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.
In Part 2 of this series, we showed you how to collect key Tomcat performance metrics and logs with open source tools. These tools are useful for quickly viewing health and performance data from Tomcat, but don’t provide much context for how those metrics and logs relate to other applications or systems within your infrastructure.
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.
Before the release of Java 9 and 10, there were several limitations to deploying and monitoring Java in Docker. This post explores how the latest versions of Java address the most common of these limitations, and includes examples of how to make the most of monitoring Java in Docker.
The use of scheduled tasks is very common in large and medium-sized systems, mainly because they have several functionalities linked to the automation of tasks without human intervention. It is common to see the use of tools like crontab (Linux) for scheduling tasks, be they in Java or another language.
While the term ‘Java troubleshooting’ can apply to many, many scenarios, this post focuses on three particular long-standing Java production scenarios: a denial of service to a Java service endpoint, a memory leak, and troubleshooting a thread deadlock or race condition. Follow along as we use Java inside Docker containers to facilitate quick testing and show you how to use open source sysdig to quickly diagnose each troubleshooting scenario.
The Java programming language comes with advanced exception handling features that help programmers manage exceptional events. In Java, exceptions are either thrown by the compiler or occur during the execution of the program. Exceptions stop the normal flow of the program, so you need to handle them appropriately.
We’re enhancing the existing Sentry Java SDK with our new Java agent. The thing is — it’s still in beta, and we need your feedback. Once downloaded, the agent will enhance your application stack traces on Sentry by adding the names and values of local variables to each frame. Specifically, the agent provides much more detail about the current state of the application at the time of the error than you would usually have.
