Posts tagged Hazelcast
Co Authored with Nicolas Frankel (Hazelcast Developer Advocate), this article is also available as a PDF.
The Hazelcast In-Memory Data Grid (IMDG) is an efficient method of storing data in a distributed way within the memory of the different processes of the cluster. Because it is distributed, searching the data locally requires 'moving' the data to your instance so it can be accessed, which is not overly efficient. Hazelcast SQL allows distributed queries which perform the search where the data is, and then transfers only the results to your process. Since the Payara products already use Hazelcast IMDG, using the Hazelcast SQL capabilities is straightforward: just add the additional JAR library to start using it.
The Domain Data Grid feature of the Payara products is powered by the Hazelcast library. It provides the necessary functionality for the Deployment Group (clustering functionality), Cache functionality, CDI cluster singleton, and monitoring data storage within Payara to name just a few features.
Since Hazelcast can cluster multiple instances into a data grid, it is possible to create a Hazelcast grid that comprises the Payara instances and some other instances included in other applications running outside of Payara.
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.