Series / Databases
A complete guide to monitoring, alerting, and capacity planning for Postgres, MySQL, Cassandra, and MongoDB at scale.
DBAs and platform engineers building or inheriting database monitoring. Covers what to measure, what thresholds matter, and how to tell signal from noise across Postgres, MySQL, Cassandra, and MongoDB.
Familiarity with at least one relational database in production. Helpful if you've used Prometheus or Datadog, but not required.
What to measure and which queries surface the right signals for PostgreSQL and MySQL/Aurora dashboards.
The eight PostgreSQL metric groups that matter for production operations — queries, connections, replication lag, autovacuum, locks, cache pressure, checkpoint behavior, and bloat — with exact SQL and alert thresholds.
The seven MySQL and Aurora metric groups that matter for production operations — threads, replication lag, InnoDB buffer pool, slow queries, connections, locks, and disk — with exact SQL, CloudWatch metrics, and alert thresholds.
Threshold design that fires on real saturation, not autovacuum and checkpoint noise.
How to set database alert thresholds that catch real failures without burning the team on autovacuum noise, checkpoint churn, and replication lag spikes — with specific values for PostgreSQL, MySQL, and Aurora.
End-to-end setup for Prometheus/Grafana and Datadog Database Monitoring — exporters, dashboards, and retention.
How to instrument PostgreSQL and MySQL with postgres_exporter and mysqld_exporter, configure Prometheus scrape jobs, and build Grafana panels that surface the metrics that matter — with working PromQL queries.
How to configure Datadog Database Monitoring for PostgreSQL, MySQL, and Aurora — query samples, explain plans, wait event analysis, and the specific Agent settings that make the difference between metric collection and real observability.