Apache Tomcat is a server for Java-based web applications, developed by the Apache Software Foundation. The Tomcat project’s source was originally created by Sun Microsystems and donated to the foundation in 1999. Tomcat is one of the more popular server implementations for Java web applications and runs in a Java Virtual Machine (JVM).
In Part 1 of this series, we discussed some key Tomcat and JVM metrics that are exposed through Java Management Extensions (JMX). Now that you are familiar with metrics necessary for monitoring Tomcat, we can look at how to collect and query that data.
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.
To build applications in AWS Lambda, you often need to use third party libraries and packages in your function code. Previously, these packages had to be included in a function’s deployment package. Today, Amazon Web Services released a new feature called Layers to simplify this process for Lambda developers. Layers allow you to deploy common components that you can reuse across functions, such as machine learning models, SDKs, or instrumentation libraries.
Envoy proxies communication among microservices. It is a key component in many service-oriented architectures—and one that offers a unique opportunity to gain visibility into your service mesh. We’re pleased to announce that Datadog integrates with Envoy as well as AWS App Mesh, a new hosted service based on Envoy that dynamically configures your service mesh proxies.
To make serverless architectures more observable, we’re excited to introduce the new Cloud Functions view in Datadog. You can now search, filter, and explore all your AWS Lambda functions in one central view, and dive straight into detailed performance data that is scoped to a single function. The Cloud Functions view brings together Lambda metrics and logs with distributed request traces from your functions, which are now available in Datadog thanks to our new integration with AWS X-Ray.
Datadog is pleased to announce the upcoming availability of Synthetics, a whole new layer of visibility on the Datadog platform. By monitoring your applications and API endpoints via simulated user requests, Synthetics helps you ensure uptime, identify regional issues, and track application performance. By unifying Synthetics with your metrics, traces, and logs, Datadog allows you to observe how all your systems are performing as experienced by your users.
Each November, AWS re:Invent draws thousands of AWS staff, partners, and users to Las Vegas for an intense week featuring all things AWS and AWS-related. As always, Datadog will be there and we’d love to meet you in person. Our engineers are excited to show off the new features they’ve been building and to answer your monitoring questions!
