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Jet 4.1 is Released

May 13, 2020
Jet 4.1 is Released

We are happy to present the new release of Hazelcast Jet 4.1. Here’s a quick overview of the new features.

Extended gRPC Support

We’ve applied the lessons learned from the Jet-Python integration and made it easier to integrate a Jet pipeline with gRPC services. The utility class GrpcServices introduces two new ServiceFactorys you can use with the mapUsingServiceAsync transform. Using this feature can be a significant performance boost vs. using the sync mapUsingService call.

Here’s a quick example on how you can use the gRPC service factory:

var greeterService = unaryService(
    () -> ManagedChannelBuilder.forAddress("localhost", 5000).usePlaintext(),
    channel -> GreeterGrpc.newStub(channel)::sayHello
);

Pipeline p = Pipeline.create();
p.readFrom(TestSources.items("one", "two", "three", "four"))
 .mapUsingServiceAsync(greeterService, (service, input) -> {
    HelloRequest request = HelloRequest.newBuilder().setName(input).build();
    return service.call(request);
}).writeTo(Sinks.logger());

In addition to the unary gRPC service, we support bidirectional streaming as well as request batching. For a more in-depth look, see the Call a gRPC Service how-to guide and the design document.

Transactional JDBC and JMS sinks

In Jet 4.0 we added support for transactional sources and sinks through the use of two-phase commit. We’re now extending this support for two additional sinks: JDBC and JMS. The support requires the broker or the database to support XA transactions. To test your database’s support for XA transactions, we’ve also released a how-to guide.

You can also see a full summary of sinks and sources and the variety of transaction support on the sources and sinks page.

Code Deployment Improvements

When you’re deploying a Jet job programmatically (not using the jet submit command-line tool), you must add every class the job needs to the job’s configuration. So far, Jet has supported adding classes one by one with JobConfig.addClass() and that wouldn’t add any of the class’s nested classes. This was especially problematic for anonymous classes, which you can’t even refer to from Java code. In 4.1 we improved addClass() so that it adds all the nested classes and we added JobConfig.addPackage() so you can add the whole package in a one-liner, and don’t have to manually maintain the list of classes as you develop your pipeline. Take a look at the design document for more details.

Job-Scoped Serializer Support

So far Jet has had a pain point in terms of serialization. The objects that travel through the pipeline must sometimes be sent from one cluster member to the other, so they must be serialized. You can let the object implement Serializable, but that’s inefficient. If you wanted to use a better serialization scheme, you had to register a serializer object with the Jet cluster and restart the whole cluster.

It is now possible to attach a serializer directly to the job you’re submitting.

JobConfig config = new JobConfig()
  .registerSerializer(Person.class, PersonSerializer.class);

jet.newJob(pipeline, config);

Jet will use these serializers only inside the job. You can read more about how serialization in Hazelcast Jet works in the serialization guide.

Protocol Buffers Support

Having added the job-level serializers, we also added an extra layer of convenience to use Google Protocol Buffers for serialization. You just need to write a simple class that delegates the work to the Protobuf compiler-generated serializer class (Person in this case):

class PersonSerializer extends ProtobufSerializer<Person> {

    private static final int TYPE_ID = 1;

    PersonSerializer() {
        super(Person.class, TYPE_ID);
    }
}

For more information, see the serialization guide.

Spring Boot Starter

Spring Boot is a framework that helps you create standalone Spring-based applications that just run. Spring Boot provides auto-configuration of some of the commonly used libraries through spring-boot-starters. Hazelcast Jet now provides its own Spring Boot Starter which can be used to auto-configure and start a Hazelcast Jet instance.

Just by adding the starter dependency to your Spring Boot application, you can start a JetInstance with the default configuration. If you want to customize the configuration, just add a configuration file (hazelcast-jet.yaml) to your classpath or working directory. The starter will pick it up and configure your Hazelcast Jet instance. If you want a client instance, add the client configuration file (hazelcast-client.yaml).

For more details, see the tutorial and the design document.

Kubernetes Operator and OpenShift Support

With version 4.1 we are introducing our Hazelcast Jet Kubernetes Operator. It’s available for both Hazelcast Jet open-source and Enterprise editions. Hazelcast Jet Enterprise Operator is also a certified by Red Hat and available on the Red Hat Marketplace.

Discovery Support for Microsoft Azure

We have extended the list of cloud environments where Hazelcast Jet instances are able to automatically discover each other and form a cluster. Self-discovery now works in the Microsoft Azure environment. Here’s a quick example on how to enable it:

hazelcast:
  network:
    join:
      multicast:
        enabled: false
      azure:
        enabled: true
        tag: TAG-NAME=HZLCAST001
        hz-port: 5701-5703

For more details, please see the discovery guide.

Full Release Notes

Members of the open source community that appear in these release notes:

  • @TomaszGaweda
  • @caioguedes
  • @SapnaDerajeRadhakrishna

Thank you for your valuable contributions!

New Features

  • [jms] Exactly-once guarantee for JMS sink (#1813)
  • [jdbc] Exactly-once guarantee for JDBC sink (#1813)
  • [core] JobConfig.addClass() automatically adds nested classes to the job (#1932)
  • [core] JobConfig.addPackage() adds a whole Java package to the job (#1932, #2077)
  • [core] Job-scoped serializer deployment (#2020, #2038, #2039, #2043, #2071, #2075, #2082, #2190)
  • [core] [006] Protobuf serializer support (#2100)
  • [pipeline-api] [007] Support gRPC for mapUsingService (#2095, #2185)

Enhancements

  • [jet-cli] Use log4j2 instead of log4j (#1981)
  • [jet-cli] Simplify default log output (#2047)
  • [core] Add useful error message when serializer not registered (#2061)
  • [jet-cli] Add hazelcast-azure cluster self-discovery plugin to the fat JAR in the distribution archive (#2079)
  • [pipeline-api] First-class support for inner hash join (@TomaszGaweda #2089)
  • [core] When Jet starts up, it now logs the cluster name (@caioguedes #2105)
  • [core] Add useful error message when trying to deploy a JDK class with JobConfig (#2108)
  • [core] Implement JobConfig.toString (@SapnaDerajeRadhakrishna #2152)
  • [core] Do not destroy Observable on shutdown (#2170)

Fixes

  • [core] Don’t send the interrupt signal to blocking threads when a job is terminating (#1971)
  • [core] Consistently prefer YAML over XML config files when both present (#2033)

Breaking Changes

  • [avro] Replace Supplier with just Schema for Avro Sink (#2005)
  • [jms] Reorder parameters in JMS source so the lambda comes last (#2062)
  • [jet-cli] Change smart routing (connecting to all cluster members) default to disabled (#2104)
  • [pipeline-api] For xUsingServiceAsync transforms, reduce the default number of concurrent service calls per processor. Before: 256; now: 4. (#2204)
About the Author

About the Author

Can Gencer

Can Gencer

Director of Engineering, Hazelcast Jet

Can is one of the founding members of the Hazelcast Jet team and is currently the engineering team lead. Prior to joining Hazelcast, he worked as a software development consultant to some of the world’s leading investment banks. He has deep interest in distributed systems, stream processing and building high-throughput, low-latency data pipelines. He is also a polyglot programmer with expertise in Java, Python, C# and functional programming.

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