Create a sink connector by Lenses.io from Apache Kafka® to MongoDB

The MongoDB Stream Reactor sink connector enables you to move data from an Aiven for Apache Kafka cluster to a MongoDB database. It uses KCQL transformations to filter and map topic data before sending it to MongoDB.

note

Aiven supports two different MongoDB sink connectors. Each has a separate implementation and configuration options:

• MongoDB sink connector by MongoDB
• MongoDB sink connector by Lenses.io

This document covers the connector by Lenses.io. For the MongoDB connector by MongoDB, see the related document.

caution

Version compatibility

Stream Reactor version 9.0.2 includes class and package name changes introduced in version 6.0.0 by Lenses. These changes standardize connector class names and converter package names.

Version 9.x is not compatible with version 4.2.0. To continue using version 4.2.0, set the connector version before upgrading.

If you are upgrading from version 4.2.0, you must recreate the connector using the updated class name. For example:

"connector.class": "io.lenses.streamreactor.connect.<connector_path>.<ConnectorClassName>"

For details about the changes, see the Stream Reactor release notes.

Prerequisites

• An Aiven for Apache Kafka service with Apache Kafka Connect enabled or a dedicated Aiven for Apache Kafka Connect cluster.

• Gather the following information for the target MongoDB database:

  • MONGODB_USERNAME: The MongoDB username.

  • MONGODB_PASSWORD: The MongoDB password.

  • MONGODB_HOST: The MongoDB hostname.

  • MONGODB_PORT: The MongoDB port.

  • MONGODB_DATABASE_NAME: The target MongoDB database name.

  • TOPIC_LIST: A comma-separated list of Kafka topics to sink.

  • KCQL_TRANSFORMATION: A KCQL statement to map topic data to MongoDB collections. Use the following format:

    INSERT | UPSERT INTO MONGODB_COLLECTION
    SELECT LIST_OF_FIELDS
    FROM APACHE_KAFKA_TOPIC

  • APACHE_KAFKA_HOST: The Apache Kafka host. Required only when using Avro.

  • SCHEMA_REGISTRY_PORT: The schema registry port. Required only when using Avro.

  • SCHEMA_REGISTRY_USER: The schema registry username. Required only when using Avro.

  • SCHEMA_REGISTRY_PASSWORD: The schema registry password. Required only when using Avro.

    note

    If you are using Aiven for MongoDB and Aiven for Apache Kafka, get all required connection details, including schema registry information, from the Connection information section on the Overview page.

    As of version 3.0, Aiven for Apache Kafka uses Karapace as the schema registry and no longer supports the Confluent Schema Registry.

For a complete list of supported parameters and configuration options, see the connector's documentation.

Create a connector configuration file

Create a file named mongodb_sink.json and add the following configuration:

{
  "name": "CONNECTOR_NAME",
  "connector.class": "com.datamountaineer.streamreactor.connect.mongodb.sink.MongoSinkConnector",
  "topics": "TOPIC_LIST",
  "connect.mongo.connection": "mongodb://MONGODB_USERNAME:MONGODB_PASSWORD@MONGODB_HOST:MONGODB_PORT",
  "connect.mongo.db": "MONGODB_DATABASE_NAME",
  "connect.mongo.kcql": "KCQL_TRANSFORMATION",
  "key.converter": "io.confluent.connect.avro.AvroConverter",
  "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "key.converter.basic.auth.credentials.source": "USER_INFO",
  "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
  "value.converter": "io.confluent.connect.avro.AvroConverter",
  "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
  "value.converter.basic.auth.credentials.source": "USER_INFO",
  "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD"
}

Parameters:

• name: The connector name. Replace CONNECTOR_NAME with your desired name.
• connect.mongo.*: MongoDB connection parameters collected in the prerequisite step.
• key.converter and value.converter: Define the message data format in the Kafka topic. This example uses io.confluent.connect.avro.AvroConverter to translate messages in Avro format. The schema is retrieved from Aiven's Karapace schema registry using the schema.registry.url and related credentials.

note

The key.converter and value.converter fields define how Kafka messages are parsed and must be included in the configuration.

When using Avro as the source format, set the following:

• value.converter.schema.registry.url: Use the Aiven for Apache Kafka schema registry URL in the format https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT. Retrieve these values from the prerequisite step.
• value.converter.basic.auth.credentials.source: Set to USER_INFO, which means authentication is done using a username and password.
• value.converter.schema.registry.basic.auth.user.info: Provide the schema registry credentials in the format SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD. These values should also be retrieved from the prerequisite step.

Setup a MongoDB sink connector with Aiven Console

The following example demonstrates how to setup a MongoDB sink connector for Apache Kafka using the Aiven Console.

Define a Kafka Connect configuration file

Define the connector configurations in a file (we'll refer to it with the name mongodb_sink.json) with the following content, creating a file is not strictly necessary but allows to have all the information in one place before copy/pasting them in the Aiven Console:

{
    "name":"CONNECTOR_NAME",
    "connector.class": "com.datamountaineer.streamreactor.connect.mongodb.sink.MongoSinkConnector",
    "topics": "TOPIC_LIST",
    "connect.mongo.connection": "mongodb://MONGODB_USERNAME:MONGODB_PASSWORD@MONGODB_HOST:MONGODB_PORT",
    "connect.mongo.db": "MONGODB_DATABASE_NAME",
    "connect.mongo.kcql": "KCQL_TRANSFORMATION",
    "key.converter": "io.confluent.connect.avro.AvroConverter",
    "key.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
    "key.converter.basic.auth.credentials.source": "USER_INFO",
    "key.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD",
    "value.converter": "io.confluent.connect.avro.AvroConverter",
    "value.converter.schema.registry.url": "https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT",
    "value.converter.basic.auth.credentials.source": "USER_INFO",
    "value.converter.schema.registry.basic.auth.user.info": "SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD"
}

The configuration file contains the following entries:

• name: the connector name, replace CONNECTOR_NAME with the name to give To the connector.
• connect.mongo.connection: sink parameters collected in the prerequisite phase.
• key.converter and value.converter: defines the messages data format in the Apache Kafka topic. The io.confluent.connect.avro.AvroConverter converter translates messages from the Avro format. To retrieve the messages schema we use Aiven's Karapace schema registry as specified by the schema.registry.url parameter and related credentials.

note

The key.converter and value.converter sections define how the topic messages will be parsed and needs to be included in the connector configuration.

When using Avro as source data format, set following parameters:

• value.converter.schema.registry.url: pointing to the Aiven for Apache Kafka schema registry URL in the form of https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT with the APACHE_KAFKA_HOST and SCHEMA_REGISTRY_PORT parameters retrieved in the previous step.
• value.converter.basic.auth.credentials.source: to the value USER_INFO, since you're going to login to the schema registry using username and password.
• value.converter.schema.registry.basic.auth.user.info: passing the required schema registry credentials in the form of SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD with the SCHEMA_REGISTRY_USER and SCHEMA_REGISTRY_PASSWORD parameters retrieved in the previous step.

Create the connector

Console

1. Go to the Aiven Console.

2. Select your Aiven for Apache Kafka or Aiven for Apache Kafka Connect service.

3. Click Connectors.

4. Click Create connector if Kafka Connect is enabled on the service. If not, click Enable connector on this service.

   To enable connectors:

   1. Click Service settings in the sidebar.
   2. In the Service management section, click Actions > Enable Kafka Connect.

5. From the list of sink connectors, select Stream Reactor MongoDB Sink and click Get started.

6. On the Common tab, go to the Connector configuration text box and click Edit.

7. Paste the contents of your mongodb_sink.json configuration file into the text box.

8. Click Create connector.

9. Verify the connector status on the Connectors page.

10. Confirm that data appears in the target MongoDB collection. The collection name corresponds to the Kafka topic name defined in the KCQL statement.

CLI

To create the connector using the Aiven CLI, run:

avn service connector create SERVICE_NAME @mongodb_sink.json

Replace:

• SERVICE_NAME: Your Kafka or Kafka Connect service name.
• @mongodb_sink.json: Path to your configuration file.

Sink topic data to MongoDB

The following examples show how to sink data from a Kafka topic to a MongoDB collection.

Insert mode

If the Kafka topic students contains the following records:

{"name": "carlo", "age": 77}
{"name": "lucy", "age": 55}
{"name": "carlo", "age": 33}

Use the following connector configuration to insert each record into a MongoDB collection named studentscol:

{
  "name": "my-mongodb-sink",
  "connector.class": "com.datamountaineer.streamreactor.connect.mongodb.sink.MongoSinkConnector",
  "connect.mongo.connection": "mongodb://MONGODB_USERNAME:MONGODB_PASSWORD@MONGODB_HOST:MONGODB_PORT",
  "connect.mongo.db": "MONGODB_DB_NAME",
  "topics": "students",
  "value.converter": "org.apache.kafka.connect.json.JsonConverter",
  "value.converter.schemas.enable": "false",
  "connect.mongo.kcql": "INSERT INTO studentscol SELECT * FROM students"
}

Replace all placeholders (such as MONGODB_HOST, MONGODB_DB_NAME, and MONGODB_USERNAME) with your actual MongoDB connection details.

This configuration does the following:

• "topics": "students": Specifies the Kafka topic to sink.
• Connection settings (connect.mongo.*): Provide the MongoDB host, port, credentials, and database name.
• "value.converter" and "value.converter.schemas.enable": Set the message format. The topic uses raw JSON without a schema.
• "connect.mongo.kcql": Defines the insert logic. Each Kafka message is written as a new document in the studentscol MongoDB collection.

After creating the connector, check the MongoDB collection to verify that the data has been inserted. You should see three documents in the studentscol collection.

Upsert mode

To ensure only one document per unique name is stored, use upsert mode. If the students topic contains:

{"name": "carlo", "age": 77}
{"name": "lucy", "age": 55}
{"name": "carlo", "age": 33}

Use the following connector configuration:

{
  "name": "my-mongodb-sink",
  "connector.class": "com.datamountaineer.streamreactor.connect.mongodb.sink.MongoSinkConnector",
  "connect.mongo.connection": "mongodb://MONGODB_USERNAME:MONGODB_PASSWORD@MONGODB_HOST:MONGODB_PORT",
  "connect.mongo.db": "MONGODB_DB_NAME",
  "topics": "students",
  "value.converter": "org.apache.kafka.connect.json.JsonConverter",
  "value.converter.schemas.enable": "false",
  "connect.mongo.kcql": "UPSERT INTO studentscol SELECT * FROM students PK name"
}

This configuration performs the following:

• Uses the same connection and converter settings as the insert mode example.
• "connect.mongo.kcql": Uses UPSERT logic with PK name to update the document based on the name field.

After the connector runs, the studentscol collection contains two documents, because the record with "name": "carlo" is upserted:

{"name": "lucy", "age": 55}
{"name": "carlo", "age": 33}