Hazelcast Jet is a distributed stream processing engine that enables developers to build fast and fault-tolerant data processing applications. In this blog post, we will explore how to handle failure scenarios in Hazelcast Jet within Java applications.

Fault Tolerance in Hazelcast Jet

Hazelcast Jet provides robust fault tolerance mechanisms to ensure that your data processing pipeline can handle failures without compromising the integrity of your data. By default, Jet automatically recovers from failures and continues processing the data.

Retry Mechanism

When a failure occurs in Hazelcast Jet, such as a node crash or an exception in processing, the job is automatically restarted by the Hazelcast Jet cluster. It attempts to resume processing from the point of failure. This retry mechanism ensures that your data pipeline continues even in the face of failures.

To configure the retry mechanism in Hazelcast Jet, you can use the withRestartStrategy method when submitting your job. It allows you to define the behavior of the job in case of failures. For example, you can specify a maximum number of retries or a delay between retries.

JobConfig jobConfig = new JobConfig();
jobConfig.withRestartStrategy(RestartStrategies.fixedDelayRestart(
    MAX_RESTARTS, 
    INITIAL_DELAY_MILLIS));

jet.newJob(pipeline, jobConfig).join();

In the above example, MAX_RESTARTS is the maximum number of restart attempts, and INITIAL_DELAY_MILLIS is the delay between retries. You can customize these values based on your application requirements.

State Snapshotting

To ensure fault tolerance, Hazelcast Jet takes regular snapshots of the job’s state and stores them in a distributed manner. If a failure occurs, Jet can restore the state from the latest snapshot and continue processing from where it left off.

The state snapshotting mechanism in Hazelcast Jet is transparent and automatic. However, you can also manually trigger a snapshot by using the SnapshotTrigger API. For example, you can trigger a snapshot based on a specific processing condition or at regular intervals.

SnapshotConfig snapshotConfig = SnapshotConfig.builder()
    .setEnabled(true)
    .setIntervalMillis(SNAPSHOT_INTERVAL_MILLIS)
    .setCoalescing(true)
    .build();

jet.newJob(pipeline, jobConfig)
   .enableSnapshotting(snapshotConfig)
   .join();

In the above example, SNAPSHOT_INTERVAL_MILLIS is the interval at which snapshots are taken. You can customize this interval based on your application’s requirements.

Conclusion

Hazelcast Jet provides powerful fault tolerance mechanisms to handle failure scenarios in Java applications. The built-in retry mechanism and state snapshotting ensure that your data processing pipeline can handle failures gracefully and continue processing with minimal disruptions.

With the ability to configure restart strategies and trigger manual snapshots, developers have fine-grained control over fault tolerance in Hazelcast Jet.

#hazelcast #faulttolerance