Google Cloud Spanner Sink Connector for Confluent Cloud

The fully-managed 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.

Confluent Cloud is available through Google Cloud Marketplace or directly from Confluent.

Note

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

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``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 Usage Examples section.

Limitations

Be sure to review the following information.

For connector limitations, see Google Cloud Spanner Sink 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 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

Authorized access to a Confluent Cloud cluster on Google Cloud.
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.
An operating Google Cloud Spanner instance and database (a table can be auto-created). For the steps necessary to create an instance using the Google Cloud Console, see Quickstart using the console.
A Google Cloud service account. You download service account credentials as a JSON file. These credentials are used when setting up the connector configuration.

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

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

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 Cloud Spanner Sink 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 Cloud Spanner Sink Connector screen, complete the following:

Select topic

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

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

Kafka credentials

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. 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.

Click Continue.

Authentication

Upload your Google Cloud credentials JSON file.
In the Spanner instance ID field, enter the ID of the Spanner instance to connect to.
In the Spanner database ID field, enter the database ID where tables are located or will be created.
Click Continue.

Configuration

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR, or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON Schema, or Protobuf).
Select an insert mode:
- INSERT: Use the standard INSERT row function. An error occurs if the row already exists in the table.
- UPSERT: This mode is similar to INSERT. However, if the row already exists, the UPSERT function overwrites column values with the new values provided.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- Table name format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.
- PK mode: The primary key mode.
- PK Fields: List of comma-separated primary key field names.
- Max batch size: The maximum number of records that can be batched into a single insert, or upsert, to Spanner.
- Auto create table: Whether to automatically create the destination table if it is missing.
- Auto add columns: Whether to automatically add columns in the table if they are missing.
Auto-restart policy
- Enable Connector Auto-restart: Control 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. In such cases, you would need to manually restart the connector.
Consumer configuration
- Max poll interval(ms): Set 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: Set 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 Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.
See Configuration Properties for all property values and definitions.
Click Continue.

Sizing

Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and Launch

Verify the connection details.
Click Launch.
The status for the connector should go from Provisioning to Running.

Step 5: Check the results in Spanner

From the Google Cloud Console, go to your Spanner project.
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 Usage Examples section.

Tip

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

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

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-database-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 Google Cloud 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.
- 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 Google Cloud credentials

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

Convert the JSON file contents into string format.

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 Google Cloud 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-database-id>",
     "auto.create": "true",
     "auto.evolve": "true",
     "tasks.max": "1"
   }

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

For example:

confluent connect cluster create --config-file 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 cluster list

Example output:

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

Step 6: Check the results in Spanner.

From the Google Cloud Console, go to your Spanner project.
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 Usage Examples section.

Tip

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

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

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

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, 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
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

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

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

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

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

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

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

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.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.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

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
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.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.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

Egress allowlist

connector.egress.whitelist

Type: string
Default: “”
Importance: high

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. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.