This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Neil is a solution architect for Hazelcast®, is the industry leading in-memory computing platform. In more than 30 years of work in IT, Neil has designed, developed and debugged a number of software systems for companies large and small.
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This example shows some of the newer features of querying, a way that joins can be achieved, and shows the pros and cons of partition aware routing. Although mainly an Hazelcast IMDG example, Hazelcast Jet puts in a guest appearance to implement the join. Background – Partitioning The most commonly used data structure in Hazelcast […]
Hazelcast provides distributed queues, an implementation of java.util.concurrent.BlockingQueue. However, an implementation of java.util.concurrent.PriorityBlockingQueue is not yet provided. In this example, we’ll see how to write this yourself, using Hazelcast’s SPI (Service Provider Interface). (Note: this is just an example, deliberately simplified. Attention is drawn to some extra steps to make this production quality.) Recap, “SPI“ […]
An example demonstrating how a near-cache configuration option can be added to an existing application to improve performance. Performance increases, no coding is required. But it’s not a universally applicable solution, there are downsides to be aware of. What is a “near-cache“? Hazelcast provides a number of distributed storage containers, for storing data in the […]
Why? The normal deployment is for a JVM to contain a single Hazelcast instance, a client or a server. This means that the instance can utilise all the resources available to that JVM. In automated tests, it can frequently be useful to run multiple Hazelcast instances in the one JVM so that it is easy […]
A step-by-step example of how to introduce Hazelcast into an existing database backed application. The example here takes a Spring JPA example and augments this with Spring Data Hazelcast for added speed and resilience, without discarding what is already there. For a developer these are baby steps, but for an architect it’s giant leaps. Hola […]
In an earlier blog post, Caching Made Bootiful: The Hazelcast Way, Hazelcast’s Viktor Gamov demonstrated the ease of doing caching with Hazelcast in Spring. In this post, we’ll continue the theme to show how trivial session clustering is to implement from a coding perspective but also how this can radically change the application architecture for […]
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