Amazon MQ is a managed ActiveMQ messaging service hosted on the AWS cloud. Amazon MQ’s brokers route messages between the nodes in a distributed application. Each broker is a managed AWS instance, so your messaging infrastructure doesn’t require the maintenance and upfront costs of a self-hosted solution.

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.

Prior to version 2.3, new versions of Kubernetes came out with point releases of Rancher and required an upgrade to Rancher before they were made available for use. Rancher 2.3 changes that pattern and now makes it possible to update the metadata store for available Kubernetes versions, disconnecting the Rancher server upgrade process from the Kubernetes cluster upgrade process.