Jvm monitoring tools open source

Apache Kafka to other systems for further analysis:. Our JVM Profiler supports a variety of use cases, most notably making it possible to instrument arbitrary Java code. Using a simple configuration change, the JVM Profiler can attach to each executor in a Spark application and collect Java method runtime metrics. Below, we touch on some of these use cases:. To make implementation as seamless as possible, JVM Profiler has a very simple and extensible design.

People can easily add additional profiler implementations to collect more metrics and also deploy their own custom reporters for sending metrics to different systems for data analysis. It consists of three main parts:. Users could implement their own reporters and specify them with -javaagent option, like:.

The we build the project by running the following command:. Next, we call the build result JAR file e. The command runs the sample Java application and reports its performance and resource usage metrics to the output console. For example:. The profiler can also send metrics to a Kafka topic via a command like the following:.

Then we use the spark-submit command line to launch the Spark application with the profiler:. At Uber, we send our metrics to Kafka topics and program background data pipelines to automatically ingest them to Hive tables. Users can set up similar pipelines and use SQL to query profiler metrics.

Below is an example of a Hive table schema:. And here is a Presto SQL example, which could be used to query these metrics:. It provides charts to visualize response time breakdown and response time percentiles, and its responsive UI will allow you to monitor your application from your mobile devices as well as your desktop.

Kamon is a reactive-friendly toolkit that is built for applications that run on top of the JVM. The tracing modules will allow recording data about functionality executed in your application, and the metrics module will allow you to control the registration of entities being tracked either by user code or by instrumentation provided with other Kamon modules. It also has other abilities such as filtering, configuring instrument factories and dispatching metrics subscriptions.

The only problem is that once you get that haystack in which the problem was found, you have to start digging around looking for the actual needle that caused it.

OverOps will not only give you the answers to where and when, it will also show you why the error happened — giving you the complete source code and variable state that caused an error, across the entire call stack.

Check it out. These are some good alternatives to the paid tools in the APM space. BUT… Some might think that going for the open source option is mostly a way to save a few bucks. Open source tools come with a price: installation, troubleshooting and of course maintenance, which will all be done in house, by your very own engineers or even you. Our 2 cents is that open source can be great, but you should also keep the other costs in mind and only then reach a decision.

Any other open source APM tools you think we should check out? Tell us about them in the comments below! Henn is a marketing manager at OverOps covering topics related to Java, Scala and everything in between.

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Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again. Next Article. It can be used to monitor a single webapp, but you can also start a centralized collect server to monitor several webapps in multiple servers, separately or aggregated. See the centralized collect server in Advanced user guide.

I can suggest another approach I've used and it seemed beneficial. I've also written a custom web app that does the same without the nagios part. Stack Overflow for Teams — Collaborate and share knowledge with a private group.

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