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.
Hazelcast has first-class support for Kubernetes. You can scale up and scale down Hazelcast clusters without data loss. There’s also the option to automatically scale a Hazelcast cluster depending on metrics triggers. These metric triggers can be any system information from Hazelcast itself or from the Kubernetes environment.
Hazelcast members form a cluster by using the Kubernetes Discovery Plugin.
There are some excellent blog posts to get you going with Hazelcast running on Kubernetes:
Rolling Upgrade Hazelcast IMDG on Kubernetes
How to Scale Hazelcast IMDG on Kubernetes
Hazelcast On Kubernetes Made Fairly Easy
If you want to work with more advanced features such as automatic scaling of the Hazelcast cluster, read this blog post by the dev lead of the integrations team on the Hazelcast project, Mesut Celik: Hazelcast Autoscaling with Horizontal Pod Autoscaler (HPA).
All of the Kubernetes work makes use of the Hazelcast Docker images which can be found on the Docker Hub.
Hazelcast can be applied to a number of use cases for your microservices. It can act as an entire hosting platform, making use of distributed locks, semaphores, and CountDownLatches to provide high availability and coordination. Your microservices could run as clients to the cluster or in an embedded model.
You could use CRDT Counters or Flake IDs. You could also use data structures such as Maps, Sets , and Lists using a database per service pattern or a shared datastore pattern.
In this webinar from Lucas Beeler and Dale Kim, you find can learn how to use Hazelcast as a cornerstone of a next-generation microservices architecture.
Rafal Leszko talks about using Hazelcast caching, sidecars, and microservices in this interesting blog post.
Find out how to use Hazelcast with Spring Boot and microservices in this white paper.
Hazelcast clusters can be run pretty much anywhere, including in all major clouds such as AWS, Azure, and GCP. Hazelcast clusters can be formed using special discovery services. You can find all of these on the Hazelcast Hub:
The Hazelcast Reference Manual provides more information on each of these discovery mechanisms.
Finally, give Hazelcast Cloud a spin; it’s an easy way to start trying out Hazelcast features without having to create your own clusters.
Get started in the cloud.