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.
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This benchmark tests the write and read performance of the Hot Restart Store, introduced in Hazelcast® Enterprise HD 3.6. All benchmarks test the performance of one Hazelcast member running on a physical server. As the Hot Restart Store is local to each member, performance is linearly scalable.
The Hazelcast Hot Restart Store is novel in that it is only ever written to when the cluster is operational. It is never read from. It is only read during start up where we read the entire store into memory before enabling operations. All operational reads are therefore always to RAM.
There are two questions on performance that are important: write performance and read performance.
All tests were performed on HP ProLiant servers running RHEL 7
Two common types of media were tested: 10k HDD and Solid State Drives.
Tests were performed using Hazelcast Simulator: