One of the biggest KPIs in the DevOps space is monitoring. There are so many tools to help any organization to complete their monitoring picture, but no tool does everything and most organizations use many tools to help complete their monitoring solution. Mashing tools together often creates a problem of its own — the tool sprawl problem.

LogicMonitor is an agentless monitoring solution. What we really mean by “agentless” is that we don’t require an agent on every monitored server (physical or virtual). One LogicMonitor Collector - a lightweight application that takes just seconds to install - can monitor hundreds or even thousands of devices, including servers, virtual machines, network switches, storage systems, cloud resources, containers, and more.

Google has effectively made the discipline of site reliability engineering (SRE) a DevOps best practice by publishing two decades’ worth of lessons in keeping alive the most scalable apps on the planet. As more organizations make the shift (or “transformation,” as it were) to becoming IT organizations, the demand for reliability increases substantially for customer-facing services.

Apache ActiveMQ is message-oriented middleware (MOM), a category of software that sends messages between applications. Using standards-based, asynchronous communication, ActiveMQ allows loose coupling of the elements in an IT environment, which is often foundational to enterprise messaging and distributed applications.

In Part 1 of this series, we looked at how ActiveMQ works, and the key metrics you can monitor to ensure proper performance of your messaging infrastructure. In this post, we’ll show you some of the tools that you can use to collect ActiveMQ metrics. This includes tools that ship with ActiveMQ, and some other tools that make use of Java Management Extensions (JMX) to monitor ActiveMQ brokers and destinations.

As you operate and scale ActiveMQ, comprehensive monitoring will enable you to rapidly identify any bottlenecks and maintain the flow of data through your applications. Earlier in this series, we introduced some key ActiveMQ metrics to watch, and looked at some tools you can use to monitor ActiveMQ.

AWS Lambda can use stream based services as invocation sources, essentially making your Lambda function a consumer of those streams. Stream sources include Kinesis Streams and DynamoDB streams. When you allow streams to invoke your Lambda function, Lambda will emit a CloudWatch metric called IteratorAge. In this post, we discuss what this metric is and how to fix it if it’s too high.