In Part 1 of this series, we saw how Amazon MQ routes messages between services in a distributed application, and we looked at some of the key metrics that describe the performance of the message broker and its destinations. Now that we’ve introduced the metrics and their meaning, we’ll look at some tools you can use to collect and query metrics from Amazon MQ:

In Part 2 of this series, we showed you how to use CloudWatch to monitor metrics and logs from Amazon MQ. With CloudWatch, you can easily create ad-hoc graphs to visualize the performance of your messaging infrastructure and other AWS services you use (such as EC2, Lambda, and S3). But to monitor your Amazon MQ brokers, destinations, and clients alongside the rest of your applications and infrastructure, you need a monitoring platform that easily integrates with your whole technology stack.

To centralize logging from your entire stack—from traditional infrastructure to serverless components—Datadog is announcing native support for the launch of FireLens for Amazon ECS. FireLens streamlines logging by enabling you to configure a log collection and forwarding tool such as Fluent Bit directly in your Fargate tasks. We’ve partnered with AWS to provide built-in Fluent Bit support for Datadog so that you can now seamlessly route container logs from AWS Fargate.

Since Kubernetes was open sourced by Google in 2014, it has steadily grown in popularity to become nearly synonymous with Docker orchestration. Kubernetes is being widely adopted by forward-thinking organizations such as Box and GitHub for a number of reasons: its active community, rapid development, and of course its ability to schedule, automate, and manage distributed applications on dynamic container infrastructure.