Get Started with Streaming Analytics + Message Hub

5 min read

Get Started with Streaming Analytics + Message Hub

Message Hub provides a simple communication mechanism built on Apache Kafka, enabling communication between loosely coupled IBM Cloud services. This article shows how to communicate with Message Hub from the IBM Cloud Streaming Analytics service using the messaging toolkit.


Creating a topic

In Message Hub, messages are transported through feeds called topics. Producers write messages to topics, and consumers read from topics.

The IBM Cloud Message Hub dashboard provides topic management tools. To create a topic click the plus sign, enter “test”, and click “Create topic”.

Create topic

We are now ready to use this new topic from Streams.

Using Streams to produce and consume messages

Now we will use the MessageHubFileSample Streams application to produce and consume messages.

Import MessageHubFileSample into Streams Studio from the downloaded toolkit directory (samples/MessageHubFileSample). The application should build successfully. Instructions for importing Streams applications can be found in Importing SPL projects.

We still need to tell the application where to find our Message Hub service.

  1. Navigate to Message Hub’s dashboard and click the “Service Credentials” tab. 

    Service Credentials
  2. Copy the credentials JSON and paste it into MessageHubFileSample’s /etc/messagehub.json file, replacing the placeholder comment.The MessageHubFileSample Streams application contains logic to both send and receive messages.

    • The “producer” part of the Streams graph (Beacon_1 → MessageHubProducer_2) uses a MessageHubProducer operator to send messages to the topic named “test” every 0.2 seconds.

    • The “consumer” part (MessageHubConsumer_3 → Custom_4) retrieves messages from Kafka using the MessageHubConsumer operator and prints them to the console.

    Build the MessageHubFileSample’s Distributed Build so that you can run it on your Streaming Analytics service on IBM Cloud.

    To run MessageHubFileSample locally in Streams Studio, see Launching a Main composite.

    To view the locally running Streams graph, see Viewing the instance graph for your application.

    Streams and Message Hub in the Cloud

    Create a Streaming Analytics service on IBM Cloud – See “Finding the service” section of Introduction to IBM Cloud Streaming Analytics.

    Building MessageHubFileSample creates a .sab file (Streams application bundle) in your workspace directory: workspace/MessageHubFileSample/output/ This file includes all necessary information for the Streaming Analytics service to run the Streams application in the cloud.

    Upload the .sab file using the Streaming Analytics console.

    1. Head to the Streaming Analytics service dashboard in IBM Cloud and click “Launch” to launch the Streams console.

    2. Click “Submit job” under the “play icon” dropdown in the top-right of the console

      Submit job
    3. Browse for the file that you built, and click Submit.


    The Streams application is working properly if the Streams console’s graph view shows that all operators are healthy (green circle).

    The Streams application

    You can also view the messages being printed by Custom_4 in the Streams log.

    1. Navigate to the Streams console log viewer on the far left.

    2. Expand the navigation tree and highlight the PE that has the Custom_4 operator.

    3. Select the “Console Log” tab.

    4. Click “Load console messages”.

    Load console messages

    If you don’t see any messages being logged, ensure that only one instance of the job is running. You can only have one Kafka consumer per topic in each consumer group.

    If you had any trouble following these instructions, check out the video tutorial.


    This article has shown how Streams can both send and receive messages using operators from the messaging toolkit. Message Hub provides powerful real-time communication between Streams and many other IBM Cloud services.

    What’s next?

    To learn more about Streaming Analytics visit:

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