RabbitMQ Sink Connector for Confluent Cloud

The fully-managed RabbitMQ Sink connector for Confluent Cloud uses the AMQP protocol to communicate with RabbitMQ servers. The RabbitMQ Sink connector reads data from one or more Apache Kafka® topics and sends the data to a RabbitMQ exchange.

Note

Features

The RabbitMQ Sink connector provides the following features:

  • At least once delivery: The connector guarantees that records are delivered at least once from the Kafka topic to the RabbitMQ exchange.
  • Dead Letter Queue: This connector supports the Dead Letter Queue (DLQ) functionality. For information about accessing and using the DLQ, see the View Connector Dead Letter Queue Errors in Confluent Cloud docs.
  • Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
  • Header forwarding: The connector supports forwarding Kafka headers and metadata to the RabbitMQ message as headers. The Kafka message key can also be forwarded as the correlationID on the RabbitMQ message
  • RabbitMQ Exchange delivery: The connector supports delivering to one configured RabbitMQ exchange. When multiple Kafka topics are specified to read from, the messages are produced to this one RabbitMQ exchange.
  • Publishes bytes as payload: The RabbitMQ message supports publishing bytes as payload. The connector supports storing raw bytes in RabbitMQ using the value.converter set to org.apache.kafka.connect.converters.ByteArrayConverter. Using the ByteArrayConverter for value, the connector stores the binary serialized form (for example, JSON, Avro, Strings, etc.) of the Kafka record values in RabbitMQ as byte arrays. Applications accessing these values can then read this information from RabbitMQ and deserialize the bytes into a usable form. If your data in Kafka is not in the format you want to persist in RabbitMQ, consider using Configure Single Message Transforms for Kafka Connectors in Confluent Cloud to change records before they are sent to RabbitMQ.
  • Supports SSL/TLS security: The connector also supports SSL/TLS security to connect to the RabbitMQ server.
  • Batches records: The connector batches the records from Kafka while publishing to RabbitMQ. This is controlled by the rabbitmq.publish.max.batch.size configuration property.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Limitations

Be sure to review the following information.

Quick Start

Use this quick start to get up and running with the Confluent Cloud RabbitMQ Sink connector. The quick start shows how to select the connector and configure it to read data from Apache Kafka® topics and persist the data to a RabbitMQ exchange.

Prerequisites
  • Kafka cluster credentials. The following lists the different ways you can provide credentials.
    • Enter an existing service account resource ID.
    • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
    • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Refer to Cloud connector limitations for additional information.

Note

There is no input.data.format configuration used with this sink connector. This is because this connector defaults to ByteArrayConverter for value and StringConverter for key. No other converter is useful for this connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the RabbitMQ Sink connector card.

RabbitMQ Sink Connector Card

Step 4: Enter the connector details

Note

  • Ensure you have all your prerequisites completed.
  • An asterisk ( * ) designates a required entry.

At the Add RabbitMQ Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Step 5: Check the RabbitMQ destination

After the connector is running, verify that messages from your Kafka topic are populated to the configured RabbitMQ exchange.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
    "name" : "RabbitMQSinkConnector_0",
    "connector.class": "RabbitMQSink",
    "topics" : "pageviews",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "rabbitmq.host" : "192.168.1.99",
    "rabbitmq.exchange" : "exchange_1",
    "rabbitmq.routing.key" : "routingkey_1",
    "rabbitmq.delivery.mode" : "PERSISTENT",
    "tasks.max" : "1"
}

Note the following property definitions:

  • "name": Sets a name for your new connector.
  • "connector.class": Identifies the connector plugin name.
  • "topics": Enter Kafka topic name or comma-separated list of topic names.

  • "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

    confluent iam service-account list

    For example:

    confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2

  • "rabbitmq.<...>": See the RabbitMQ Sink configuration properties for property values and definitions. Note that the connector configuration defaults to host port 5672 (i.e., "rabbitmq.port" : "5672").

  • "tasks.max": Enter the number of tasks that the connector uses. The connector supports running one or more tasks. More tasks may improve performance.

For TLS connections, you must supply the keystore and/or truststore file contents and the file passwords when creating JSON connector configuration. The truststore and keystore files are binary files. For the rabbitmq.https.ssl.keystorefile and rabbitmq.https.ssl.truststorefile properties, you must do the following:

  1. Encode the truststore or keystore file in base64.

  2. Take the encoded string and add the data:text/plain:base64 prefix.

  3. Use the entire string as the property entry. For example:

    "rabbitmq.https.ssl.keystorefile" : "data:text/plain;base64,/u3+7QAAAAIAAAACAAAAAQAGY2xpZ...omitted...=="

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this sink connector.

See Configuration Properties for all property values and definitions.

Step 4: Load the properties file and create the connector

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file rabbitmq-sink.json

Example output:

Created connector RabbitMQSinkConnector_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |            Name           | Status  |  Type
+-----------+---------------------------+---------+-------+
lcc-ix4dl   | RabbitMQSinkConnector_0   | RUNNING | sink

Step 6: Check the RabbitMQ destination.

After the connector is running, verify that messages are populating from your Kafka topic to the configured RabbitMQ exchange.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

Which topics do you want to get data from?

topics

Identifies the topic name or a comma-separated list of topic names.

  • Type: list
  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string
  • Default: default
  • Importance: medium

How should we connect to your data?

name

Sets a name for your connector.

  • Type: string
  • Valid Values: A string at most 64 characters long
  • Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

  • Type: string
  • Default: KAFKA_API_KEY
  • Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
  • Importance: high
kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high
kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

  • Type: string
  • Importance: high
kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

  • Type: password
  • Importance: high

RabbitMQ Publishing

rabbitmq.publish.max.batch.size

Maximum number of messages in a batch to block on for acknowledgements. Maximum allowed size is 10000.

  • Type: int
  • Default: 100
  • Valid Values: [1,…,10000]
  • Importance: medium
rabbitmq.publish.ack.timeout

Period of time to wait for message acknowledgement in milliseconds. Minimum allowed timeout is 1 millisecond. Maximum allowed timeout is 60 seconds.

  • Type: int
  • Default: 10000
  • Valid Values: [1,…,60000]
  • Importance: medium
rabbitmq.publish.max.retries

Number of retries for un-acked or n-acked messages.

  • Type: int
  • Default: 1
  • Valid Values: [0,…]
  • Importance: medium

Security

rabbitmq.security.protocol

The security protocol to use when connection to RabbitMQ. Values can be PLAINTEXT or SSL.

  • Type: string
  • Default: PLAINTEXT
  • Importance: medium
rabbitmq.https.ssl.key.password

The password of the private key in the key store file or the PEM key specified in ssl.keystore.key. This is required for clients only if two-way authentication is configured.

  • Type: password
  • Importance: high
rabbitmq.https.ssl.keystorefile

The key store containing server certificate. Only required if using https

  • Type: password
  • Default: [hidden]
  • Importance: high
rabbitmq.https.ssl.keystore.password

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured. Key store password is not supported for PEM format.

  • Type: password
  • Importance: high
rabbitmq.https.ssl.truststorefile

The trust store containing server CA certificate. Only required if using https

  • Type: password
  • Default: [hidden]
  • Importance: high
rabbitmq.https.ssl.truststore.password

The password for the trust store file. If a password is not set, trust store file configured will still be used, but integrity checking is disabled. Trust store password is not supported for PEM format.

  • Type: password
  • Importance: high
rabbitmq.https.ssl.keystore.type

The file format of the key store file. This is optional for client.

  • Type: string
  • Default: JKS
  • Importance: medium
rabbitmq.https.ssl.truststore.type

The file format of the trust store file.

  • Type: string
  • Default: JKS
  • Importance: medium

Connection

rabbitmq.host

RabbitMQ host to connect to.

  • Type: string
  • Importance: high
rabbitmq.port

RabbitMQ port to connect to.

  • Type: int
  • Default: 5672
  • Valid Values: [0,…,65535]
  • Importance: medium
rabbitmq.username

Username to authenticate to RabbitMQ with.

  • Type: string
  • Importance: high
rabbitmq.password

Password to authenticate to RabbitMQ with.

  • Type: password
  • Importance: high
rabbitmq.virtual.host

The virtual host to use when connecting to the broker.

  • Type: string
  • Default: /
  • Importance: low

RabbitMQ

rabbitmq.routing.key

RabbitMQ routing key that dictates how the message travels once it reaches RabbitMQ.

  • Type: string
  • Importance: high
rabbitmq.delivery.mode

PERSISTENT or TRANSIENT, decides message durability in RabbitMQ.

  • Type: string
  • Importance: high
rabbitmq.forward.kafka.key

If enabled, the Kafka record key is converted to a string and forwarded on the correlationID property of the RabbitMQ Message. In case the Kafka record key is null and this value is true, no correlationID will be sent.

  • Type: boolean
  • Importance: low
rabbitmq.forward.kafka.metadata

If enabled, metadata from the Kafka record is forwarded on the RabbitMQ Message as headers. This includes the record’s topic, partition, and offset. The topic name is applied as a header named KAFKA_TOPIC, the partition value is applied as a header named KAFKA_PARTITION, and the offset value is applied as a header named KAFKA_OFFSET.

  • Type: boolean
  • Importance: low
rabbitmq.forward.kafka.headers

If enabled, Kafka record headers are added to the RabbitMQ Message as headers.

  • Type: boolean
  • Importance: low
rabbitmq.exchange

The destination RabbitMQ exchange where messages need to be delivered. The connector will deliver messages to this one RabbitMQ exchange even when the connector consumes from multiple specified Kafka topics.

  • Type: string
  • Importance: high

Consumer configuration

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

  • Type: long
  • Default: 300000 (5 minutes)
  • Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
  • Importance: low
max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

  • Type: long
  • Default: 500
  • Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
  • Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

  • Type: int
  • Valid Values: [1,…]
  • Importance: high

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

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