This is achieved by defining and configuring a split-brain protection cluster quorum. Hazelcast’s Split-Brain Protection enables you to specify the minimum cluster size required for operations to occur. For all datatypes other than Maps, updates to the smaller cluster (or an arbitrary cluster if the split is even) are thrown away. As we have seen in prior Jepsen analyses, all of these techniques can result in the loss of committed updates. The built-in policies are a pair of non-commutative heuristics (larger or smaller cluster wins), last-write-wins, or higher-hits-wins. The documentation goes on to discuss split-brain merging: for Maps (and only Maps a footnote explains that other datatypes are not merged), conflicting values for the same key are merged using a configurable merge policy. The documentation does not mention that this problem also arises without a backing database-users could be forgiven for assuming that they won’t experience lost updates if they use Hazelcast as a purely in-memory store. They go on to discuss a lost-update scenario when the MapStore persistence layer is enabled for Maps: both clusters could write conflicting entries to their backing database. In section 26 of the manual, Network Partitioning - Split-Brain Syndrome, Hazelcast explains that in the event of a network partition, each component of the network continues to run independently. What the documentation for this datatype does not mention is that compare-and-set on AtomicReferences is not, in point of fact, atomic. For instance, the features page claims that Hazelcast’s AtomicReferences offers guaranteed atomic compare-and-set across a cluster, and the AtomicReference documentation confirms this claim. Hazelcast’s high-level documentation, and the names of these objects themselves, imply that operations on these objects provide certain safety guarantees. Either way, Hazelcast offers transparent distribution for rich datatypes, with familiar Java APIs for sets, lists, maps, locks, semaphores, queues, atomic objects, id generators, and more. Users may embed a full Hazelcast node directly into their JVM application, or use a lightweight network client (available in many languages) to talk to a dedicated Hazelcast cluster. It is often deployed as a synchronization service for databases, caches, session stores, messaging buses, or for service discovery. Hazelcast provides easy-to-use distributed data structures with rich, intuitive APIs and optional persistence. The literature is somewhat unclear on this point. : Java’s AtomicReferences may only be sequential (and linearizable in certain cases, like CaS updates), rather than linearizable. : IMap is not the only datatype supporting merge: ICache is mergable as well.
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