Best Practices for Detecting Downtime

Downtime can be costly—leading to lost revenue, frustrated users, and damage to your brand’s reputation. As a developer, ensuring high availability and quickly detecting outages is critical.

Relying on a single monitoring method can lead to blind spots. Instead, use a combination of:

• Synthetic Monitoring (Probes): Simulate user interactions from different locations to detect failures before real users do.
• Application Monitoring: Gather traces internal from your application, and look for errors or long timespans that indicate trouble. Send logs to a single locality to help you investigate problems after one is detected.
• Infrastructure Monitoring: Use tools like Prometheus, Grafana, or Nagios to keep an eye on the underlying health of your system.

Avoid “alert fatigue” by ensuring alerts are meaningful and actionable:

• Use Multi-Signal Confirmation: Trigger alerts only when multiple checks fail (e.g., HTTP status + latency + DNS resolution). For example, Checkly makes it easy to require 2 or more failures before sending alerts in its retry configuration.
• Prioritize Alerts: Distinguish between critical (full outage) and warning (degraded performance). Any time your on-call team gets an alert that doesn’t require action, this should be counted as a false alarm and call for improvements to your alert thresholds.
• Avoid Static Thresholds: When a service that usually responds in 4.98 seconds starts responding after 5.1 seconds, it’s a negative trend that might call for some investigation. But it certainly does not call for a midnight alert to the on-call team. As just one example, Playwright’s assertions about page elements use an auto-waiting system that is more flexible than static time thresholds.

Your application may depend on APIs, CDNs, or cloud providers—any of which can fail.

• Check External Endpoints: Regularly test third-party services. With Checkly’s API monitors, you can send frequent quick checks to external API’s you rely on, and get reliable notifications if something isn’t working, before users report a problem.
• Implement Fallbacks: Gracefully degrade functionality if a dependency fails. For core dependencies this might go as far as switching to a backup payment provider if your primary service fails.

Proactively simulate outages to ensure your monitoring works:

• Monitor Proactively: Use synthetic monitoring to send requests to your service. Ideally a sophisticated automation tool like Playwright to simulate full user paths. For example on your eCommerce site Playwright could test the process of visiting the site, browsing inventory, adding items to a cart, and checking out.
• Chaos Engineering: Large teams with dedicated platform engineers can use tools like Chaos Monkey to intentionally break systems and verify detection.
• Run Scheduled Drills: Periodically trigger false outages to test alerting responsiveness. Note this is only necessary if you aren’t having frequent real incidents! It is useful to schedule a drill whenever the process for incident management changes, so that your team has a chance for a dry run of the procedure.

Downtime may only affect certain regions due to DNS, CDN, or cloud provider issues.

• Distributed Monitoring: Deploy checks across multiple geographic locations.
• Monitor DNS & CDN Health: Use tools like Checkly to detect regional outages.

When downtime occurs, speed is crucial.

• Auto-Remediation: For known issues (e.g., a stuck process), automatically restart services. Tools like Kubernetes have automatic remediation as part of their core function.
• Escalation Policies: Ensure alerts reach the right team (SMS, Slack, PagerDuty). And that incidents are tracked when the team is responding.
• Postmortem Culture: Analyze outages to improve future detection.

Detecting downtime quickly requires a proactive, multi-layered approach. By combining synthetic checks, real-user monitoring, intelligent alerting, and global visibility, you can minimize downtime and maintain a reliable user experience.

Further Reading:

• Reducing Mean Time to Repair