Lowering MTTR with Amazon CloudWatch and Amazon Web Services X-Ray

by Erich Wolz and Jesse Sullivan | on

Customers running microservice-based workloads in a serverless environment frequently have issues with troubleshooting incidents as the data they need can be distributed across hundreds or thousands of components. In this blog post, I will demonstrate how you can reduce the mean time to resolution (MTTR, or the average time it takes to repair or mitigate the impact of a failure) for your serverless workloads using Amazon CloudWatch ServiceLens and Amazon Web Services X-Ray . A successful monitoring strategy relies on all three pillars of observability (metrics, logs, and traces) and can surface insights from all of them in a single view.

Understanding your dependencies

CloudWatch ServiceLens helps you visualize your distributed environment and understand the dependencies between services. It brings metrics, logs, and traces together into a service map so that you can focus on the integrations between components and pinpoint issues as they occur. Your resources and instrumented applications appear as nodes on a directed graph, with the edges between nodes representing the transactions that occur between them. Nodes experiencing issues will have a colored outline according to the type of error and can help highlight points in your architecture that are experiencing faults or are currently in an alarm state.

Screenshot of the ServiceLens Map showing multiple services from the PetAdoptions application used by the AWS One Observability workshop.

Figure 1: CloudWatch ServiceLens Map

By default, the size of each node and the edges between them represent the number of requests they are receiving. This can be changed by selecting the gear icon to load the Preferences dialog box and choosing an alternate metric to base sizing on such as Latency. Selecting a node will open a panel with additional metrics showing you latency, requests, and faults over time as well as providing links to your logs, traces, and a more detailed dashboard.

Screenshot of metrics shown when the node representing a DynamoDB table is selected from the ServiceLens Map.

Figure 2: DynamoDB table metrics

This panel will also surface any alerts associated with the resource. In the preceding image, you can see that two alerts for a DynamoDB table are currently in alarm.

Screenshot of active alarms shown when the node representing a DynamoDB table is selected from the ServiceLens Map.

Figure 3: DynamoDB table alerts

Returning to the service map, let’s look at one of the nodes experiencing faults and select View connections to focus on all of the incoming and outgoing requests for that specific resource. Alternatively, you can use X-Ray groups to focus on a subset of nodes in your service graph. For a further deep dive, checkout a previous blog post on groups and their use cases .

Screenshot of incoming and outgoing traffic for a single node from the ServiceLens Map. Incoming traffic from one client has a 24% error rate.

Figure 4: Incoming and outgoing requests for the PetSearch microservice

From the preceding image, you can see that there are three nodes making requests to the back-end PetSearch microservice and only two of them are experiencing issues. Selecting one of these edges brings up a panel with a Response time distribution graph summarizing the traffic along that path. If you were troubleshooting latency, you could highlight a portion of the graph showing slow response times and open the View filtered traces button to get a list of traces in the selected time window. In this case, I am more concerned with the 24% error rate from the front-end PetSite service so I select the checkbox next to 24% Faults (5xx) and open View filtered traces.

Screenshot of response time distribution shown when an edge between two nodes on the ServiceLens Map is selected.

Figure 5: Response time distribution

This loads a new page where you can further refine the query if needed. Selecting the first trace from the table, you are brought to the Trace details page.

Screenshot of details for a single trace showing the segment timeline and the stack trace associated to the exception raised in one of the subsegments.

Figure 6: Trace details

With the Segment Timeline, you get a clear picture of what happened across the distributed system and can clearly see the status of each response as well as how long the request took to answer. Selecting a segment from the timeline loads a panel with additional details, allowing you to see on the Exceptions tab that the root cause of the failure is a permission issue leading to an authorization error. This page also displays a correlated list of log messages for all of the services associated with the trace, allowing you to see logs from different log groups in a single place.

Integrating with Amazon Web Services X-Ray

ServiceLens builds its ServiceMap using traces from X-Ray to understand dependencies between services. While some interactions you want to capture will require instrumenting your workloads , many Amazon Web Services services support X-Ray integration out of the box and only require you to opt-in. For a full list, please see integrating Amazon Web Services X-Ray with other Amazon Web Services services.

To enable X-Ray for your Amazon Web Services Lambda functions

  1. Open the Amazon Web Services Lambda console
  2. Select your function function from the list
  3. Choose the Configuration tab and then choose Monitoring and operations tools
  4. Choose Edit and then toggle Active tracing under Amazon Web Services X-Ray
Screenshot showing which monitoring tools are enabled for a Lambda function.

Figure 7: Amazon Web Services Lambda configuration tools

Screenshot of toggle used to enable AWS X-Ray with a single click for a Lambda function.

Figure 8: Enabling Amazon Web Services X-Ray

While on the page to enable active tracing for your Lambda functions, you’ll also see an option to enable CloudWatch Lambda Insights under the Enhanced monitoring heading. This feature is used to collect, aggregate, and summarize system-level metrics; for further information, see the previous blog article detailing how to monitor Amazon Web Services Lambda errors .

Conclusion

In this article, I reviewed some of the ways you can use CloudWatch ServiceLens and Amazon Web Services X-Ray to monitor and observe your distributed workloads, focusing on the integrations between services. Once you’ve been alerted to an issue, ServiceLens can help you drill down into the relevant traces so that you can diagnose the failure and start working on a plan to return to normal business operations.

Interested in learning more about monitoring and observability? Review the Amazon Web Services observability best practices and guide for monitoring your Lambda workloads using Amazon Web Services native tooling . For a guided hands-on experience using Amazon Web Services native or managed open-source tools, check out the One Observability Workshop .

About the author

Erich Wolz author photo

Erich Wolz

Erich Wolz is a Senior Technical Account Manager based in Denver, Colorado, with a background in network engineering and Python development.

Jesse Sullivan author photo

Jesse Sullivan

Jesse Sullivan is a Senior Technical Account Manager based in Clarksville, Tennessee, who specializes in cloud native monitoring and observability.