Posts tagged Clustering
Note: This blog post is an update to Creating a Simple Cluster, which was written for Payara Server 4.
Continuing our introductory blog series, this blog will demonstrate how to set up a simple Hazelcast deployment group containing two instances. Deployment groups were introduced with Payara 5 to replace clusters. They provide a looser way of managing servers, allowing instances to cluster by sharing the same configuration whilst providing a single deployment target for all of them. See here to read more about Deployment Groups.
You probably already know what clustering is, but you might not know that Payara Server 5 automatically clusters. If you use Payara Server 4, you have to manually set up clustering. Payara Server 5 introduces a convenient feature called Deployment Groups. Deployment groups do exactly as the name suggests: it allows you to group a collection of instances that will be the targets of your deployment.
When it comes to clustering and distributed computing performance, some of the challenges you have to overcome involve cache invalidation and coordination. Fortunately, both Payara Server and Payara Micro come with EclipseLink, which supports cache coordination and invalidation out of the box. This blog will explain how to configure this feature for your Payara Data Grid. We would also like to thank Sven Diedrichsen who is the community member that created the Hazelcast cache coordination.
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.
In Payara Server 5 we will be introducing some major changes to the way clustering is working by creating the Domain Data Grid (see documentation for more info). The Domain Data Grid will be easier to use, more scalable, more flexible and ideally suited for cloud environments and cloud-native architectures. All Payara Server instances will join a single domain-wide data grid for sharing of in-memory data like web sessions, JCache, SSO and Stateful EJBs.
Payara Micro supports Hazelcast out of the box, and can be used for clustering. This allows members in the cluster to distribute data between themselves, amongst other things. By default, Hazelcast comes with multiple ways to discover other members in the same network. A multicast discovery strategy is commonly used for this purpose; a multicast request is sent to all members in a network and the members respond with their IP addresses. Another strategy must be employed if a member cannot or does not wish to provide their IP address.
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.