Streaming pipeline

Set up Kafka

If you don’t have Kafka already setup, here is a quick guide on how to run and use Kafka locally:

Using Kafka locally

  1. In your terminal, clone this repository: git clone
  2. Change directory into that repository: cd kafka-docker.
  3. Edit the docker-compose.yml file to match this:
    version: "2"
        image: wurstmeister/zookeeper:3.4.6
          - "2181:2181"
        build: .
        container_name: docker_kafka
          - "9092:9092"
          - "9093"
          KAFKA_ADVERTISED_LISTENERS: INSIDE://kafka:9093,OUTSIDE://localhost:9092
          KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
          - /var/run/docker.sock:/var/run/docker.sock
  4. Start Docker: docker-compose up
  5. Start a terminal session in the running container:
    docker exec -i -t -u root $(docker ps | grep docker_kafka | cut -d' ' -f1) /bin/bash
  6. Create a topic:
    $KAFKA_HOME/bin/ --create --partitions 4 --bootstrap-server kafka:9092 --topic test
  7. List all available topics in Kafka instance:
    $KAFKA_HOME/bin/ --bootstrap-server kafka:9092 --list
  8. Start a producer on topic named test:
    $KAFKA_HOME/bin/ --broker-list kafka:9092 --topic=test
  9. Send messages to the topic named test by typing the following in the terminal:
    >this is a test
    >test 1
    >test 2
    >test 3
  10. Open another terminal and start a consumer on the topic named test:
$KAFKA_HOME/bin/ --from-beginning --bootstrap-server kafka:9092 --topic=test
  1. The output should look something like this:
test 1
test 3
this is a test
test 2

Original source of instructions.

Build streaming pipeline

Start Mage

Using Kafka locally in a Docker container

Start Mage using Docker. Run the following command to run Docker in network mode:

docker run -it -p 6789:6789 -v $(pwd):/home/src \
  --env AWS_ACCESS_KEY_ID=your_access_key_id \
  --env AWS_SECRET_ACCESS_KEY=your_secret_access_key \
  --env AWS_REGION=your_region \
  --network kafka-docker_default \
  mageai/mageai /app/  mage start default_repo

Change the environment variables argument depending on your cloud provider.

If the network named kafka-docker_default doesn’t exist, create a new network:

docker network create -d bridge kafka-docker_default

Check that it exists:

docker network ls

If you can’t connect to Kafka locally in a Docker container using Mage in a Docker container, do the following:

  1. Clone Mage: git clone
  2. Change directory into Mage: cd mage-ai.
  3. Edit the docker-compose.yml file to match this:
    version: '3'
        ... (original config)
          - kafka
        ... (original config)
        name: kafka-docker_default
        external: true
  4. Run the following script in your terminal: ./scripts/

This will run Mage in development mode; which runs it in a Docker container using docker compose instead of docker run.

Using Kafka without a Docker container

Start Mage using Docker. If you haven’t done this before, refer to the setup guide.

Create a new pipeline

  1. Open Mage in your browser.

  2. Click + New pipeline, then select Streaming.

  3. Add a data loader block, select Kafka, and paste the following:

    connector_type: kafka
    bootstrap_server: "localhost:9092"
    topic: test
    consumer_group: unique_consumer_group
    batch_size: 100
    1. By default, the bootstrap_server is set to localhost:9092. If you’re running Mage in a container, the bootstrap_server should be kafka:9093.
    2. Messages are consumed from source in micro batch mode for better efficiency. The default batch size is 100. You can adjust the batch size in the source config.
  4. Add a transformer block and paste the following:

    from typing import Dict, List
    if 'transformer' not in globals():
        from mage_ai.data_preparation.decorators import transformer
    def transform(messages: List[Dict], *args, **kwargs):
        for msg in messages:
        return messages
  5. Add a data exporter block, select OpenSearch and paste the following:

    connector_type: opensearch
    index_name: python-test-index
    1. Change the host to match your OpenSearch domain’s endpoint.
    2. Change the index_name to match the index you want to export data into.

Test pipeline

Open the streaming pipeline you just created, and in the right side panel near the bottom, click the button Execute pipeline to test the pipeline.

You should see an output like this:

[streaming_pipeline_test] Start initializing kafka consumer.
[streaming_pipeline_test] Finish initializing kafka consumer.
[streaming_pipeline_test] Start consuming messages from kafka.

Publish messages using Python

  1. Open a terminal on your local workstation.

  2. Install kafka-python:

    pip install kafka-python
  3. Open a Python shell and write the following code to publish messages:

    from kafka import KafkaProducer
    from random import random
    import json
    topic = 'test'
    producer = KafkaProducer(
    def publish_messages(limit):
        for i in range(limit):
            data = {
                'title': 'test_title',
                'director': 'Bennett Miller',
                'year': '2011',
                'rating': random(),
            producer.send(topic, json.dumps(data).encode('utf-8'))

Once you run the code snippet above, go back to your streaming pipeline in Mage and the output should look like this:

[streaming_pipeline_test] Start initializing kafka consumer.
[streaming_pipeline_test] Finish initializing kafka consumer.
[streaming_pipeline_test] Start consuming messages from kafka.
[streaming_pipeline_test] [Kafka] Receive message 2:16: v=b'{"title": "test_title", "director": "Bennett Miller", "year": "2011", "rating": 0.7010424523477785}', time=1665618592.226788
[streaming_pipeline_test] [Kafka] Receive message 0:16: v=b'{"title": "test_title", "director": "Bennett Miller", "year": "2011", "rating": 0.7886308380991354}', time=1665618592.2268753
[streaming_pipeline_test] [Kafka] Receive message 0:17: v=b'{"title": "test_title", "director": "Bennett Miller", "year": "2011", "rating": 0.0673276352704153}', time=1665618592.2268832
[streaming_pipeline_test] [Kafka] Receive message 3:10: v=b'{"title": "test_title", "director": "Bennett Miller", "year": "2011", "rating": 0.37935417366095525}', time=1665618592.2268872
[streaming_pipeline_test] [Kafka] Receive message 3:11: v=b'{"title": "test_title", "director": "Bennett Miller", "year": "2011", "rating": 0.21110511524126563}', time=1665618592.2268918
[streaming_pipeline_test] {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.7010424523477785}
[streaming_pipeline_test] {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.7886308380991354}
[streaming_pipeline_test] {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.0673276352704153}
[streaming_pipeline_test] {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.37935417366095525}
[streaming_pipeline_test] {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.21110511524126563}
[streaming_pipeline_test] [Opensearch] Batch ingest data [{'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.7010424523477785}, {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.7886308380991354}, {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.0673276352704153}, {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.37935417366095525}, {'title': 'test_title', 'director': 'Bennett Miller', 'year': '2011', 'rating': 0.21110511524126563}], time=1665618592.2294626

Consume messages using Python

If you want to programmatically consume messages from a Kafka topic, here is a code snippet:

from kafka import KafkaConsumer
import time

topic = 'test'
consumer = KafkaConsumer(

for message in consumer:
    print(f"{message.partition}:{message.offset}: v={message.value}, time={time.time()}")

Run in production

  1. Create a trigger.
  2. Once trigger is created, click the Start trigger button at the top of the page to make the streaming pipeline active.