# Google BigQuery Sink V2 Connector for Confluent Cloud The fully managed Google BigQuery Sink V2 connector for Confluent Cloud streams data from Apache Kafka® topics into Google BigQuery tables using the BigQuery Storage Write API for high-throughput, exactly-once delivery. The connector supports Avro, JSON Schema, Protobuf, JSON (schemaless), and String input data formats. 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/). With version 2 of the Google BigQuery Sink connector, you can perform upsert and delete actions on ingested data. #### NOTE The Google BigQuery Sink V2 connector for Confluent Cloud connects to resources in the same region and cloud provider as your Confluent Cloud cluster. If your Google BigQuery instance is in a different region or cloud provider than your Confluent Cloud cluster, contact [Confluent Support](https://support.confluent.io/hc/en-us) to enable cross-region or cross-cloud connectivity before you configure the connector. ## Features * The connector supports the following functionalities for data ingestion: - **Upsert functionality**: With the upsert functionality, you can insert new data or update existing matching key data. For more details, see [Stream table updates with change data capture](https://cloud.google.com/bigquery/docs/change-data-capture). - **Upsert and delete functionality**: With the upsert and delete functionality, you can insert new data, update existing matching key data, or delete matching key data for tombstone records. For more details, see [Stream table updates with change data capture](https://cloud.google.com/bigquery/docs/change-data-capture). - Google Cloud [BigQuery Storage Write API](https://cloud.google.com/bigquery/docs/write-api). The BigQuery Storage Write API combines streaming ingestion and batch loading into a single high-performance API. For more information, see [Batch load and stream data with BigQuery Storage Write API](https://cloud.google.com/bigquery/docs/write-api). Note that using the Storage Write API may provide a [cost-benefit](https://cloud.google.com/bigquery/pricing?hl=en#data_ingestion_pricing) for your BigQuery project. Also, note that [BigQuery API quotas](https://cloud.google.com/bigquery/quotas#write-api-limits) apply. Review the following table for details about the differences between using BATCH LOADING versus STREAMING mode with the BigQuery API. For more information, see [Introduction to loading data](https://cloud.google.com/bigquery/docs/loading-data). | BATCH LOADING | STREAMING | |---------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------| | Records are available after the commit interval has expired and may not be provided in real time | Records are available immediately after an append call (minimal latency) in near real time | | Requires creating specific application streams to handle the records | Uses the default stream | | May cost less | May cost more | | Multiple API quota limits with more restrictions (for example, maximum number of streams and buffering) | Lower quota with fewer restrictions | * **Provider integration support**: The connector supports Google Cloud’s native identity authorization using Confluent Provider Integration. For more information about provider integration setup, see the [connector authentication](#cc-gcp-bigquery-storage-sink-setup-connection). * **Client-side encryption (CSFLE and CSPE) support**: The connector supports CSFLE and CSPE for sensitive data. For more information about CSFLE or CSPE setup, see the [connector configuration](#cc-gcp-bigquery-storage-sink-setup-connection). * The connector supports OAuth 2.0 for connecting to BigQuery. Note that OAuth is only available when creating a connector using the Cloud Console. OAuth 2.0 support is available in two variations: - **Shared app**: Use the application provided by Confluent, shared with all users, for an easy and quick start without the hassle of application management. - **Bring your own app**: Register and manage your own connected application on the OAuth platform, maintaining control over your authentication setup. * The connector supports streaming from a list of topics into corresponding tables in BigQuery. * Even though the connector streams records by default (as opposed to running in batch mode), the connector is scalable because it contains an internal thread pool that allows it to stream records in parallel. The internal thread pool defaults to 10 threads. Note that this is only applicable for `BATCH LOADING` and `STREAMING` mode and not `UPSERT` and `UPSERT_DELETE` mode. * The connector supports several time-based table partitioning strategies. * The connector supports routing invalid records to the [DLQ](dead-letter-queue.md#ccloud-dlq-topics). This includes any records that have gRPC status code INVALID_ARGUMENT from the BigQuery Storage Write API. #### NOTE DLQ routing does not work if **Auto update schemas** (`auto.update.schemas`) is enabled and the connector detects that the failure is due to schema mismatch. * The connector supports `default_missing_value_interpretation`. For more information about default value interpretation for missing value, see [Google BigQuery default value settings](https://cloud.google.com/bigquery/docs/default-values#write_api). Contact Confluent [Support](https://support.confluent.io/) to enable default value settings in your connector. * The connector supports `Avro`, `JSON Schema`, `Protobuf`, `JSON (schemaless)`, or `String` input data formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use Schema Registry-based formats such as Avro, JSON_SR (JSON Schema), or Protobuf. When you use the `String` input format, the connector treats the message as a raw string even if it contains JSON. The connector does not parse the JSON content. BigQuery expects data as a `Struct` or a `Map`, so you must convert the string to one of those formats before the connector writes data to BigQuery. You can convert your data using the following methods: - Set the `string.value.column.name` configuration to specify the BigQuery column name where the connector stores the raw string value. The connector wraps the string into a single-column record using this name. The connector ignores this configuration if the record value is already formatted as a `Struct` or a `Map`. For example, if an SMT has already transformed the data into one of those formats. - Apply an SMT to convert the raw string into a `Struct` or a `Map` before the connector writes the data to BigQuery. For example, using an SMT to wrap the string into a structured record allows the connector to chain additional SMTs for adding metadata fields. * For Avro, JSON_SR, and Protobuf, the connector provides the following configuration properties that support automated table creation and schema updates. You can select these properties in the UI or add them to the connector configuration, if using the Confluent CLI. - `auto.create.tables`: Automatically create BigQuery tables if they don’t already exist. The connector expects that the BigQuery table name is the same as the topic name. If you create the BigQuery tables manually, make sure the table name matches the topic name. This property adds/updates Kafka record keys as primary keys if `ingestion.mode` is set to `UPSERT` or `UPSERT_DELETE`. Note that you must adhere to the BiqQuery’s [primary key constraints](https://cloud.google.com/bigquery/docs/information-schema-table-constraints#limitations). - `auto.update.schemas`: Automatically update BigQuery schemas. Note that new fields are added as [NULLABLE](https://cloud.google.com/bigquery/docs/schemas#modes) in the BigQuery schema. This property adds/updates Kafka record keys as primary keys if `ingestion.mode` is set to `UPSERT` or `UPSERT_DELETE`. Note that you must adhere to the BiqQuery’s [primary key constraints](https://cloud.google.com/bigquery/docs/information-schema-table-constraints#limitations). - `sanitize.topics`: Automatically sanitize topic names before using them as BigQuery table names. If not enabled, topic names are used as table names. If enabled, the table names created may be different from the topic names. - `sanitize.field.names` Automatically sanitize field names before using them as column names in BigQuery. - `sanitize.field.names.in.array`: Automatically sanitize field names inside array-type objects before using them as column names in BigQuery. #### NOTE New tables and schema updates may take a few minutes to be detected by the Google Client Library. For more information see the Google Cloud [BigQuery API guide](https://cloud.google.com/bigquery/docs/error-messages#metadata-errors-for-streaming-inserts). * **Clustering Support**: Clustering organizes data within a table based on one or more specified columns. The order of the clustered columns determines the sort order of the data, thus enhancing the query performance. Clustering is beneficial for queries that include filter clauses or aggregate large datasets, as it reduces the amount of data scanned. The `topic2ClusteringFieldsMap` configuration allow users to define clustering columns for each topic. Clustering is only applicable when `auto.create.table` is enabled and applies only to tables created through the connector. For more information, check the following resources: - For limits on clustering, see [BigQuery Clustering Limitations](https://cloud.google.com/bigquery/docs/clustered-tables#limitations) - For supported data types, see [BigQuery Clustering Data Types](https://cloud.google.com/bigquery/docs/clustered-tables#cluster_column_types_and_ordering) * **\_CHANGE_SEQUENCE_NUMBER in CDC mode**: The connector supports the [\_CHANGE_SEQUENCE_NUMBER](https://cloud.google.com/bigquery/docs/change-data-capture#format) pseudo-column to maintain correct record ordering in CDC upsert mode. For identical primary keys, the record with the higher sequence number takes precedence. If both primary key and sequence number are identical, the later ingested record is retained. Note that `_CHANGE_SEQUENCE_NUMBER` is a pseudo-column and is not included in the BigQuery table schema. When enabled, this is an optional column and the value for this needs to be added in the data. #### NOTE - Disabling the `_CHANGE_SEQUENCE_NUMBER` feature will not remove existing values from records. To remove these values, contact GCP Support. **Hexadecimal converter**: BigQuery requires `_CHANGE_SEQUENCE_NUMBER` values to [be in hexadecimal format](https://cloud.google.com/bigquery/docs/change-data-capture#format). You must ensure these values are in the correct format or use this converter to transform decimal string values to hexadecimal. Note the following: - The converter expects decimal numeric values as strings. - Applies only when `_CHANGE_SEQUENCE_NUMBER` is enabled and the connector is in `UPSERT` or `UPSERT_DELETE` mode. - The converter supports multi-part sequence numbers separated by forward slashes where each part is converted independently. For example, `"100/200/300"` becomes `"64/C8/12C"`. - The converter allows a maximum of four parts with up to 16 hexadecimal characters per part, per BigQuery CDC specifications. - If the converter is enabled and the connector receives values that are already in hexadecimal format (for example, `"FF"`), the connector treats them as non-numeric and sends them to the Dead Letter Queue (DLQ), if configured. Do not enable the converter if your source data already produces hexadecimal values. * **Secret manager integration**: The connector supports secret manager integration. For `Google Cloud service account` based authentication, the connector can retrieve the following configuration from an integrated secret manager at runtime as needed. | **Secret manager managed configuration** | **Type** | |--------------------------------------------|------------| | `keyfile` | `PASSWORD` | For more information, see [Create a secret manager integration in Confluent Cloud](secret-manager-integration/overview.md#cloud-secret-manager-quickstart). 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 BigQuery Sink V2 Connector](limits.md#google-bigquery-storage-sink-limits) limitations. * If you plan to use OAuth 2.0 for connecting to BigQuery, see [OAuth Limitations](#cc-gcp-bigquery-storage-sink-oauth-limits). * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). * If you plan to consume schemaless JSON, contact Confluent Support. ## Supported data types The following sections describe supported BigQuery data types and the associated connector mapping. ### General mapping The following is a general mapping of supported data types for the Google BigQuery Sink V2 connector. Note that this mapping applies even if a table is created or updated outside of the connector lifecycle. For automatic table creation and schema update mapping, see [the following section](#cc-gcp-bigquery-table-schema-mapping). | BigQuery Data Type | Connector Mapping | Conditions | |----------------------|-------------------------|----------------------------------------------------------------------------------------------| | JSON | STRING | | | GEOGRAPHY | STRING | | | INTEGER | STRING | | | INTEGER | INT32 | | | INTEGER | INT64 | | | FLOAT | INT8 | | | FLOAT | INT16 | | | FLOAT | INT32 | | | FLOAT | INT64 | | | FLOAT | STRING | | | FLOAT | FLOAT32 | | | FLOAT | FLOAT64 | | | BOOL | BOOLEAN | | | BOOL | STRING | “true” or “false” | | BYTES | BYTES | | | STRING | STRING | | | BIGNUMERIC | INT16 | | | BIGNUMERIC | INT32 | | | BIGNUMERIC | INT64 | | | BIGNUMERIC | STRING | | | BIGNUMERIC | FLOAT32 | | | BIGNUMERIC | FLOAT64 | | | NUMERIC | INT16 | | | NUMERIC | INT32 | | | NUMERIC | INT64 | | | NUMERIC | STRING | | | NUMERIC | FLOAT32 | | | NUMERIC | FLOAT64 | | | DATE | STRING | YYYY-MM-DD | | DATE | INT32 | Number of days since epoch. The valid range is -719162 (0001-01-01) to 2932896 (9999-12-31). | | DATE | INT64 | Number of days since epoch. The valid range is -719162 (0001-01-01) to 2932896 (9999-12-31). | | DATETIME | STRING | YYYY-MM-DD[t|T]HH:mm:ss[.F] | | TIMESTAMP | STRING | YYYY-MM-DD HH:mm:SS[.F] | | TIMESTAMP | INT64 | microseconds since epoch | | TIME | STRING | HH:mm:SS[.F] | | TIMESTAMP | Logical TIMESTAMP | | | TIME | Logical TIME | | | DATE | Logical DATE | | | DATE | Debezium Date | | | TIME | Debezium MicroTime | | | TIME | Debezium Time | | | TIMESTAMP | Debezium MicroTimestamp | | | TIMESTAMP | Debezium TIMESTAMP | | | TIMESTAMP | Debezium ZonedTimestamp | | ### Table creation and schema update mapping The following mapping applies if the connector creates a table automatically or updates schemas (that is, if either `auto.create.tables` or `auto.update.schemas` is not disabled). | BigQuery Data Type | Connector Mapping | |----------------------|-------------------------| | STRING | String | | FLOAT | INT8 | | FLOAT | INT16 | | INTEGER | INT32 | | INTEGER | INT64 | | FLOAT | FLOAT32 | | FLOAT | FLOAT64 | | BOOLEAN | Boolean | | BYTES | Bytes | | TIMESTAMP | Logical TIMESTAMP | | TIME | Logical TIME | | DATE | Logical DATE | | FLOAT | Logical Decimal | | DATE | Debezium Date | | TIME | Debezium MicroTime | | TIME | Debezium Time | | TIMESTAMP | Debezium MicroTimestamp | | TIMESTAMP | Debezium TIMESTAMP | | TIMESTAMP | Debezium ZonedTimestamp | #### NOTE Enable `use.integer.for.int8.int16` configuration property to store INT8 and INT16 as **INTEGER** in BigQuery. ## Quick Start Use this quick start to get up and running with the Confluent Cloud Google BigQuery Storage API Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a BigQuery data warehouse. Prerequisites : - An active Google Cloud account with authorization to create resources. - A BigQuery project is required. The project can be created using the [Google Cloud Console](https://console.cloud.google.com/). A [BigQuery dataset](https://cloud.google.com/bigquery/docs/datasets) is required in the project. - To use the [Storage Write API](https://cloud.google.com/bigquery/docs/write-api#required_permissions), the connector [user account](#cc-gcp-bigquery-storage-sink-authentication) authenticating with BigQuery must have `bigquery.tables.updateData` permissions. The minimum permissions are: ```none bigquery.datasets.get bigquery.tables.create bigquery.tables.get bigquery.tables.getData bigquery.tables.list bigquery.tables.update bigquery.tables.updateData ``` - You must create a BigQuery table before using the connector, *if* you leave **Auto create tables** (`auto.create.tables`) disabled (the default). - You may need to create a schema in BigQuery, depending on how you set the **Auto update schemas** property (`auto.update.schemas`). * Auto update schemas set to **ADD NEW FIELDS**: You do not have to create a schema. * Auto update schemas set to **DISABLED** (the default): You must create a schema in BigQuery (as shown below). The connector does not automatically update the table.  - 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 Confluent CLI [installed and configured](https://docs.confluent.io/confluent-cli/current/install.html) for the cluster. - [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 BigQuery Sink V2** connector card.  #### Step 4: Enter the connector details #### NOTE * Be sure you have all the [prerequisites](#cc-gcp-bigquery-storage-sink-prereqs) completed. * An asterisk ( \* ) in the Cloud Console designates a required entry. At the **Add Google BigQuery Sink V2** screen, complete the following: ### Topic selection If you’ve already added 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: **Authentication method** - **Authentication method**: Under **GCP credentials**, select one of the following authenticatation methods with GCP: * **OAuth 2.0 (Shared app)**: (Default) Select this option to use shared OAuth 2.0 credentials. * **OAuth 2.0 (Bring your own app)**: Select this option to use client credentials you configure for OAuth 2.0. To configure Google OAuth 2.0 to bring your own app, see [Set up Google OAuth (Bring your own app)](#cc-gcp-bigquery-storage-sink-oauth). * **Google Cloud service account**: Select this option if you want to authenticate using the GCP credentials file. * **Google service account impersonation**: Select this option to use Provider integration for authentication. - **Use secret manager**: Fetch sensitive configuration values from a secret manager. **GCP credentials** - **Provider Integration**: If you select **Google service account impersonation**, choose an existing integration name under **Provider integration name** dropdown that has access to your resource or create a new provider integration. For more information, see [Manage a Google Cloud Provider Integration](provider-integration.md#connector-gcp-pi). - **GCP credentials file**: If you select **Google Cloud service account**, upload your Google Cloud credentials JSON file with write permissions for BigQuery. For additional details, see [Create a Service Account](#cc-gcp-bigquery-storage-sink-service-account). **Secret manager configuration** - **Secret manager**: Select the secret manager to use for retrieving sensitive data. - **Configurations from Secret manager**: Select the configurations whose values Confluent Cloud should fetch from the secret manager. - **Provider Integration**: If you select **Google service account impersonation**, choose an existing integration name under **Provider integration name** dropdown that has access to your resource or create a new provider integration. For more information, see [Manage a Google Cloud Provider Integration](provider-integration.md#connector-gcp-pi). **Setting up client credentials** - **Client ID**: Enter the OAuth client ID returned when setting up OAuth 2.0. Applicable when authenication method is set to **OAuth 2.0 (Bring your own app)**. - **Client secret**: Enter the client secret for the client ID. Applicable when authenication method is set to **OAuth 2.0 (Bring your own app)**. **Add project details** - **Project ID**: In the **Project ID** field, enter the ID for the Google Cloud project where BigQuery is located. - **Dataset**: Enter the name for the BigQuery dataset the connector writes to in the **Dataset** field. 2. Click **Continue**. ### Configuration - **Ingestion Mode**: Select the **Ingestion Mode**: `STREAMING`, `BATCH LOADING`, `UPSERT`, or `UPSERT_DELETE`. Defaults to `STREAMING`. Review the following table for details about the differences between using `BATCH LOADING` and `STREAMING mode` with the BigQuery API. For more information, see [Introduction to loading data](https://cloud.google.com/bigquery/docs/loading-data). | BATCH LOADING | STREAMING | |----------------------------------------------------------------------|-----------------------------------------------------------------------------------| | Records are available after a stream commit and may not be real time | Records are available for reading immediately after append call (minimal latency) | | Requires creation of application streams | Default stream is used | | May cost less | May cost more | | More API quota limits (max streams, buffering, etc.) | Less quota limits | Note that [BigQuery API quotas](https://cloud.google.com/bigquery/quotas#write-api-limits) apply. - **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), or PROTOBUF. 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). **Data decryption** - Enable **Client-Side Field Level Encryption** for data decryption. Specify a **Service Account** to access the Schema Registry and associated encryption rules or keys with that schema. Select 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. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](csfle.md#connect-csfle). ### **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**: Select the input Kafka record key format to set the data format for incoming record keys. Valid entries are: Avro, Bytes, JSON, JSON Schema, Protobuf, or String. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. #### NOTE If the `ingestion.mode` is set to `UPSERT` or `UPSERT_DELETE`, you must set the record keys to one of the following formats: Avro, JSON, JSON Schema, or Protobuf. - **Commit Interval**: Used only when **BATCH LOADING** (`ingestion.mode`) is enabled. Enter an interval, in seconds, in which the connector attempts to commit records. You can set this to a minimum of `60` seconds up to `14,400` seconds (four hours). BigQuery API quotas apply. - **Kafka Topic to BigQuery Table Map**: Map of topics to tables (optional). The required format is comma-separated tuples. For example, `:,:,...` Note that a topic name must not be modified using a regex single message transform (SMT) if using this option. Note that if this property is used, `sanitize.topics` is ignored. Also, if the topic-to-table map doesn’t contain the topic for a record, the connector creates a table with the same name as the topic name. - **Sanitize topics**: Specifies whether to automatically sanitize topic names before using them as table names in BigQuery. If not enabled, topic names are used as table names. If enabled, the table names created may be different from the topic names. - **Auto update schemas**: Defaults to `DISABLED`. Designates whether or not to automatically update BigQuery schemas. If `ADD NEW FIELDS` is selected, new fields are added with mode [NULLABLE](https://cloud.google.com/bigquery/docs/schemas#modes) in the BigQuery schema. Note that this property is applicable for AVRO, JSON_SR, and PROTOBUF message formats only. - **Sanitize field names**: Specifies whether to automatically sanitize field names before using them as field names in BigQuery. - **Sanitize array fields**: Whether to automatically sanitize field names inside arrays. When enabled, field names inside arrays are also sanitized according to BigQuery naming rules. This setting only takes effect if `sanitize.field.names` is enabled. - **Auto create tables**: Designates whether to automatically create BigQuery tables. Defaults to `DISABLED`. Note that this property is applicable for AVRO, JSON_SR, and PROTOBUF message formats only. The other options available are listed below. * **NON-PARTITIONED**: The connector creates non-partitioned tables. * **PARTITION by INGESTION TIME**: The connector creates tables partitioned by ingestion time. Uses **Partitioning type** to set partitioning. * **PARTITION by FIELD**: The connector creates tables partitioned using a field in a Kafka record value. The field name is set using the property **Timestamp partition field name** (`timestamp.partition.field.name`). Uses Partitioning type to set partitioning. New tables and schema updates may take a few minutes to be detected by the Google Client Library. For more information see the Google Cloud [BigQuery API guide](https://cloud.google.com/bigquery/docs/error-messages#metadata-errors-for-streaming-inserts). - **Partitioning type**: The time partitioning type to use when creating new partitioned tables. Existing tables are not altered to use this partitioning type. Defaults to `DAY`. - **Timestamp partition field name**: The name of the field in the value that contains the timestamp to partition by in BigQuery. Used when **PARTITION by FIELD** is used. - **Enable change sequence number**: Enable the `_CHANGE_SEQUENCE_NUMBER` pseudo-column for CDC operations to ensure record are ordered correctly during upsert mode. When primary keys match, the record with the higher sequence number takes precedence. Because `_CHANGE_SEQUENCE_NUMBER` requires hexadecimal values, enable the hexadecimal converter if your source data produces decimal values. For more information, see [\_CHANGE_SEQUENCE_NUMBER in CDC mode](https://docs.confluent.io/cloud/current/connectors/cc-gcp-bigquery-storage-sink.html#:~:text=_CHANGE_SEQUENCE_NUMBER%20in%20CDC%20mode). - **Use hexadecimal converter for change sequence number**: Use this converter when `use.change.sequence.number` is enabled to convert the `_CHANGE_SEQUENCE_NUMBER` string field from decimal to hexadecimal format for BigQuery. The converter expects decimal numeric values as strings (for example, “255” becomes “FF”). For multi-part sequence numbers separated by forward slashes, the converter processes each part independently (for example, “100/200/300” becomes “64/C8/12C”). It supports up to four parts with a maximum of 16 hexadecimal characters per part. If configured, non-numeric values are sent to DLQ. For more information, see [Hexadecimal converter](https://docs.confluent.io/cloud/current/connectors/cc-gcp-bigquery-storage-sink.html#:~:text=Hexadecimal%20converter). - **Use Date Time Formatter**: Specify whether to use a `DateTimeFormatter` to support a wide range of epochs. Setting this true will use `DateTimeFormatter` over default `SimpleDateFormat`. The output might vary for same input between the two formatters. - **Kafka Topic to Clustering Fields Map**: Maps topics to their corresponding table clustering fields (optional). It accepts a list of comma-separated topic-to-field mappings, for example, `topic1:[col1|col2], topic2:[col1|col2|..], ...`. The fields specified are used to cluster data in BigQuery. The order of the fields determines the clustering precedence.” - **Use INTEGER for INT8 and INT16**: Determines how the connector stores INT8 (BYTE) and INT16 (SHORT) data types in BigQuery during auto table creation and schema update. When set to `false``(default), these values are stored as FLOAT. When set to ``true`, INT8 (BYTE) and INT16 (SHORT) values are stored as INTEGER. - **String value column name**: When using the `STRING` input format, this configuration specifies the BigQuery column name to store the raw string value. The connector wraps the string into a single-column record using this name. If you do not set this configuration, you must use an SMT such as `HoistField$Value` to convert the string into a `Struct` or `Map` type before writing to the BigQuery table. This configuration is ignored if the record value is already a `Struct` or `Map` type, for example, when transformed by an SMT like `HoistField`. **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. 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). - 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 BigQuery 1. From the [Google Cloud Console](https://console.cloud.google.com/), go to your BigQuery project. 2. Query your datasets and verify that new records are being added. 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 Be sure you have all the [prerequisites](#cc-gcp-bigquery-storage-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 example connector properties. ```none { "name" : "confluent-bigquery-storage-sink", "connector.class" : "BigQueryStorageSink", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key" : "", "kafka.api.secret" : "", "keyfile" : "....", "project" : "", "datasets" : "", "ingestion.mode" : "STREAMING" "input.data.format" : "AVRO", "auto.create.tables" : "DISABLED" "sanitize.topics" : "true" "sanitize.field.names" : "false" "sanitize.field.names.in.array" : "false" "tasks.max" : "1" "topics" : "pageviews", } ``` Note the following property definitions: * `"name"`: Sets a name for the new connector. * `"connector.class"`: Identifies the connector plugin name. * `"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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command: ```bash confluent iam service-account list ``` For example: ```bash 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 ``` * `"topics"`: Identifies the topic name or a comma-separated list of topic names. * `"keyfile"`: This contains the contents of the downloaded Google Cloud credentials JSON file for a [Google Cloud service account](#cc-gcp-bigquery-storage-sink-service-account). See [Format service account keyfile credentials](#cc-bigquery-storage-api-json-config-format) for details about how to format and use the contents of the downloaded credentials file as the `"keyfile"` property value. * `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR**, **PROTOBUF**, or **JSON**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"ingestion.mode"`: Sets the connector to use either STREAMING or BATCH LOADING. Defaults to STREAMING. Review the following table for details about the differences between using BATCH LOADING versus STREAMING mode with the BigQuery API. For more information, see [Introduction to loading data](https://cloud.google.com/bigquery/docs/loading-data). | BATCH LOADING | STREAMING | |----------------------------------------------------------------------|-----------------------------------------------------------------------------------| | Records are available after a stream commit and may not be real time | Records are available for reading immediately after append call (minimal latency) | | Requires creation of application streams | Default stream is used | | May cost less | May cost more | | More API quota limits (max streams, buffering, etc.) | Less quota limits | Note that [BigQuery API quotas](https://cloud.google.com/bigquery/quotas#write-api-limits) apply. The following are additional properties you can use. See [Configuration Properties](#cc-gcp-bigquery-storage-sink-config-properties) for all property values and definitions. * `"auto.create.tables"`: Designates whether to automatically create BigQuery tables. Defaults to `DISABLED`. Note that this property is applicable for AVRO, JSON_SR, and PROTOBUF message formats only. The other options available are listed below. - `NON-PARTITIONED`: The connector creates non-partitioned tables. - `PARTITION by INGESTION TIME`: The connector creates tables partitioned by ingestion time. Uses `partitioning.type` to set partitioning. - `PARTITION by FIELD`: The connector creates tables partitioned using a field in a Kafka record value. The field name is set using the property `timestamp.partition.field.name`. Uses `partitioning.type` to set partitioning. #### NOTE New tables and schema updates may take a few minutes to be detected by the Google Client Library. For more information see the Google Cloud [BigQuery API guide](https://cloud.google.com/bigquery/docs/error-messages#metadata-errors-for-streaming-inserts). * `"auto.update.schemas"`: Defaults to `DISABLED`. Designates whether or not to automatically update BigQuery schemas. If `ADD NEW FIELDS` is selected, new fields are added with mode [NULLABLE](https://cloud.google.com/bigquery/docs/schemas#modes) in the BigQuery schema. Note that this property is applicable for AVRO, JSON_SR, and PROTOBUF message formats only. - `"partitioning.type"`: The time partitioning type to use when creating new partitioned tables. Existing tables are not altered to use this partitioning type. Defaults to `DAY`. - `"timestamp.partition.field.name"`: The name of the field in the value that contains the timestamp to partition by in BigQuery. Used when **PARTITION by FIELD** is used. * `"sanitize.topics"`: Designates whether to automatically sanitize topic names before using them as table names. If not enabled, topic names are used as table names. If enabled, the table names created may be different from the topic names. Source topic names must comply with [BigQuery naming conventions](https://cloud.google.com/bigquery/docs/tables) even if `sanitize.topics` is set to `true`. * `"sanitize.field.names"`: Designates whether to automatically sanitize field names before using them as column names in BigQuery. BigQuery specifies that field names can only contain letters, numbers, and underscores. The sanitizer replaces invalid symbols with underscores. If the field name starts with a digit, the sanitizer adds an underscore in front of the field name. Defaults to `false`. * `sanitize.field.names.in.array`: Determines whether to sanitize field names that are nested inside array-type objects. By default, the connector does not sanitize fields inside array-type objects during record ingestion. To enable sanitization for these fields, set `sanitize.field.names.in.array` to `true`. #### NOTE This property requires `sanitize.field.names=true`. To sanitize all fields, including those in arrays, you must set both properties to `true`. * `"use.date.time.formatter"`: Defaults to `false` and the connector uses the `SimpleDateFormat`. If set to `true`, the connector uses `DateTimeFormatter`, which supports a wide range of epochs. Use `DateTimeFormatter` for time-based field such as Logical TIMESTAMP, Logical TIME, Logical DATE, Debezium Date , Debezium MicroTime, Debezium Time , Debezium MicroTimestamp, Debezium TIMESTAMP, or Debezium ZonedTimestamp. The output can vary for the same input across the two formatters. For example, with the epoch `-62104147200000`, the connector performs the following: - With `SimpleDateFormat`: Writes the timestamp as `0002-01-02T00:00:00` to the destination. - With `DateTimeFormatter`: Writes the timestamp as `0001-12-31 00:00:00.000` to the destination. #### 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](csfle.md#connect-csfle). **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). See [Configuration Properties](#cc-gcp-bigquery-storage-sink-config-properties) for all property values and definitions. #### 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 bigquery-storage-sink-config.json ``` Example output: ```none Created connector confluent-bigquery-storage-sink 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 | confluent-bigquery-storage-sink | RUNNING | sink ``` #### Step 6: Check the results in BigQuery. 1. From the [Google Cloud Console](https://console.cloud.google.com/), go to your BigQuery project. 2. Query your datasets and verify that new records are being added. 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. ## Connector authentication You can use a Google Cloud service account or Google OAuth to authenticate the connector with BigQuery. Note that OAuth is only available when using the Confluent Cloud UI to create the connector. ### Create a Service Account For details about how to create a Google Cloud service account and get a JSON credentials file, see [Create and delete service account keys](https://cloud.google.com/iam/docs/keys-create-delete). Note the following when creating the service account: * The service account must have write access to the BigQuery project containing the dataset. * To use the [Storage Write API](https://cloud.google.com/bigquery/docs/write-api#required_permissions), the connector must have `bigquery.tables.updateData` permissions. The minimum permissions are: ```none bigquery.datasets.get bigquery.tables.create bigquery.tables.get bigquery.tables.getData bigquery.tables.list bigquery.tables.update bigquery.tables.updateData ``` * You create and download a key when creating a service account. The key must be downloaded as a JSON file. It resembles the example below: ```json { "type": "service_account", "project_id": "confluent-123456", "private_key_id": ".....", "private_key": "-----BEGIN PRIVATE ...omitted... =\n-----END PRIVATE KEY-----\n", "client_email": "confluent2@confluent-123456.iam.gserviceaccount.com", "client_id": "....", "auth_uri": "https://accounts.google.com/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/metadata/confluent2%40confluent-123456.iam.gserviceaccount.com" } ``` ### Format service account keyfile credentials Formatting the keyfile is only required when using the CLI or Terraform to create the connector, where you must add the service account key directly into the connector configuration. The contents of the downloaded service account credentials JSON 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" : "confluent-bigquery-sink", "connector.class" : "BigQueryStorageSink", "kafka.api.key" : "", "kafka.api.secret" : "", "topics" : "pageviews", "keyfile" : "{\"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\"}", "project": "", "datasets":"", "data.format":"AVRO", "tasks.max" : "1" } ``` 3. Add all the converted string content to the `"keyfile"` credentials section of your configuration file as shown in the example above. ### Set up Google OAuth (Bring your own app) Complete the following steps to set up Google OAuth and get the **Client ID** and **Client secret**. This is applicable for the **OAuth 2.0 (Bring your own app)** BigQuery authentication option. For additional information, see [Setting up OAuth 2.0](https://support.google.com/googleapi/answer/6158849). 1. Create a Project in Google Cloud Console. If you haven’t already, create a new project in the [Google Cloud Console](https://console.cloud.google.com/project). 2. Enable the BigQuery API. * Navigate to the [API Library](https://console.cloud.google.com/apis/library) in the Google Cloud Console. * Search for the **BigQuery API**. * Select it and enable it for your project. 3. Configure the OAuth Consent Screen. * Go to the [OAuth consent screen](https://console.cloud.google.com/apis/credentials/consent) in the Credentials section of the Google Cloud Console. * Select **Internal** when prompted to set the User Type. This means only users within your organization can consent to the connector accessing data in BigQuery. * In the **Scopes** section of the Consent screen, enter the following scope: ```bash https://www.googleapis.com/auth/bigquery ``` * Fill in the remaining required fields and save. 4. Create OAuth 2.0 Credentials. * In the Google Cloud Console, go to the [Credentials page](https://console.cloud.google.com/apis/credentials). * Click on **Create Credentials** and select **OAuth client ID**. * Choose the application type **Web application**. * Set up the authorized redirect URI. Enter the following Confluent-provided URI: ```bash https://confluent.cloud/api/connect/oauth/callback ``` 5. Click **Create**. You are provided with the Client ID and Client secret. #### Authorization Code grant flow The following are the two stages of the authorization code grant flow when using OAuth 2.0. 1. Get the authorization code: * The Google OAuth API endpoint is invoked by passing query parameters (for example, `client_id`, `redirect_uri`, and `scope`). * The user handles authentication (AuthN) and authorization (AuthZ) with Google. Note that Confluent does not have access to the username and password. * Google delivers the authorization code as a query parameter to the `redirect_uri` passed in the original API request. 2. Exchange the authorization code for an `access_token` and `refresh_token`. * After invoking the Google OAuth API endpoint and passing the authorization code and other parameters, an `access_token` is returned and is used as the Bearer Token to access Google BigQuery. * When the `access_token` expires, a new token is requested by passing the `refresh_token` request to the OAuth API endpoint. No human intervention occurs. #### OAuth Limitations Note the following limitations when using OAuth for the connector. * If you enter the wrong **Client ID** when authenticating, the UI attempts to launch the consent screen, and then displays the error for an invalid client. Click the back button and re-enter the connector configuration. This is applicable for the **OAuth 2.0 (Bring your own app)** BigQuery authentication option. * If you enter the correct **Client ID** but enter the wrong **Client secret**, the UI successfully launches the consent screen and the user is able to authorize. However, the API request fails. The user is redirected to the connect UI provisioning screen. This is applicable for the **OAuth 2.0 (Bring your own app)** BigQuery authentication option. * You must manually revoke permissions for the connector from [Third-party apps & services](https://myaccount.google.com/connections) if you delete the connector or switch to a different auth mechanism. * Google Cloud allows a maximum of [100 refresh tokens](https://developers.google.com/identity/protocols/oauth2#:~:text=There%20is%20currently%20a%20limit,oldest%20refresh%20token%20without%20warning) for a single user using a single OAuth 2.0 client ID. For example, a single user can create a maximum of 100 connectors using the same client ID. When this user creates connector 101 using the same client ID, the refresh token for the first connector created is revoked. If you want to run more than 100 connectors, you can use multiple Client IDs or User IDs. Or, you can use a [service account](#cc-gcp-bigquery-storage-sink-service-account). ## Legacy to V2 Connector Migration The [BigQuery Sink Legacy connector](cc-gcp-bigquery-sink.md#cc-gcp-bigquery-sink) uses the [BigQuery legacy streaming API](https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery) and will reach end of life (EOL) on March 31, 2026. Confluent recommends migrating to the BigQuery Sink V2 connector. The [BigQuery Sink V2 connector](#cc-gcp-bigquery-storage-api-sink) uses the [BiqQuery storage write API](https://cloud.google.com/bigquery/docs/write-api), which offers higher throughput, improved reliability, and better schema evolution support than the legacy connector. Due to fundamental differences between the underlying Google Cloud APIs, a direct in-place upgrade is not possible. The following sections provide the migration approach with no data loss, including the known API changes, and mappings for key configuration properties. You can choose one of two paths for your migration: - **Automated migration** (recommended): Use the [Bigquery V2 Sink migration tool](https://github.com/confluentinc/confluent-connector-migration-tool/tree/master/bigquery-v2-sink) provided by Confluent. This is the simplest and fastest method, as it automates the transfer of configurations and offsets. - **Manual migration**: Set up a parallel pipeline, validate data, and manually cut over from the legacy to the V2 connector. This approach offers more granular control but requires more hands-on effort. Prerequisites : - Confluent Cloud: An active account with access to the legacy connector. - Google Cloud: A Google Cloud service account with the necessary BigQuery permissions. - Automated migration script: If you choose to use the automated script, ensure your local machine has Python 3.6 or later installed. ### Using the automated migration For a streamlined approach, use the [Bigquery V2 Sink migration tool](https://github.com/confluentinc/confluent-connector-migration-tool/tree/master/bigquery-v2-sink). This tool is designed to simplify the process by automating the transfer of existing offsets and topic mappings from your legacy connector configuration to the new V2 connector. The migration tool, [migrate-to-bq-v2-sink.py](https://github.com/confluentinc/confluent-connector-migration-tool/blob/master/bigquery-v2-sink/migrate-to-bq-v2-sink.py), is available in the official Confluent GitHub repository. #### Quick start Clone or download the repository containing the script. Run the migration tool from your local machine: ```bash python3 migrate-to-bq-v2-sink.py --legacy_connector "" --environment "" --cluster_id "" ``` The tool will guide you through configuring the new V2 connector, including: 1. **Check V1 Sink connector status**: The tool shows your connector’s current status. - If testing on dummy tables: You can keep the existing connector running. - For production tables: It’s recommended to pause the V1 connector to avoid data duplication. 2. **Get environment details**: Fetch the environment name and cluster ID from your Kafka cluster’s URL in Confluent Cloud. 3. **Set credentials**: The tool supports three methods for providing your Confluent Cloud credentials. - Environment variables: Export your email and password as environment variables. Note that the environment variables can be visible in process lists and command history. ```none export EMAIL="your-email@example.com" export PASSWORD="your-password" ``` - Credentials file (recommended): Create a JSON file with your credentials. ```none { "email": "your-email@example.com", "password": "your-password" } ``` - Secure input: Enter credentials interactively when prompted. 4. **Follow the interactive prompts**: The tool will guide you through configuring the new V2 connector, including: - Connector Name: Enter a name for the new V2 connector. - Ingestion Mode: Select a mode for ingestion. Defaults to `STREAMING`. * `STREAMING`: Lower latency, higher cost (default) * `BATCH LOADING`: Higher latency, lower cost * `UPSERT`: For upsert operations (requires key fields) * `UPSERT_DELETE`: For upsert and delete operations (requires key fields) - Int8/Int16 casting for Byte and Short fields: Choose between `FLOAT` (default) or `INTEGER`. - Commit Interval: For `BATCH LOADING` mode, set interval between 60-14400 seconds. - Auto Create Tables: Configure table creation (defaults to `DISABLED`). * `DISABLED`: Do not auto-create tables (default) * `NON-PARTITIONED`: Create tables without partitioning * `PARTITION by INGESTION TIME`: Create time-partitioned tables * `PARTITION by FIELD`: Create field-partitioned tables - Partitioning: Set partitioning type and field (if applicable). * `HOUR`: Partition by hour * `DAY`: Partition by day * `MONTH`: Partition by month * `YEAR`: Partition by year - Topic to Table Mapping: For testing purposes, you can configure `topic2table.map` to redirect data to different tables. * Use existing mapping: If already configured in the legacy connector. * Configure new mapping: For testing without affecting production tables. For example, `my-topic:my-test-table,another-topic:another-test-table`. * Skip mapping: Use default table names. - Date Time Formatter: Choose between `SimpleDateFormat` or `DateTimeFormatter`. - GCP Service Account Keyfile: Provide your credentials via file path, environment variable, or direct input. * File Path: Provide the path to your JSON keyfile * Environment Variable: Set `GCP_KEYFILE_PATH` environment variable * Direct Input: Paste the JSON content directly - Credentials: Choose a secure method for Confluent Cloud authentication. 5. **Review and Confirm**: The tool displays the final configuration and asks for confirmation before creating the connector. #### Considerations - Preserve offsets: The V2 connector will start from the latest offset of your legacy connector. - Prevent data loss: The tool automatically transfers offsets to avoid data duplication. - Review breaking changes: Before you migrate, review the breaking changes section. - Test first: Always test with a small dataset first before upgrading a production connector. - Monitor after migration: Monitor the new connector for any issues after migration. - Check connector status: The tool shows the connector status and recommends actions based on whether you’re testing or migrating production data. ### Using the manual migration Follow these steps to migrate your pipelines from the legacy connector to the V2 connector. - **Understand the key changes**: Review the differences in [API behavior](#cc-gcp-bigquery-sink-legacy-v2-migration-breaking-api), data type handling, and [configuration properties](#cc-gcp-bigquery-sink-legacy-v2-migration-changed-behavior). Pay close attention to how `TIMESTAMP` values are handled. - **Set up a parallel pipeline**: Never attempt to upgrade a production connector in place. A parallel environment is essential for validation without impacting your production data flow. Create a new BigQuery Sink V2 connector in a test environment, pointing it to a new, separate BigQuery table for validation. To directly compare fields, data types, and stored data between legacy and V2, you can continue using the same Kafka topic as your production connector. - **Validate data integrity**: Run the legacy and V2 connectors in parallel, writing to separate tables. Compare the data in both tables to ensure consistency. * Confirm that `TIMESTAMP`, `DATE`, and `DATETIME` fields are handled correctly. * Ensure all fields are mapped as `NULLABLE` in BigQuery, as expected. * Check for any inconsistencies with nested or complex data schemas. - **Cutover to V2**: After validating the V2 pipeline, perform the following: * Gracefully stop the legacy BigQuery sink connector. * Start or resume the new V2 connector from the last committed offsets of the V1 connector. * After confirming the V2 connector is running stably, decommission the legacy connector and update any monitoring systems to use the new connector. ### Breaking API changes The following table lists API differences that were identified during connector development and testing. Be aware that there may be additional breaking changes that were not identified. | BigQuery Schema Types | Legacy InsertAll API | Storage Write API (V2) | |------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------| | TIMESTAMP | Input value is considered seconds since epoch | Input value is considered microseconds since epoch | | DATE | INT is not supported | INT in range -719162 (0001-01-01) to 2932896 (9999-12-31) is supported | | TIMESTAMP, JSON, INTEGER, BIGNUMERIC, NUMERIC, STRING, DATE, TIME, DATETIME, BYTES | Supports most primitive data type inputs | See [Supported data types](#cc-gcp-bigquery-storage-supported-datatypes) | | DATE, TIME, DATETIME, TIMESTAMP | Supports entire [datetime canonical format](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#canonical_format_for_datetime_literals) | Supports a subset of the [datetime canonical format](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#canonical_format_for_datetime_literals) | ### New V2 properties These properties provide new functionality that wasn’t available in the legacy connector. | V2 property | Default value | Description | |------------------------------|-----------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `ingestion.mode` | `STREAMING` | Selects the data ingestion strategy. `STREAMING` offers lower latency and higher cost,
while `BATCH LOADING` offers higher latency and lower cost. `UPSERT` and
`UPSERT_DELETE` modes handle record-level updates and deletes. | | `auto.create.tables` | `DISABLED` | Provides more granular control over table creation. Options include:
`NON-PARTITIONED`, `PARTITION by INGESTION TIME`, and `PARTITION by FIELD`. | | `use.date.time.formatter` | `false` (uses `SimpleDateFormat`) | Allows switching from the default `SimpleDateFormat` to the more modern
`DateTimeFormatter` for handling date and time types. | | `use.integer.for.int8.int16` | `FLOAT` | Determines how `INT8` and `INT16` Kafka types are cast. | ### Discontinued properties The following legacy configuration properties have been removed in V2. | Discontinued legacy property | V2 behavior & equivalent | |-----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `allowBigQueryRequiredFieldRelaxation` | Not supported. All fields in the V2 connector are created as `NULLABLE` by default.
To make a field `REQUIRED`, you must manually alter the table schema in the BigQuery UI. | | `allowSchemaUnionization` | Removed. This functionality is now part of the `auto.update.schemas` property, which
handles schema evolution for both primitive and complex types (structs and arrays). | | `bigQueryRetry` and `bigQueryRetryWait` | Not user-configurable. The V2 connector uses built-in retry mechanisms with a
default retry count of 10 and a wait time of 1,000 ms. | | `defaultDataset` | Removed. Datasets are specified directly in the connector configuration. | | `schemaRetriever` | Removed. Its functionality for creating and updating tables is now handled by the
`auto.create.tables` and `auto.update.schemas` properties. | ### Properties with changed behavior The following configuration properties exist in both the legacy and V2 versions but have different implementations or capabilities in V2. Note the legacy to V2 mapping for the key configuration properties: | Configuration property | Legacy behavior | V2 behavior | |----------------------------------------------------------|---------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------| | `auto.update.schemas` | Allowed automatic updates for new primitive fields only. | Supports schema updates for both primitive and complex field types (structs and arrays), making it more robust. | | `all.bq.fields.nullable` | Could be set to `false` to enforce `REQUIRED` fields based on the schema. | Removed. All fields are created as `NULLABLE` by default, with no override configuration available. | | `partitioning.type` and `timestamp.partition.field.name` | Offered basic partitioning options. | These configurations offer more granular control and work in tandem with the enhanced `auto.create.tables` options. | | `convert.double.special.values` | User-configurable | Not exposed in Confluent Cloud. The default behavior is `false`, passing non-finite
values (`NaN`, `+Infinity`, `-Infinity`) as-is without conversion. | ### Migration FAQs The frequently asked questions (FAQs) address common questions and issues encountered while migrating from the legacy to BigQuery Sink V2 connector for Confluent Cloud. #### Why should I migrate to the V2 connector? The V2 connector, which uses the underlying Storage Write API, offers significant benefits: - **Higher throughput**: Sustains much higher data ingestion rates. - **Future-proof**: Aligns with Google Cloud’s official recommendation for streaming data into BigQuery. - **Improved reliability**: Provides better error handling and built-in retry mechanisms. - **Expanded type support**: Natively supports `NUMERIC` and `BIGNUMERIC` data types. - **Enhanced schema evolution**: More robustly handles changes to Kafka topic schemas. - **Flexible write strategies**: Supports multiple delivery modes, including `STREAMING`, `BATCH LOADING`, `UPSERT`, and `UPSERT_DELETE`. #### What happens if I don’t migrate before the legacy connector EOL? The legacy BigQuery streaming API is already deprecated, and in alignment with this, the legacy BigQuery connector is scheduled for EOL on March 31, 2026. After this date: - You can no longer create new legacy BigQuery connectors. - Existing connectors may continue to function, but they will not receive any updates or fixes. To ensure long-term stability and support, migrate to the [BigQuery Sink V2 connector](#cc-gcp-bigquery-storage-api-sink) as soon as possible. #### Are these two connectors just different versions of the same software? No. They are two completely different connectors from an architectural standpoint. The legacy connector is built on BigQuery’s old `InsertAll` API, while the V2 connector was written from the ground up to leverage the modern [BiqQuery storage write API](https://cloud.google.com/bigquery/docs/write-api). This fundamental difference in the underlying technology is why a direct, in-place upgrade is not possible and requires a parallel migration strategy. #### Is there a price difference between the legacy and V2 connectors? No, Confluent prices both the legacy and V2 connectors the same. #### Are there any changes to my Google Cloud costs? While Confluent’s pricing for the connector remains the same, the underlying Google Cloud APIs have different pricing models. The legacy `InsertAll` API and the new `Storage Write` API are billed differently by Google. You should review Google Cloud’s [BigQuery pricing documentation](https://cloud.google.com/bigquery/pricing) to estimate the cost impact for your specific workload. #### Do I need to stop my legacy connector before starting V2 connector? No. During the validation phase, you should run them in parallel. However, once you cut over to the V2 connector for production traffic, you must stop the legacy connector to prevent duplicate data writes. #### Will my old offsets work with the V2 connector? Yes, but you should not reuse them manually. Offsets are stored per partition and can be reused by the V2 connector to ensure continuity. The [Bigquery V2 Sink migration tool](https://github.com/confluentinc/confluent-connector-migration-tool/tree/master/bigquery-v2-sink) leverages these offsets so the V2 connector starts from the last committed offset of each partition, avoiding skipped data or duplication. You should always use the migration tool to correctly map offsets. #### Can I use the BigQuery V2 Sink migration tool mentioned in the docs? Yes. Confluent provides a [Bigquery V2 Sink migration tool](https://github.com/confluentinc/confluent-connector-migration-tool/tree/master/bigquery-v2-sink) to automate the migration process for users looking for a more automated approach. However, we still strongly recommend that you understand the manual steps and perform thorough validation before switching production workloads. #### Does the connector support a single topic with multiple schemas to write to multiple tables? No. The BigQuery V2 Sink connector does not support splitting data from a single Kafka topic into multiple BigQuery tables based on different schemas. Each Kafka topic maps to one dedicated BigQuery table. However, a single connector instance can consume from multiple Kafka topics, with each topic mapping to its own respective table and schema. #### Is `allow.schema.unionization` configuration property available in V2 connector? No. The `allow.schema.unionization` property is removed in V2. In the V1 connector, you needed this property enabled to update complex fields (structs and arrays). In the V2 connector, the `auto.update.schemas` property handles schema updates for both primitive and complex field types. #### Why are all fields mapped as `NULLABLE` in V2? The V2 connector uses this design to ensure maximum compatibility and prevent load failures that occur when a source record is missing an optional field. Unlike the legacy connector (`all.bq.fields.nullable`), V2 does not currently offer a configuration to override this behavior. If you require a field to be `REQUIRED`, manually update the table schema in the BigQuery UI after the table is created. #### Why is the `convert.double.special.values` configuration not available in BigQuery Sink V2? The V2 connector supports the `convert.double.special.values` configuration internally, but it is not exposed in Confluent Cloud. The default value is `false`, which means non-finite double values (`NaN`, `+Infinity`, `-Infinity`) are passed through as-is and are not converted to maximum (`Double.MAX_VALUE`) or minimum values (`Double.MIN_VALUE`). #### Does the BigQuery V2 connector require users to include `_CHANGE_TYPE` in Kafka records? No. In `UPSERT_DELETE` mode, the connector automatically interprets a record containing only primary key fields (with all other fields as null) as a delete operation. The connector’s backend then internally sets `_CHANGE_TYPE=DELETE` before applying the change in BigQuery. #### Will there be a V2 connector for self-managed Confluent Platform? At this time, there are no plans to release a self-managed V2 connector. We are currently evaluating customer demand. If you need this capability, submit a Feature Request through your Confluent account team so we can track and prioritize interest. ## Frequently asked questions Find answers to common questions about the Google BigQuery Sink V2 connector for Confluent Cloud. ### Why are there duplicate records in my BigQuery table? The connector guarantees at-least-once delivery, which means some duplicates are expected after a connector restart or rebalance. This is by design to prevent data loss. To minimize duplicate records in your BigQuery table: * **Check the source topic**: If the source Kafka topic already contains duplicates, work with the producer application to address that upstream. * **Use UPSERT or UPSERT_DELETE ingestion mode**: Switch the `ingestion.mode` property to `UPSERT` (or `UPSERT_DELETE` if you also need to process deletes) and configure a primary key. In these modes, duplicate records with the same primary key value override the existing row instead of creating a new row, ensuring no duplicate entries accumulate in the BigQuery table. * **Manually remove duplicates**: If you are using `STREAMING` mode, you can manually remove duplicates in BigQuery. For instructions, see [Manually removing duplicates](https://cloud.google.com/bigquery/docs/streaming-data-into-bigquery#manually_removing_duplicates) in the Google BigQuery documentation. ### What causes a `BigQueryException: Not found: Dataset` error? This error occurs when the dataset specified in the connector configuration does not exist in the target BigQuery project, or when the service account does not have permission to access it. To resolve this: 1. Verify the dataset exists in your BigQuery project. You can check in the [Google Cloud Console](https://console.cloud.google.com/). 2. Confirm that the service account used by the connector has the `bigquery.datasets.get` and `bigquery.tables.create` permissions on the dataset. 3. Check that the `datasets` property in the connector configuration matches the exact name of the dataset in BigQuery (case-sensitive). 4. If your connector is in a different Google Cloud project from your dataset, ensure the service account has access to the dataset project. ### What causes the `failed to update the table schema` error? This error occurs when the connector attempts to evolve a BigQuery table schema to match the incoming Kafka record schema, but the schema change is incompatible. Common causes include: * **Type changes**: BigQuery does not allow changing a column’s data type (for example, from `RECORD` to `STRING`). If a field’s type changes in the Kafka schema, the connector cannot automatically update the table. * **Manual table modifications**: If the BigQuery table schema was manually altered in a way that conflicts with the incoming records. To resolve this, manually update the BigQuery table schema to align with the new schema, or update the Kafka records to match the existing BigQuery table schema. Note that `auto.update.schemas` in V2 only supports adding new fields as `NULLABLE`. It does not support changing field types or removing fields. Also note that `auto.update.schemas` is supported only with AVRO, JSON_SR, and PROTOBUF input data formats. ### What is the maximum number of columns (fields) per BigQuery table? BigQuery enforces a hard limit of 10,000 fields per table, counting all leaf fields including those within nested records. If the connector attempts to create or auto-update a table schema with more than 10,000 fields, it fails with an error similar to: ```text Connector failed to create a table in BigQuery as the total number of fields are X which is higher than the allowed limit of 10000 fields per table. ``` To avoid hitting this limit: * Use stream processing (for example, Confluent Cloud for Apache Flink or ksqlDB) to normalize or trim your schema upstream before it reaches the connector. * Use the `ReplaceField$Value` SMT with an `include` list to select only the required fields before writing to BigQuery. * If you have already exceeded the limit, create a new BigQuery table with a bounded schema and point the connector to it. ### Does the connector support cross-region or cross-cloud connectivity? The Google BigQuery Sink V2 connector for Confluent Cloud connects to resources in the same region and cloud provider as your Confluent Cloud cluster by default. If your Google BigQuery instance is in a different region or cloud provider than your Confluent Cloud cluster, contact [Confluent Support](https://support.confluent.io/hc/en-us) to enable cross-region or cross-cloud connectivity before you configure the connector. ### What causes a VPC Service Controls policy violation error? If your Google Cloud environment has VPC Service Controls enabled, the connection from Confluent Cloud to Google BigQuery might be blocked, resulting in a `403 Permission Denied` error. This is a Google Cloud environment configuration issue, not a connector configuration issue. To resolve this, check your Google Cloud environment’s VPC Service Controls configuration and add an access rule that allows requests from the Confluent Cloud connector. Contact your cloud operations team with the `vpcServiceControlsUniqueIdentifier` from the error message to identify and update the relevant access policy. ## Configuration Properties Use the following configuration properties with this connector. ### 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.key.format` : Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF. 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: BYTES * Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING * Importance: high `input.data.format` : Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, and JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, or PROTOBUF.
* Type: string * Default: JSON * 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 ### Authentication method `authentication.method` : Select how you want to authenticate with BigQuery.
* Type: string * Default: Google cloud service account * Importance: high `secret.manager.enabled` : Fetch sensitive configuration values from a secret manager.
* Type: boolean * Default: false * Importance: high ### Secret manager configuration `secret.manager` : Select the secret manager to use for retrieving sensitive data.
* Type: string * Importance: high `secret.manager.managed.configs` : Select the configurations to fetch their values from the secret manager.
* Type: list * Importance: high `secret.manager.provider.integration.id` : Select an existing provider integration that has access to your secret manager.
* Type: string * Importance: high ### GCP credentials `provider.integration.id` : Select an existing integration that has access to your resource. In case you need to integrate a new Google Service Account, use provider integration
* Type: string * Importance: high `keyfile` : GCP service account JSON file with write permissions for BigQuery.
* Type: password * Importance: high `oauth.client.id` : Client ID of your Google OAuth application.
* Type: string * Importance: high `oauth.client.secret` : Client secret of your Google OAuth application.
* Type: password * Importance: high `oauth.refresh.token` : OAuth 2.0 refresh token for BigQuery.
* Type: password * Importance: high ### BigQuery details `project` : ID for the GCP project where BigQuery is located.
* Type: string * Importance: high `datasets` : Name of the BigQuery dataset where table(s) is located.
* Type: string * Importance: high ### Ingestion Mode details `ingestion.mode` : Select a mode to ingest data into the table. Select STREAMING for reduced latency. Select BATCH LOADING for cost savings. Select UPSERT for upserting records. Select UPSERT_DELETE for upserting and deleting records.
* Type: string * Default: STREAMING * Importance: high ### Insertion and DDL support `commit.interval` : The interval, in seconds, the connector attempts to commit streamed records. Set the interval between 60 seconds (1 minute) and 14,400 seconds (4 hours). Be careful when setting the commit interval as on every commit interval, a task calls the `CreateWriteStream` API which is subject to a quota . For example, if you have five tasks (may belong to different connectors also) set to commit every 60 seconds to a project, there will be five calls to `CreateWriteStream` API on that project every minute. If the count exceeds the allowed quota, some tasks may fail.
* Type: int * Default: 60 * Valid Values: [60,…,14400] * Importance: high `topic2table.map` : Map of topics to tables (optional). The required format is comma-separated tuples. For example, :,:,… Note that a topic name must not be modified using a regex SMT while using this option. If this property is used, `sanitize.topics` is ignored. Also, if the topic-to-table map doesn’t contain the topic for a record, the connector creates a table with the same name as the topic name.
* Type: string * Default: “” * Importance: medium `sanitize.topics` : Designates whether to automatically sanitize topic names before using them as table names in BigQuery. If not enabled, topic names are used as table names.
* Type: boolean * Default: true * Importance: high `sanitize.field.names` : Whether to automatically sanitize field names before using them as field names in BigQuery. BigQuery specifies that field names can only contain letters, numbers, and underscores. The sanitizer replaces invalid symbols with underscores. If the field name starts with a digit, the sanitizer adds an underscore in front of field name. Caution: Key duplication errors can occur if different fields are named a.b and a_b, for instance. After being sanitized, field names a.b and a_b will have same value.
* Type: boolean * Default: false * Importance: high `auto.create.tables` : Designates whether or not to automatically create BigQuery tables. Note: Supports AVRO, JSON_SR, and PROTOBUF message format only.
* Type: string * Default: DISABLED * Importance: high `auto.update.schemas` : Designates whether or not to automatically update BigQuery schemas. New fields in record schemas must be nullable. Note: Supports AVRO, JSON_SR, and PROTOBUF message format only.
* Type: string * Default: DISABLED * Importance: high `sanitize.field.names.in.array` : Whether to automatically sanitize field names inside arrays. When enabled, field names inside arrays will also be sanitized according to BigQuery naming rules. This setting only takes effect if ‘sanitize.field.names’ is also enabled.
* Type: boolean * Default: false * Importance: medium `partitioning.type` : The time partitioning type to use when creating new partitioned tables. Existing tables will not be altered to use this partitioning type.
* Type: string * Default: DAY * Importance: low `timestamp.partition.field.name` : The name of the field in the value that contains the timestamp to partition by in BigQuery. This also enables timestamp partitioning for each table.
* Type: string * Importance: low `use.change.sequence.number` : Enables the \_CHANGE_SEQUENCE_NUMBER pseudo-column for CDC operations to ensure correct record ordering in upsert mode. For identical primary keys, the record with the higher sequence number takes precedence. Because \_CHANGE_SEQUENCE_NUMBER requires hexadecimal values, enable the hexadecimal converter if your source data produces decimal values. For more information, see [\_CHANGE_SEQUENCE_NUMBER in CDC mode](https://docs.confluent.io/cloud/current/connectors/cc-gcp-bigquery-storage-sink.html#:~:text=_CHANGE_SEQUENCE_NUMBER%20in%20CDC%20mode).
* Type: boolean * Default: false * Importance: medium `use.hexadecimal.converter.for.change.sequence.number` : Only applies when `use.change.sequence.number` is enabled. Converts the \_CHANGE_SEQUENCE_NUMBER string field from decimal to hexadecimal format, as required by BigQuery. Expects decimal numeric values as strings (for example, “255” converts to “FF”). Supports multi-part sequence numbers separated by forward slashes where each part is converted independently (for example, “100/200/300” becomes “64/C8/12C”). Supports a maximum of four parts with a maximum of 16 hex characters per part. Non-numeric values are sent to the DLQ, if configured. For more information, see [Hexadecimal converter](https://docs.confluent.io/cloud/current/connectors/cc-gcp-bigquery-storage-sink.html#:~:text=Hexadecimal%20converter).
* Type: boolean * Default: false * Importance: low `use.date.time.formatter` : Specify whether to use a `DateTimeFormatter` to support a wide range of epochs. Setting this true will use `DateTimeFormatter` over default `SimpleDateFormat`. The output might vary for same input between the two formatters.
* Type: boolean * Default: false * Importance: high `topic2clustering.fields.map` : Maps topics to their corresponding table clustering fields (optional). Format: comma-separated tuples, e.g., topic1:[col1|col2], topic2:[col1|col2|..], … Specifies the fields used to cluster data in BigQuery. The order of the fields determines the clustering precedence.
* Type: string * Default: “” * Importance: low `use.integer.for.int8.int16` : Determines how the connector stores INT8 (BYTE) and INT16 (SHORT) data types in BigQuery during auto table creation and schema update. When set to `false``(default), these values are stored as FLOAT. When set to ``true`, INT8 (BYTE) and INT16 (SHORT) values are stored as INTEGER.
* Type: boolean * Default: false * Importance: low `string.value.column.name` : When using the STRING input format, this configuration specifies the BigQuery column name to store the raw string value. The connector wraps the string into a single-column record using this name. If you don’t set this configuration, you must use an SMT such as HoistField$Value to convert the string into a Struct or Map type before writing to the BigQuery table. This configuration is ignored if the record value is already a Struct or Map type, for example, when transformed by an SMT like HoistField.
* Type: string * 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 ## 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)