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

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 <https://docs.lenses.io/5.0/integrations/connectors/stream-reactor/sinks/mongosinkconnector/>`_ 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:
    
    * The standard `MongoDB sink connector by MongoDB <https://docs.mongodb.com/kafka-connector/current/>`_
    * The `MongoDB sink connector by Lenses.io <https://docs.lenses.io/connectors/sink/mongo.html>`_

    This document refers to the MongoDB sink connector by Lenses.io, you can browse the MongoDB implementation in the :doc:`related document <mongodb-sink-mongo>`
    
    You can check the full set of available parameters and configuration options in the `connector's documentation <https://docs.lenses.io/connectors/sink/mongo.html>`_.

.. _connect_mongodb_lenses_sink_prereq:

Prerequisites
-------------

To setup a MongoDB sink connector, you need an Aiven for Apache Kafka service :doc:`with Kafka Connect enabled <enable-connect>` or a :ref:`dedicated Aiven for Apache Kafka Connect cluster <apache_kafka_connect_dedicated_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 <https://console.aiven.io/>`_ service *Overview tab* or via the dedicated ``avn service get`` command with the :ref:`Aiven CLI <avn_service_get>`.

    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 <https://help.aiven.io/en/articles/5651983>`_

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 <https://console.aiven.io/>`_.

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 <https://console.aiven.io/>`_:

.. code-block:: json

    {
        "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 :ref:`prerequisite <connect_mongodb_lenses_sink_prereq>` 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 <https://github.com/aiven/karapace>`_ 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 :ref:`retrieved in the previous step <connect_mongodb_lenses_sink_prereq>`.
    * ``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 :ref:`retrieved in the previous step <connect_mongodb_lenses_sink_prereq>`. 


Create a Kafka Connect connector with the Aiven Console
'''''''''''''''''''''''''''''''''''''''''''''''''''''''
To create a Apache Kafka Connect connector, follow these steps: 

1. Log in to the `Aiven Console <https://console.aiven.io/>`_ and select the Aiven for Apache Kafka® or Aiven for Apache Kafka Connect® service where the connector needs to be defined. 
2. Select **Connectors** from the left sidebar. 
3. Select **Create New Connector**, the button is enabled only for services :doc:`with Kafka Connect enabled <enable-connect>`.
4. Select **Stream Reactor MongoDB Sink**.
5. In the **Common** tab, locate the **Connector configuration** text box and select on **Edit**.
6. Paste the connector configuration (stored in the ``mongodb_sink.json`` file) in the form.
7. Select **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.

8. After all the settings are correctly configured, select **Create connector**.
9. Verify the connector status under the **Connectors** screen. 
10. Verify the presence of the data in the target MongoDB service, the index name is equal to the Apache Kafka topic name.

.. Note::

    You can also create connectors using the :ref:`Aiven CLI command <avn_service_connector_create>`.

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:

.. code-block::

    {"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``:

.. code-block:: json

    {
        "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:

.. code-block::

    {"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``:

.. code-block:: json

    {
        "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:

.. code-block::

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