Google BigQuery Sink Connector for Confluent Cloud

Note

If you are installing the connector locally for Confluent Platform, see Google BigQuery Sink Connector for Confluent Platform.

The Google BigQuery Sink Connector is used to stream data into BigQuery tables. The BigQuery table schema is based upon information in the Apache Kafka® schema for the topic.

Important

Once this connector moves from Preview to Generally Availability (GA), it will require a subscription for Confluent Cloud commitment for Confluent Cloud Enterprise customers. Without a Confluent Cloud commitment, Confluent Cloud Enterprise customers will not have access to these connectors in GA. Contact your Confluent Account Executive to learn more and to update your subscription, if necessary.

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, which inserts records one at a time. The records are immediately available in the table for querying.
  • The connector supports streaming from a list of topics into corresponding tables in BigQuery.
  • 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 Avro and schemaless JSON (schema validation is disabled for JSON).

Refer to Cloud connector limitations for additional information.

Caution

Preview connectors are not currently supported and are not recommended for production use. For specific connector limitations, see Cloud connector limitations.

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.

Prerequisites
  • An active GCP account with authorization to create resources.

  • A BigQuery project is required. The project can be created using the Google Cloud Console.

  • 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
    

Additionally, make sure that either one of the following credential types is generated to use for the Kafka cluster credentials fields:

  • A Confluent Cloud API key and secret. After you have created your cluster, go to Cluster settings > API access > Create Key.
  • A Confluent Cloud service account.

Important

A BigQuery table must exist before running this connector. Topic names are mapped to BigQuery table names. When you create the BigQuery table, make sure to enable Partitioning: Partition by ingestion time and define a schema as shown in the example below:

Enable partition by ingestion time and define a schema

Enable partition by ingestion time and define a schema

Using the Confluent Cloud GUI

Step 1: Launch your Confluent Cloud cluster.

See the Confluent Cloud Quick Start for installation instructions.

Step 2: Add a connector.

Click Connectors > Add connector.

Add a connector

Step 3: Select your connector.

Click the Google BigQuery Sink connector icon.

Step 4: Set up the connection.

Note

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

Complete the following and click Continue.

  1. Select one or more topics.
  2. Enter a Connector Name.
  3. Enter your Kafka Cluster credentials. The credentials are either the API key and secret or the service account API key and secret.
  4. Enter your BigQuery credentials. Open the JSON file you downloaded when creating the service account. Copy and paste all file contents into the credentials field.
  5. Enter your BigQuery project and datasets.
  6. Select the message format.
  7. Add the storage account name, account key, and container name.
  8. Enter the number of tasks in use by the connector. See Confluent Cloud connector limitations for additional task information.

Configuration properties that are not shown in the Confluent Cloud UI use the default values. For default values and property definitions, see Google BigQuery Sink Connector Configuration Properties.

Step 5: Launch the connector.

Verify the connection details and click Launch.

Launch the connector

Step 6: Check the connector status.

The status for the connector should go from Provisioning to Running.

Check the connector status

Step 7: 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 additional information about this connector, see Google BigQuery Sink Connector for Confluent Platform. Note that not all Confluent Platform connector features are provided in the Confluent Cloud connector.

Using the Confluent Cloud CLI

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors.

Enter the following command to list available connectors:

ccloud connector-catalog list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

ccloud connector-catalog describe <connector-catalog-name>

For example:

ccloud connector-catalog describe BigQuerySink

Example output:

Following are the required configs:
connector.class
name
kafka.api.key
kafka.api.secret
topics
keyfile
project
datasets
data.format
tasks.max

Step 3: Create the connector configuration file.

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

{
    "name" : "confluent-bigquery-sink",
    "connector.class": "BigQuerySink",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "topics" : "pageviews",
    "keyfile" : "omitted",
    "project": "<my-BigQuery-project>",
    "datasets":"<my-BigQuery-dataset>",
    "data.format":"AVRO",
    "tasks.max" : "1"
}

Note the following property definitions:

  • "name": Sets a name for your new connector.
  • "connector.class": Identifies the connector plugin name.
  • "topics": Identifies the topic name or a comma-separated list of topic names.
  • "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.
  • "data.format": Sets the message format. Valid entries are AVRO, JSON, or BYTES.

Configuration properties that are not listed use the default values. For default values and property definitions, see Google BigQuery Sink Connector Configuration Properties.

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. You can use an online converter tool to do this. For example: JSON to String Online Converter.

  2. Add \ 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 additional \ characters where needed. See Stringify GCP Credentials.

      {
          "name" : "confluent-bigquery-sink",
          "connector.class" : "GcsSink",
          "kafka.api.key" : "<my-kafka-api-keyk>",
          "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:

ccloud connector create --config <file-name>.json

For example:

ccloud connector create --config bigquery-sink-config.json

Example output:

Created connector confluent-bigquery-sink jtt-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

ccloud connector list

Example output:

ID          |       Name              | Status  | Type
+-----------+-------------------------+---------+------+
jtt-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 additional information about this connector, see Google BigQuery Sink Connector for Confluent Platform. Note that not all Confluent Platform connector features are provided in the Confluent Cloud connector.