MQTT Sink Connector for Confluent Cloud

The fully-managed MQTT Sink connector for Confluent Cloud streams data from Apache Kafka® to an MQTT broker.

Note

Features

The MQTT Sink connector provides the following features:

  • At least once delivery: The connector guarantees that records are delivered at least once to the MQTT topic.
  • Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
  • Schemas: The connector supports Avro, JSON Schema, and Protobuf input data formats. Schema Registry must be enabled to use a Schema Registry-based format. Note that the connector only supports bytes and string schemas. It does not support structs. If you want to have struct type schemas, you can store the struct data as bytes and select bytes in the connector.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

Quick Start

Use this quick start to get up and running with the Confluent Cloud MQTT sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to an MQTT broker.

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.

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 MQTT Sink connector card.

MQTT 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 MQTT 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 results on the broker

Verify that new records are being added to the MQTT broker

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

{
  "connector.class": "MqttSink",
  "name": "MqttSink_0",
  "input.data.format": "AVRO",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "mqtt.server.uri" : ""tcp://192.0.0.1:1881",
  "topics" : "kafka_topic_0",
  "tasks.max" : "1"
}

Note the following property definitions:

  • "name": Sets a name for your new connector.
  • "connector.class": Identifies the connector plugin name.
  • "input.data.format": Supports AVRO, BYTES, JSON, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format.

  • "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

  • "mqtt.server.uri": The MQTT broker URI. Must be in the format <PROTOCOL>//:URI. The supported protocols are TCP, SSL, WS, and WSS. For TLS connections you must additionally provide credentials and upload Keystore and Truststore files. See the MQTT Sink configuration properties for these property values and definitions.

    Note

    If the MQTT broker does not support anonymous mode, you must add the following two additional properties:

    • "mqtt.username": "<mqtt_broker_username>"
    • "mqtt.password": "<user_password>"

  • "topics": The Kafka topic name (or comma-separated topic names) where the data for the MQTT broker is located.

  • "tasks.max": Enter the number of tasks in use by the connector. The connector supports multiple tasks. More tasks may improve performance.

Note

The MQTT topic name where data lands is the same as the Kafka topic name.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and definitions.

Step 4: Load the configuration 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 mqtt-server-sink-config.json

Example output:

Created connector MqttSink_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect plugin list

Example output:

ID          |       Name   | Status  | Type
+-----------+--------------+---------+------+
lcc-ix4dl   | MqttSink_0   | RUNNING | sink

Step 6: Check the results in the database.

Verify that new records are being added to the MQTT database.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

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

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

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

  • Type: string
  • 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

How should we connect to MQTT Broker?

mqtt.server.uri

The URI of the MQTT broker. This must be given in the format <PROTOCOL>//:URI. The supported protocols are tcp, ssl, ws, wss. Note that for a connection that uses TLS, you must provide the required key stores and trust stores.

  • Type: list
  • Importance: high
mqtt.username

Username to connect with, or blank if a username is not required. Note: username field is masked as it may contain sensitive information

  • Type: password
  • Importance: high
mqtt.password

Password to connect with, or blank if a password is not required.

  • Type: password
  • Default: [hidden]
  • Importance: high

MQTT secure connection

mqtt.ssl.key.store.file

The location of the Java KeyStore file containing the private key to use for authenticating with the server.

  • Type: password
  • Default: [hidden]
  • Importance: low
mqtt.ssl.key.store.password

Password used to open the Java KeyStore file.

  • Type: password
  • Default: [hidden]
  • Importance: medium
mqtt.ssl.key.password

Password for the client certificate contained in the Java KeyStore.

  • Type: password
  • Default: [hidden]
  • Importance: high
mqtt.ssl.trust.store.file

The location of the Java TrustStore file containing the certificates required to validate the SSL connection to the server.

  • Type: password
  • Default: [hidden]
  • Importance: medium
mqtt.ssl.trust.store.password

Password used to open the Java TrustStore file.

  • Type: password
  • Default: [hidden]
  • Importance: medium

Connection Details

mqtt.clean.session.enabled

Sets whether the client and server should remember state across restarts and reconnects. Note that for unreceived messages to be received after reconnect you should set the QOS to 1 or above.

  • Type: boolean
  • Default: false
  • Importance: medium
mqtt.connect.timeout.seconds

Sets the connection timeout value in seconds.

  • Type: int
  • Default: 30
  • Importance: medium
mqtt.keepalive.interval.seconds

This value, measured in seconds, defines the maximum time interval between messages sent or received. In the absence of a data-related message during the time period, the client sends a very small “ping” message, which the server will acknowledge.

  • Type: int
  • Default: 60
  • Importance: medium
max.retry.time.ms

The maximum time in milliseconds (ms) the connector will spend backing off and retrying failed operations (connecting to the MQTT broker and publishing records).

  • Type: int
  • Default: 30000 (30 seconds)
  • Importance: medium
mqtt.retained.enabled

Set it to true for messages to be retained for future clients.

  • Type: boolean
  • Default: true
  • Importance: medium
mqtt.qos

The QOS level to write messages to the MQTT broker with.

  • Type: int
  • Default: 0
  • Importance: medium

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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