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Kafka to IBM MQ with Kafka Connector

This extended scenario supports different labs going from simple to more complex and addresses how to integrate between IBM MQ and Kafka. It uses Kafka Confluent or Open Source Strimzi, IBM MQ as part of Cloud Pak for Integration and Kafka connect with MQ connectors.

This Lab focuses on using Strimzi 2.7. To run the same Lab with Kafka Confluent see the ibm-cloud-architecture/eda-lab-mq-to-kafka readme file.

Pre-requisites to all labs

  • Clone the lab git repository

    git clone
  • Get access to a Cloud Pak for Integration installation, with MQ deployed.

  • Get access to the OpenShift Console

Lab 1: MQ source to Kafka

The following figure illustrates what we will deploy:

mq kafka lab1
  • The Sell Store simulator is a separate application available in the refarch-eda-store-simulator public github and the docker image is accessible from the registry or the docker hub one.

  • Login to the OpenShift Console and get the access token to use oc cli.

  • Create new project with a command like: oc new-project mq-to-k-lab

  • Deploy Strimzi operator and create one Strimzi cluster. This is done by using the following commands under the eda-lab-mq-to-kafka folder:

# Wait one or two minutes to get an lab-kafka-entity-operator- pod running.
oc apply -k kustomize/environment/strimzi

It may take one minute to get Kafka deploy, but a oc get pods should return 3 zookeepers and 3 kafka broker pods:

lab-kafka-cruise-control-d94ffbcb5-7g7h2 2/2 Running 0 75s
lab-kafka-entity-operator-65fc8644c6-hlpn2 3/3 Running 0 117s
lab-kafka-kafka-0 1/1 Running 0 2m30s
lab-kafka-kafka-1 1/1 Running 0 2m30s
lab-kafka-kafka-2 1/1 Running 0 2m30s
lab-kafka-zookeeper-0 1/1 Running 0 3m24s
lab-kafka-zookeeper-1 1/1 Running 0 3m24s
lab-kafka-zookeeper-2 1/1 Running 0 3m24s
  • Deploy MQ queue manager: IBM MQ queue managers that are exposed by a Route on OpenShift require TLS-enabled security, so we will first create a SSL certificate pair and truststore for both queue manager and client use, respectively.
  • Get MQ connection characteristics, like service name and port number using the MQ console
  • Define the Kafka MQ connector configuration

To run this lab with Kafka Confluent see the eda-lab-mq-to-kafka readme file.

Lab 2: MQ Sink from Kafka

The target deployment looks like in the following diagram:


Kafka is runnning in its own namespace, and we are using Event Streams from Cloud Pack for Integration.

Setup MQ Channel

Open a shell on the remote container or use the user interface to define the communication channel to use for the Kafka Connection.

  • Using CLI: Change the name of the Q manager to reflect what you defined in MQ configuration.
runmqsc EDAQMGR1
# Define a app channel using server connection
# Set the channel authentication rules to accept connections requiring userid and password
# Set identity of the client connections
  • As an alternate you can use the MQ administration console.


Setup Kafka Connect Cluster

Connectors can be added to a Kafka Connect environment using OpenShift CLI commands and the source to image customer resource. We will use the Strimzi operator console to setup kafka connect environment.

Once you downloaded the zip file, which is a yaml manifest, define the configuration for a KafkaConnectS2I instance. The major configuration settings are the server certificate settings and the authentication using Mutual TLS authentication, something like:

bootstrapServers: sandbox-rp-kafka-bootstrap.eventstreams.svc:9093
- secretName: sandbox-rp-cluster-ca-cert
certificate: ca.crt
type: tls
certificate: user.crt

If you change the name of the connect cluster in the metadata, modify also the name: spec.template.pod.metadata.annotations. productChargedContainers accordingly.

The secrets used above, need to be accessible from the project where the connector is deployed. The simple way to do so is to copy the source certificates from the Event streams project to your current project with the commands like:

oc get secret sandbox-rp-cluster-ca-cert -n eventstreams --export -o yaml | oc apply -f -
oc get secret sandbox-rp-tls-cred -n eventstreams --export -o yaml | oc apply -f -

If you do not have a TLS client certificate from a TLS user, use this note to create one.

  • Deploy the connector cluster
oc apply -f kafka-connect-s2i.yaml

An instance of this custom resource represents a Kafka Connect distributed worker cluster. In this mode, workload balancing is automatic, scaling is dynamic, and tasks and data are fault-tolerant. Each connector is represented by another custom resource called KafkaConnector.

oc describe kafkaconnects2i eda-connect-cluster

Add the mq-sink connector

The product documentation details the available MQ connectors and the configuration process. Using the event streams console, the process is quite simple to get a connector configuration as json file. Here is an example of the final form to generate the json file:


  • Once the json is downloaded, complete the settings
"name": "mq-sink",
"connector.class": "",
"tasks.max": "1",
"topics": "inventory",
"key.converter": "",
"value.converter": "",
  • To run the connector within the cluster, we need to connector jar. You can download this jar file from the Event Stream adming console > Toolkit > Add connector to your Kafka Connect environment > Add Connector > IBM MQ Connectors,

    es mq sink 1

or as an alternate, we cloned the mq-sink code, so a mvn package command under kafka-connect-mq-sink folder will build the jar. Copy this jar under cp4i/my-plugins folder.

  • Build the connector with source to image component.

With the correct credentials for IBM EventStreams and IBM MQ, Kafka Connect should connect to both services and pull data from the EventStreams topic configured to the MQ Queue configured. You will see signs of success in the container output (via oc logs, or in the UI):

+ curl -X POST -H Content-Type: application/json http://localhost:8083/connectors --data @/opt/kafka-connect-mq-sink/config/mq-sink.json
[2020-06-23 04:26:26,054] INFO Creating task mq-sink-connector-0 (org.apache.kafka.connect.runtime.Worker:419)
...[2020-06-23 04:26:26,449] INFO Connection to MQ established (
[2020-06-23 04:26:26,449] INFO WorkerSinkTask{id=mq-sink-connector-0} Sink task finished initialization and start (org.apache.kafka.connect.runtime.WorkerSinkTask:306)

You should now have the Kafka Connector MQ Sink running on OpenShift.

MQ Sink Connector on virtual or baremetal server, MQ and Event Streams on IBM Cloud

In this second option, we are using our own laptop for the baremetal dedployment, but the current solution will work the same on virtual server.



We assume that you have an instance of IBM Event Streams already running on IBM Cloud or on OpenShift with at least administrator credentials created. The credentials will be needed later on for configuring the Kafka Connect framework to be able to connect and work with your IBM Event Streams instance. Also, this scenario requires a topic called inventory in your IBM Event Streams instance. For gathering the credentials and creating the topic required for this scenario, please review the Common pre-requisites. IMPORTANT: if you are sharing your IBM Event Streams instance, append some unique identifier to the topic you create in IBM Event Streams.

Create Local IBM MQ Instance

In this section we are going to use Docker to create a local IBM MQ instance to simulate an IBM MQ instance somewhere in our datacenter.

  1. Create a data directory to mount onto the container.

    mkdir qm1data
  2. Run the IBM MQ official Docker image by execting the following command.

    docker run \
    --name mq \
    --detach \
    --publish 1414:1414 \
    --publish 9443:9443 \
    --publish 9157:9157 \
    --volume qm1data:/mnt/mqm \
    --env LICENSE=accept \
    --env MQ_QMGR_NAME=QM1 \

    where we can see that out container will be called mq, it will run in detached mode (i.e. in the background), it will expose the ports IBM MQ uses for communication and the image we are actually running ibmcom/mq.

  3. You could make sure your IBM MQ Docker image is running by listing the Docker containers in your workstation.

    $ docker ps
    a7b2a115a3c6 ibmcom/mq "runmqdevserver" 6 minutes ago Up 6 minutes>1414/tcp,>9157/tcp,>9443/tcp mq
  4. You should also be able to log into the MQ server on port 9443 (https://localhost:9443) with default user admin and password passw0rd.


Now, we need to configure our local IBM MQ instance Queue Manager in order to define a server connection (KAFKA.CHANNEL) with authentication (user admin, password admin) and the queue (INVENTORY) where our messages from Kafka will be sinked to. And we are going to so it using the IBM MQ CLI.

  1. Get into the IBM MQ Docker container we have started above by executing the following command that will give us a bash interactive terminal:

    docker exec -ti mq bash
  2. Now that we are in the container, start the queue manager QM1 by executing:

    strmqm QM1
  3. Start the runmqsc tool to configure the queue manager by executing:

    runmqsc QM1
  4. Create a server-connection channel called KAFKA.CHANNEL by executing:

  5. Set the channel authentication rules to accept connections requiring userid and password by executing:

  6. Set the identity of the client connections based on the supplied context, the user ID by executing:

  7. Refresh the connection authentication information by executing:

  8. Create the INVENTORY queue for the connector to use by executing:

  9. Authorize admin to connect to and inquire the queue manager by executing:

  10. Finally authorize admin to use the queue by executing:

  11. End runmqsc by executing:


You should see the following output:

9 MQSC commands read.
No commands have a syntax error.
One valid MQSC command could not be processed.

Exit the container by executing:


If you check your IBM MQ dashboard you should see your newly created INVENTORY queue:


Create MQ Kafka Connector Sink

The MQ Connector Sink can be downloaded from this Github. The Github site includes exhaustive instructions for further detail on this connector and its usage.

  1. Clone the repository with the following command:

    git clone
  2. Change directory into the kafka-connect-mq-sink directory:

    cd kafka-connect-mq-sink
  3. Build the connector using Maven:

    mvn clean package
  4. Create a directory (if it does not exist yet) to contain the Kafka Connect framework configuration and cd into it.

    mkdir config
    cd config
  5. Create a configuration file called for the Kafka Connect framework with the following properties in it:

    # A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.

    IMPORTANT: You must replace all occurences of the following placeholders in the properties file above with the appropriate values for the Kafka Connect framework to work with your IBM Event Streams instance:

    • REPLACE_WITH_YOUR_BOOTSTRAP_URL: Your IBM Event Streams bootstrap url.
    • REPLACE_WITH_YOUR_SCRAM_USERNAME: Your SCRAM service credentials username.
    • REPLACE_WITH_YOUR_SCRAM_PASSWORD: Your SCRAM service credentials password.
    • REPLACE_WITH_UNIQUE_IDENTIFIER: A unique identifier so that the resources your kafka connect cluster will create on your IBM Event Streams instance don’t collide with other users’ resources (Note: there are 4 placeholders of this type. Replace them all).

    Review the Common pre-requisites instructions if you don’t know how to find out any of the config properties above.

  6. Download the IBM Event Streams TLS certificate so that your Kafka Connect framework local instance can establish secure communication with your IBM Event Streams instance. IMPORTANT: download the PKCS12 certificate. How to get the certificate in the Common pre-requisites section.

  7. Create a log4j configuration file named based on the template below.

    log4j.rootLogger=DEBUG, stdout
    log4j.appender.stdout.layout.ConversionPattern=[%d] %p %m (%c:%L)%n
  8. Back out of the config directory to the kafka-connect-mq-sink directory:

    cd ..
  9. Build the Docker image for your Kafka Connect framework that will contain the IBM MQ Sink connector and all the properties files you have created and tailored earlier so that your Kafka Connect framework can work with your IBM Event Streams instance we have set up previously in this exercise (mind the dot at the end of the command. It is necessary):

    docker build -t kafkaconnect-with-mq-sink:1.3.0 .
  10. Run the Kafka Connect MQ Sink container.

    docker run \
    --name mq-sink \
    --detach \
    --volume $(pwd)/config:/opt/kafka/config \
    --publish 8083:8083 \
    --link mq:mq \
  11. Now that we have a Kafka Connect framework running with the configuration to connect to our IBM Event Streams instance and the IBM MQ Sink Connector jar file in it, is to create a JSON file called mq-sink.json to startup an instance of the IBM MQ Sink connector in our Kafka Connect framework with the appropriate config to work read messages from the IBM Event Streams topic we desire and sink those to the IBM MQ instance we have running locally. IMPORTANT: If you are sharing your IBM Event Streams instance and followed the instructions on this readme, you should have appended a unique identifier to the name of the topic (inventory) that you are meant to create in IBM Event Streams. As a result, modify the line "topics": "inventory" in the following JSON object accordignly.

    "name": "mq-sink",
    "connector.class": "",
    "tasks.max": "1",
    "topics": "inventory",
    "key.converter": "",
  12. Last piece of the puzzle is to tell our Kafka Connect framework to create and startup the IBM MQ Sink connector based on the configuration we have in the previous json file. To do so, execute the following POST request.

    curl -X POST -H "Content-Type: application/json" http://localhost:8083/connectors --data "@./mq-sink.json"
    # The response returns the metadata about the connector
  13. You can query your Kafka Connect framework to make sure of this. Execute the following command that should return all the connectors up and running in your Kafka Connect cluster.

    curl localhost:8083/connectors

You should now have a working MQ Sink connector getting messages from your topic in IBM Event Streams and sending these to your local IBM MQ instance. In order to check that out working, we first need to send few messages to that IBM Event Streams topic and then check out our INVENTORY queue in our local IBM MQ instance.

  1. In order to send messages to IBM Event Streams, we are going to use the IBM Event Streams Starter application. You can find the instruction either in the IBM Event Streams official documentation [here] or on the IBM Event Streams dashboard:

  2. Follow the instructions to run the IBM Event Streams starter application from your workstation. IMPORTANT: When generating the properties for your IBM Event Streams starter application, please choose to connect to an existing topic and select the topic you created previously as part of this exercise so that the messages we send into IBM Event Streams end up in the appropriate topic that is being monitored by your IBM MQ Sink connector runnning on the Kafka Connect framework.

  3. Once you have your application running, open it up in your web browser, click on Start producing, let the application produce a couple of messages and then click on Stop producing

  4. Check out your those messages got into the Kafka topic

  5. Check out your messages in the Kafka topic have already reached your INVENTORY queue in your local IBM MQ instance

  1. You could also inspect the logs of your Kafka Connect Docker container running on your workstation:

    docker logs mq-sink
    [2021-01-19 19:44:17,110] DEBUG Putting record for topic inventory, partition 0 and offset 0 (
    [2021-01-19 19:44:17,110] DEBUG Value schema Schema{STRING} (
    [2021-01-19 19:44:18,292] DEBUG Flushing up to topic inventory, partition 0 and offset 1 (
    [2021-01-19 19:44:18,292] INFO WorkerSinkTask{id=mq-sink-0} Committing offsets asynchronously using sequence number 636: {inventory-0=OffsetAndMetadata{offset=1, leaderEpoch=null, metadata=''}} (org.apache.kafka.connect.runtime.WorkerSinkTask:346)
    [2021-01-19 19:44:18,711] DEBUG Putting record for topic inventory, partition 0 and offset 1 (
    [2021-01-19 19:44:18,711] DEBUG Value schema Schema{STRING} (
    [2021-01-19 19:44:20,674] DEBUG Putting record for topic inventory, partition 0 and offset 2 (

To cleanup your environment, you can do

  1. Remove the connector from your Kafka Connect framework instance (this isn’t really needed if you are going to stop and remove the Kafka Connect Docker container)

    curl -X DELETE http://localhost:8083/connectors/mq-sink
  2. Stop both IBM MQ and Kakfa Connect Docker containers running on your workstation

    docker stop mq mq-sink
  3. Remove both IBM MQ and Kakfa Connect Docker containers from your workstation

    docker rm mq mq-sink