Posts tagged Hazelcast
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.
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.
Both Payara Server and Payara Micro can cluster together and share data using Hazelcast. Out-of-the-box, there is no configuration needed, since Hazelcast uses multicast to discover and join other cluster members. However, when running in cloud environments like AWS, for example, there are a lot of things which can stop discovery being quite so straightforward. The key thing is that Multicast is not available, meaning another discovery strategy is needed; the most common generic alternative is to use TCP, but this assumes that you know at least the intended subnet that your cluster members will be in ahead of time.
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.
In order to make a cluster of servers appear as one server, you need to introduce a load balancer. A load balancer will accept a request, and redirect it to one of the members of the cluster depending on a given configuration. A web server such as NGINX or Apache can act as this load balancer as well as a reverse proxy, which allows the web server to load balance requests across the cluster, act as a termination point for SSL connections to reduce strain on the cluster, as well as cache server content for quicker access. In this blog, we will set up NGINX as a reverse proxy and secure it using SSL.