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MongoDB Atlas Source Connector for Confluent Cloud

Note

This is a Quick Start for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see MongoDB Source Connector for Confluent Platform.

The Kafka Connect MongoDB Atlas Source connector for Confluent Cloud moves data from a MongoDB replica set into an Apache Kafka® cluster. The connector configures and consumes change stream event documents and publishes them to a Kafka topic.

Features

Note

This connector supports MongoDB Atlas only and will not work with a self-managed MongoDB database.

The MongoDB Atlas Source connector provides the following features:

• Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <prefix>.<database-name>.<collection-name>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. You add the prefix when setting up the connection in the Quick Start steps. For more information, see Maximum message size. Note that if you want to create topics with specific settings, create the topics before running this connector.
• Database authentication: Uses password authentication.
• Output data formats: Supports Avro, Byte, JSON (schemaless), JSON Schema, Protobuf or String output data. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• Large size records: Supports MongoDb documents up to 8 MB in size on dedicated Kafka clusters and 2 MB on other clusters.
• Select configuration properties:
  • poll.await.time.ms: The amount of time to wait before checking for new results in the change stream.
  • poll.max.batch.size: The maximum number of change stream documents to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Limitations

Be sure to review the following information.

• For connector limitations, see MongoDB Atlas Source Connector limitations.
• If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
• If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Maximum message size

This connector creates topics automatically. When it creates topics, the internal connector configuration property max.message.bytes is set to the following:

• Basic cluster: 2 MB
• Standard cluster: 2 MB
• Dedicated cluster: 20 MB

For more information about Confluent Cloud clusters, see Confluent Cloud Features and Limits by Cluster Type.

Quick Start

Use this quick start to get up and running with the Confluent Cloud MongoDB Atlas Source connector. The quick start provides the basics of selecting the connector and configuring it to consume data from Kafka and persist the data to a MongoDB database.

Note

This connector supports MongoDB Atlas only and will not work with a self-managed MongoDB database.

Prerequisites

• Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud Platform (GCP).
• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
• Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• Access to a MongoDB database. Note that the connection user must have privileged action “find” to query the MongoDB database. For more information, see Query and Write Actions.
• The connector automatically creates Kafka topics using the naming convention: <prefix>.<database-name>.<collection-name>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. If you want to create topics with specific settings, please create the topics before running this connector.
• If you have a VPC-peered cluster in Confluent Cloud, consider configuring a PrivateLink Connection between MongoDB Atlas and the VPC. For additional networking considerations, see Networking, DNS, and service endpoints. To use static egress IPs, see Static Egress IP Addresses.

• Kafka cluster credentials. The following lists the different ways you can provide credentials.
  • Enter an existing service account resource ID.
  • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
  • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector.

In the left navigation menu, click Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the MongoDB Atlas Source connector card.

Step 4: Enter the connector details.

Note

• Make sure you have all your prerequisites completed.
• An asterisk ( * ) designates a required entry.

At the MongoDB Atlas Source Connector screen, complete the following:

Define a topic prefixKafka credentialsAuthenticationConfigurationSizingReview and Launch

In the Topic prefix field, define a topic prefix your connector will use to publish to Kafka topics. The connector will Kafka topics using the following naming convention: <topic.prefix><tableName>.

Step 5: Check the Kafka topic.

After the connector is running, verify that MongoDB documents are populating the Kafka topic. If the config copy.existing is set to true and the connector restarts due to any reason, you may see duplicate records in the topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

• Make sure you have all your prerequisites completed.
• The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe MongoDbAtlasSource

Example output:

Following are the required configs:
connector.class: MongoDbAtlasSource
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
topic.prefix
connection.host
connection.user
connection.password
database
output.data.format
tasks.max

Step 3: Create the connector configuration file.

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

 {
     "connector.class": "MongoDbAtlasSource",
     "name": "<my-connector-name>",
     "kafka.auth.mode": "KAFKA_API_KEY",
     "kafka.api.key": "<my-kafka-api-key>",
     "kafka.api.secret": "<my-kafka-api-secret>",
     "topic.prefix": "<topic-prefix>",
     "connection.host": "<database-host-address>",
     "connection.user": "<database-username>",
     "connection.password": "<database-password>",
     "database": "<database-name>",
     "collection": "<database-collection-name>",
     "poll.await.time.ms": "5000",
     "poll.max.batch.size": "1000",
     "copy.existing": "true",
     "output.data.format": "JSON"
     "tasks.max": "1"
}

Note the following property definitions:

• "connector.class": Identifies the connector plugin name.
• "name": Sets a name for your new connector.

• "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

  confluent iam service-account list
  

  For example:

  confluent iam service-account list
  
     Id     | Resource ID |       Name        |    Description
  +---------+-------------+-------------------+-------------------
     123456 | sa-l1r23m   | sa-1              | Service account 1
     789101 | sa-l4d56p   | sa-2              | Service account 2
  

• (Optional) "topic.prefix": Enter a topic prefix. The connector automatically creates Kafka topics using the naming convention: <prefix>.<database-name>.<collection-name>. The tables are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. If you want to create topics with specific settings, create the topics before running this connector. If you are using a dedicated cluster and have a MongoDb document of size more than 2MB, create the topic beforehand with topic config max.message.bytes set equals to more than the largest document size (max is 8388608 bytes).

• (Optional) "topic.namespace.map": A JSON map that maps change stream document namespaces to topics. For example: {\"db\": \"dbTopic\", \"db.coll\": \"dbCollTopic\"} will map all change stream documents from the db database to dbTopic.<collectionName> apart from any documents from the db.coll namespace which map to the dbCollTopic topic. If you want to map all messages to a single topic use *. For example: {\"*\": \"everyThingTopic\", \"db.coll\": \"exceptionToTheRuleTopic\"} will map all change stream documents to the everyThingTopic apart from the db.coll messages. Note that any prefix configuration will still apply. If multiple collections with records having varying schema are mapped to a single topic with AVRO, JSON_SR, and PROTOBUF, then multiple schemas will be registered under a single subject name. If these schemas are not backward compatible to each other, the connector will fail until you change the schema compatibility in Confluent Cloud Schema Registry.

• "connection.host": The MongoDB host. Use a hostname address and not a full URL. For example: cluster4-r5q3r7.gcp.mongodb.net.

• (Optional) "collection": The collection name. If the property is not used, all collections are watched in the supplied database.

• (Optional) "poll.await.time.ms": The amount of time to wait before checking for new results in the change stream. If not used, this property defaults to 5000 ms (5 seconds).

• (Optional) "poll.max.batch.size": The maximum number of change stream documents to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector. If not used, this property defaults to 1000 records.

• (Optional) "pipeline": An array of JSON objects that represents the pipeline operations to filter or modify the change stream output. For example: [{"$match": {"ns.coll": {"$regex": /^(collection1|collection2)$/}}}] sets the connector to listen to the collection1 and collection2 collections only. If not used, this property defaults to an empty array.

• (Optional) "copy.existing": Select whether to copy existing data from source collections and convert them to change stream events on the respective topics. Any change to the data that occurs during the copy process is applied once the copy is completed. Note that setting copy.existing to true can lead to duplicate records if the connector restarts. For example: when using Schema Registry-based output format, if the schemas are not backward compatible to each other, the connector will fail and restart, which creates duplicate records. If not used, this property defaults to false.

• (Optional) "copy.existing.namespace.regex": Regex that matches the namespaces from which the existing documents are copied. A namespace is represented as databaseName.collectionName. For example, stats\.page.* matches all collections that start with page in the stats database.

• (Optional) "copy.existing.pipeline": An array of JSON objects that describes the pipeline operations to run when copying existing data. It is applied to existing documents that are being copied. If not used, this property defaults to an empty array.

• "output.data.format": Sets the output Kafka record value format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

• (Optional) "heartbeat.interval.ms": The number of milliseconds the connector waits between sending heartbeat messages. If not used, this property defaults to 0. Thus, no heartbeat message is sent by default. If set to a positive number, the connector sends heartbeat messages when source records are not published in the specified interval. This mechanism improves resumability of the connector for low volume namespaces. See the Invalid Resume Token page in MongoDb documentation for more information on this feature. When using SMTs, use predicates to prevent SMTs from processing the heartbeat messages. For example, if the heartbeat topic name is __mongodb_heartbeats and the connector is writing the actual database records into topics that do not share common prefix with the heartbeat topic; use the following configuration to prevent heartbeat messages from being processed by the transform with an alias say, mongoTransform: "predicates": "isHeartbeatTopicPrefix", "predicates.isHeartbeatTopicPrefix.type": "org.apache.kafka.connect.transforms.predicates.TopicNameMatches", "predicates.isHeartbeatTopicPrefix.pattern": "__mongodb.*", "transforms.mongoTransform.predicate": "isHeartbeatTopicPrefix", "transforms.mongoTransform.negate": "true".

• (Optional) "heartbeat.topic.name": The name of the topic on which the connector should publish heartbeat messages. You must provide a positive value in the heartbeat.interval.ms setting to enable this feature. If setting the heartbeat messages for multiple connectors, you must ensure that the heartbeat topic names for the connectors are unique. If not set, this defaults to __mongodb_heartbeats.

• "tasks.max": The connector supports running a single task.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and definitions.

Step 4: Load the properties file and create the connector.

Enter the following command to load the configuration and start the connector:

confluent connect create --config <file-name>.json

For example:

confluent connect create --config mongo-db-source.json

Example output:

Created connector confluent-mongodb-source lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |            Name           | Status  | Type
+-----------+---------------------------+---------+-------+
lcc-ix4dl   | confluent-mongodb-source  | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that MongoDB documents are populating the Kafka topic. If the config copy.existing is set to true and the connector restarts due to any reason, you may see duplicate records in the topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect section.

Configuration Properties

Use the following configuration properties with this connector.

Note

These are properties for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see MongoDB Source Connector for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

• Type: string
• Valid Values: A string at most 64 characters long
• Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

• Type: string
• Default: KAFKA_API_KEY
• Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
• Importance: high

kafka.api.key

• Type: password
• Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

• Type: string
• Importance: high

kafka.api.secret

• Type: password
• Importance: high

How do you want to name your topic(s)?

topic.prefix

Prefix to prepend to table names to generate the name of the Apache Kafka® topic to publish data to.

• Type: string
• Importance: high

topic.namespace.map

JSON object that maps change stream document namespaces to topics. Any prefix configuration will still apply. In case multiple collections with records having varying schema are mapped to single topic with AVRO, JSON_SR, and PROTOBUF, then multiple schemas will be registered under single subject name. If these schemas are not backward compatible to each other, the connector will fail until you change the schema compatibility in Confluent Cloud Schema Registry.

• Type: string
• Default: “”
• Importance: low

How should we connect to your MongoDB Atlas database?

connection.host

MongoDB Atlas connection host (e.g. confluent-test.mycluster.mongodb.net).

• Type: string
• Default: “”
• Importance: high

connection.user

MongoDB Atlas connection user.

• Type: string
• Importance: high

connection.password

MongoDB Atlas connection password.

• Type: password
• Importance: high

database

MongoDB Atlas database name. If not set, all databases in the cluster are watched.

• Type: string
• Importance: high

Database details

collection

Single MongoDB Atlas collection to watch. If not set, all collections in the specified database are watched.

• Type: string
• Importance: medium

Connection details

poll.await.time.ms

The amount of time to wait before checking for new results on the change stream.

• Type: int
• Default: 5000 (5 seconds)
• Valid Values: [1,…]
• Importance: low

poll.max.batch.size

Maximum number of change stream documents to include in a single batch when polling for new data. This setting can be used to limit the amount of data buffered internally in the connector.

• Type: int
• Default: 100
• Valid Values: [1,…,1000]
• Importance: low

pipeline

An array of JSON objects describing the pipeline operations to filter or modify the change events output. For example, [{“$match”: {“ns.coll”: {“$regex”: /^(collection1|collection2)$/}}}] will set your source connector to listen to the “collection1” and “collection2” collections only.

• Type: string
• Default: []
• Importance: medium

copy.existing

Whether to copy existing data from source collections. Setting this to true may cause record duplications.

• Type: boolean
• Default: false
• Importance: high

copy.existing.namespace.regex

Regular expression that matches the namespaces (databaseName.collectionName) from which to copy data. For example, stats.page.* matches all collections that starts with “page” in “stats” database.

• Type: string
• Default: “”
• Importance: medium

copy.existing.pipeline

An array of JSON objects describing the pipeline operations to run when copying existing data. It will only be applied for existing documents which are being copied.

• Type: string
• Default: []
• Importance: medium

batch.size

The number of documents to return in a batch.

• Type: int
• Default: 0
• Valid Values: […,50]
• Importance: low

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, STRING or BSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

• Type: string
• Default: STRING
• Importance: high

publish.full.document.only

Only publish the changed document instead of the full change stream document. Sets the change.stream.full.document=updateLookup automatically so updated documents will be included.

• Type: boolean
• Default: false
• Importance: high

change.stream.full.document

Determines what to return for update operations when using a Change Stream. When set to ‘updateLookup’, the change stream for partial updates will include both a delta describing the changes to the document as well as a copy of the entire document that was changed from some point in time after the change occurred.

• Type: string
• Default: default
• Importance: high

json.output.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

• Type: string
• Default: BASE64
• Importance: low

output.json.format

The output format of json strings can be configured to be either: DefaultJson: The legacy strict json formatter. ExtendedJson: The fully type safe extended json formatter. SimplifiedJson: Simplified Json, with ObjectId, Decimals, Dates and Binary values represented as strings. Users can provide their own implementation of the com.mongodb.kafka.connect.source.json.formatter.

• Type: string
• Default: DefaultJson
• Importance: high

topic.separator

Separator to use when joining prefix, database, collection, and suffix values. This generates the name of the Kafka topic to publish data to. Used by the ‘DefaultTopicMapper’.

• Type: string
• Default: .
• Importance: low

topic.suffix

Suffix to append to database and collection names to generate the name of the Kafka topic to publish data to.

• Type: string
• Importance: low

Error handling

heartbeat.interval.ms

The number of milliseconds the connector waits between sending heartbeat messages. The connector sends heartbeat messages when source records are not published in the specified interval. This mechanism improves resumability of the connector for low volume namespaces. When using SMTs, use predicates to prevent SMTs from processing the heartbeat messages. See connector documentation for more details.

• Type: int
• Default: 0
• Importance: medium

heartbeat.topic.name

The name of the topic on which the connector should publish heartbeat messages. You must provide a positive value in the “heartbeat.interval.ms” setting to enable this feature.

• Type: string
• Default: __mongodb_heartbeats
• Importance: medium

mongo.errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior differently from the Connect framework’s.

• Type: string
• Default: NONE
• Importance: medium

mongo.errors.deadletterqueue.topic.name

Whether to output conversion errors to the dead letter queue. Stops poison messages when using schemas, any message will be outputted as extended json on the specified topic. By default messages are not outputted to the dead letter queue. Also requires errors.tolerance=all.

• Type: string
• Importance: medium

Server API

server.api.version

The server API version to use. Disabled by default.

• Type: string
• Importance: low

server.api.deprecation.errors

Sets whether the connector requires use of deprecated server APIs to be reported as errors.

• Type: boolean
• Default: false
• Importance: low

server.api.strict

Sets whether the application requires strict server API version enforcement.

• Type: boolean
• Default: false
• Importance: low

Number of tasks for this connector

tasks.max

• Type: int
• Valid Values: [1,…,1]
• Importance: high

Suggested Reading

Blog post: Using the Fully Managed MongoDB Atlas Connector in a Secure Environment

Blog post: Announcing the MongoDB Atlas Sink and Source connectors in Confluent Cloud

Next Steps

See also

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.