Jepsen then uses the Hazelcast client library to connect to various nodes, and perform operations against Hazelcast datatypes. We apply majority quorum constraints to every datatype that supports them. Cluster membership is fixed at startup, and all nodes are connected via direct TCP connections to every other node. We tune the Hazelcast configuration to speed up test times, making heartbeats more frequent and timeouts shorter. We write a trivial server application which starts a Hazelcast instance on each of five nodes. We use the Jepsen distributed systems testing library to evaluate Hazelcast’s safety. In this analysis, we experimentally confirm this hypothesis, by testing the behavior of several Hazelcast datatypes when the network is allowed to fail. That implies that operations on most Hazelcast datatype may be lost when the network is unreliable. For this reason, there will be a time window between the network partitioning and the application of Split-Brain Protection. It is normally seconds or tens of seconds before the cluster is adjusted to exclude unreachable members….
Hazel cast update#
This implies that we should be able to update Maps, Transactional Maps, Caches, Locks, and Queues safely, so long as we choose a majority quorum policy for those data structures. The key point is that rather than applications continuing in error with stale data, they are prevented from doing so.
Any application instances connected to the cluster with sizes below the defined quorum will be receiving exceptions which, depending on the programming and monitoring setup, should generate alerts. Your application continues its operations on the remaining operating cluster. If the cluster size is below the defined quorum, the operations are rejected and the rejected operations return a QuorumException to their callers. 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.
Hazel cast full#
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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