kafka-connect-mq-sink is a Kafka Connect sink connector for copying data from Apache Kafka into IBM MQ.
The connector is supplied as source code which you can easily build into a JAR file.
Note: A source connector for IBM MQ is also available on GitHub.
- Building the connector
- Running the connector
- Running the connector with Docker
- Deploying the connector to Kubernetes
- Data formats
- Security
- Configuration
- Troubleshooting
- Support
- Issues and contributions
- License
To build the connector, you must have the following installed:
- git
- Maven 3.0 or later
- Java 8 or later
Clone the repository with the following command:
git clone https://github.com/ibm-messaging/kafka-connect-mq-sink.gitChange directory into the kafka-connect-mq-sink directory:
cd kafka-connect-mq-sinkRun the unit tests:
mvn testRun the integration tests (requires Docker):
mvn integration-testBuild the connector using Maven:
mvn clean packageOnce built, the output is a single JAR target/kafka-connect-mq-sink-<version>-jar-with-dependencies.jar which contains all of the required dependencies.
For step-by-step instructions, see the following guides for running the connector:
- connecting to Apache Kafka running locally
- connecting to an installation of IBM Event Streams
To run the connector, you must have:
- The JAR from building the connector
- A properties file containing the configuration for the connector
- Apache Kafka 2.6.2 or later, either standalone or included as part of an offering such as IBM Event Streams
- IBM MQ v9 or later, or the IBM MQ on Cloud service
The connector can be run in a Kafka Connect worker in either standalone (single process) or distributed mode. It's a good idea to start in standalone mode.
You need two configuration files, one for the configuration that applies to all of the connectors such as the Kafka bootstrap servers, and another for the configuration specific to the MQ sink connector such as the connection information for your queue manager. For the former, the Kafka distribution includes a file called connect-standalone.properties that you can use as a starting point. For the latter, you can use config/mq-sink.properties in this repository.
The connector connects to MQ using either a client or a bindings connection. For a client connection, you must provide the name of the queue manager, the connection name (one or more host/port pairs) and the channel name. In addition, you can provide a user name and password if the queue manager is configured to require them for client connections. If you look at the supplied config/mq-sink.properties, you'll see how to specify the configuration required. For a bindings connection, you must provide provide the name of the queue manager and also run the Kafka Connect worker on the same system as the queue manager.
To run the connector in standalone mode from the directory into which you installed Apache Kafka, you use a command like this:
bin/connect-standalone.sh connect-standalone.properties mq-sink.propertiesYou need an instance of Kafka Connect running in distributed mode. The Kafka distribution includes a file called connect-distributed.properties that you can use as a starting point, or follow Running with Docker or Deploying to Kubernetes.
To start the MQ connector, you can use config/mq-sink.json in this repository after replacing all placeholders and use a command like this:
curl -X POST -H "Content-Type: application/json" http://localhost:8083/connectors \
--data "@./config/mq-sink.json"This repository includes an example Dockerfile to run Kafka Connect in distributed mode. It also adds in the MQ sink connector as an available connector plugin. It uses the default connect-distributed.properties and connect-log4j.properties files.
mvn clean packagedocker build -t kafkaconnect-with-mq-sink:<TAG> .docker run -p 8083:8083 kafkaconnect-with-mq-sink:<TAG>
NOTE: To provide custom properties files create a folder called config containing the connect-distributed.properties and connect-log4j.properties files and use a Docker volume to make them available when running the container like this:
docker run -v $(pwd)/config:/opt/kafka/config -p 8083:8083 kafkaconnect-with-mq-sink:<TAG>To start the MQ connector, you can use config/mq-sink.json in this repository after replacing all placeholders and use a command like this:
curl -X POST -H "Content-Type: application/json" http://localhost:8083/connectors \
--data "@./config/mq-sink.json"This repository includes a Kubernetes yaml file called kafka-connect.yaml. This will create a deployment to run Kafka Connect in distributed mode and a service to access the deployment.
The deployment assumes the existence of a Secret called connect-distributed-config and a ConfigMap called connect-log4j-config. These can be created using the default files in your Kafka install, however it is easier to edit them later if comments and whitespaces are trimmed before creation.
Create Secret for Kafka Connect configuration:
cp kafka/config/connect-distributed.properties connect-distributed.properties.origsed '/^#/d;/^[[:space:]]*$/d' < connect-distributed.properties.orig > connect-distributed.propertieskubectl -n <namespace> create secret generic connect-distributed-config --from-file=connect-distributed.properties
Create ConfigMap for Kafka Connect Log4j configuration:
cp kafka/config/connect-log4j.properties connect-log4j.properties.origsed '/^#/d;/^[[:space:]]*$/d' < connect-log4j.properties.orig > connect-log4j.propertieskubectl -n <namespace> create configmap connect-log4j-config --from-file=connect-log4j.properties
NOTE: You will need to build the Docker image and push it to your Kubernetes image repository. Remember that the supplied Dockerfile is just an example and you will have to modify it for your needs. You might need to update the image name in the kafka-connect.yaml file.
- Update the namespace in
kafka-connect.yaml kubectl -n <namespace> apply -f kafka-connect.yamlcurl <serviceIP>:<servicePort>/connector-pluginsto see whether the MQ sink connector is available to use
This repository includes a Kubernetes yaml file called strimzi.kafkaconnector.yaml for use with the Strimzi operator. Strimzi provides a simplified way of running the Kafka Connect distributed worker, by defining either a KafkaConnect resource or a KafkaConnectS2I resource.
The KafkaConnectS2I resource provides a nice way to have OpenShift do all the work of building the Docker images for you. This works particularly nicely combined with the KafkaConnector resource that represents an individual connector.
The following instructions assume you are running on OpenShift and have Strimzi 0.16 or later installed.
- Create a file called
kafka-connect-s2i.yamlcontaining the definition of a KafkaConnectS2I resource. You can use the examples in the Strimzi project to get started. - Configure it with the information it needs to connect to your Kafka cluster. You must include the annotation
strimzi.io/use-connector-resources: "true"to configure it to use KafkaConnector resources so you can avoid needing to call the Kafka Connect REST API directly. oc apply -f kafka-connect-s2i.yamlto create the cluster, which usually takes several minutes.
mvn clean packageto build the connector JAR.mkdir my-pluginscp target/kafka-connect-mq-sink-*-jar-with-dependencies.jar my-pluginsoc start-build <kafkaconnectClusterName>-connect --from-dir ./my-pluginsto add the MQ sink connector to the Kafka Connect distributed worker cluster. Wait for the build to complete, which usually takes a few minutes.oc describe kafkaconnects2i <kafkaConnectClusterName>to check that the MQ sink connector is in the list of available connector plugins.
cp deploy/strimzi.kafkaconnector.yaml kafkaconnector.yaml- Update the
kafkaconnector.yamlfile to replace all of the values in<>, adding any additional configuration properties. oc apply -f kafkaconnector.yamlto start the connector.oc get kafkaconnectorto list the connectors. You can useoc describeto get more details on the connector, such as its status.
Kafka Connect is very flexible but it's important to understand the way that it processes messages to end up with a reliable system. When the connector encounters a message that it cannot process, it stops rather than throwing the message away. Therefore, you need to make sure that the configuration you use can handle the messages the connector will process.
Each message in Kafka Connect is associated with a representation of the message format known as a schema. Each Kafka message actually has two parts, key and value, and each part has its own schema. The MQ sink connector does not currently use message keys, but some of the configuration options use the word Value because they refer to the Kafka message value.
When the MQ sink connector reads a message from Kafka, it is processed using a converter which chooses a schema to represent the message format and creates a Java object containing the message value. The MQ sink connector then converts this internal format into the message it sends to MQ using a message builder.
There are three converters built into Apache Kafka. The following table shows which converters to use based on the incoming message encoding.
| Incoming Kafka message | Converter class |
|---|---|
| Any | org.apache.kafka.connect.converters.ByteArrayConverter |
| String | org.apache.kafka.connect.storage.StringConverter |
| JSON, may have schema | org.apache.kafka.connect.json.JsonConverter |
There are three message builders supplied with the connector, although you can write your own. The basic rule is that if you're using a converter that uses a very simple schema, the default message builder is probably the best choice. If you're using a converter that uses richer schemas to represent complex messages, the JSON message builder is good for generating a JSON representation of the complex data. The following table shows some likely combinations.
| Converter class | Message builder class | Outgoing MQ message |
|---|---|---|
| org.apache.kafka.connect.converters.ByteArrayConverter | com.ibm.eventstreams.connect.mqsink.builders.DefaultMessageBuilder | Binary data |
| org.apache.kafka.connect.storage.StringConverter | com.ibm.eventstreams.connect.mqsink.builders.DefaultMessageBuilder | String data |
| org.apache.kafka.connect.json.JsonConverter | com.ibm.eventstreams.connect.mqsink.builders.JsonMessageBuilder | JSON, no schema |
When you set mq.message.body.jms=true, the MQ messages are generated as JMS messages. This is appropriate if the applications receiving the messages are themselves using JMS.
There's no single configuration that will always be right, but here are some high-level suggestions.
- Message values are treated as byte arrays, pass byte array into MQ message
value.converter=org.apache.kafka.connect.converters.ByteArrayConverter
- Message values are treated as strings, pass string into MQ message
value.converter=org.apache.kafka.connect.storage.StringConverter
The messages received from Kafka are processed by a converter which chooses a schema to represent the message and creates a Java object containing the message value. There are three basic converters built into Apache Kafka.
| Converter class | Kafka message encoding | Value schema | Value class |
|---|---|---|---|
| org.apache.kafka.connect.converters.ByteArrayConverter | Any | OPTIONAL_BYTES | byte[] |
| org.apache.kafka.connect.storage.StringConverter | String | OPTIONAL_STRING | java.lang.String |
| org.apache.kafka.connect.json.JsonConverter | JSON, may have schema | Depends on message | Depends on message |
The MQ sink connector uses a message builder to build the MQ messsages from the schema and value. There are three built-in message builders.
The DefaultMessageBuilder is best when the schema is very simple, such as when the ByteArrayConverter or StringConverter are being used.
| Value schema | Value class | Outgoing message format | JMS message type | Outgoing message body |
|---|---|---|---|---|
| null | Any | MQFMT_STRING | TextMessage | Java Object.toString() of value |
| BYTES | byte[] | MQFMT_NONE | BytesMessage | Byte array |
| STRING | java.lang.String | MQFMT_STRING | TextMessage | String |
| Everything else | Any | MQFMT_STRING | TextMessage | Java Object.toString() of value |
If you use the JsonConverter with the DefaultMessageBuilder, the output message will not be JSON; it will be a Java string representation of the value instead. That's why there's a JsonMessageBuilder too which behaves like this:
| Value schema | Value class | Outgoing message format | JMS message type | Outgoing message body |
|---|---|---|---|---|
| Any | Any | MQFMT_STRING | TextMessage | JSON representation of the value |
To make the differences clear, here are some examples.
| Input message | Converter | Value schema | Message builder | Output message body | Comment |
|---|---|---|---|---|---|
| ABC | StringConverter | STRING | DefaultMessageBuilder | ABC | OK |
| ABC | StringConverter | STRING | JsonMessageBuilder | "ABC" | Quotes added to give a JSON string |
| "ABC" | JsonConverter | STRING | DefaultMessageBuilder | ABC | Quotes removed, not a JSON string |
| "ABC" | JsonConverter | STRING | JsonMessageBuilder | "ABC" | OK |
| {"A":"B"} | JsonConverter | Compound (STRUCT) | DefaultMessageBuilder | STRUCT{A=B} | Probably not helpful |
| {"A":"B"} | JsonConverter | Compound (STRUCT) | JsonMessageBuilder | {"A":"B"} | OK |
Note that the order of JSON structures is not fixed and fields may be reordered.
To handle the situation in which you already have a Kafka converter that you want to use to build the MQ message payload, the ConverterMessageBuilder is the one to use. Then you would end up using two Converters - one to convert the Kafka message to the internal SinkRecord, and the second to convert that into the MQ message. Since the Converter might also have its own configuration options, you can specify them using a prefix of mq.message.builder.value.converter. For example, the following configuration gets the ConverterMessageBuilder to work the same as the JsonMessageBuilder.
mq.message.builder=com.ibm.eventstreams.connect.mqsink.builders.ConverterMessageBuilder
mq.message.builder.value.converter=org.apache.kafka.connect.json.JsonConverter
mq.message.builder.value.converter.schemas.enable=false
By default, the connector does not use the keys for the Kafka messages it reads. It can be configured to set the JMS correlation ID using the key of the Kafka records. To configure this behavior, set the mq.message.builder.key.header configuration value.
| mq.message.builder.key.header | Key schema | Key class | Recommended value for key.converter |
|---|---|---|---|
| JMSCorrelationID | STRING | String | org.apache.kafka.connect.storage.StringConverter |
| JMSCorrelationID | BYTES | byte[] | org.apache.kafka.connect.converters.ByteArrayConverter |
In MQ, the correlation ID is a 24-byte array. As a string, the connector represents it using a sequence of 48 hexadecimal characters. The Kafka key will be truncated to fit into this size.
The connector can be configured to set the Kafka topic, partition and offset as JMS message properties using the mq.message.builder.*.property configuration values. If configured, the topic is set as a string property, the partition as an integer property and the offset as a long property. Because these values are set using JMS message properties, they only have an effect if mq.message.body.jms=true is set.
The connector supports authentication with user name and password and also connections secured with TLS using a server-side certificate and mutual authentication with client-side certificates. You can also choose whether to use connection security parameters (MQCSP) depending on the security settings you're using in MQ.
To enable use of TLS, set the configuration mq.ssl.cipher.suite to the name of the cipher suite which matches the CipherSpec in the SSLCIPH attribute of the MQ server-connection channel. Use the table of supported cipher suites for MQ 9.1 here as a reference. Note that the names of the CipherSpecs as used in the MQ configuration are not necessarily the same as the cipher suite names that the connector uses. The connector uses the JMS interface so it follows the Java conventions.
You will need to put the public part of the queue manager's certificate in the JSSE truststore used by the Kafka Connect worker that you're using to run the connector. If you need to specify extra arguments to the worker's JVM, you can use the EXTRA_ARGS environment variable.
You will need to put the public part of the client's certificate in the queue manager's key repository. You will also need to configure the worker's JVM with the location and password for the keystore containing the client's certificate. Alternatively, you can configure a separate keystore and truststore for the connector.
For troubleshooting, or to better understand the handshake performed by the IBM MQ Java client application in combination with your specific JSSE provider, you can enable debugging by setting javax.net.debug=ssl in the JVM environment.
The configuration options for the Kafka Connect sink connector for IBM MQ are as follows:
| Name | Description | Type | Default | Valid values |
|---|---|---|---|---|
| topics or topics.regex | List of Kafka source topics | string | topic1[,topic2,...] | |
| mq.queue.manager | The name of the MQ queue manager | string | MQ queue manager name | |
| mq.connection.mode | The connection mode - bindings or client | string | client | client, bindings |
| mq.connection.name.list | List of connection names for queue manager | string | host(port)[,host(port),...] | |
| mq.channel.name | The name of the server-connection channel | string | MQ channel name | |
| mq.queue | The name of the target MQ queue | string | MQ queue name | |
| mq.user.name | The user name for authenticating with the queue manager | string | User name | |
| mq.password | The password for authenticating with the queue manager | string | Password | |
| mq.user.authentication.mqcsp | Whether to use MQ connection security parameters (MQCSP) | boolean | true | |
| mq.ccdt.url | The URL for the CCDT file containing MQ connection details | string | URL for obtaining a CCDT file | |
| mq.message.builder | The class used to build the MQ message | string | Class implementing MessageBuilder | |
| mq.message.body.jms | Whether to generate the message body as a JMS message type | boolean | false | |
| mq.time.to.live | Time-to-live in milliseconds for messages sent to MQ | long | 0 (unlimited) | [0,...] |
| mq.persistent | Send persistent or non-persistent messages to MQ | boolean | true | |
| mq.ssl.cipher.suite | The name of the cipher suite for TLS (SSL) connection | string | Blank or valid cipher suite | |
| mq.ssl.peer.name | The distinguished name pattern of the TLS (SSL) peer | string | Blank or DN pattern | |
| mq.ssl.keystore.location | The path to the JKS keystore to use for SSL (TLS) connections | string | JVM keystore | Local path to a JKS file |
| mq.ssl.keystore.password | The password of the JKS keystore to use for SSL (TLS) connections | string | ||
| mq.ssl.truststore.location | The path to the JKS truststore to use for SSL (TLS) connections | string | JVM truststore | Local path to a JKS file |
| mq.ssl.truststore.password | The password of the JKS truststore to use for SSL (TLS) connections | string | ||
| mq.ssl.use.ibm.cipher.mappings | Whether to set system property to control use of IBM cipher mappings | boolean | ||
| mq.message.builder.key.header | The JMS message header to set from the Kafka record key | string | JMSCorrelationID | |
| mq.kafka.headers.copy.to.jms.properties | Whether to copy Kafka headers to JMS message properties | boolean | false | |
| mq.message.builder.value.converter | The class and prefix for message builder's value converter | string | Class implementing Converter | |
| mq.message.builder.topic.property | The JMS message property to set from the Kafka topic | string | Blank or valid JMS property name | |
| mq.message.builder.partition.property | The JMS message property to set from the Kafka partition | string | Blank or valid JMS property name | |
| mq.message.builder.offset.property | The JMS message property to set from the Kafka offset | string | Blank or valid JMS property name | |
| mq.reply.queue | The name of the reply-to queue | string | MQ queue name or queue URI | |
| mq.retry.backoff.ms | Wait time, in milliseconds, before retrying after retriable exceptions | long | 60000 | [0,...] |
Some of the connection details for MQ can be provided in a CCDT file by setting mq.ccdt.url in the MQ sink connector configuration file. If using a CCDT file the mq.connection.name.list and mq.channel.name configuration options are not required.
KIP 297 introduced a mechanism to externalize secrets to be used as configuration for Kafka connectors.
Given a file mq-secrets.properties with the contents:
secret-key=password
Update the worker configuration file to specify the FileConfigProvider which is included by default:
# Additional properties for the worker configuration to enable use of ConfigProviders
# multiple comma-separated provider types can be specified here
config.providers=file
config.providers.file.class=org.apache.kafka.common.config.provider.FileConfigProvider
Update the connector configuration file to reference secret-key in the file:
mq.password=${file:mq-secret.properties:secret-key}
To use a file for the mq.password in Kubernetes, you create a Secret using the file as described in the Kubernetes docs.
You may receive an org.apache.kafka.common.errors.SslAuthenticationException: SSL handshake failed error when trying to run the MQ sink connector using SSL to connect to your Kafka cluster. In the case that the error is caused by the following exception: Caused by: java.security.cert.CertificateException: No subject alternative DNS name matching XXXXX found., Java may be replacing the IP address of your cluster with the corresponding hostname in your /etc/hosts file. For example, to push Docker images to a custom Docker repository, you may add an entry in this file which corresponds to the IP of your repository e.g. 123.456.78.90 mycluster.icp. To fix this, you can comment out this line in your /etc/hosts file.
When configuring TLS connection to MQ, you may find that the queue manager rejects the cipher suite, in spite of the name looking correct. There are two different naming conventions for cipher suites (https://www.ibm.com/support/knowledgecenter/SSFKSJ_9.1.0/com.ibm.mq.dev.doc/q113220_.htm). Setting the configuration option mq.ssl.use.ibm.cipher.mappings=false often resolves cipher suite problems.
Commercial support for this connector is available for customers with a support entitlement for IBM Event Automation or IBM Cloud Pak for Integration.
For issues relating specifically to this connector, please use the GitHub issue tracker. If you do want to submit a Pull Request related to this connector, please read the contributing guide first to understand how to sign your commits.
Copyright 2017, 2020, 2023 IBM Corporation
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
(http://www.apache.org/licenses/LICENSE-2.0)
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.The project is licensed under the Apache 2 license.