Payara Micro is packed with most of the features and APIs that come with Payara Server Full Profile even though it doesn't entirely support whole Jakarta EE Full Profile. As an example, Payara Micro supports persistent EJB Timers, which are only required by the Jakarta EE Full Profile and not by the Web Profile. In Payara Micro, it's possible to use persistent EJB Timers, which are stored across your micro instances inside the distributed data grid as long as at least one instance in the data grid is up and running.

When Payara Server has been logging monitoring data to the server log for a short while, the metrics that Logstash outputs to Elasticsearch can be visualised using Kibana. In this blog post, we will create a date histogram displaying used heap memory as a percentage of the maximum heap memory.

 Following the first part of this series of blog posts, you should now have a Payara Server installation which monitors the HeapMemoryUsage MBean and logs the used, max, init and committed values to the server.log file.  As mentioned in the introduction of the previous post, the Monitoring Service logs metrics in a way which allows for fairly hassle-free integration with tools such as Logstash and fluentd.

Often, you might find it useful to store your monitoring data in a search engine such as Elasticsearch or a time series database such as InfluxDB. One way of getting the monitoring data from your server.log into one of these datastores is to use Logstash.

This blog post covers how to get monitoring data from your server.log file and store it in Elasticsearch using Logstash.

(note: there is an updated version of this blog post available here https://blog.payara.fish/making-use-of-payara-servers-jmx-monitoring-service-part-1-setting-up-the-service)

 With the release of version 4.1.1.163, Payara Server includes a JMX Monitoring Service (technical preview) which can be used to log information from MBeans to the server log. Using the Monitoring Service, you can monitor information about the JVM runtime such as heap memory usage and threading, as well as more detailed information about the running Payara Server instance. The information is logged as a series of key-value pairs prefixed with the string PAYARA-MONITORING:, making it easy to filter the output using tools such as Logstash or fluentd. 

In this blog series we're going to show you exactly how to use the new Payara Server Monitoring Service. First, we'll take a look at setting up the service - let's get started!

As we enter the third quarter of the year, that can only mean one thing: Payara Server 163 is here! With this release, we’ve managed to cram in 44 bug fixes, 34 enhancements, 6 new features and 6 component upgrades. One of these new features is the tech preview of our new Request Tracing service, which I’ll explain in more detail below.

As part of the new features introduced with the release of Payara Server 162, you can now form a cluster of Payara Server instances using the concept of Hazelcast “Lite Nodes”. This can help you create more flexible cluster topologies.

The new Java Magazine is out now, featuring a lot of useful articles about enterprise Java - not so much Java EE as a platform, but individual services that can be useful as part of a larger solution. See below for an introduction to my article on Custom Servlet Authentication Using JASPIC, also featured in the magazine. 

Take a look at this quick demo to see some of Payara Micro's  dynamic clustering capabilities. I'm running the demo without any extra tools, just Payara Micro itself. To show how Payara Micro dynamically rebalances the cluster, I used JCache and Payara Micro's --autoBindHttp feature.