Google Cloud Spanner Sink Connector for Confluent Cloud

Note

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

The Kafka Connect Google Cloud Spanner Sink connector for Confluent Cloud moves data from Apache Kafka® to a Google Cloud Spanner database. It writes data from a topic in Kafka to a table in the specified Spanner database. Table auto-creation and limited auto-evolution are supported.

Features

The Google Cloud Spanner Sink connector provides the following features:

  • The connector inserts and upserts Kafka records into a Google Cloud Spanner database.
  • 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).
  • auto.create and auto-evolve are supported. If tables or columns are missing, they can be created automatically.
  • PK modes supported are kafka, none, and record_value. Used in conjunction with the PK Fields property.

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

Limitations

Be sure to review the following information.

Quick Start

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

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

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster.

See the Quick Start for Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

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

Step 3: Select your connector.

Click the Google Cloud Spanner Sink connector icon.

Google Cloud Spanner Sink Connector Icon

Step 4: Enter the connector details.

Note

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

At the Add Google Cloud Spanner Sink Connector screen, complete the following:

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 Spanner.

  1. From the Google Cloud Console, go to your Spanner project.
  2. Verify that new records are being added to the Spanner database.

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

Tip

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

See also

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.

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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.
  • The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe SpannerSink

Example output:

Following are the required configs:
connector.class: SpannerSink
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
topics
input.data.format
gcp.spanner.credentials.json
gcp.spanner.instance.id
gcp.spanner.database.id
tasks.max

Step 3: Create the connector configuration file.

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

{
  "connector.class": "SpannerSink",
  "name": "spanner-sink-connector",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key?",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "topics": "pageviews",
  "input.data.format": "AVRO",
  "gcp.spanner.credentials.json": "<my-gcp-credentials>",
  "gcp.spanner.instance.id": "<my-spanner-instance-id>",
  "gcp.spanner.database.id": "<my-spanner-dabase-id>",
  "auto.create": "true",
  "auto.evolve": "true",
  "tasks.max": "1"
 }

Note the following property definitions:

  • "connector.class": Identifies the connector plugin name.
  • "name": Sets a name for your new connector.
  • "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.

  • "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).

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

  • "tasks.max": Maximum number of tasks the connector can run. See Confluent Cloud connector limitations for additional task information.

Optional

  • "auto.create" (tables) and "auto-evolve" (columns): Sets whether to automatically create tables or columns if they are missing relative to the input record schema. If not entered in the configuration, both default to false.
  • "pk.mode": (Optional) Supported modes are listed below:
    • kafka: Kafka coordinates are used as the primary key. Must be used with the PK Fields property.
    • none: No primary keys used.
    • record_value: Fields from the Kafka record value are used. This must be a struct type.
  • "pk.fields": A list of comma-separated primary key field names. The runtime interpretation of this property depends on the pk.mode selected. Options are listed below:
    • kafka: Must be three values representing the Kafka coordinates. If left empty, the coordinates default to __connect_topic,__connect_partition,__connect_offset.
    • none: PK Fields not used.
    • record_value: Used to extract fields from the record value. If left empty, all fields from the value struct are used.

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 GCP 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 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 additional escape \ characters where needed. See Stringify GCP Credentials.

       {
         "connector.class": "SpannerSink",
         "name": "spanner-sink-connector",
         "kafka.api.key": "<my-kafka-api-key?",
         "kafka.api.secret": "<my-kafka-api-secret>",
         "topics": "pageviews",
         "input.data.format": "AVRO",
         "gcp.spanner.credentials.json": "{\"type\":\"service_account\",\"project_id\":\"connect-
         1234567\",\"private_key_id\":\"omitted\",
         \"private_key\":\"-----BEGIN PRIVATE KEY-----
         \\nMIIEvAIBADANBgkqhkiG9w0BA
         \\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5pzsIh
         \\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn
         \\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J
         \\n...rest of key omitted...
         \\n-----END PRIVATE KEY-----\\n\",
         \"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\",
         \"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/
         auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/
         token\",\"auth_provider_x509_cert_url\":\"https:\/\/
         www.googleapis.com\/oauth2\/v1\/
         certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/
         robot\/v1\/metadata\/x509\/pub-sub%40connect-
         123456789.iam.gserviceaccount.com\"}",
         "gcp.spanner.instance.id": "<my-spanner-instance-id>",
         "gcp.spanner.database.id": "<my-spanner-dabase-id>",
         "auto.create": "true",
         "auto.evolve": "true",
         "tasks.max": "1"
       }
    
  3. Add all the converted string content to the 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 create --config <file-name>.json

For example:

confluent connect create --config spanner-sink-config.json

Example output:

Created connector spanner-sink-connector lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |       Name              | Status  | Type
+-----------+-------------------------+---------+------+
lcc-ix4dl   | spanner-sink-connector  | RUNNING | sink

Step 6: Check the results in Spanner.

  1. From the Google Cloud Console, go to your Spanner project.
  2. Verify that new records are being added to the Spanner database.

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

Tip

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

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

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, or 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
  • 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
  • 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
  • Type: password
  • Importance: high

GCP credentials

gcp.spanner.credentials.json

GCP service account JSON file with write permissions for Spanner.

  • Type: password
  • Importance: high

How should we connect to your Spanner?

gcp.spanner.instance.id

The ID of the Spanner instance to connect to.

  • Type: string
  • Importance: high
gcp.spanner.database.id

Database ID where tables are located or will be created.

  • Type: string
  • Importance: high

Database details

insert.mode

The insertion mode to use.

  • Type: string
  • Default: INSERT
  • Importance: high
table.name.format

A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name.

For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.

Spanner constraints for table names are {a—z|A—Z}[{a—z|A—Z|0—9|_}+].

  • Type: string
  • Default: ${topic}
  • Importance: medium

Primary Key

pk.mode

The primary key mode, also refer to pk.fields documentation for interplay. Supported modes are:

none: No keys utilized.

kafka: Apache Kafka® coordinates are used as the PK.

record_value: Field(s) from the record value are used, which must be a struct.

  • Type: string
  • Importance: high
pk.fields

List of comma-separated primary key field names. The runtime interpretation of this config depends on the pk.mode:

none: Ignored as no fields are used as primary key in this mode.

kafka: Must be a trio representing the Kafka coordinates, defaults to __connect_topic,__connect_partition,__connect_offset if empty.

record_value: If empty, all fields from the value struct will be used, otherwise used to extract the desired fields.

  • Type: list
  • Importance: high

SQL/DDL Support

auto.create

Whether to automatically create the destination table if it is missing.

  • Type: boolean
  • Default: false
  • Importance: medium
auto.evolve

Whether to automatically add columns in the table if they are

  • Type: boolean
  • Default: false
  • Importance: medium

Connection details

max.batch.size

The maximum number of records that can be batched into a single insert, or upsert to Spanner.

  • Type: int
  • Default: 1000
  • Valid Values: [1,…,5000]
  • Importance: medium

Number of tasks for this connector

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

Next Steps

See also

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.

../_images/topology.png