In this article we explore the serverless metrics that are critical to the health of your Amazon Web Services application.
Troubleshooting serverless applications means tying together many different resources. Lambda functions run on-demand, with hardware that exists only for the duration of requests, and logs that can be spread out across multiple resources. Therefore, the first line of defense when making a serverless application more maintainable is getting a handle on the metrics that matter, and what they mean. Aggregate analysis of metrics will often be the primary signal that you receive when problems occur in production, and knowing which metrics to track is half the battle. In this article, we’ll explore the serverless metrics that are critical to your application’s health.
Let’s dive in by setting some boundaries for our discussion. In this exploration, we’ll focus on three general categories of metrics: operational, load-related, and other. These metrics will each represent a portion of your application’s execution process. To build a coherent view of your application’s production performance, you’ll need to take them all into account.
The easiest metrics to understand are operational metrics, which track the operational performance of your serverless functions by comparing the results of calls. This allows you to build thresholds for alerting, establishing the critical metrics that help you ensure your application continues to run without issue. In AWS Lambda, we primarily focus on two operational metrics: aggregate error count and aggregate execution count.
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