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At Hazelcast we take reliability very seriously. With the new CP Subsystem module, Hazelcast has become the first and only IMDG that offers a linearizable distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. In addition to well-grounded designs and proven algorithms, reliability also requires a substantial amount of testing. We […]
“Distributed locks aren’t real”, some like to remind us. “Anyone who’s trying to sell you a distributed lock is selling you sawdust and lies.” This may sound rather bleak, but it doesn’t say that locking itself is impossible in a distributed system: it’s just that all of the system’s components must participate in the protocol. […]
The CP Subsystem of Hazelcast IDMG 3.12 offers a new linearizable implementation of Hazelcast’s concurrency APIs on top of the Raft consensus algorithm. These implementations live alongside AP data structures in the same Hazelcast IMDG cluster (new BFFs, yay!). You can store large data sets on hundreds of Hazelcast members and coordinate your operations using […]
We put Java locks on steroids! Hazelcast is now the first and only In-Memory Data Grid (IMDG) to offer a linearizable and distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. Sounds interesting? Just keep calm and carry on reading… Hazelcast IMDG has been offering a set of concurrency APIs for […]
In Hazelcast Jet 0.5, we introduced fault tolerance for streaming computations. Hazelcast Jet periodically takes snapshots of the state of a running job and stores these snapshots in Hazelcast IMaps. In case of a failure, the job is restarted from the last successful snapshot. Hazelcast Jet 0.6 uses the same snapshotting mechanism to enable dynamic […]
Stream processing is a paradigm for on-the-fly processing of unbounded data feeds. We have been witnessing that stream processing engines (SPEs) get more attention every day in the era of fast data and become a fundamental component of data processing pipelines. They usually run in distributed settings to be able to cope with the flood […]
Stream processing is an emerging computational paradigm for on-the-fly processing of live data feeds, targeting low latency and high throughput. Streaming applications are usually deployed on multiple servers to achieve these requirements. Since even a single failure may lead to incorrect results or long interruptions in result delivery, fault tolerance is of paramount importance in […]