What are the key metrics to monitor in a relational database?

When monitoring a relational database, three primary categories of metrics are essential: performance, resource utilization, and availability/errors. These metrics help developers maintain system health, optimize queries, and prevent outages. Below, we’ll break down specific examples in each category and explain why they matter.

Performance Metrics Start by tracking query execution time and throughput. Slow-running queries can bottleneck the entire system, so tools like PostgreSQL’s pg_stat_statements or MySQL’s slow query log are critical for identifying inefficient operations. For example, a query taking 5 seconds to fetch user data might indicate missing indexes or poor schema design. Throughput metrics, such as transactions per second (TPS) or queries per second (QPS), reveal workload patterns. A sudden drop in TPS could signal contention issues like lock waits. Additionally, monitor connection pool usage—if active connections consistently hit the database’s limit, applications may fail to connect, requiring configuration adjustments.

Resource Utilization CPU, memory, disk I/O, and storage are foundational. High CPU usage (e.g., 90%+) might stem from unoptimized queries or insufficient indexing. Memory metrics like buffer cache hit ratios (e.g., in PostgreSQL’s pg_buffercache) show how often data is retrieved from memory versus disk—a low ratio suggests inadequate RAM for common workloads. Disk I/O latency (measured in milliseconds) and throughput (MB/s) help spot storage bottlenecks. For example, a sudden spike in read latency could indicate disk hardware issues. Storage capacity is equally critical: a table growing at 10GB/day might require archiving or partitioning to avoid filling the disk.

Availability and Errors Track uptime and replication lag if using read replicas. A replica lagging by minutes (e.g., 300 seconds in MySQL’s SHOW REPLICA STATUS) risks serving stale data. Error rates, such as deadlocks or failed logins, are early warnings of deeper issues. For instance, frequent deadlocks might require transaction logic changes. Log monitoring for events like query timeouts or authentication failures is also vital. Finally, ensure backups complete successfully and test restore processes—a failed backup job for a critical table could leave data unrecoverable. Tools like automated alerts for these metrics help teams act before minor issues escalate.