Create a MongoDB sink connector by Lenses.io#

The MongoDB sink connector enables you to move data from an Aiven for Apache Kafka® cluster to a MongoDB database. The Lenses.io implementation enables you to write KCQL transformations on the topic data before sending it to the MongoDB database.

Note

Aiven offers two distinct MongoDB sink connectors, each one having different implementation and parameters:

This document refers to the MongoDB sink connector by Lenses.io, you can browse the MongoDB implementation in the related document

You can check the full set of available parameters and configuration options in the connector’s documentation.

Prerequisites#

To setup a MongoDB sink connector, you need an Aiven for Apache Kafka service with Kafka Connect enabled or a dedicated Aiven for Apache Kafka Connect cluster.

Furthermore you need to collect the following information about the target MongoDB database upfront:

  • MONGODB_USERNAME: The database username to connect

  • MONGODB_PASSWORD: The password for the username selected

  • MONGODB_HOST: the MongoDB hostname

  • MONGODB_PORT: the MongoDB port

  • MONGODB_DATABASE_NAME: The name of the MongoDB database

  • TOPIC_LIST: The list of topics to sink divided by comma

  • KCQL_TRANSFORMATION: The KCQL syntax to parse the topic data, should be in the format:

    INSERT | UPSERT
    INTO MONGODB_COLLECTION_NAME
    SELECT LIST_OF_FIELDS
    FROM APACHE_KAFKA_TOPIC
    
  • APACHE_KAFKA_HOST: The hostname of the Apache Kafka service, only needed when using Avro as data format

  • SCHEMA_REGISTRY_PORT: The Apache Kafka’s schema registry port, only needed when using Avro as data format

  • SCHEMA_REGISTRY_USER: The Apache Kafka’s schema registry username, only needed when using Avro as data format

  • SCHEMA_REGISTRY_PASSWORD: The Apache Kafka’s schema registry user password, only needed when using Avro as data format

Note

The Apache Kafka related details are available in the Aiven console service Overview tab or via the dedicated avn service get command with the Aiven CLI.

The SCHEMA_REGISTRY related parameters are available in the Aiven for Apache Kafka® service page, Overview tab, and Schema Registry subtab

As of version 3.0, Aiven for Apache Kafka no longer supports Confluent Schema Registry. For more information, read the article describing the replacement, Karapace

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 you want to use for 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, you need to 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 a Kafka Connect connector with the Aiven Console#

To create the connector, access the Aiven Console and select the Aiven for Apache Kafka® or Aiven for Apache Kafka Connect® service where the connector needs to be defined, then:

  1. Click on the Connectors tab

  2. Clink on Create New Connector, the button is enabled only for services with Kafka Connect enabled.

  3. Select the Stream Reactor MongoDB Sink

  4. Under the Common tab, locate the Connector configuration text box and click on Edit

  5. Paste the connector configuration (stored in the mongodb_sink.json file) in the form

  6. Click on Apply

Note

The Aiven Console parses the configuration file and fills the relevant UI fields. You can review the UI fields across the various tab and change them if necessary. The changes will be reflected in JSON format in the Connector configuration text box.

  1. After all the settings are correctly configured, click on Create new connector

  2. Verify the connector status under the Connectors tab

  3. Verify the presence of the data in the target MongoDB service, the index name is equal to the Apache Kafka topic name

Note

Connectors can be created also using the dedicated Aiven CLI command.

Example: Create a MongoDB sink connector in insert mode#

If you have a topic named students containing the following data that you want to move to MongoDB:

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

You can sink the students topic to MongoDB with the following connector configuration, after replacing the placeholders for MONGODB_HOST, MONGODB_PORT, MONGODB_DB_NAME, MONGODB_USERNAME and MONGODB_PASSWORD:

{
    "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"
}

The configuration file contains the following peculiarities:

  • "topics": "students": setting the topic to sink

  • "database": "MONGODB_DB_NAME": the database used is the one referenced by the placeholder MONGODB_DB_NAME

  • "value.converter": "org.apache.kafka.connect.json.JsonConverter" and "value.converter.schemas.enable": "false": the topic value is in JSON format without a schema

  • "connect.mongo.kcql": "INSERT into studentscol SELECT * FROM students": the connector logic is to insert every topic message as new document into a collection called studentscol.

Once the connector is created successfully, you should see a collection named studentscol in the MongoDB database referenced by the MONGODB_DB_NAME placeholder with three documents in it.

Example: Create a MongoDB sink connector in upsert mode#

If you have a topic named students containing the following data that you want to move to MongoDB, but having one document per person name in the following messages:

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

You can sink the students topic to MongoDB with the following connector configuration, after replacing the placeholders for MONGODB_HOST, MONGODB_PORT, MONGODB_DB_NAME, MONGODB_USERNAME and MONGODB_PASSWORD:

{
    "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"
}

The configuration file contains the following peculiarities:

  • "topics": "students": setting the topic to sink

  • "database": "MONGODB_DB_NAME": the database used is the one referenced by the placeholder MONGODB_DB_NAME

  • "value.converter": "org.apache.kafka.connect.json.JsonConverter" and "value.converter.schemas.enable": "false": the topic value is in JSON format without a schema

  • "connect.mongo.kcql": "UPSERT into studentscol SELECT * FROM students PK name": the connector logic is to upsert every topic message as new document into a collection called studentscol, the primary key is set to the name field (PK name).

Once the connector is created successfully, you should see a collection named studentscol in the MongoDB database referenced by the MONGODB_DB_NAME placeholder. The collection should contain two documents since the name carlo was present two times:

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