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.
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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 […]
Hazelcast Ringbuffer is a new data-structure added to Hazelcast 3.5 that in some cases can be a more practical alternative to queues. Think of Ringbuffer as a circular array with fixed capacity. Just as with an array, each item in a Ringbuffer is uniquely identified with a sequence id (a long). Ringbuffer is an append […]
Performance, especially predictable performance, becomes a higher priority with every Hazelcast release. One of the performance issues in releases prior to 3.5 is that between benchmarks – every benchmark gets a fresh cluster – there often is a large performance variation, even though the performance during a benchmark is pretty stable. e.g. If we have […]
Internally the Performance/QA team is using a tool called the Simulator to simulate load on a Hazelcast cluster and see how it behaves when I apply this load for hours or even for day. This tool helps us to detect performance and stability problems early. The simulator can be used on a predefined set of […]
In Hazelcast 3.2 client/member performance is not the same as member/member performance. For example, if we get String values from an IMap using 4 machines so that each machine has 1 client and 1 cluster-member. And we use 40 threads per client, 8 char keys and 100 char values, then the clients do roughly 190k […]
This is the second of five blogposts how to improve performance in Hazelcast applications. When accessing data in Hazelcast, e.g. a distributed IMap, the data needs to be serialized to be transferred over the line. To deal with serialization, Hazelcast supports various serialization options. The most basic serialization option is the java.io.Serializable. Unfortunately java.io.Serializable is […]
This is the first in a series of 5 blogposts about how to speed up Hazelcast applications. Some modifications are a bit more work and some are minor. Each of the blogpost will be backed up by a JMH benchmark to verify the performance improvement. All the benchmarks for this blog-series can be found in […]
One of the new features in Hazelcast 3.2, which is going to be released in January, is the IAtomicReference. The IAtomicReference is the distributed version of the Java AtomicReference and can be created like this: This will create/load the IAtomicReference with the name ‘foo’. We already had the IAtomicLong, but the IAtomicReference is more versatile […]
A week ago I was called in to help a large online webshop with a problem. They are using Hazelcast as a large cache, since with Hazelcast the data can be distributed over multiple machines and with a database this is a lot more complicated. The problem was that they could not keep enough products […]
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