TIBCO Source Connector for Confluent Platform

The Kafka Connect TIBCO Source connector is used to move messages from TIBCO Enterprise Messaging Service (EMS) to Apache Kafka®.

Messages are consumed from the TIBCO EMS broker using the configured message selectors and written to a single Kafka topic. A Single Message Transformation can be used to route messages to multiple Kafka topics.

The connector currently supports consuming JMS TextMessage and BytesMessage but not ObjectMessage or StreamMessage.

Note

If you are required to use the Java Naming and Directory Interface™ (JNDI) to connect to TIBCO EMS, there is a general JMS Source connector for Confluent Platform available that uses a JNDI-based mechanism to connect to the JMS broker.

Features

At least once delivery

This connector guarantees that records are delivered to the Kafka topic at least once. If the connector restarts, there may be some duplicate records in the Kafka topic.

Multiple tasks

The TIBCO Source connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. This can lead to performance gains when multiple files need to be parsed.

Schemas

The TIBCO Source connector produces messages with keys and values that adhere to the schemas described in the following sections.

io.confluent.connect.jms.Key

This schema is used to store the incoming MessageID on the message interface. This will ensure that when the same message ID arrives it will end up in the same partition. In practice this should never occur. The schema defines the following fields:

Name Schema Required Default Value Documentation
messageID STRING yes   This field stores the value of Message.getJMSMessageID().

io.confluent.connect.jms.Value

This schema is used to store the value of the JMS message. The schema defines the following fields:

Name Schemna Required Default Value Documentation
messageID STRING yes   This field stores the value of Message.getJMSMessageID().
messageType STRING yes   This field stores the type of message that was received. This corresponds to the sub-interfaces of Message. BytesMessage = bytes, MapMessage = map, ObjectMessage = object, StreamMessage = stream and TextMessage = text. The corresponding field will be populated with the values from the respective message sub-interface.
timestamp INT64 yes   Data from the getJMSTimestamp() method.
deliveryMode INT32 yes   This field stores the value of Message.getJMSDeliveryMode(). method.
correlationID STRING no   This field stores the value of Message.getJMSCorrelationID(). method.
replyTo Destination no   This schema is used to represent a JMS Destination, and is either queue or topic.
destination Destination no   This schema is used to represent a JMS Destination, and is either queue or topic.
redelivered BOOLEAN yes   This field stores the value of Message.getJMSRedelivered().
type STRING no   This field stores the value of Message.getJMSType().
expiration INT64 no   This field stores the value of Message.getJMSExpiration().
priority INT32 no   This field stores the value of Message.getJMSPriority().
properties Map of STRING, PropertyValue yes   This field stores the data from all of the properties for the Message indexed by their propertyName.
bytes BYTES no   This field stores the value from BytesMessage.html.readBytes(byte[]).
map Map of STRING, PropertyValue no   This field stores the data from all of the map entries returned from MapMessage.getMapNames() for the Message indexed by their key.
text STRING no   This field stores the value from TextMessage.html.getText().

io.confluent.connect.jms.Destination

This schema is used to represent a JMS Destination, and is either queue or topic.

The schema defines the following fields:

Name Schema Required Default Value Documentation
destinationType STRING yes   The type of JMS Destination, and either queue or topic.
name STRING yes   The name of the destination. This will be the value of Queue.getQueueName() or Topic.getTopicName().

io.confluent.connect.jms.PropertyValue

This schema is used to store the data that is found in the properties of the message. To ensure that the proper type mappings are preserved field propertyType stores the value type for the field. The corresponding field in the schema will contain the data for the property. This ensures that the data is retrievable as the type returned by Message.getObjectProperty(). The schema defines the following fields:

Name Schema Required Default Value Documentation
propertyType STRING yes   The Java type of the property on the Message. One of boolean, byte, short, integer, long, float, double, or string.
boolean BOOLEAN no   The value stored as a boolean. Null unless propertyType is set to boolean.
byte INT8 no   The value stored as a byte. Null unless propertyType is set to byte.
short INT16 no   The value stored as a short. Null unless propertyType is set to short.
integer INT32 no   The value stored as a integer. Null unless propertyType is set to integer.
long INT64 no   The value stored as a long. Null unless propertyType is set to long.
float FLOAT32 no   The value stored as a float. Null unless propertyType is set to float.
double FLOAT64 no   The value stored as a double. Null unless propertyType is set to double.
string STRING no   The value stored as a string. Null unless propertyType is set to string.

License

You can use this connector for a 30-day trial period without a license key.

After 30 days, you must purchase a connector subscription which includes Confluent enterprise license keys to subscribers, along with enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, you can contact Confluent Support at support@confluent.io for more information.

For license properties, see Confluent Platform license. For information about the license topic, refer to License topic configuration.

Configuration

This section describes how the connector internally configures the acknowledgement mode. For a complete list of configuration properties for this connector, see TIBCO Source Connector Configuration Properties.

For an example of how to get Kafka Connect connected to Confluent Cloud, see Connect Self-Managed Kafka Connect to Confluent Cloud.

Acknowledgement mode

The connector internally uses CLIENT_ACKNOWLEDGE mode to receive and acknowledge messages from the JMS broker. In this mode, acknowledging any message will acknowledge every message received (see section 6.2.10 in the JMS 2.0 Specification). To prevent messages from being prematurely acknowledged, the connector processes only one message at time. In other words, the connector will not try to receive new messages until the last message is committed to a Kafka topic. This might compromise the throughput of the connector, but messages will be transferred to Kafka successfully.

Install the TIBCO Source connector

You can install this connector by using the confluent connect plugin install command, or by manually downloading the ZIP file.

Prerequisites

  • You must install the connector on every machine where Connect will run.
  • Kafka Broker: Confluent Platform 3.3.0 or later, or Kafka 0.11.0 or later.
  • Connect: Confluent Platform 4.1.0 or later, or Kafka 1.1.0 or later (requires header support in Connect).
  • TIBCO EMS with JMS 1.1 support.
  • tibjms Client Library. For more details, see Installing the TIBCO JMS client library.
  • Java 1.8.
  • The latest (latest) connector version.

Install the connector using Confluent CLI

To install the latest connector version, navigate to your Confluent Platform installation directory and run the following command:

confluent connect plugin install confluentinc/kafka-connect-tibco-source:latest

You can install a specific version by replacing latest with a version number as shown in the following example:

confluent connect plugin install confluentinc/kafka-connect-tibco-source:1.0.0-preview

TIBCO Client library

The Kafka Connect TIBCO Source connector does not include the TIBCO JMS client library.

If you are running a multi-node Connect cluster, the connector and TIBCO JMS client JAR must be installed on every Connect worker in the cluster. The following section for details.

Installing the TIBCO JMS client library

This connector relies on a provided tibjms client JAR that is included in the TIBCO EMS installation. The connector will fail to create a connection to TIBCO EMS if you have not installed the JAR on each Connect worker node.

The installation steps are:

  1. Download and Install TIBCO Enterprise Message Service™ (Mac or Linux). If you have already installed TIBCO EMS, skip to the next step.
  2. Unzip the download and copy only the tibco/ems/{version}/lib/tibjms.jar file into the share/java/kafka-connect-tibco-source directory of your Confluent Platform installation on each worker node. If you are using a different installation, find the location of the Confluent TIBCO Source connector JAR files and place the tibjms JAR file into the same directory.
  3. Restart all of the Connect worker nodes.

Install the connector manually

Download and extract the ZIP file for your connector and then follow the manual connector installation instructions.

Quick start

This quick start uses the TIBCO Source connector to consume records from TIBCO Enterprise Message Service™ - Community Edition and sends them to Kafka.

  1. Download TIBCO Enterprise Message Service™ - Community Edition (Mac or Linux) and run the appropriate installer. For more details, see the TIBCO Enterprise Message Service™ Installation Guide. Similar documentation is available for each version of TIBCO EMS.

  2. Install the connector through the Confluent Hub Client.

    # run from your Confluent Platform installation directory
    confluent connect plugin install confluentinc/kafka-connect-tibco-source:latest
    
  3. Install the TIBCO JMS Client Library.

  4. Start Confluent Platform.

    confluent local start
    
  5. Create a connector-quickstart queue with the TIBCO Admin Tool.

    # connect to TIBCO with the Admin Tool (PASSWORD IS EMPTY)
    tibco/ems/8.4/bin/tibemsadmin -server "tcp://localhost:7222" -user admin
    
    > create queue connector-quickstart
    
  6. Compile the TIBCO Java samples so that they can be run in the following step.

    # setup Java's classpath so that the Java compiler can find the imports of the samples
    cd tibco/ems/8.4/samples/java
    export TIBEMS_JAVA=tibco/ems/8.4/lib
    CLASSPATH=${TIBEMS_JAVA}/jms-2.0.jar:${CLASSPATH}
    CLASSPATH=.:${TIBEMS_JAVA}/tibjms.jar:${TIBEMS_JAVA}/tibjmsadmin.jar:${CLASSPATH}
    export CLASSPATH
    
    # compile the java classes (run from the tibco/ems/8.4/samples/java directory)
    javac *.java
    
  7. Produce a set of messages to the connector-quickstart queue.

    cd tibco/ems/8.4/samples/java
    
    # produce 5 test messages
    java tibjmsMsgProducer -user admin -queue connector-quickstart m1 m2 m3 m4 m5
    
    ------------------------------------------------------------------------
    tibjmsMsgProducer SAMPLE
    ------------------------------------------------------------------------
    Server....................... localhost
    User......................... admin
    Destination.................. connector-quickstart
    Send Asynchronously.......... false
    Message Text.................
    m1
    m2
    m3
    m4
    m5
    ------------------------------------------------------------------------
    
    Publishing to destination 'connector-quickstart'
    
    Published message: m1
    Published message: m2
    Published message: m3
    Published message: m4
    Published message: m5
    
  8. Create a tibco-source.json file with the following contents:

    {
      "name": "TibcoSourceConnector",
      "config": {
        "connector.class": "io.confluent.connect.tibco.TibcoSourceConnector",
        "tasks.max": "1",
        "kafka.topic": "from-tibco-messages",
        "tibco.url": "tcp://localhost:7222",
        "tibco.username": "admin",
        "tibco.password": "",
        "jms.destination.type": "queue",
        "jms.destination.name": "connector-quickstart",
        "key.converter": "org.apache.kafka.connect.storage.StringConverter",
        "value.converter": "org.apache.kafka.connect.storage.StringConverter",
        "confluent.topic.bootstrap.servers": "localhost:9092",
        "confluent.topic.replication.factor": "1"
      }
    }
    
  9. Load the TIBCO Source connector.

    Caution

    You must include a double dash (--) between the topic name and your flag. For more information, see this post.

    confluent local load tibco --config tibco-source.json
    
  10. Confirm that the connector is in a RUNNING state.

    confluent local status TibcoSourceConnector
    
  11. Confirm the messages were delivered to the from-tibco-messages topic in Kafka.

    confluent local consume from-tibco-messages --from-beginning