How does observability work in highly available databases?

Observability in highly available databases involves collecting and analyzing data to monitor system health, detect issues, and ensure continuous operation. It relies on three core components: metrics, logs, and distributed tracing. Metrics track performance indicators like query latency, replication lag, or node availability. Logs record events such as failed connections, slow queries, or replication errors. Distributed tracing follows requests across database nodes to identify bottlenecks or failures in distributed transactions. Together, these tools provide visibility into the system’s state, enabling teams to maintain uptime and respond to issues before they escalate.

For example, a database like PostgreSQL with streaming replication might use tools like pg_stat_activity to monitor active queries and pg_stat_replication to track replication delays. In a distributed system like Apache Cassandra, observability could involve monitoring read/write latencies per node and using logs to detect hints (pending data repairs) during network partitions. Tools like Prometheus scrape metrics from database exporters, while centralized logging systems like Elasticsearch aggregate logs from all nodes. Distributed tracing frameworks like Jaeger or OpenTelemetry help map requests across shards or replicas, making it easier to pinpoint where a multi-node query failed.

Observability directly supports high availability by enabling rapid detection and resolution of failures. For instance, if a replica node falls behind due to network congestion, metrics on replication lag trigger alerts, allowing operators to reroute traffic or provision additional resources. Automated systems can use these signals to initiate failover processes, such as promoting a standby node to primary. Real-time dashboards (e.g., Grafana) visualize cluster health, while anomaly detection algorithms flag deviations from baseline performance. By combining these approaches, teams ensure minimal downtime, meet SLAs, and maintain consistency across nodes during outages or scaling events.