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Getting Started with the MongoDB Kafka Sink Connector

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  • Get Started with the MongoDB Kafka Sink Connector
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Follow this tutorial to learn how to configure a MongoDB Kafka sink connector to read data from an Apache Kafka topic and write it to a MongoDB collection.


Complete the steps in the Kafka Connector Tutorial Setup to start the the Confluent Kafka Connect and MongoDB environment.


Create an interactive shell session on the tutorial Docker Container using the following command:

docker exec -it mongo1 /bin/bash

Create a source configuration file called simplesink.json with the following command:

nano simplesink.json

Paste the following configuration information into the file and save your changes:

"name": "mongo-tutorial-sink",
"config": {
"connector.class": "com.mongodb.kafka.connect.MongoSinkConnector",
"topics": "Tutorial2.pets",
"connection.uri": "mongodb://mongo1",
"key.converter": "",
"value.converter": "org.apache.kafka.connect.json.JsonConverter",
"value.converter.schemas.enable": false,
"database": "Tutorial2",
"collection": "pets"


The highlighted lines in the configuration properties specify converters which instruct the connector how to translate the data from Kafka.

Run the following command in the shell to start the sink connector using the configuration file you created:

cx simplesink.json


The cx command is a custom script included in the tutorial development environment. This script runs the following equivalent request to the Kafka Connect REST API to create a new connector:

curl -X POST -H "Content-Type: application/json" -d @simplesink.json http://connect:8083/connectors -w "\n"

Run the following command in the shell to check the status of the connectors:


If your sink connector started successfully, you should see the following output:

Kafka topics:
The status of the connectors:
sink | mongo-tutorial-sink | RUNNING | RUNNING | com.mongodb.kafka.connect.MongoSinkConnector
Currently configured connectors

In the same shell, create a Python script to write data to a Kafka topic.


Paste the following code into the file and save your changes:

from kafka import KafkaProducer
import json
from json import dumps
p = KafkaProducer(bootstrap_servers = ['broker:29092'], value_serializer = lambda x:dumps(x).encode('utf-8'))
data = {'name': 'roscoe'}
p.send('Tutorial2.pets', value = data)

Run the Python script:


In the same shell, connect to MongoDB using mongosh, the MongoDB shell by running the following command:

mongosh "mongodb://mongo1"

After you connect successfully, you should see the following MongoDB shell prompt:

rs0 [direct: primary] test>

At the prompt, type the following commands to retrieve all the documents in the Tutorial2.pets MongoDB namespace:

use Tutorial2

You should see the following document returned as the result:

{ _id: ObjectId("62659..."), name: 'roscoe' }

Exit the MongoDB shell by entering the command exit.


After you complete this tutorial, free resources on your computer by stopping or removing Docker assets. You can choose to remove both the Docker containers and images, or exclusively the containers. If you remove the containers and images, you must download them again to restart your MongoDB Kafka Connector development environment, which is approximately 2.4 GB in size. If you exclusively remove the containers, you can reuse the images and avoid downloading most of the large files in the sample data pipeline.


More Tutorials

If you plan to complete any more MongoDB Kafka Connector tutorials, consider removing only containers. If you don't plan to complete any more MongoDB Kafka Connector tutorials, consider removing containers and images.

Select the tab that corresponds to the removal task you want to run.

To restart the containers, follow the same steps required to start them in the Tutorial Setup.

In this tutorial, you configured a sink connector to save data from a Kafka topic to a collection in a MongoDB cluster.

Read the following resources to learn more about concepts mentioned in this tutorial:

  • Sink Connector Configuration Properties

  • Introduction to Kafka Connector Converters

  • Kafka Connect REST API

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