MongoDB Atlas Sink Connector for Confluent Cloud¶

The fully-managed MongoDB Atlas Sink connector for Confluent Cloud maps and persists events from Apache Kafka® topics directly to a MongoDB Atlas database collection. The connector supports Avro, JSON Schema, Protobuf, JSON (schemaless), String, or BSON data from Apache Kafka® topics. The connector ingests events from Kafka topics directly into a MongoDB Atlas database, exposing the data to services for querying, enrichment, and analytics.

Note

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

Features¶

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

The connector provides the following features:

Collections: Collections can be auto-created based on topic names.
Database authentication: Uses password authentication.
Input data formats: The connector supports Avro, JSON Schema, Protobuf, JSON (schemaless), String, or BSON input data formats. 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.
Select configuration properties:
- "max.num.retries": How often retries should be attempted on write errors.
- "max.batch.size": The maximum number of sink records to batch together for processing.
- "delete.on.null.values": Whether the connector should delete documents with matching key values when the value is null.
- "doc.id.strategy": The strategy to generate a unique document ID (_id).
- "write.strategy": Defines the behavior of bulk write operations made on a MongoDB collection.
See Configuration Properties for all property values and definitions.

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

Limitations¶

Be sure to review the following information.

For connector limitations, see MongoDB Atlas Sink 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.

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud MongoDB Atlas sink 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.
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.
The MongoDB database service endpoint and the Kafka cluster must be in the same region.
The MongoDB hostname address must provide a service record (SRV). A standard connection string does not work.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
If you have a VPC-peered cluster in Confluent Cloud, consider configuring a PrivateLink Connection between MongoDB Atlas and the VPC.

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.

Adding an IP Whitelist Entry¶

Important

For networking considerations, see Networking and DNS.
To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

By default, MongoDB Atlas does not allow external network connections from the Internet. To allow external connections, you can add a specific IP or a CIDR IP range using the IP Whitelist entry dialog box under the Network Access menu in MongoDB.

In order for Confluent Cloud to connect to MongoDB Atlas, you need to specify the public IP address of your Confluent Cloud cluster. Add all of the Confluent Cloud egress IP addresses to the whitelist entry to your MongoDB Atlas cluster.

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 Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector¶

Click the MongoDB Atlas Sink connector card.

Step 4: Enter the connector details¶

Note

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

At the Add MongoDB Atlas Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Under MongoDB Atlas credentials, enter the following MongoDB Atlas credentials:
- Connection host: Use only the hostname and not a full URL. For example: cluster4-r5q3r7.gcp.mongodb.net. The hostname address must provide a service record (SRV). A standard connection string does not work.
- Connection user: The MongoDB Atlas connection user.
- Connection password: The MongoDB Atlas connection password. When entering the password, make sure that any special characters are URL encoded.
Under MongoDB Atlas Database Details, enter the following MongoDB Atlas database details:
- Database name: The MongoDB Atlas database name.
- Collection name: The collection name to write to. If the connector is sinking data from multiple topics, this is the default collection the topics are mapped to.
Click Continue.

Note

See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON (schemaless), STRING or BSON. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- Change data capture handler: Sets the class name of CDC handler to use for processing. You can capture CDC events with this connector and perform corresponding insert, update, and delete operations to a destination MongoDB cluster. Valid options are None, MongoDbChangeStreamHandler, DebeziumMongoDbHandler, DebeziumMySqlHandler, DebeziumPostgresHandler, or QlikRdbmsHandler. If not used, this property defaults to None. For more information, see mongodb-sink-cdc.
- Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry. This property defaults to TopicNameStrategy. Set this property to RecordNameStrategy to derive the subject name from the record name. For more information, see Subject name strategy.
Connection details
- Max number of retries: Enter the maximum number of retries on a write error. The default value is 3 retries.
- Retry defer timeout (ms): Enter value in milliseconds (ms) that a retry gets deferred. The default is 5000 ms (5 seconds).
Writes
- Delete on null values: Select whether or not the connector deletes documents with matching key values when the value is null. The default is false.
- Max batch size: Enter the maximum number of records to batch together for processing. The default is 0 (uses the server default setting).
- Use ordered bulk writes: When batch processing is used, this property sets whether the connector writes the batches using ordered bulk writes. Defaults to true.
- Rate limiting timeout: After a rate limit is reached, this sets how long in milliseconds (ms) the connector waits before continuing to process data. Defaults to 0.
- Rate limiting batch number: The number of processed batches that trigger a rate limit. Defaults to 0 (that is, no rate limiting).
ID strategies
- Document ID Strategy: Select the strategy to generate a unique document ID _id. To delete a document when the value is null, this has to be set to FullKeyStrategy, PartialKeyStrategy, or ProvidedInKeyStrategy. The default is BsonOidStrategy. For more information, see DocumentIdAdder.
- Document ID strategy overwrite existing: Whether the connector should overwrite existing values in the _id field when the strategy defined in doc.id.strategy is applied.
- Document ID strategy UUID format: The BSON output format when using UuidStrategy. Options are String or Binary.
- Document ID strategy key projection type: For use with the PartialKeyStrategy. Allows custom key fields to be projected for the ID strategy. Use either AllowList or BlockList.
- Document ID strategy key projection list: For use with the PartialKeyStrategy. Allows custom key fields to be projected for the ID strategy. A comma-separated list of fields names for key projection.
- Document ID strategy value projection type: For use with the PartialValueStrategy. Allows custom value fields to be projected for the ID strategy. Use either AllowList or BlockList.
- Document ID strategy value projection list: For use with the PartialValueStrategy. Allows custom value fields to be projected for the ID strategy. A comma-separated list of field names for value projection.
- Write Model Strategy: The class that specifies the WriteModel to use for bulk writes. Defaults to DefaultWriteModelStrategy. Valid entries are DefaultWriteModelStrategy, ReplaceOneDefaultStrategy, InsertOneDefaultStrategy, ReplaceOneBusinessKeyStrategy, DeleteOneDefaultStrategy, UpdateOneTimestampsStrategy, UpdateOneBusinessKeyTimestampStrategy, or UpdateOneDefaultStrategy. For time-series collections, the DefaultWriteModelStrategy will internally default to InsertOneDefaultStrategy. For normal collections, it defaults to ReplaceOneDefaultStrategy. For detailed information about each write strategy, see Strategies.
Time Series configuration
- Timefield: The name of the top-level time field that contains the date in each time-series document. Setting this config will create a time-series collection where each document will have a BSON date value for the time field. Time-series collections were introduced in MongoDB v5.0, which is only available for dedicated clusters in MongoDB Atlas.
- Auto Conversion: Whether to convert the data in the time field to BSON date format. Supported formats include integer, long and string.
- Auto Convert Date Format: The string pattern to convert the source data from format to convert the source data from. The setting expects the string representation to contain both date and time information and uses the Java DateTimeFormatter.ofPattern(pattern, locale) API for the conversion. If the string only contains date information, then the time since epoch is from the start of that day. If a string representation does not contain time-zone offset, then the setting interprets the extracted date and time as UTC.
- Locate Language Tag: The DateTimeFormatter locale language tag to use with the date pattern.
- Metafield: The name of the top-level field that contains metadata in each time-series document. The metadata in the specified field should be data that is used to label a unique series of documents. The field can be of any type except array.
- Expire After Seconds: The amount of seconds the data remains in MongoDB before MongoDB deletes it. Omitting this field means data will not be deleted automatically.
- Granularity: The expected interval between subsequent measurements for a time-series. Set this to None or leave it empty if the data is not time-series.
Server API
- Server API version: The MongoDB server API version to use. This property is disabled by default.
- Deprecation errors: Whether or not to require the connector to report the use of deprecated server APIs as errors. This property is disabled by default.
- Server API: Whether or not to require strict server API version enforcement. This property is disabled by default.
Namespace mapping
- Namespace mapper class: The class that determines the namespace where the connector sinks data. Defaults to DefaultNamespaceMapper, which is based on the database configuration and either the topic name or the collection configuration. Can be set to FieldPathNamespaceMapper, which is based on the data’s field values. For more information, see the MongoDB documentation.
- Key field for destination database name: The name of the key document field that specifies the name of the database to write to.
- Key field for database collection name: The name of the key document field that specifies the name of the collection to write to.
- Value field for destination database name: The name of the value document field that specifies the name of the database to write to.
- Value field for destination collection name: The name of the value document field that specifies the name of the collection to write to.
- Mapped field error: Whether to throw an exception when either the document is missing the mapped field or it has an invalid BSON type. When set to true, the connector does not process documents missing the mapped field or containing an invalid BSON type. Defaults to false.
Click Continue.

Step 5: Check MongoDB¶

After the connector is running, verify that messages are populating your MongoDB database.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

Step 1: List the available connectors¶

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties¶

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

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": "MongoDbAtlasSink",
    "name": "confluent-mongodb-sink",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "input.data.format" : "JSON",
    "connection.host": "<database-host-address>",
    "connection.user": "<my-username>",
    "connection.password": "<my-password>",
    "topics": "<kafka-topic-name>",
    "max.num.retries": "3",
    "retries.defer.timeout": "5000",
    "max.batch.size": "0",
    "database": "<database-name>",
    "collection": "<collection-name>",
    "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
```
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, STRING, or BSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"connection.host": The MongoDB host. Use a hostname address and not a full URL. For example: cluster4-r5q3r7.gcp.mongodb.net. The hostname address must provide a service record (SRV). A standard connection string does not work.
"collection": The MongoDB collection name. For multiple topics, this is the default collection the topics are mapped to.

The following are optional (with the exception of the number of tasks).
"max.num.retries": How often retries should be attempted on write errors. If not used, this property defaults to 3.
"retries.defer.timeout": How long (in milliseconds) a retry should get deferred. If not used, the default is 5000 ms.
"max.batch.size": The maximum number of sink records to batch together for processing. If not used, this property defaults to 0.
"delete.on.null.values": Whether the connector should delete documents with matching key values, when the value is null. If not used, this property defaults to false.
"doc.id.strategy": Sets the strategy to generate a unique document ID (_id). Enter the strategy to generate a unique document ID (_id). Valid entries are BsonOidStrategy, KafkaMetaDataStrategy, FullKeyStrategy, PartialKeyStrategy, PartialValueStrategy, ProvidedInKeyStrategy, ProvidedInValueStrategy, or UuidStrategy. To delete the document when the value is null, you must set the strategy to FullKeyStrategy, PartialKeyStrategy, or ProvidedInKeyStrategy. The default value is BsonOidStrategy. For more information, see DocumentIdAdder.
Depending on the selected strategy, add the appropriate Document ID strategy projection list:
- "key.projection.type": For use with PartialKeyStrategy. Use either allowlist or blocklist to allow or block the custom key fields to be projected for ID strategy. If not used, this property defaults to none.
- "key.projection.list": For use with PartialKeyStrategy. A comma-separated list of key fields to be projected for ID strategy.
- "value.projection.type": For use with PartialValueStrategy. Use either allowlist or blocklist to allow or block the custom value fields to be projected for ID strategy. If not used, this property defaults to none.
- "value.projection.list": For use with PartialValueStrategy. A comma-separated list of value fields to be projected for ID strategy.
"write.strategy": Sets the write model for bulk write operations. Valid entries are DefaultWriteModelStrategy, ReplaceOneDefaultStrategy, InsertOneDefaultStrategy, ReplaceOneBusinessKeyStrategy, DeleteOneDefaultStrategy, UpdateOneTimestampsStrategy, UpdateOneBusinessKeyTimestampStrategy, or UpdateOneDefaultStrategy. If not used, this property defaults to DefaultWriteModelStrategy. For time-series collections, the DefaultWriteModelStrategy will internally default to InsertOneDefaultStrategy. For normal collections, it defaults to ReplaceOneDefaultStrategy. For detailed information about each write strategy, see Strategies.
"cdc.handler": Sets the class name of CDC handler to use for processing. You can capture CDC events with the MongoDB Kafka Sink connector and perform corresponding insert, update, and delete operations to a destination MongoDB cluster. Valid entries are None, MongoDbChangeStreamHandler, DebeziumMongoDbHandler, DebeziumMySqlHandler, DebeziumPostgresHandler, or QlikRdbmsHandler. If not used, this property defaults to None. For more information, see mongodb-sink-cdc.
"timeseries.timefield": Sets the name of the top-level time field that contains the date in each time-series document. Setting this property will create a time-series collection where each document will have a BSON date as the value for the time field. Time-series collections were introduced in MongoDB v5.0, which is only available for dedicated clusters in MongoDB Atlas.
"timeseries.timefield.auto.convert": Whether to convert the data in the time field to BSON date format. Supported formats for data include integer, long and string. If not used, this property defaults to false.
"timeseries.timefield.auto.convert.date.format": Sets the DateTimeFormatter format to convert the source data from. The setting expects the string representation to contain both date and time information and uses the Java DateTimeFormatter.ofPattern(pattern, locale) API for the conversion. If the string only contains date information, then the time since epoch is from the start of that day. If a string representation does not contain time-zone offset, then the setting interprets the extracted date and time as UTC. If not used, this property defaults to yyyy-MM-dd[['T'][ ]][HH:mm:ss[[.][SSSSSS][SSS]][ ]VV[ ]'['VV']'][HH:mm:ss[[.][SSSSSS][SSS]][ ]X][HH:mm:ss[[.][SSSSSS][SSS]]].
"timeseries.timefield.auto.convert.locale.language.tag": Sets the DateTimeFormatter locale language tag to use with the date pattern. See Language tags in HTML and XML for more information on constructing tags. If not used, this property defaults to en.
"timeseries.metafield": Sets the name of the top-level field that contains metadata in each time-series document. The metadata in the specified field should be data that is used to label a unique series of documents. The field can be of any type except array.
"timeseries.expire.after.seconds": Sets the number of seconds after which the document expires. MongoDB deletes expired documents automatically. If not used, this property default to 0, which means data will not be deleted automatically.
"ts.granularity": Sets the interval granularity for subsequent measurements for a time-series. Valid entries are None, seconds, minutes, or hours. If not used, this property defaults to None. For normal collections, None is the only applicable value. For time-series collections, all entries are applicable and None internally defaults to seconds.
Enter the number of tasks for the connector. Refer to Confluent Cloud connector limitations for additional information.

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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file mongo-db-sink.json

Example output:

Created connector confluent-mongodb-sink lcc-ix4dl

Step 5: Check the connector status¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

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

Step 6: Check MongoDB¶

After the connector is running, verify that records are populating your MongoDB database.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

Configuration Properties¶

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors 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

Schema Config¶

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages¶

input.data.format

Sets the input 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
Importance: high

cdc.handler

The class name of the CDC handler to use for processing. You can capture CDC events with the MongoDB Kafka sink connector and perform corresponding insert, update, and delete operations to a destination MongoDB cluster.

Type: string
Default: None
Importance: low

value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

Type: string
Default: TopicNameStrategy
Valid Values: RecordNameStrategy, TopicNameStrategy
Importance: medium

Writes¶

delete.on.null.values

Whether or not the connector should try to delete documents based on key when value is null.

Type: boolean
Default: false
Importance: low

max.batch.size

The maximum number of sink records to possibly batch together for processing.

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

bulk.write.ordered

Whether the batches controlled by ‘max.batch.size’ must be written via ordered bulk writes.

Type: boolean
Default: true
Importance: low

rate.limiting.timeout

How long in ms processing should wait before continuing after triggering a rate limit.

Type: int
Default: 0
Importance: low

rate.limiting.every.n

The number of processed batches that will trigger rate limiting. The default value of 0 sets no rate limiting.

Type: int
Default: 0
Importance: low

write.strategy

The class that specifies the WriteModel to use for bulk writes.

Type: string
Default: DefaultWriteModelStrategy
Importance: low

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

Kafka API Key. Required when kafka.auth.mode==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

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

Which topics do you want to get data from?¶

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

How should we connect to your MongoDB Atlas database?¶

connection.host

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

Type: string
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.

Type: string
Importance: high

Database details¶

collection

Collection name to write to. If the connector is sinking data from multiple topics, this is the default collection the topics are mapped to.

Type: string
Importance: medium

ID strategies¶

doc.id.strategy

The IdStrategy class name to use for generating a unique document id (_id).

Type: string
Default: BsonOidStrategy
Importance: low

doc.id.strategy.overwrite.existing

Whether the connector should overwrite existing values in the _id field when the strategy defined in doc.id.strategy is applied.

Type: boolean
Default: false
Importance: low

document.id.strategy.uuid.format

The bson output format when using the UuidStrategy. Can be either String or Binary.

Type: string
Default: string
Importance: low

key.projection.type

For use with the PartialKeyStrategy allows custom key fields to be projected for the ID strategy. Use either AllowList or BlockList.

Type: string
Default: none
Importance: low

key.projection.list

For use with the PartialKeyStrategy allows custom key fields to be projected for the ID strategy. A comma-separated list of field names for key projection.

Type: string
Importance: low

value.projection.type

For use with the PartialValueStrategy allows custom value fields to be projected for the ID strategy. Use either AllowList or BlockList.

Type: string
Default: none
Importance: low

value.projection.list

For use with the PartialValueStrategy allows custom value fields to be projected for the ID strategy. A comma-separated list of field names for value projection.

Type: string
Importance: low

Namespace mapping¶

namespace.mapper.class

The class that determines the namespace to write the sink data to. By default this will be based on the ‘database’ configuration and either the topic name or the ‘collection’ configuration.

Type: string
Default: DefaultNamespaceMapper
Importance: low

namespace.mapper.key.database.field

The key field to use as the destination database name.

Type: string
Importance: low

namespace.mapper.key.collection.field

The key field to use as the destination collection name.

Type: string
Importance: low

namespace.mapper.value.database.field

The value field to use as the destination database name.

Type: string
Importance: low

namespace.mapper.value.collection.field

The value field to use as the destination collection name.

Type: string
Importance: low

namespace.mapper.error.if.invalid

Whether to throw an error if the mapped field is missing or invalid. Defaults to false.

Type: boolean
Default: false
Importance: low

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

Connection details¶

max.num.retries

How many retries should be attempted on write errors.

Type: int
Default: 3
Valid Values: [0,…]
Importance: low

retries.defer.timeout

How long a retry should get deferred.

Type: int
Default: 5000
Valid Values: [0,…]
Importance: low

Time Series configuration¶

timeseries.timefield

The name of the top-level field which contains the date in each time series document. Setting this config will create a time series collection where each document will have a BSON date as the value for the timefield.

Type: string
Default: “”
Importance: low

timeseries.timefield.auto.convert

Whether to convert the data in the field into a BSON Date format. Supported formats include integer, long, and string.

Type: boolean
Default: false
Importance: low

timeseries.timefield.auto.convert.date.format

The string pattern to convert the source data from. The setting expects the string representation to contain both date and time information and uses the Java DateTimeFormatter.ofPattern(pattern, locale) API for the conversion. If the string only contains date information, then the time since epoch is from the start of that day. If a string representation does not contain time-zone offset, then the setting interprets the extracted date and time as UTC.

Type: string
Default: yyyy-MM-dd[[‘T’][ ]][HH:mm:ss[[.][SSSSSS][SSS]][ ]VV[ ]’[‘VV’]’][HH:mm:ss[[.][SSSSSS][SSS]][ ]X][HH:mm:ss[[.][SSSSSS][SSS]]]
Importance: low

timeseries.timefield.auto.convert.locale.language.tag

The DateTimeFormatter locale language tag to use with the date pattern.

Type: string
Default: en
Importance: low

timeseries.metafield

The name of the top-level field that contains metadata in each time series document. This field groups related data. It can be of any type except array.

Type: string
Default: “”
Importance: low

timeseries.expire.after.seconds

The amount of seconds the data remains in MongoDB before MongoDB deletes it. Omitting this field means data will not be deleted automatically.

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

ts.granularity

The expected interval between subsequent measurements for a time-series. Set this to None or leave it empty if the data is not time-series

Type: string
Default: None
Importance: low

Consumer configuration¶

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector¶

tasks.max

Maximum number of tasks for the connector.

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

Next Steps¶

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.

MongoDB Atlas Sink Connector for Confluent Cloud¶

Features¶

Limitations¶

Quick Start¶

Adding an IP Whitelist Entry¶

Using the Confluent Cloud Console¶

Step 1: Launch your Confluent Cloud cluster¶

Step 2: Add a connector¶

Step 3: Select your connector¶

Step 4: Enter the connector details¶

Step 5: Check MongoDB¶

Using the Confluent CLI¶

Step 1: List the available connectors¶

Step 2: List the connector configuration properties¶

Step 3: Create the connector configuration file¶

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

Step 5: Check the connector status¶

Step 6: Check MongoDB¶

Configuration Properties¶

How should we connect to your data?¶

Schema Config¶

Input messages¶

Writes¶

Kafka Cluster credentials¶

Which topics do you want to get data from?¶

How should we connect to your MongoDB Atlas database?¶

Database details¶

ID strategies¶

Namespace mapping¶

Server API¶

Connection details¶

Time Series configuration¶

Consumer configuration¶

Number of tasks for this connector¶

Suggested Reading¶

Next Steps¶