Posts tagged Developer
Avanzando más nuestra serie de blogs de introducción, esta entrada mostrará como puedes escalar de forma dinámica tu cluster, y como Payara Server maneja la conmutación por fallas entre miembros del cluster.
La conmutación por fallas es la habilidad de continuar proporcionando acceso a nuestro sitio web o aplicación en el caso de que un servidor falle. Es una parte importante de un servicio que goza de alta disponibilidad, cuyo objetivo es minimizar los tiempos de inactividad a lo largo de tu infraestructura de servicios.
Taking our introductory series onwards, this blog will look at how you set up a simple Payara Server cluster on Windows using the native remote control protocol, DCOM. We will set up two instances on Windows 10, controlled by a third Domain Administration Server (DAS) instance on Windows 7 via DCOM, and cluster them together using Hazelcast. Finally, we will deploy our trusty clusterjsp application to demonstrate how the data is being shared across our instances.
Further developing our introductory blog series, this post will look at how you can dynamically scale your cluster, and how Payara Server handles failover between cluster members.
Failover is the ability to continue to provide access to your website or application in the event of a server failing. It is an important part of high availability hosting, which aims to minimise downtime across your server infrastructure.
By clustering our Payara Servers together and balancing traffic between them with Apache Web Server we keep the benefits of having our application accessible from a single URL and gain the resilience and expansion prospects from having our application deployed across multiple instances.
Continuing our introductory blog series, this blog will demonstrate how to add load balancing capability to Apache Web Server and forward to our simple Payara Server cluster.
A load balancer can redirect requests to multiple instances, primarily for the purpose of distributing incoming requests between cluster members based on pre-determined rules. This could be a simple "round-robin" algorithm, where the workload is distributed to each instance in turn, or a weighted algorithm where requests are delivered based on a pre-determined weight for each cluster member.
Continuando con nuestra serie de blogs de introducción, este blog va a demostrar como añadir la capacidades de balanceo de carga a un Servidor Web Apache y asi re-enviar las peticiones HTTP a nuestro cluster de Payara Server.
Many applications, especially complex legacy ones that are packaged with a large number of libraries, may contain libraries that are also shipped with Payara Server (like Google Guava or Jackson for example). These types of conflicts can be very hard to track down and solve. Starting from the 171 release of Payara Server, there is now another solution in the toolbox which can help with resolving these dependency conflicts.
Hibernate is the object/relational mapping tool that handles mapping of Java classes to relational tables and Java types to SQL data type. It’s a well-known framework in the Enterprise Java eco-system since it’s being actively developed for the last 16 years.
With this article, I’m going to show the ways of using Hibernate inside a sample application – source code available here – and deploy it onto Payara Server. I will be using the latest version of Hibernate, which is 5.2.10.Final at the time of writing.
Since Java EE 6 it's possible to define data sources in a portable way.
This does mean though that the data source is embedded in the application archive. For some use cases, this is exactly what's needed, but for others it may not be ideal.