Create a MongoDB sink connector (Lenses.io) for Aiven for Apache Kafka®

Use the MongoDB sink connector by Lenses.io to write data from Apache Kafka® topics into a MongoDB database. This connector supports KCQL transformations to filter and map topic data before inserting it into MongoDB.

Aiven supports two MongoDB sink connectors with different capabilities:

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

caution

Version compatibility

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

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

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

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

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

Prerequisites

• An Aiven for Apache Kafka® service with Kafka Connect enabled, or a dedicated Aiven for Apache Kafka Connect® service
• A MongoDB database with accessible connection credentials
• The following MongoDB and Kafka connection details:

  • MONGODB_USERNAME: MongoDB username

  • MONGODB_PASSWORD: MongoDB password

  • MONGODB_HOST: MongoDB hostname

  • MONGODB_PORT: MongoDB port

  • MONGODB_DATABASE_NAME: MongoDB database name

  • TOPIC_LIST: Comma-separated list of Kafka topics to sink

  • KCQL_TRANSFORMATION: KCQL mapping statement. For example:

    INSERT INTO MONGODB_COLLECTION
    SELECT * FROM APACHE_KAFKA_TOPIC

  • Schema Registry details (required only for Avro format):

    • APACHE_KAFKA_HOST: Kafka host
    • SCHEMA_REGISTRY_PORT: Schema Registry port
    • SCHEMA_REGISTRY_USER: Schema Registry username
    • SCHEMA_REGISTRY_PASSWORD: Schema Registry password
• Access to one of the following setup methods:
  • Aiven Console
  • Aiven CLI
  • Aiven Provider for Terraform
• Authentication configured for your Aiven project (for example, set the AIVEN_API_TOKEN environment variable if using CLI or Terraform)

tip

Aiven for Apache Kafka® uses Karapace as the built-in Schema Registry.

Find connection details in the Aiven Console under Overview > Connection information > Schema Registry, or using the Aiven CLI command avn service get.

Create a MongoDB sink connector configuration file

Create a file named mongodb_sink.json with the following configuration:

{
  "name": "mongodb-sink",
  "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: Connector name
• connector.class: Class name of the MongoDB sink connector
• connect.mongo.connection: MongoDB connection URI with credentials
• connect.mongo.db: MongoDB database name
• connect.mongo.kcql: KCQL statement defining how Kafka topic data maps to MongoDB
• key.converter and value.converter: Configure the data format and schema registry details
• topics: Kafka topics to sink data from

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, configure the following parameters:

• value.converter.schema.registry.url: Schema Registry URL in the format https://APACHE_KAFKA_HOST:SCHEMA_REGISTRY_PORT. Retrieve these values from the prerequisites.
• value.converter.basic.auth.credentials.source: Set to USER_INFO to authenticate with a username and password.
• value.converter.schema.registry.basic.auth.user.info: Schema Registry credentials in the format SCHEMA_REGISTRY_USER:SCHEMA_REGISTRY_PASSWORD. Retrieve these values from the prerequisites.

For advanced configurations such as batch size, write strategy, or custom transformations, see the Lenses.io MongoDB sink connector reference.

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, enable it under Service settings > Actions > Enable Kafka Connect.
5. From the list of sink connectors, select Stream Reactor MongoDB Sink, and click Get started.
6. In the Common tab, find the Connector configuration text box and click Edit.
7. Paste the configuration from your mongodb_sink.json file.
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 KCQL mapping.

CLI

To create the connector using the Aiven CLI, run:

avn service connector create SERVICE_NAME @mongodb_sink.json

Replace:

• SERVICE_NAME: Name of your Aiven for Apache Kafka or Kafka Connect service
• @mongodb_sink.json: Path to your configuration file

Example: Sink data to MongoDB

The following examples show how to write Kafka topic data to MongoDB collections using KCQL transformations.

Insert mode

If the Kafka topic students contains:

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

Use this configuration to insert all records into a MongoDB collection named studentscol:

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

Upsert mode

To ensure only one document per unique name is stored, use upsert mode:

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

This configuration updates existing records based on the name field instead of inserting duplicates.

After the connector runs, the MongoDB collection studentscol contains:

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

Related pages

• MongoDB sink connector for Aiven for Apache Kafka®
• MongoDB source connector for Aiven for Apache Kafka®