In the first blog of this series, we configured our Apache Web Server. Our next step will be to set up request forwarding to send traffic to Payara Server. If you need guidance on installing Payara Server on Ubuntu, we already have a blog post covering the installation of a JDK and Payara Server which we would recommend you read before continuing with this blog.

 

In this blog series, we will aim to give an overview of the basics of using Payara Server in a production scenario using Apache Web Server (sometimes called httpd) and Ubuntu 16.04. Many of the concepts described in these blogs do not rely on the tools we are using here and can be applied to other scenarios.

En esta serie de blogs, intentaremos dar una visión general de los fundamentos de utilizar Payara Server en un entorno de producción utilizando Apache Web Server ( comúnmente conocido como httpd) y Ubuntu 16.04. Muchos de los conceptos descritos en estos blogs no se basan en las herramientas que estamos usando aquí y se pueden aplicar a otros escenarios.

See here for the original version in English language. 

As a follow up to our 'Getting Started with Payara Micro Demo', we have another introductory video for you, this time demonstrating the Payara Micro API, using NetBeans and Maven.

 

 

In this short introductory demo we will show you how to start Payara Micro and demonstrate how simple it is to use its dynamic clustering feature.

 

 

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!

 

In this quick blog I will explain how you can install Zulu JVM and run Payara Server on it. I will be using Ubuntu 14.04 LTS but it shouldn’t require many modifications in case you prefer using other Linux-based platforms.

 

En este pequeño blog voy a explicar como puedes instalar la JVM (Java Virtual Machine) Zulu y ejecutar Payara Server en ella. Para esta demostración voy a utilizar Ubuntu 14.04 LTS, pero en caso de que prefieras usar otras plataformas basadas en Linux, no se requieren muchas modificaciones.