Hazelcast Jet is an open-source distributed data processing engine that provides fault-tolerant processing of big data and stream processing workloads. Fault tolerance is a crucial aspect in distributed systems to ensure that your application keeps running even when failures occur. In this blog post, we will explore how to leverage Hazelcast Jet’s fault tolerance features in Java applications.
Why is Fault Tolerance Important?
In distributed systems, failures can occur at any time due to various reasons, such as network issues, hardware failures, or software bugs. To ensure the continuity of data processing and prevent data loss, it is crucial to have mechanisms in place to handle failures gracefully.
Understanding Hazelcast Jet’s Fault Tolerance Mechanisms
Hazelcast Jet offers several features to handle faults and ensure data processing continues uninterrupted:
Job Restartability
Hazelcast Jet allows you to restart failed jobs automatically. When a job fails due to a node failure, network issue, or any other reason, the JobTracker component automatically restarts the job on another available node. This ensures that the processing continues seamlessly without manual intervention.
Exactly-Once Processing
Hazelcast Jet provides exactly-once processing semantics, which guarantees that every event is processed exactly once, even in the presence of failures. This is achieved using a combination of write-ahead logs and a coordinated checkpoint mechanism. By enabling exactly-once processing, you can ensure the integrity of your data and avoid duplicate processing.
Snapshotting and State Recovery
Hazelcast Jet supports periodic snapshotting of the job’s state, which captures the in-memory intermediate results of the processing. In the event of a failure, the job can recover its state from the latest snapshot and resume processing from where it left off. This feature ensures that data is not lost and the job can continue from a consistent state.
Configuring Fault Tolerance in Hazelcast Jet
To leverage fault tolerance features in Hazelcast Jet, you need to configure the job and enable the desired fault tolerance mechanism. Below is an example of configuring fault tolerance for a Jet job:
Pipeline pipeline = Pipeline.create();
// Add processing stages to the pipeline
JobConfig config = new JobConfig();
config.setProcessingGuarantee(ProcessingGuarantee.EXACTLY_ONCE);
Job job = jet.newJob(pipeline, config);
In the above example, we create a pipeline that represents the data processing stages of our job. We then create a JobConfig object and set the ProcessingGuarantee to EXACTLY_ONCE, enabling the exactly-once processing semantics. Finally, we submit the job using jet.newJob().
Conclusion
Ensuring fault tolerance is vital for Java applications that handle big data and stream processing workloads. Hazelcast Jet provides robust fault tolerance mechanisms such as job restartability, exactly-once processing, and snapshotting. These features enable you to build highly resilient applications and handle failures gracefully. By configuring the fault tolerance settings in Hazelcast Jet, you can ensure the continuity of your data processing even in the face of failures.