Setting Up Alerts for High-Availability APIs: What Metrics to Use?

In today’s fast-paced software environment, maintaining high availability for APIs is crucial. As developers and operators, we must ensure that our systems are not only functional but also resilient and responsive to user needs. A vital aspect of this is setting up effective alerting systems. But with countless metrics to choose from, how do we determine which ones warrant alerts, and how do we avoid alert fatigue?

Understanding the Challenge

The challenge of alerting lies in balancing responsiveness with noise reduction. Many teams face the frustration of receiving hundreds of alerts daily, many of which are irrelevant or quickly resolved. This leads to alert fatigue, where important notifications are ignored due to the overwhelming volume of alerts. Therefore, crafting an alerting strategy that is both meaningful and manageable is essential.

Metrics to Consider for Alerts

1. Error Rates

• 5xx Errors: Alerting on server errors is crucial. However, rather than triggering an alert for every single occurrence, consider implementing a threshold. For instance, alerting for 50 5xx errors within a minute can serve as a good tripwire.
• 4xx Errors: While some teams choose to ignore 4xx errors (client errors), monitoring unhandled 4xx errors could provide insights into user experience issues or misconfigurations.

2. Latency

• P75 and P99 Latency: Monitoring percentile latency (e.g., p75, p99) helps in understanding response times under load. Alerts can be set for significant regressions, such as p75 latency exceeding 500ms for internal calls or 2 seconds for external API calls.
• Health Checks: Implementing uptime and latency checks on health endpoints can help quickly identify when a service goes down. Such checks should be lightweight, ensuring minimal impact on system performance.

3. Service Level Objectives (SLOs)

• Defining clear SLOs helps in creating actionable alerts. For instance, if your SLO states that 99% of requests should complete within 200ms, alerting on violations of this SLO can be a direct indicator of issues impacting users.

4. Traffic and Resource Spikes

• Monitoring traffic patterns and resource usage (CPU, memory, etc.) can help identify potential bottlenecks before they become critical issues. Alerts can be structured to notify the responsible teams (e.g., DevOps for resource spikes, engineers for application issues).

Strategies for Reducing Alert Noise

1. Focus on Customer Impact

• Prioritize alerts based on customer impact. If an alert indicates a current or impending problem for users, it should trigger immediate attention. This can help ensure that only the most critical alerts reach the team.

2. Define Clear Ownership

• Clearly define team ownership for specific alerts. For example, DevOps can handle infrastructure-related alerts, while application engineers can focus on application performance issues. This allows for a more structured escalation process.

3. Implement a Ground Truth

• Establish a baseline by creating a “ground truth” for your API. This could involve having reference clients send requests at regular intervals to measure expected performance and identify deviations from normal behavior.

4. Involve Teams in Alert Development

• Encourage the teams who will respond to alerts to be involved in defining and refining them. This increases buy-in and ensures that alerts are relevant to those who will be acting on them.

5. Regular Review of Alerts

• Periodically review the alerting strategy and metrics used. As systems evolve, so should the alerting criteria. Remove or adjust alerts that have proven unhelpful or overly noisy.

Conclusion

Setting up alerts for high-availability APIs is a nuanced endeavor that requires careful thought and consideration. By focusing on key metrics, prioritizing alerts based on customer impact, and involving teams in the alerting process, organizations can create a robust monitoring strategy that minimizes noise while ensuring they remain responsive to critical issues.

As the industry continues to evolve, staying informed about methodologies like the RED metrics (Rate, Error, Duration) and principles from observability engineering can further enhance our approach to monitoring and alerting. The goal should always be to create a system that not only alerts us to problems but empowers us to resolve them effectively.

For further insights, I recommend exploring resources such as Observability Engineering and the RED metrics approach to better understand how to instrument and monitor your services effectively.