Zero instrumentation observability for AWS Lambda

Zero instrumentation observability for AWS Lambda

Learn how to get instant visibility into your AWS Lambda functions with zero instrumentation using Serverless Framework. We recently launched a few features which make troubleshooting Serverless Framework services much easier. Before diving into the details, I’d like to share a personal story. About three years ago I was vacationing with my family in Michigan. At the time I was acting as interim backend engineer for a startup. We were out on the lake with friends enjoying the afternoon sun when PagerDuty started going off.

We recently launched a few features which make troubleshooting Serverless Framework services much easier. Before diving into the details, I’d like to share a personal story.

About three years ago I was vacationing with my family in Michigan. At the time I was acting as interim backend engineer for a startup. We were out on the lake with friends enjoying the afternoon sun when PagerDuty started going off. Each component started falling over one by one and the angry customer emails about the thousands of dollars they were losing each minute started piling up. After getting off the boat, getting online and pulling my hair out for three hours, we finally identified the root cause - Facebook’s APIs were responding about 40% slower than usual causing cascading slower response times in upstream dependencies and ultimately timeouts. This was an experience I would never wish upon anyone and I deeply sympathize with anyone who experiences such outages as a developer.

Such issues likely would have been avoided with a serverless architecture, but a few lessons are still applicable. Amongst them is the importance of monitoring the performance of each dependent service. And secondly, that it’s hard to do.

Given this experience, I am especially proud to show you how you can use the new invocation explorer and functions spans feature to troubleshoot issues similar to the one I experienced a few years ago.

To begin our troubleshooting journey, let’s assume we have a service with a few functions handling tens of thousands of invocations each hour. We are informed of degraded performance, but we have little to go on, other than knowing that there is a slow response time on a particular endpoint.

Invocation Explorer

First we need to identify the needle in the haystack. Luckily, we at least know what to look for. We know that the durations are probably taking longer than expected, we know the most likely functions causing the issue, and a time period during which the issue was reported.

Using the invocation explorer we can quickly and easily identify the individual function invocations that match these characteristics by filtering on the function, time range, error state, cold start state, memory usage, and durations.

Once you apply the filter you’ll be able to see the timestamp, duration, memory utilization, cold start state, and the error if one occurred for every individual invocation which meets the filter criteria.

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