Google Cloud BigQuery Sink (Legacy) Connector for Confluent Cloud

Tip

Confluent recommends using version 2 of this connector. For more information, see Google BigQuery Sink V2 Connector for Confluent Cloud and Legacy to V2 Connector Migration.

You can use the Kafka Connect Google BigQuery Sink connector for Confluent Cloud to export Avro, JSON Schema, Protobuf, or JSON (schemaless) data from Apache Kafka® topics to BigQuery. The BigQuery table schema is based upon information in the Apache Kafka® schema for the topic.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Google BigQuery Sink connector for Confluent Platform.

Features

• The connector supports insert operations and attempts to detect duplicates. See BigQuery troubleshooting for additional information.

• The connector uses the BigQuery insertAll streaming api. The records are immediately available in the table for querying.

• The connector supports streaming from a list of topics into corresponding tables in BigQuery.

  Note

  Make sure to review BigQuery rate limits if you are planning to use multiple connectors with a high number of tasks.

• Even though the connector streams records one at a time 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.

• The connector supports several time-based table partitioning strategies using the property partitioning.type.

• The connector supports routing invalid records to the DLQ. This includes any records having a 400 code (invalid error message) from BigQuery.

  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 Avro, JSON Schema, Protobuf, or JSON (schemaless) input data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

• For Avro, JSON_SR, and PROTOBUF, the connector provides the following configuration properties that support automated table creation and 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.
  • 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.
  • auto.update.schemas: Automatically update BigQuery tables.
  • sanitize.field.names Automatically sanitize field names before using them as column names in BigQuery. Note that Kafka field names become 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.

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

Limitations

Be sure to review the following information.

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

Quick Start

Use this quick start to get up and running with the Confluent Cloud Google BigQuery Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a BigQuery data warehouse.

Tip

Confluent recommends using Version 2 of this connector. For more information, see Google BigQuery Sink V2 Connector for Confluent Cloud.

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.

• The data system the sink connector is connecting to should be in the same region as your Confluent Cloud cluster. If you use a different region or cloud platform, be aware that you may incur additional data transfer charges. Contact your Confluent account team or Confluent Support if you need to use Confluent Cloud and connect to a data system that is in a different region or on a different cloud platform.

• A BigQuery dataset is required in the project.

• A service account that can access the BigQuery project containing the dataset. You can create this service account in the Google Cloud Console.

• The service account must have access to the BigQuery project containing the dataset. 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:

  {
   "type": "service_account",
   "project_id": "confluent-842583",
   "private_key_id": "...omitted...",
   "private_key": "-----BEGIN PRIVATE ...omitted... =\n-----END PRIVATE KEY-----\n",
   "client_email": "confluent2@confluent-842583.iam.gserviceaccount.com",
   "client_id": "...omitted...",
   "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-842583.iam.gserviceaccount.com"
  }
  

  According to GCP specifications, the service account will either have to have the BigQueryEditor primitive IAM role or the bigquery.dataEditor predefined IAM role. The minimum permissions are:

  bigquery.datasets.get
  bigquery.tables.create
  bigquery.tables.get
  bigquery.tables.getData
  bigquery.tables.list
  bigquery.tables.update
  bigquery.tables.updateData
  

• Kafka cluster credentials. The following lists the different ways you can provide credentials.

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

• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.

• Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

• You must create a BigQuery table before using the connector, if you leave Auto create tables (auto.create.tables) set to false (the default).

• You may need to create a schema in BigQuery, depending on how you set the Auto update schemas property (or auto.update.schemas).

  • Auto update schemas set to true: You do not have to create a schema.

  • Auto update schemas set to false (the default): You must create a schema in BigQuery (as shown below). The connector does not automatically update the table.

    

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector

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

Step 3: Select your connector

Click the Google BigQuery Sink (Legacy) connector card.

Step 4: Enter the connector details

Note

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

At the Add Google BigQuery Sink (Legacy) Connector screen, complete the following:

Select topicKafka credentialsAuthenticationConfigurationSizingReview and Launch

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

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

Step 5: Check the results in BigQuery

1. From the Google Cloud Console, 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 Managed and Custom Connectors section.

Tip

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

Note

Make sure to review BigQuery rate limits if you are planning to use multiple connectors with a high number of tasks.

Using the Confluent CLI

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Tip

Confluent recommends using Version 2 of this connector. For more information, see Google BigQuery Sink V2 Connector for Confluent Cloud.

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

{
    "name" : "confluent-bigquery-sink",
    "connector.class" : "BigQuerySink",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key" : "<my-kafka-api-key>",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "keyfile" : "omitted",
    "project" : "<my-BigQuery-project>",
    "datasets" : "<my-BigQuery-dataset>",
    "input.data.format" : "AVRO",
    "auto.create.tables" : "true"
    "sanitize.topics" : "true"
    "auto.update.schemas" : "true"
    "sanitize.field.names" : "true"
    "tasks.max" : "1"
    "topics" : "pageviews",
}

Note the following property definitions:

• "name": Sets a name for your 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, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

  confluent iam service-account list
  

  For example:

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

• "topics": Identifies the topic name or a comma-separated list of topic names.

• "keyfile": This contains the contents of the downloaded JSON file. See Formatting keyfile credentials for details about how to format and use the contents of the downloaded credentials file.

• "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).

The following are additional properties you can use. See Configuration Properties for all property values and definitions.

• "auto.create.tables": Designates whether to automatically create BigQuery tables. Defaults to false. 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. Note that this property is available for AVRO only.

  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.

• "auto.update.schemas": Defaults to false. Designates whether or not to automatically update BigQuery schemas. If true is selected, new fields are added with mode NULLABLE in the BigQuery schema. Note that this property is available for AVRO only.

• "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 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.

  Caution

  Fields a.b and a_b will have the same value after sanitizing, which could cause a key duplication error. If not used, field names are used as column names.

• "partitioning.type": Select a partitioning type to use:

  • "INGESTION_TIME": To use this type, existing tables must be partitioned by ingestion time. The connector writes to the partition for the current wall clock time. When "auto.create.tables" is true, the connector creates tables partitioned by ingestion time.
  • "NONE": The connector relies only on how the existing tables are set up. When "auto.create.tables" is true, the connector creates non-partitioned tables.
  • "RECORD_TIME": To use this type, existing tables must be partitioned by record time. The connector writes to the partition that corresponds to the Kafka record’s timestamp. When "auto.create.tables" is true, the connector creates tables partitioned by record time.
  • "TIMESTAMP_COLUMN": The connector relies only on how existing tables are set up. When "auto.create.tables" is true, the connector creates tables partitioned using a field in a Kafka record value.

• "time.partitioning.type": When using INGESTION_TIME, RECORD_TIME, or TIMESTAMP_COLUMN, enter a time span for time partitioning. If you enter NONE, the connector honors the existing BigQuery table partitioning. When "auto.create.tables" is true, the connector creates a table without a specific partitioning strategy.

• "timestamp.partition.field.name": To use this property, "partitioning.type" must be TIMESTAMP_COLUMN and "auto.create.tables" must be set to true. Enter the field name for the value that contains the timestamp to partition in BigQuery. This enables timestamp partitioning for each table.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

See Configuration Properties for all property values and definitions.

Formatting keyfile credentials

The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration.

1. Convert the JSON file contents into string format.

2. Add the escape character \ before all \n entries in the Private Key section so that each section begins with \\n (see the highlighted lines below). The example below has been formatted so that the \\n entries are easier to see. Most of the credentials key has been omitted.

   Tip

   A script is available that converts the credentials to a string and also adds the additional escape characters where needed. See Stringify GCP Credentials.

     {
         "name" : "confluent-bigquery-sink",
         "connector.class" : "BigQuerySink",
         "kafka.api.key" : "<my-kafka-api-key>",
         "kafka.api.secret" : "<my-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": "<my-BigQuery-project>",
         "datasets":"<my-BigQuery-dataset>",
         "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.

Step 4: Load the configuration file and create the connector

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

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

For example:

confluent connect cluster create --config-file bigquery-sink-config.json

Example output:

Created connector confluent-bigquery-sink lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

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

Step 6: Check the results in BigQuery.

1. From the Google Cloud Console, 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 Managed and Custom Connectors section.

Tip

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

Note

Make sure to review BigQuery rate limits if you are planning to use multiple connectors with a high number of tasks.

Configuration Properties

Use the following configuration properties with this connector.

Which topics do you want to get data from?

topics

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

• Type: list
• Importance: high

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, or STRING. Note that you must 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 must 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.

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

kafka.api.key

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

• Type: password
• Importance: high

kafka.service.account.id

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

• Type: string
• Importance: high

kafka.api.secret

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

• Type: password
• Importance: high

GCP credentials

keyfile

GCP service account JSON file with write permissions for BigQuery.

• Type: password
• Importance: high

BigQuery details

project

GCP Project ID where BigQuery is located.

• Type: string
• Importance: high

datasets

Name for the BigQuery dataset that Kafka topics write to.

• Type: string
• Importance: high

SQL/DDL Support

partitioning.type

The partitioning type to use.

NONE: The connector relies only on how the existing tables are set up. If the Auto create tables property is enabled, the connector creates non-partitioned tables.

INGESTION_TIME: Existing tables must be partitioned by ingestion time. The connector writes to the partition for the current wall clock time. If the Auto create tables property is enabled, the connector creates tables partitioned by ingestion time.

RECORD_TIME: Existing tables must be partitioned by ingestion time. The connector writes to the partition based on the Kafka record timestamp. If the Auto create tables property is enabled, the connector creates tables partitioned by ingestion time. The only supported time.partitioning.type value for RECORD_TIME is DAY.

TIMESTAMP_COLUMN: The connector relies only on how the existing tables are set up. If the Auto create tables property is enabled, the connector creates tables partitioned by the timestamp.partition.field.name used.

• Type: string
• Default: INGESTION_TIME
• Importance: high

topic2table.map

Map of topics to tables (optional). The required format is comma-separated tuples. For example, <topic-1>:<table-1>,<topic-2>:<table-2>,… Note that a topic name must not be modified using a regex SMT while 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.

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

auto.create.tables

Designates whether or not to automatically create BigQuery tables. Note: Supports AVRO, JSON_SR, and PROTOBUF message format only.

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

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

time.partitioning.type

The time partitioning type to use when creating new tables for partitioning.type INGESTION_TIME, RECORD_TIME or TIMESTAMP_COLUMN. Existing tables are not 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 enable timestamp partitioning for tables. The connector ignores this property if partitioning.type is not TIMESTAMP_COLUMN or if auto.create.tables is false.

• Type: string
• Importance: low

allow.schema.unionization

If enabled, when performing schema updates, record schemas are combined with the current schema of the BigQuery table. This can be useful if there are Kafka records with schemas that are missing fields that correspond to columns already present in the BigQuery table schema. Note that when this is enabled, unrelated records could be produced to a topic consumed by the connector. The reason for this is instead of failing on invalid data, the connector appends the field for the unrelated record schema to the BigQuery table schema.

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

all.bq.fields.nullable

If true, no fields in any produced BigQuery schema are required. All non-nullable Avro fields are translated as NULLABLE (or REPEATED, if arrays).

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

convert.double.special.values

Designates whether +Infinity is converted to Double.MAX_VALUE and whether -Infinity and NaN are converted to Double.MIN_VALUE to ensure successful delivery to BigQuery.

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

Consumer configuration

max.poll.interval.ms

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

• Type: long
• Default: 300000 (5 minutes)
• Valid Values: [60000,…,1800000]
• 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]
• Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

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

Next Steps

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