How do you prioritize alerts in database observability?

Prioritizing alerts in database observability involves assessing the impact, urgency, and root cause of issues to focus on what matters most. The goal is to resolve critical problems first while avoiding alert fatigue caused by low-priority noise. This requires a combination of predefined rules, contextual analysis, and automation to triage alerts effectively.

First, categorize alerts based on severity and business impact. For example, alerts tied to system downtime (e.g., database unresponsive) or data integrity issues (e.g., corruption detected) should rank highest. These directly affect user-facing services or risk permanent data loss. High-priority alerts might also include sudden spikes in error rates for core transactions, like payment failures in an e-commerce system. Medium-priority alerts could involve performance degradation, such as slower query responses that haven’t yet impacted users. Low-priority alerts might include temporary resource spikes (e.g., CPU usage hitting 80% for a few seconds) or non-critical warnings like infrequent connection timeouts. Tools like Prometheus or Datadog can help automate this categorization using custom rules, such as tagging alerts as “critical” if they exceed predefined error thresholds for more than two minutes.

Next, use context to refine prioritization. For instance, an alert about high disk usage becomes urgent if the database is nearing its storage limit and risks crashing, but less urgent if cleanup processes are already running. Correlate multiple alerts to identify root causes: a sudden increase in query latency paired with a CPU spike might indicate a misconfigured index or a runaway query. Tools like Elasticsearch or Splunk can help aggregate logs and metrics to provide this context. Additionally, integrate alerts with team expertise—flag issues that align with recent code deployments or infrastructure changes. For example, if a schema update was rolled out an hour before a deadlock alert, prioritize investigating potential conflicts in the new schema.

Finally, automate responses where possible to reduce manual triage. Use tools like PagerDuty or Opsgenie to route critical alerts directly to on-call engineers via SMS or Slack, while sending low-priority issues to a ticketing system. Implement automated remediation for known issues—for example, restarting a stuck connection pool or killing long-running queries that exceed time limits. Regularly review alert patterns to eliminate false positives and adjust thresholds. For example, if nightly backups consistently trigger a “high I/O” warning but cause no harm, suppress or reclassify those alerts. This iterative process ensures the team focuses on actionable issues, balancing responsiveness with sustainable workload management.