Category: Hazelcast

Feed: Blog – Hazelcast. Author: Ensar Basri Kahveci. The CP Subsystem of Hazelcast IDMG 3.12 offers a new linearizable implementation of Hazelcast’s concurrency APIs on top of the Raft consensus algorithm. These implementations live alongside AP data structures in the same Hazelcast IMDG cluster (new BFFs, yay!). You can store large data sets on hundreds of Hazelcast members and coordinate your operations using the new safe concurrency primitives that reside in the very same cluster. This design poses a significant challenge. Hazelcast IMDG is known for its dynamic and elastic clustering capabilities. You can separately configure backups for your data ... Read More

Feed: Blog – Hazelcast. Author: David Brimley. The past year has seen in-memory data grids (IMDG) continue to gain traction with the development community and large organisations alike. As you’ll see in the 2019 IMDG LinkedIn Survey results below, adoption of IMDG as a skill in LinkedIn profiles has risen by 43% YoY. Companies are turning to IMDGs as replacements for RDBMS and NoSQL solutions that struggle to perform at scale. The trend sees IMDGs used as a cornerstone for projects related to Digital Transformation strategies. A key driver for adoption is the ease in which IMDGs drop into new deployment ... Read More

Feed: Blog – Hazelcast. Author: Dan Ortega. Knock-knock Your business is already under assault, whether you know it or not. It doesn’t matter whether you’re B2B or B2C; the same technology variables are driving pressure on your IT infrastructure. The growth of the global technology ecosystem has always been organic. While new technologies often layer on top of previous enablers (e.g. mobility over the top of the internet, social media on top of both), the introduction of something new is rarely coordinated or systematic, which means sectors are always trying to catch up with or leap-frog each other. The bigger ... Read More

Feed: Blog – Hazelcast. Author: Dan Ortega. The data we continuously generate and use operates on an incredibly vast scale (think of Google, Amazon, Facebook, that level of data). Because of the breadth and depth of infrastructure required to stream incoming data and execute against it (and to avoid single points of failure), the ingestion and processing of data is distributed across systems in nodes, which group together to form clusters. The clusters can form and reform (or partition) the nodes that comprise them, based on service demand, network latency, system availability, etc. This forming/reforming normally occurs automatically, and the ... Read More

Feed: Blog – Hazelcast. Author: Peter Veentjer. Hazelcast IMDG 3.12 contains a new performance optimization called pipelining. If you have a client/server with a 1ms round trip time, then a single thread will only be able to make 1/001=1000 operations per second. The most obvious way to improve performance is to add a second thread because with two threads you can do 2*(2/001)=2000 operations/second. But it isn’t always possible or convenient to introduce multiple threads and sometimes even multiple threads will not be able to reach optimal throughput. The critical part for increased performance is that you have multiple inflight ... Read More

Feed: Blog – Hazelcast. Author: Kelly Herrell. As your organization moves into the era of in-memory computing to leverage its powerful data capabilities of speed at scale, recognize that the new breed of vendors has a lot to live up to in terms of expectations. You’re selecting from a critically-important, yet a relatively young set of new tech providers. The Balancing Act The general rule that most young enterprise software companies follow is to focus on the exciting new capabilities that are attractive to customers for initial evaluation while promising to smooth the “rough edges” of the product over time ... Read More

Feed: Blog – Hazelcast. Author: David Brimley. What’s New in Hazelcast IMDG 3.12 The Hazelcast IMDG® 3.12 release provides major new enhancements for Open Source users, it’s no exaggeration to say this is the biggest release we’ve ever made to the Open Source community. Hazelcast IMDG® 3.12 now provides a new level of consistency with the inclusion of our CP Subsystem based on the RAFT protocol.  There is […] Voxxed Days Zurich Join the Hazelcast® team at Voxxed Days Zurich! Voxxed Days Zürich returns to Sihlcity Cinema on March 19th, 2019. The entire developer community can meet and hear from ... Read More

Feed: Blog – Hazelcast. Author: Ensar Basri Kahveci. TL;DR: We put Java locks on steroids! Hazelcast is now the first and only In-Memory Data Grid (IMDG) to offer a linearizable and distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. Sounds interesting? Just keep calm and carry on reading… Hazelcast IMDG has been offering a set of concurrency APIs for a long time. We have IAtomicLong. IAtomicReference, ILock, ISemaphore, ICountDownLatch APIs, which are basically distributed versions of Java’s fundamental concurrency APIs. Although Hazelcast is mostly used for storing large amounts of data in memory, these concurrency ... Read More

Feed: Hazelcast Blog. Author: Jaromir Hamala. In the previous post we have seen how LockSupport.parkNanos() is implemented on Linux, what behavior to expect and how we can tune it. The post was well-received and a few people asked how parkNanos() behaves on Windows. I’ve used Linux as a daily driver for over a decade and I didn’t feel like exploring Windows. It’s a closed source operating system and that makes it hard to explore it. Moreover, my knowledge of Windows API is virtually non-existent and I felt the learning curve would be too steep to justify the time investment. However, ... Read More

Feed: Hazelcast Blog. Author: Rafal Leszko. Hazelcast IMDG is a perfect fit for your (micro)services running on Kubernetes since it can be used in the embedded mode and therefore scale in and out together with your service replicas. This blog post presents a step-by-step description of how to embed Hazelcast into a Spring Boot application and deploy it in the Kubernetes cluster. The source code for this example can be found here. Step 1: Configure Hazelcast to work on Kubernetes Add the following Maven dependencies: com.hazelcast hazelcast 3.11 com.hazelcast hazelcast-kubernetes 1.3.1 Then, configure the Kubernetes Discovery Strategy. You can do ... Read More