Category: Hazelcast

Feed: Blog – Hazelcast. Author: Neil Stevenson. It’s said a wise person learns from their mistakes, and a wiser person learns from the mistakes of others. In this blog, we’ll look at some common mistakes made when deploying Hazelcast, so you can avoid them and be that wiser person. In other words, what are the “anti-patterns” that don’t ensure failure, but do make problems more likely? Here we’ll look at the top 5. The good news is it’s not a top 10. The following 5 are the ones we see the most. 1. A 2-Node Cluster Hazelcast is a distributed ... Read More

Feed: Blog – Hazelcast. Author: Neil Stevenson. Uniformity and balance are key principles for data grids. All grid members should hold the same amount of data, do the same amount of compute and have the same amount of resources (CPU, etc) available. At least approximately. It doesn’t have to be exactly even, but pretty close. A hot spot in the grid means an overloaded member and a cold spot means under-utilized resources. So let’s look at the data spread. A Recap on Sharding Hazelcast uses a sharded approach for record storage. The potential range of data records is partitioned into ... Read More

Feed: Blog – Hazelcast. Author: Neil Stevenson. We can send an SQL query to Hazelcast and get results, a great feature. But did you know the data doesn’t have to be stored in Hazelcast to be queryable via Hazelcast? It could be somewhere like a Kafka topic or a CSV file, and you can still query it using Hazelcast’s SQL. This makes Hazelcast a “one-stop-shop” for all your querying needs. Example Imagine this query: SELECT * FROM xyz Where is the data stored that it is querying? An IMap? Somewhere else? The answer, if you are a user, is that ... Read More

Feed: Blog – Hazelcast. Author: Neil Stevenson. One of the features of Hazelcast Enterprise is the ability to share data between multiple Hazelcast clusters, whether geographically distant or adjacent. In this blog, we’ll look at the practicalities of keeping the data in multiple clusters synchronized. Business continuity wins There are two conflicting needs here. Data replication happens fastest the nearer the clusters are to each other. A good network helps too. Business continuity dictates (ISO22301 etc) to keep the clusters as far apart as possible. Business continuity wins every time here. Even if your data centers are currently close in ... Read More

Feed: Blog – Hazelcast. Author: Nicolas Frankel. Where would you draw the line between a data store and a cache? Persistence? Hazelcast allows you to write your in-memory data on disk. Derived data vs. source of truth? If the cost of creating the data is cheap, why would you persist them? Let’s agree that there’s no clear-cut defining property but a blurry continuum between those two concepts. The raison d’être for caches is two-fold: performance, data is available faster, and availability, data is “always” available (at least the availability is higher than the underlying store). Therefore, it stands to reason ... Read More

Feed: Blog – Hazelcast. Author: Zoltan Baranyi. The mission of the PMDK team has always been and will always be to make programming persistent memory easier for the community of software developers. One of our goals is to help simplify the integration of persistent memory into software solutions by making it transparent as possible. Adopting ground-breaking and disruptive technology creates a chasm, which is challenging to cross at first. To help with that, PMDK offers a multi-layered stack of solutions so that developers can choose a library that is the best fit for their desired level of abstraction and purpose ... Read More

Feed: Blog – Hazelcast. Author: Sergii Bishyr. The JDBC API was the first way to connect and execute queries on databases in Java and is still widely used today. To connect to a specific database, you add the relevant driver to the classpath which implements the JDBC API. On the other side, the application code only uses the SQL language: JDBC acts as a façade, shielding the code from the specifics of the database. JDBC being a standard API means that many libraries, frameworks, and tools (e.g. Spring Data JPA, sqlline, Tableau) use it for integration with a whole lot ... Read More

Feed: Blog – Hazelcast. Author: Marko Topolnik. Today we’re releasing Hazelcast Jet 4.5, the second release this year! We’re bringing Jet closer to IMDG, unifying their SQL syntax and features. Our goal is to have a single SQL dialect that seamlessly uses the features of both Jet and IMDG. This version of Jet is built on Hazelcast IMDG 4.2. Improved SQL Experience There used to be an important inconvenience when using IMap from outside Java — in order to populate it, you first had to create a Java class for its value, or at best a portable ClassDefinition, which is declarative ... Read More

Feed: Blog – Hazelcast. Author: Nicolas Frankel. Java developers are particularly spoiled when using Hazelcast. Because Hazelcast is developed in Java, it’s available as a JAR, and we can integrate it as a library in our application. Just add it to the application’s classpath, start a node, and we’re good to go. However, I believe that once you start relying on Hazelcast as a critical infrastructure component, embedding limits your options. In this post, I’d like to dive a bit deeper into the subject. As mentioned above, the easiest way for Java developers to start their journey using Hazelcast is ... Read More

Feed: Blog – Hazelcast. Author: Jiri Holusa. It’s a pleasure to announce the general availability of Hazelcast IMDG 4.2! In this blog post, we highlight the most notable features and enhancements in this release. We resolved almost 150 GitHub issues and merged roughly 350 pull requests and you can find the complete list of changes in the release notes. Don’t hesitate to download it and get started. Shoutout to Community Accepting contributions from the community is one of the many pleasures of being an open source company. Big thanks to those who contributed to IMDG 4.2. A complete list of ... Read More