Google Cloud Firestore Sink Connector for Confluent Cloud

The fully-managed Google Cloud Firestore Sink connector for Confluent Cloud streams data from Apache Kafka® topics into Google Cloud Firestore collections using the Firestore endpoint.

The connector writes to Firestore through its Firestore MongoDB-compatible API. Consequently, you address Firestore data by using MongoDB driver concepts: databases, collections, documents, binary JSON (BSON), and the _id field. This applies even though the underlying datastore is Firestore. Property names and ID strategies in this document reflect the MongoDB model.

Features

The Google Cloud Firestore Sink connector provides the following features:

Database authentication: Authenticates to the Firestore MongoDB-compatible endpoint by using a SCRAM username and password provisioned in the Google Cloud console. The connector does not use service account JSON authentication.
Input data formats: Supports Avro, JSON Schema (JSON_SR), Protobuf, or JSON (schemaless) input data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
Configurable write strategies: Controls the behavior of bulk write operations, including upserts, inserts, replacements, and deletes.
Flexible document ID strategies: Supports multiple strategies for generating unique document IDs, including BSON OID, full key, Kafka metadata, and custom field projections.
Delete on null: When enabled, the connector deletes a Firestore document when the corresponding Kafka record value is null.
Client-side encryption (CSFLE and CSPE) support: Supports CSFLE and CSPE for sensitive data. For more information about CSFLE or CSPE setup, see Manage Client-Side Encryption for Fully-Managed Connectors in Confluent Cloud.
Offset management capabilities: Supports offset management. For more information, see Manage offsets for Sink Connectors.

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

Limitations

Be sure to review the following information.

For connector limitations, see Google Cloud Firestore Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.

If you plan to use one or more Custom SMTs, see Custom SMT limitations.

Quick start

Use this quick start to get up and running with the Confluent Cloud Google Cloud Firestore Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events from Kafka topics into Firestore collections.

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).
A Google Cloud project with Firestore enabled and the MongoDB-compatible API activated. For details, see Use Firestore with MongoDB drivers.
A Firestore database with the hostname and port.
A Firestore user with read and write permissions on the target database.

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

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

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 Google Cloud Firestore Sink connector card.

Step 4: Enter the connector details

Before configuring the connector settings, ensure that you complete all prerequisites.

Note

An asterisk ( * ) designates a required entry.

At the Google Cloud Firestore Sink Connector screen, complete the following:

Select topic

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.

Kafka credentials

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note
Freight clusters support only service accounts for Kafka authentication.
Click Continue.

Authentication

Configure the authentication properties:
Firestore connection
- Connection host: The host name and port of the Google Cloud Firestore MongoDB-compatible endpoint. Expected format: hostname:port.
Authentication
- Connection user: The Google Cloud Firestore connection user.
- Connection password: The Google Cloud Firestore connection password.
Firestore Database Details
- Database name: The Google Cloud Firestore database name.
- Collection name: The Google Cloud Firestore collection name. If the connector sinks data from multiple topics, this is the default collection the topics are mapped to.
TLS Configuration
- TLS: Whether TLS is enabled on the connection. Default is true.
Click Continue.

Configuration

Note

Configuration properties that are not shown in the Cloud Console use the default values. For all property values and definitions, see Configuration properties.

Kafka record value format: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF or JSON. You must have Confluent Cloud Schema Registry configured to use a schema-based message format (for example, AVRO, JSON_SR, and PROTOBUF).

Data encryption

Enable Client-Side Field Level Encryption for data encryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

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?.
Kafka record key format: Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING.
Topic override map: A JSON map to override sink connector properties for specific topics. Use the map as {"<topicName>.<property>": "<value>"}. For example, {"orders.collection": "orders_collection", "orders.database": "orders_db"} routes the orders topic to the orders_collection in the orders_db database. Supported overridable properties include collection, database, namespace.mapper.*, delete.on.null.values, writemodel.strategy, delete.writemodel.strategy, document.id.strategy.*, key.projection.*, value.projection.*, field.renamer.*, post.processor.chain, errors.tolerance, errors.log.enable, errors.deadletterqueue.topic.name, max.batch.size, and rate.limiting.timeout. Properties that cannot be overridden include connection.host, connection.user, connection.password, topics, and topic.override.*.

Additional Configs

Value Converter Replace Null With Default: Specifies whether to replace fields that have a default value and that are null to the default value. When set to true, the connector uses the default value; otherwise, it uses null. Applies to the JSON converter.
Value Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
Value Converter Reference Subject Name Strategy: Sets the subject reference name strategy for values. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. You can use this strategy only with PROTOBUF format; the default strategy is DefaultReferenceSubjectNameStrategy.
Schema ID For Value Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Value Converter Schemas Enable: Includes schema within each of the serialized values. Input messages must contain schema and payload fields and must not contain additional fields. For plain JSON data, set this to false. Applies to the JSON converter.
Errors Tolerance: Use this property to configure the connector’s error handling behavior.
Warning
Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to all, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to none, the connector task fails on errant records.
Value Converter Ignore Default For Nullables: When set to true, this property ensures that the corresponding record in Kafka is null, instead of showing the default column value. Applies to the AVRO, PROTOBUF, and JSON_SR converters.
Key Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
Value Converter Decimal Format: Specifies the JSON or 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 DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.
Schema GUID For Key Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Schema GUID For Value Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Value Converter Connect Meta Data: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
Value Converter Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
Key Converter Key Subject Name Strategy: Determines how to construct the subject name for key schema registration.
Schema ID For Key Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Auto-restart policy

Enable Connector Auto-restart: Enables the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.

Consumer configuration

Max poll interval(ms): Sets the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes).
Max poll records: Sets the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records.

Write strategies

Write model strategy: The class that specifies the write model to use for bulk writes.
Maximum batch size: The maximum number of sink records to batch together for processing. 0 means no limit.
Ordered bulk writes: Whether bulk writes should be ordered.
Rate limit timeout (ms): How long to wait, in milliseconds, before sending the next batch if rate limit is enabled. 0 means no rate limit.
Rate limit every N batches: How many batches of records to send before applying rate limit. 0 means no rate limit.
Delete on null values: Whether the connector should delete documents when the value is null.
Delete write model strategy: The class that handles how to build the delete write models for the sink documents.

ID strategies

Document ID strategy: The strategy to use for generating a unique document ID.
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 key projection type: For use with 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 PartialKeyStrategy, allows custom key fields to be projected for the ID strategy. Provide a comma-separated list of field names for key projection.
Document ID strategy value projection type: For use with 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 PartialValueStrategy, allows custom value fields to be projected for the ID strategy. Provide a comma-separated list of field names for value projection.

Namespace mapping

Namespace mapper: The class that determines the database and collection to write to.
Key database field: The field in the key to use for the database name when using FieldPathNamespaceMapper.
Key collection field: The field in the key to use for the collection name when using FieldPathNamespaceMapper.
Value database field: The field in the value to use for the database name when using FieldPathNamespaceMapper.
Value collection field: The field in the value to use for the collection name when using FieldPathNamespaceMapper.
Error if invalid namespace: Whether to throw an error if the namespace mapping is invalid.

Error handling

Error tolerance: Error tolerance level. Use none to fail on errors, or all to skip errors.

Transforms

Single Message Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.

For all property values and definitions, see Configuration properties.

Click Continue.

Sizing

Based on the number of topic partitions you select, you are provided with a recommended number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and Launch

Verify the connection details.
Click Launch.
The status for the connector should go from Provisioning to Running.

Step 5: Check the results in Firestore

Check your Firestore database to verify that the connector has written documents to the target collection.

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

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud 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:

{
  "name": "FirestoreSinkConnector_0",
  "config": {
    "topics": "<topic-name>",
    "connector.class": "FirestoreSink",
    "name": "FirestoreSinkConnector_0",
    "input.data.format": "AVRO",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "connection.host": "<my-firestore-endpoint>:<port>",
    "connection.user": "<my-firestore-user>",
    "connection.password": "<my-firestore-password>",
    "database": "<my-database>",
    "tasks.max": "1"
  }
}

Note the following property definitions:

"name": Sets a name for your new connector.
"connector.class": Identifies the connector plugin name.
"topics": Identifies the topic name or a comma-separated list of topic names.
"input.data.format": Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured to use a schema-based message format.

"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
```

"connection.host": The host name and port of the Google Cloud Firestore MongoDB-compatible endpoint. Use the format hostname:port.
"connection.user": The Google Cloud Firestore connection user.
"connection.password": The Google Cloud Firestore connection password.
"database": The Google Cloud Firestore database name.
"tasks.max": The maximum number of tasks for the connector to use.

Note

To enable CSFLE or CSPE for data encryption, specify the following properties:

csfle.enabled: Flag to indicate whether the connector honors CSFLE or CSPE rules.
sr.service.account.id: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.
csfle.onFailure: Configures the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption.

When using CSFLE or CSPE with connectors that route failed messages to a Dead Letter Queue (DLQ), be aware that data sent to the DLQ is written in unencrypted plaintext. This poses a significant security risk as sensitive data that should be encrypted may be exposed in the DLQ.

Do not use DLQ with CSFLE or CSPE in the current version. If you need error handling for CSFLE- or CSPE-enabled data, use alternative approaches such as:

Setting the connector behavior to ERROR to throw exceptions instead of routing to DLQ
Implementing custom error handling in your applications
Using NONE to pass encrypted data through without decryption

For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

For all properties and definitions, see Configuration properties.

Step 4: Load the configuration 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 gcp-firestore-sink-config.json

Example output:

Created connector FirestoreSinkConnector_0 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   | FirestoreSinkConnector_0      | RUNNING | sink

Step 6: Check the results in Firestore

Check your Firestore database to verify that the connector has written documents to the target collection.

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

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See View Connector Dead Letter Queue Errors in Confluent Cloud 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 /platform/current/Self-managed connectors for .

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 or JSON. You must have Confluent Cloud Schema Registry configured to use a schema-based message format (for example, AVRO, JSON_SR, and PROTOBUF).

Type: string
Default: JSON
Importance: high

input.key.format

Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING.

Type: string
Default: STRING
Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
Importance: high

Write strategies

write.strategy

The class that specifies the write model to use for bulk writes.

Type: string
Default: DefaultWriteModelStrategy
Valid Values: DefaultWriteModelStrategy, DeleteOneDefaultStrategy, InsertOneDefaultStrategy, ReplaceOneBusinessKeyStrategy, ReplaceOneDefaultStrategy, UpdateOneBusinessKeyTimestampStrategy, UpdateOneDefaultStrategy, UpdateOneTimestampsStrategy
Importance: low

max.batch.size

The maximum number of sink records to batch together for processing. 0 means no limit.

Type: int
Default: 0
Importance: medium

bulk.write.ordered

Whether bulk writes are ordered.

Type: boolean
Default: true
Importance: medium

rate.limiting.timeout

How long to wait, in milliseconds, before sending the next batch if rate limit is enabled. 0 means no rate limit.

Type: int
Default: 0
Importance: low

rate.limiting.every.n

How many batches of records to send before applying rate limit. 0 means no rate limit.

Type: int
Default: 0
Importance: low

delete.on.null.values

Whether the connector deletes documents when the value is null.

Type: boolean
Default: false
Importance: medium

delete.write.strategy

The class that handles how to build the delete write models for the sink documents.

Type: string
Default: DeleteOneDefaultStrategy
Importance: low

topic.override.map

A JSON map to override sink connector properties for specific topics. Use the map as {"<topicName>.<property>": "<value>"}. For example, {"orders.collection": "orders_collection", "orders.database": "orders_db"} routes the orders topic to the orders_collection in the orders_db database.

Supported overridable properties include collection, database, namespace.mapper.*, delete.on.null.values, writemodel.strategy, delete.writemodel.strategy, document.id.strategy.*, key.projection.*, value.projection.*, field.renamer.*, post.processor.chain, errors.tolerance, errors.log.enable, errors.deadletterqueue.topic.name, max.batch.size, and rate.limiting.timeout.

Properties that cannot be overridden include connection.host, connection.user, connection.password, topics, and topic.override.*.

Type: string
Default: {}
Importance: low

ID strategies

doc.id.strategy

The strategy to use for generating a unique document ID.

Type: string
Default: BsonOidStrategy
Valid Values: BsonOidStrategy, FullKeyStrategy, KafkaMetaDataStrategy, PartialKeyStrategy, PartialValueStrategy, ProvidedInKeyStrategy, ProvidedInValueStrategy, UuidStrategy
Importance: high

doc.id.strategy.overwrite.existing

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

Type: boolean
Default: false
Importance: low

key.projection.type

When using the PartialKeyStrategy, this allows custom key fields to be projected for the ID strategy. Use either allowlist or blocklist.

Type: string
Default: none
Valid Values: allowlist, blocklist, none
Importance: low

key.projection.list

When using the PartialKeyStrategy, this allows custom key fields to be projected for the ID strategy. Provide a comma-separated list of field names for key projection.

Type: string
Importance: low

value.projection.type

When using the PartialValueStrategy, this allows custom value fields to be projected for the ID strategy. Use either allowlist or blocklist.

Type: string
Default: none
Valid Values: allowlist, blocklist, none
Importance: low

value.projection.list

When using the PartialValueStrategy, this allows custom value fields to be projected for the ID strategy. Provide a comma-separated list of field names for value projection.

Type: string
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, whenever possible.

Type: string
Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
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.regex

A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.

Type: string
Importance: low

topics

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

Type: list
Importance: high

errors.deadletterqueue.topic.name

The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: dlq-${connector}
Importance: low

Firestore connection

connection.host

The host name and port of the Firestore MongoDB-compatible endpoint. The expected format is hostname:port.

Type: string
Default: “”
Importance: high

connection.user

The name of the connection user connecting to the Firestore database.

Type: string
Importance: high

connection.password

The password for the connection user connecting to the Firestore database.

Type: password
Importance: high

database

The name of the Firestore database to connect to.

Type: string
Importance: high

collection

The name of the Firestore collection name used to store events from Kafka topics.

Type: string
Importance: medium

connection.tls.enabled

Specify whether to use Transport Layer Security (TLS) to connect to the Firestore database.

Type: boolean
Default: true
Importance: high

Namespace mapping

namespace.mapper.class

The class that determines the database and collection to write to.

Type: string
Default: DefaultNamespaceMapper
Valid Values: DefaultNamespaceMapper, FieldPathNamespaceMapper
Importance: low

namespace.mapper.key.database.field

The field in the key to use for the database name when using FieldPathNamespaceMapper.

Type: string
Default: “”
Importance: low

namespace.mapper.key.collection.field

The field in the key to use for the collection name when using FieldPathNamespaceMapper.

Type: string
Default: “”
Importance: low

namespace.mapper.value.database.field

The field in the value to use for the database name when using FieldPathNamespaceMapper.

Type: string
Default: “”
Importance: low

namespace.mapper.value.collection.field

The field in the value to use for the collection name when using FieldPathNamespaceMapper.

Type: string
Default: “”
Importance: low

namespace.mapper.error.if.invalid

Whether to throw an error if the namespace mapping is invalid.

Type: boolean
Default: false
Importance: low

Error handling

firestore.errors.tolerance

The error tolerance level. Use none to fail on errors, or all to skip errors.

Type: string
Default: none
Valid Values: all, none
Importance: medium

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

Additional Configs

consumer.override.auto.offset.reset

Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset

Type: string
Importance: low

consumer.override.isolation.level

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level

Type: string
Importance: low

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

Type: string
Importance: low

key.converter.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

key.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

Type: boolean
Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

Type: boolean
Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

Type: boolean
Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

Type: boolean
Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

value.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

Type: boolean
Importance: low

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

Type: string
Default: all
Importance: low

key.converter.key.schema.id.deserializer

The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
Importance: low

key.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Key Converter.

Type: boolean
Default: true
Importance: low

key.converter.schemas.enable

Include schemas within each of the serialized keys. Input message keys must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Key Converter.

Type: boolean
Default: false
Importance: low

value.converter.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

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

Type: string
Default: DefaultReferenceSubjectNameStrategy
Importance: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

Type: boolean
Default: true
Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

Type: boolean
Default: false
Importance: low

value.converter.value.schema.id.deserializer

The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
Importance: low

value.converter.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
Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

Next steps

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