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Stream MQTT Data into Apache Kafka

Apache Kafka is a widely-used open-source distributed event streaming platform capable of handling real-time data flows between applications and systems. However, Kafka is not built for edge IoT communication, as Kafka clients require stable network connections and more hardware resources. In the IoT domain, data generated by devices and applications are transmitted using the lightweight MQTT protocol. The integration of EMQX Cloud with Kafka/Confluent allows users to seamlessly stream MQTT data into Kafka. MQTT data streams are introduced into Kafka topics, ensuring real-time processing, storage, and analysis. Currently, EMQX Cloud only supports forwarding data to Kafka.

This page provides a detailed introduction to the functional features of Kafka Data Integration and offers practical guidance for creating it. The content includes creating Kafka connectors, creating rules, and testing rules. It demonstrates how to report simulated temperature and humidity data to EMQX Cloud via the MQTT protocol and store the data in Kafka through configured data integration.

How It Works

Apache Kafka Data Integration is an out-of-the-box feature in EMQX Cloud, bridging MQTT-based IoT data and Kafka's powerful data processing capabilities. Through its built-in rule engine component, the integration simplifies the data flow and processing between the two platforms without complex coding.

The basic workflow for forwarding message data to Kafka is as follows:

  1. Message Publishing: Devices successfully connect to the EMQX Cloud deployment via the MQTT protocol and periodically publish messages containing status data. When EMQX Cloud receives these messages, it initiates the matching process in its rule engine.
  2. Message Data Processing: These MQTT messages can be processed based on topic-matching rules through the built-in rule engine. When a message arrives and passes through the rule engine, the engine evaluates predefined processing rules for that message. If any rules specify payload transformations, these transformations are applied, such as data format conversion, filtering specific information, or enriching the payload with additional context.
  3. Sending to Kafka: Rules defined in the rule engine trigger the action of forwarding messages to Kafka. Using Kafka Data Integration, MQTT topics are mapped to predefined Kafka topics, and all processed messages and data are written into Kafka topics.

Features and Advantages

Data integration with Apache Kafka brings the following features and advantages to your business:

  • Payload Transformation: During transmission, message payloads can be processed through defined SQL rules. For example, payloads containing real-time metrics like total message count, successful/failed delivery count, and message rate can undergo data extraction, filtering, enrichment, and transformation before being input into Kafka.
  • Effective Topic Mapping: Through configured Kafka data integration, numerous IoT business topics can be mapped to Kafka topics. EMQX supports mapping MQTT user properties to Kafka headers and employs various flexible topic mapping methods, including one-to-one, one-to-many, many-to-many, and support for MQTT topic filters (wildcards).
  • Flexible Partition Selection Strategy: Supports forwarding messages to the same Kafka partition based on MQTT topics or clients.
  • Processing Capability under High Throughput: EMQX Kafka producers support synchronous and asynchronous write modes, allowing you to flexibly balance between real-time priority and performance priority data write strategies.
  • Runtime Metrics: Supports viewing runtime metrics for each data bridge, such as total message count, success/failure count, current rate, etc.

These features enhance integration capabilities and flexibility, helping you build an effective and robust IoT platform architecture. Your growing IoT data can be transmitted under stable network connections and further effectively stored and managed.

Before You Start

This section introduces the preparatory work needed to create Kafka Data Integration in EMQX Cloud.


Install Kafka and Create Topics

  1. Install Kafka.

    # Install Zookeeper
    docker run -d --restart=always \
        --name zookeeper \
        -p 2181:2181 \
    # Install Kafka, opening port 9092
    docker run -d  --restart=always --name mykafka \
        -p 9092:9092 \
        -e HOST_IP=localhost \
        -e KAFKA_ADVERTISED_PORT=9092 \
        -e KAFKA_BROKER_ID=1 \
        -e KAFKA_ZOOKEEPER_CONNECT=<Server IP>:2181 \
        -e ZK=<Server IP> \
  2. Create a topic.

    # Enter the Kafka instance and create the emqx topic
    $ docker exec -it mykafka /opt/kafka/bin/ --zookeeper <broker IP>:2181 --replication-factor 1 --partitions 1 --topic emqx --create

"Created topic emqx." indicates successful creation.

Create a Kafka Connector

Before creating data integration rules, you need to first create a Kafka connector to access the Kafka server.

  1. Go to your deployment. Click Data Integration from the left-navigation menu.
  2. If it is the first time for you to create a connector, select Kafka under the Data Forward category. If you have already created connectors, select New Connector and then select Kafka under the Data Forward category.
  3. On the New Connector page, configure the following options:
    • Connector Name: The system will automatically generate a connector name, or you can name it yourself. In this example, you can use my_kafkaserver.
    • Bootstrap Hosts: Fill in the host list, ensuring your Kafka service can be normally accessed through the network.
    • Use default values for other settings, or configure them according to your business needs.
  4. Click the Test button. If the Kafka service is accessible, a success prompt will be returned.
  5. Click the New button to complete the creation.

Create Rules

Next, you need to create a rule to specify the data to be written and add corresponding actions in the rule to forward the processed data to Kafka.

  1. Click New Rule in Rules area or click the New Rule icon in the Actions column of the connector you just created.

  2. Enter the rule matching SQL statement in the SQL editor. The following SQL example reads the message reporting time up_timestamp, client ID, and message body (Payload) from messages sent to the temp_hum/emqx topic, extracting temperature and humidity.

    payload.temp as temp, 
    payload.hum as hum

    You can use Enable Test to simulate data input and test the results.

  3. Click Next to add an action.

  4. Select the connector you just created from the Connector dropdown box.

  5. Configure the following information:

    • Action Name: The system will automatically generate an action name, or you can name it yourself.

    • Kafka Topic Name: Fill in the previously created topic emqx.

    • Kafka Headers: Define Kafka header according to your business needs.

    • In the message body settings, the Message Key defaults to the client ID obtained from the rule, but you can modify it as needed. In the Message Value, you can enter the temperature and humidity values to be forwarded.

      # Kafka message value
      {"temp": ${temp}, "hum": ${hum}}
    • Use default values for other settings, or configure them according to your business needs.

  6. Click the Confirm button to complete the rule creation.

  7. In the Successful new rule pop-up, click Back to Rules, thus completing the entire data integration configuration chain.

Test Rules

You are recommended to use MQTTX to simulate temperature and humidity data reporting, but you can also use any other client.

  1. Use MQTTX to connect to the deployment and send messages to the following Topic.

    • topic: temp_hum/emqx

    • payload:

        "temp": "27.5",
        "hum": "41.8"
  2. Check if the message has been forwarded to Kafka.

    # Enter the Kafka instance and view the emqx topic
    $ docker exec -it mykafka /opt/kafka/bin/ --bootstrap-server <broker IP>:9092  --topic emqx --from-beginning
  3. View operational data in the console. Click the rule ID in the rule list, and you can see the statistics of the rule and the statistics of all actions under this rule.