Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
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.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
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The in-memory speed you count on, with the convenience and scalability of cloud.
This is a comparison between a Redis 3.2.8 cluster and a Hazelcast IMDG® 3.8 cluster.
Read our previous benchmark here >>
Hazelcast IMDG was up to 56% faster than Redis.
Note that near cache was disabled for Hazelcast®.
As you can see from our previous benchmark, enabling near cache makes us 5 times faster.
Hazelcast IMDG was up to 44% faster on puts.
We think Hazelcast IMDG is faster because of the following design differences:
This is the second performance test we have done where Hazelcast IMDG beats Redis. See our earlier Redis 3.0.7 vs Hazelcast IMDG 3.6 Benchmark. We have extended our performance lead over Redis with Hazelcast IMDG 3.8.
3 physical boxes dedicated to cluster members, 5 physical boxes for clients.
Hazelcast IMDG uses a map configured with HD in memory format and async backups, by default read from backups is disabled.
Redis master Slave replication is async, by default Redis allows read from backups.
<native-memory allocator-type="POOLED" enabled="true">
<size unit="GIGABYTES" value="100" />