSQL configuration MonPG recommends and how to apply it on each provider.
Invite teammates, assign roles, transfer ownership.
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.
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.
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.
MonPG gives teams query history, alerts, index guidance, vacuum visibility, replication signals, and cloud PostgreSQL monitoring in one place.
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