# Google Cloud BigTable Sink Connector for Confluent Cloud The fully managed Google Cloud BigTable Sink connector for Confluent Cloud moves data from Apache Kafka® to Google Cloud BigTable. It writes data from a topic in Kafka to a table in the specified BigTable instance. Confluent Cloud is available through [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/confluent-prod/apache-kafka-on-confluent-cloud?inv=1&invt=Ab2Ryw) or [directly from Confluent](https://www.confluent.io/get-started/). #### NOTE This is a Quick Start for the fully managed cloud connector. If you are installing the connector locally for Confluent Platform, see [Google Cloud BigTable Sink Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/gcp-bigtable/current/). ## Features * **Supports Inserts and Upserts**: The connector can insert rows and update rows in Google Cloud BigTable. * **Automatically create tables and column families**: The connector can create missing tables and can create missing column families. * **Row key can be constructed from record fields**: A comma-separated list of Kafka record key field names can be concatenated to form the row key. * **At least once delivery**: The connector guarantees that records are delivered at least once. * **Supports multiple tasks**: The connector supports running one or more tasks. * **Input data formats:** Supports Avro, JSON Schema, or Protobuf input data. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ## Limitations Be sure to review the following information. * For connector limitations, see [Google BigTable Sink Connector](limits.md#google-bigtable-sink-limits) limitations. * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). ## Quick Start Use this quick start to get up and running with the Google Cloud BigTable Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a BigTable instance. Prerequisites : * Authorized access to a BigTable instance on Google Cloud. * A Google Cloud [service account JSON key file](https://cloud.google.com/docs/authentication/production). You create and download a key when creating a service account. The key must be downloaded as a JSON file. The service account must have [write permissions for BigTable](https://cloud.google.com/bigtable/docs/access-control). The minimum permissions are: ```text bigtable.tables.create bigtable.tables.mutateRows bigtable.tables.get bigtable.tables.update bigtable.tables.readRows bigtable.tables.list bigtable.tables.delete ``` - Kafka cluster credentials. The following lists the different ways you can provide credentials. - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID. - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). 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](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *or* you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector. * The BigTable instance and the Kafka cluster should be in the same region. * The Confluent CLI installed and configured for the cluster. See [Install and Configure the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). * [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). ### 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](../get-started/index.md#cloud-create-kafka-cluster). #### 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 BigTable Sink** connector card.  #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-gcp-bigtable-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Google Cloud BigTable Sink Connector** screen, complete the following: ### Topic selection 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 access 1. 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](service-account.md#s3-cloud-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. 2. Click **Continue**. ### Authentication 1. Configure the authentication properties: - **GCP credentials file**: Upload your **GCP credentials file**, which is the Google Cloud service account JSON file with write permissions for Cloud Bigtable. - **Cloud Bigtable project ID**: Enter your BigTable **Project ID**, which is the ID of the Cloud Bigtable project to connect to. - **Cloud Bigtable instance ID**: Enter your BigTable **Instance ID**-, which is the ID of the Cloud Bigtable instance to connect to. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-gcp-bigtable-sink-config-properties) for all property values and definitions. - **Input Kafka record value format**: Select the input Kafka record value format (data coming from the Kafka topic). Valid entires AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON, BYTES. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). - **Insert mode**: Select an **Insert mode**: The insertion mode to use: - `INSERT`: Use the standard `INSERT` row function. An error occurs if the row already exists in the table. - `UPSERT`: This mode is similar to `INSERT`. However, if the row already exists, the `UPSERT` function overwrites column values with the new values provided. ### **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?](../sr/faqs-cc.md#faq-schema-contexts). - **Input Kafka record key format**: AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON, STRING, or BYTES. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. - **Max batch size**: The maximum number of records that can be batched into a batch of upserts. Note that since only a batch size of 1 for inserts is supported, `max.batch.size` must be exactly `1` when Insert mode is set to `INSERT`. - **Table name format**: A format string for the destination table name, which may contain `${topic}` as a placeholder for the originating topic name. For example, to create a table named `kafka-orders` based on a Kafka topic named `orders`, you would enter `kafka-${topic}` in this field. - **Row key definition**: A comma separated list of Kafka Record key field names that specifies the order of Kafka key fields to be concatenated to form the row key. #### NOTE If the Row key definition property is left empty and the Kafka record key is a struct, all the fields in the struct are used to construct the row key. If the record key is a byte array, the row key is set to the byte array as is. If the record key is a primitive, the row key is set to the primitive (stringified). - **Row key delimiter**: The delimiter used in concatenating Kafka key fields in the row key. If this configuration is empty or unspecified, the key fields are concatenated together directly. - **Auto create tables**: Whether to automatically create the destination table if it is found to be missing. - **Auto create column families**: Designates whether to automatically create column families if they don’t already exist. **Additional Configs** - **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. - **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. - **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`. - **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers. - **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 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 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. - **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. - **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. If disabled, you must 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. **Transforms** - **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui). For more information about unsupported SMTs, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). **Processing position** - **Set offsets**: Click **Set offsets** to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets). See [Configuration Properties](#cc-gcp-bigtable-sink-config-properties) for all property values and definitions. - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. 1. To change the number of recommended tasks, enter the number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks** field. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details. 2. Click **Launch**. The status for the connector should go from **Provisioning** to **Running**. #### Step 5: Check the results in BigTable Check your BigTable instance to verify that the table is being populated. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ### 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](#cc-gcp-bigtable-sink-prereqs) completed. #### Step 1: List the available connectors Enter the following command to list available connectors: ```none confluent connect plugin list ``` #### Step 2: List the connector configuration properties Enter the following command to show the connector configuration properties: ```none confluent connect plugin describe ``` 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. ```json { "name": "BigTableSinkConnector_0", "config": { "topics": "pageviews", "input.data.format": "AVRO", "input.key.format": "STRING", "connector.class": "BigTableSink", "name": "BigTableSinkConnector_0", "kafka.api.key": "****************", "kafka.api.secret": "*************************************************", "gcp.bigtable.credentials.json": "*", "gcp.bigtable.project.id": "connect-123456789", "gcp.bigtable.instance.id": "confluent", "insert.mode": "INSERT", "auto.create.tables": "true", "auto.create.column.families": "true", "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 (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR**, or **PROTOBUF**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"input.key.format"`: Sets the input record key format (data coming from the Kafka topic). Valid entries are **AVRO**, **BYTES**, **JSON**, **JSON_SR** (JSON Schema), **PROTOBUF**, or **STRING**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format. * `"gcp.bigtable.credentials.json"`: This property contains the contents of the downloaded JSON file. See [Formatting keyfile credentials](#cc-bigtable-json-config-format) for details about how to format and use the contents of the downloaded credentials file. * `"insert.mode"`: Enter an insert mode. The default mode is `UPSERT`. - `"INSERT"`: This option provides the standard insert row function. An error occurs if the row already exists in the table. - `"UPSERT"`: This mode is similar to `INSERT`. However, if the row already exists, the `UPSERT` function overwrites column values with the new values provided. * `max.batch.size`: (Optional) The maximum number of records that can be batched into a single insert or upsert for the table. When `insert.mode` is `INSERT`, the max batch size should be set to `1`. The default value is `1000`. * `"auto.create.tables"`: Designates to automatically create tables if they don’t already exist. The default is `false`. * `"auto.create.column.families"`: Designates whether to automatically create column families if they don’t already exist. The default is `false`. See [Configuration Properties](#cc-gcp-bigtable-sink-config-properties) for all property values and descriptions. #### Step 4: Load the configuration file and create the connector Enter the following command to load the configuration and start the connector: ```none confluent connect cluster create --config-file .json ``` For example: ```none confluent connect cluster create --config-file bigtable-sink-config.json ``` Example output: ```none Created connector BigTableSinkConnector_0 lcc-ix4dl ``` #### Step 5: Check the connector status Enter the following command to check the connector status: ```none confluent connect cluster list ``` Example output: ```none ID | Name | Status | Type +-----------+-------------------------+---------+------+ lcc-ix4dl | BigTableSinkConnector_0 | RUNNING | sink ``` #### Step 6: Check the results in BigTable. Check your BigTable instance to verify that the table is being populated. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ## Formatting keyfile credentials The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration. 1. Convert the JSON file contents into string format. 2. Add the escape character `\` before all `\n` entries in the Private Key section so that each section begins with `\\n` (see the highlighted lines below). The example below has been formatted so that the `\\n` entries are easier to see. Most of the credentials key has been omitted. ```json { "name" : "BigTableSinkConnector_0", "connector.class" : "BigTableSink", "kafka.api.key" : "", "kafka.api.secret" : "", "input.data.format": "AVRO", "topics" : "pageviews", "gcp.bigtable.credentials.json" : "{\"type\":\"service_account\",\"project_id\":\"connect- 1234567\",\"private_key_id\":\"omitted\", \"private_key\":\"-----BEGIN PRIVATE KEY----- \\nMIIEvAIBADANBgkqhkiG9w0BA \\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5\opzsIh \\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn \\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J \\n...rest of key omitted... \\n-----END PRIVATE KEY-----\\n\", \"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\", \"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/ auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/ token\",\"auth_provider_x509_cert_url\":\"https:\/\/ www.googleapis.com\/oauth2\/v1\/ certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/ robot\/v1\/metadata\/x509\/pub-sub%40connect- 123456789.iam.gserviceaccount.com\"}", "gcp.bigtable.project.id": "", "gcp.bigtable.instance.id": " ## 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](/platform/current/connect/kafka_connectors.html). ### 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 ### 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 or BYTES. 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: JSON * Importance: high `input.key.format` : Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. 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: JSON * Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING * Importance: high ### 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, 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 ### GCP credentials `gcp.bigtable.credentials.json` : GCP service account JSON file with write permissions for Cloud Bigtable.
* Type: password * Importance: high ### How should we connect to your Cloud BigTable instance? `gcp.bigtable.project.id` : The ID of the Cloud Bigtable project to connect to.
* Type: string * Importance: high `gcp.bigtable.instance.id` : The ID of the Cloud Bigtable instance to connect to.
* Type: string * Importance: high ### Database details `insert.mode` : The insertion mode to use.
* Type: string * Default: UPSERT * Valid Values: INSERT, UPSERT * Importance: high ### Connection details `max.batch.size` : The maximum number of records that can be batched into a batch of upserts. Note that since only a batch size of 1 for inserts is supported, max.batch.size must be exactly 1 when insert.mode is set to INSERT.
* Type: int * Default: 1000 * Valid Values: [1,…,5000] * Importance: medium ### Data mapping `table.name.format` : A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.
* Type: string * Default: ${topic} * Importance: medium `bigtable.row.key.definition` : A comma separated list of Kafka Record key field names that specifies the order of Kafka key fields to be concatenated to form the row key.
For example the list: ‘username, post_id, time_stamp’ when applied to a Kafka key: {‘username’: ‘bob’,’post_id’: ‘213’, ‘time_stamp’: ‘123123’} and with delimiter # gives the row key ‘bob#213#123123’. You can also access terms nested in the key by using . as a delimiter. If this configuration is empty or unspecified and the Kafka Message Key is a: STRUCT: all the fields in the struct are used to construct the row key. BYTE ARRAY: the row key is set to the byte array as is. PRIMITIVE: the row key is set to the primitive stringified.
If prefixes, more complicated delimiters, and string constants are required in your Row Key, consider configuring an SMT to add relevant fields to the Kafka Record key.
* Type: list * Default: “” * Importance: medium `bigtable.row.key.delimiter` : The delimiter used in concatenating Kafka key fields in the row key. If this configuration is empty or unspecified, the key fields will be concatenated together directly.
* Type: string * Default: “” * Importance: low `auto.create.tables` : Whether to automatically create the destination table if it is found to be missing.
* Type: boolean * Default: false * Importance: medium `auto.create.column.families` : Whether to automatically create missing columns families in the table relative to the record schema.
* Type: boolean * Default: false * 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](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](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 ## FAQs Find answers to common questions about the Google Cloud BigTable Sink connector for Confluent Cloud. ### The connector fails with “Invalid Grant: Account not found”. What does this mean? This error indicates that the authentication process to Google Cloud failed because the service account or its associated credentials could not be found or validated. Common causes include: * The service account credentials JSON file is invalid, expired, or was accidentally corrupted. * The service account specified in the credentials file (`client_email`) does not exist in the Google Cloud project, or has been deleted, disabled, or renamed. * The `private_key` field in the credentials JSON is missing or malformed. To resolve this: 1. Verify that the service account exists in the Google Cloud Console under **IAM & Admin > Service Accounts**, and confirm the `client_email` in your credentials matches an active service account. 2. Generate a new service account key in the Google Cloud Console, download it as a JSON file, and update the connector configuration with the new credentials. 3. If you suspect the key is compromised or expired, rotate the credentials immediately and update all systems using them. ### The connector fails with “Requested column family not found”. What causes this? This error occurs when the connector attempts to write to a column family in BigTable that does not exist, and `auto.create.column.families` is set to `false` (the default). To resolve this, choose one of the following: * **Enable automatic column family creation**: Set `auto.create.column.families` to `true` in the connector configuration. The connector automatically creates missing column families. * **Create the column family manually**: Create the required column families in your BigTable instance before starting the connector. For details on how column families and columns are mapped from Kafka records to BigTable, see the data mapping properties in [Configuration Properties](#cc-gcp-bigtable-sink-config-properties). ### What IAM permissions does the service account need? The service account used by the connector must have write permissions for BigTable. At a minimum, the following permissions are required: ```text bigtable.tables.create bigtable.tables.mutateRows bigtable.tables.get bigtable.tables.update bigtable.tables.readRows bigtable.tables.list bigtable.tables.delete ``` You can grant these permissions by assigning the **Bigtable User** predefined IAM role to the service account. Ensure that the required BigTable API is also enabled for the Google Cloud project. ### What is the difference between INSERT and UPSERT modes? The connector supports two insert modes, configured with the `insert.mode` property: * **INSERT**: Performs a standard row insert. An error occurs if the row already exists in the table. When using `INSERT` mode, set `max.batch.size` to `1` to avoid issues with duplicate row detection. * **UPSERT** (default): Inserts the row if it does not exist, or overwrites column values with new values if the row already exists. Use UPSERT mode for most use cases, because it handles idempotent writes. ### Does the BigTable instance need to be in the same region as my Confluent Cloud cluster? Yes. For optimal performance and to avoid cross-region data transfer costs, the BigTable instance and the Kafka cluster should be in the same region. ### Does the connector support egress for private networking clusters? Yes. If your Confluent Cloud dedicated cluster uses private networking, you must configure egress access so the connector can reach the Google Cloud BigTable endpoint. For details, see [Manage networking for Confluent Cloud connectors](networking/internet-resource.md#clusters-connect-cloud). ## 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](/platform/current/tutorials/examples/cloud-etl/docs/index.html). This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud. [](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)