# Google Cloud Spanner CDC Source (Debezium) Connector for Confluent Cloud The fully managed Google Cloud Spanner Change Data Capture (CDC) Source (Debezium) connector for Confluent Cloud monitors a Google Cloud Spanner change stream and streams row-level inserts, updates, and deletes from Spanner tables to Apache Kafka® topics in near-real time. If you require private networking for fully managed connectors, make sure to set up the proper networking beforehand. For more information, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ## Features The Google Cloud Spanner CDC Source (Debezium) connector provides the following features: * **Topics created automatically**: Automatically creates Kafka topics using the naming convention `.`. Per-row ordering is preserved regardless of topic partition count, because records are partitioned by the table’s primary key. For global ordering across all rows, use a single topic partition. * **Tables included and Tables excluded**: Sets whether a table is or is not monitored for changes. By default, the connector monitors every table tracked by the change stream. * **Tombstones on delete**: Configures whether a tombstone event should be generated after a delete event. Default is `true`. * **Low watermark**: A timestamp that guarantees all events with an earlier commit timestamp have been streamed to Kafka. Enable it when downstream consumers need to confirm event completeness. * **Database role support**: Supports Spanner fine-grained access control using database roles. * **Output data formats**: Supports Avro, JSON_SR (JSON Schema), and Protobuf output record value formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled. For more information, see [Limits for Fully Managed Connectors for Confluent Cloud](limits.md#connect-ccloud-limits). * **Client-side encryption (CSFLE and CSPE) support**: Supports CSFLE and CSPE for sensitive data. For more information about CSFLE or CSPE setup, see [Manage CSFLE or CSPE for connectors](csfle.md#connect-csfle). 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 * For connector limitations, see [Google Cloud Spanner CDC Source (Debezium) Connector](limits.md#google-spanner-cdc-source-debezium-connector-limits) 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 use one or more Custom SMTs, see [Custom SMT limitations](configure-custom-single-message-transforms/custom-smt-limitations-support.md#cc-custom-smt-limitations). ## Quick start Select the connector and configure it to stream change events from a Spanner change stream to Kafka. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud. - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - [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). For more information, see [Limits for Fully Managed Connectors for Confluent Cloud](limits.md#connect-ccloud-limits). - A Google Cloud project with a Spanner instance and database. - A Spanner change stream configured on the database. For details, see [Create a change stream](https://docs.cloud.google.com/spanner/docs/change-streams/manage#create). - A Google Cloud service account with the following IAM roles on the Spanner instance: * `roles/spanner.databaseReader` * `roles/spanner.viewer` - The Google Cloud service account key in JSON format. - For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
- 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. ### 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 Cloud Spanner CDC Source** connector card. ![Google Cloud Spanner CDC Source Connector Card](images/ccloud-google-spanner-source-icon.png) #### Step 4: Enter the connector details Before configuring the connector settings, ensure that you complete all [prerequisites](#cc-google-spanner-cdc-source-debezium-prereqs). #### NOTE * An asterisk ( \* ) designates a required entry. At the **Google Cloud Spanner CDC Source (Debezium) Connector** screen, complete the following: ### 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: **Google Cloud Spanner connection** - **GCP credentials file**: The Google Cloud service account key in JSON format. - **GCP project ID**: The Google Cloud project ID that contains the Spanner instance. - **Spanner instance ID**: The Spanner instance ID. - **Spanner database ID**: The Spanner database ID. - **Spanner host**: The custom Spanner host endpoint. Leave empty to use the default Google Cloud Spanner endpoint. - **Database role**: The Spanner database role to use for fine-grained access control. Leave empty if not using database roles. 2. Click **Continue**. ### Configuration Configuration properties that are not shown in the Cloud Console use the default values. For all property values and definitions, see [Configuration properties](#cc-google-spanner-cdc-source-debezium-config-properties). - **Select output record value format**: Sets the output Kafka record value format. Valid entries are `AVRO`, `JSON_SR`, or `PROTOBUF`. #### NOTE You must have Confluent Cloud Schema Registry configured if using a schema-based message format like `AVRO`, `JSON_SR`, or `PROTOBUF`. **Connector configuration** - **Change stream name**: The name of the Spanner change stream to monitor. - **Tables included**: A comma-separated list of regular expressions matching fully-qualified table names to include. If not specified, all tables are included. - **Tables excluded**: A comma-separated list of regular expressions matching fully-qualified table names to exclude from monitoring. - **Start time**: The timestamp to start reading changes from, in ISO 8601 Instant format (for example, `2024-01-15T10:00:00Z`). If not specified, starts from the current time. The start time must fall within the change stream’s retention window. - **End time**: The timestamp to stop reading changes, in ISO 8601 Instant format (for example, `2024-01-15T18:00:00Z`). If not specified, the connector runs indefinitely. Must be after the start time. **Data encryption** - Enable **Client-Side Field Level Encryption** for data encryption. Specify a **Service Account** to access the Schema Registry and associated encryption rules or keys with that schema. 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). **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. **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 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`. - **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. - **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. - **Key Converter Schema ID Serializer**: The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers. - **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. - **Value Converter Schema ID Serializer**: The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers. **Connector configuration** - **Topic auto-creation partitions**: The number of partitions for automatically created data change topics. Per-row ordering is preserved regardless of partition count, because records are partitioned by primary key. Increasing this value allows higher consumer parallelism but loses global ordering across different rows. - **Tombstones on delete**: Controls whether a tombstone event should be generated after a delete event. - `true`: a delete operation is represented by a delete event and a subsequent tombstone event. - `false`: only a delete event is emitted. Emitting a tombstone event, which is the default behavior, allows Kafka to completely delete all events associated with the key of the deleted row when log compaction is enabled for the topic. - **Heartbeat interval**: The interval at which heartbeat messages are sent, in milliseconds. The value must be between 100 and 300000. - **Max missed heartbeats**: The maximum number of missed heartbeats before the connector identifies that a partition is stuck. - **Schema name adjustment mode**: Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Use one of the following: - `none` does not apply any adjustment. - `avro` replaces the characters that cannot be used in the Avro type name with an underscore. - `avro_unicode` replaces the underscore or characters that cannot be used in the Avro type name with corresponding unicode like `_uxxxx`. #### NOTE `_` is an escape sequence like backslash in Java. - **Field name adjustment mode**: Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Use one of the following: - `none` does not apply any adjustment. - `avro` replaces the characters that cannot be used in the AVRO type name with an underscore. - `avro_unicode` replaces the underscore or characters that cannot be used in the AVRO type name with corresponding unicode like `_uxxxx`. Note that `_` is an escape sequence like backslash in Java. - **Enable low watermark**: Enables low-watermark tracking for monitoring connector progress. - **Low watermark update period**: The low-watermark update period for each task, in milliseconds. - **Low watermark stamp interval**: The interval, in milliseconds, at which the connector periodically sends low-watermark stamp messages to all table topics and Kafka partitions. **Sync topic configuration** - **Sync topic cleanup policy**: The sync topic cleanup policy. - **Sync topic retention**: The sync topic retention, in milliseconds. - **Sync topic segment time**: The sync topic segment time, in milliseconds. **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). For more information about the Debezium SMTs **ExtractNewRecordState** and **EventRouter (Debezium)**, see [Debezium transformations](single-message-transforms.md#cc-single-message-transforms-debezium-unwrap). For all property values and definitions, see [Configuration properties](#cc-google-spanner-cdc-source-debezium-config-properties). - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you are provided with a recommended number of tasks. 1. To change the number of tasks, use the Range Slider to select the desired number of tasks. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details by previewing the running configuration. 2. After you’ve validated that the properties are configured to your satisfaction, click **Launch**. The status for the connector should go from **Provisioning** to **Running**. #### Step 5: Check the Kafka topic After the connector is running, verify that messages are populating your Kafka topics. The connector creates one topic per monitored Spanner table, using the naming convention `.`. 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 Before configuring the connector settings, ensure that you complete all [prerequisites](#cc-google-spanner-cdc-source-debezium-prereqs). Complete the following steps to set up and run the connector using the Confluent CLI. #### 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 required and optional connector properties: ```none { "connector.class": "SpannerCdcSource", "name": "SpannerCdcSourceConnector_0", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret": "", "gcp.spanner.credentials.json": "", "gcp.spanner.project.id": "", "gcp.spanner.instance.id": "", "gcp.spanner.database.id": "", "gcp.spanner.change.stream": "", "output.data.format": "AVRO", "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](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 ``` * `"gcp.spanner.credentials.json"`: The Google Cloud service account key in JSON format. The service account must have read access to the Spanner instance. * `"gcp.spanner.project.id"`: The Google Cloud project ID that contains the Spanner instance. * `"gcp.spanner.instance.id"`: The Spanner instance ID. * `"gcp.spanner.database.id"`: The Spanner database ID. * `"gcp.spanner.change.stream"`: The name of the Spanner change stream to monitor. * `"output.data.format"`: Sets the output Kafka record value format. Valid entries are `AVRO`, `JSON_SR`, or `PROTOBUF`. You must have Confluent Cloud Schema Registry configured. * `"tasks.max"`: The maximum number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. #### 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. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](csfle.md#connect-csfle). **Single Message Transformations**: For details about adding SMTs using the Confluent CLI, see [Single Message Transformations (SMT)](single-message-transforms.md#cc-single-message-transforms). For more information about the Debezium SMTs **ExtractNewRecordState** and **EventRouter (Debezium)**, see [Debezium transformations](single-message-transforms.md#cc-single-message-transforms-debezium-unwrap). For all properties and definitions, see [Configuration properties](#cc-google-spanner-cdc-source-debezium-config-properties). #### Step 4: Load the properties 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 google-spanner-cdc-source.json ``` Example output: ```none Created connector SpannerCdcSourceConnector_0 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 | SpannerCdcSourceConnector_0 | RUNNING | source ``` #### Step 6: Check the Kafka topic After the connector is running, verify that messages are populating your Kafka topics. The connector creates one topic per monitored Spanner table. 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. ## Configuration properties Use the following configuration properties with the fully managed connector. ### 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 ### Output messages `output.data.format` : Sets the output Kafka record value format. Valid entries are `AVRO`, `JSON_SR`, or `PROTOBUF`. Note that you must have Confluent Cloud Schema Registry configured to use a schema-based message format such as `AVRO`, `JSON_SR`, or `PROTOBUF`.
* Type: string * Default: JSON_SR * Importance: high ### Google Cloud Spanner connection `gcp.spanner.credentials.json` : The Google Cloud service account key in JSON format.
* Type: password * Importance: high `gcp.spanner.project.id` : The Google Cloud project ID that contains the Spanner instance.
* Type: string * Importance: high `gcp.spanner.instance.id` : The Spanner instance ID.
* Type: string * Importance: high `gcp.spanner.database.id` : The Spanner database ID.
* Type: string * Importance: high `gcp.spanner.host` : The custom Spanner host endpoint. Leave empty to use the default Google Cloud Spanner endpoint.
* Type: string * Default: “” * Importance: low `gcp.spanner.database.role` : The Spanner database role to use for fine-grained access control. Leave empty if not using database roles.
* Type: string * Default: “” * Importance: medium ### Connector configuration `gcp.spanner.change.stream` : The name of the Spanner change stream to monitor.
* Type: string * Importance: high `table.include.list` : A comma-separated list of regular expressions matching fully-qualified table names to include. If not specified, all tables are included.
* Type: list * Importance: medium `table.exclude.list` : A comma-separated list of regular expressions matching fully-qualified table names to exclude from monitoring.
* Type: list * Importance: medium `gcp.spanner.start.time` : The timestamp to start reading changes from, in ISO 8601 Instant format (e.g., `2024-01-15T10:00:00Z`). If not specified, starts from the current time. The start time must fall within the change stream’s retention window.
* Type: string * Importance: medium `gcp.spanner.end.time` : The timestamp to stop reading changes, in ISO 8601 Instant format (e.g., `2024-01-15T18:00:00Z`). If not specified, the connector runs indefinitely. Must be after the start time.
* Type: string * Importance: low `topic.creation.default.partitions` : The number of partitions for automatically created data change topics. Per-row ordering is preserved regardless of partition count, because records are partitioned by primary key. Increasing this value allows higher consumer parallelism but loses global ordering across different rows.
* Type: int * Default: 1 * Importance: low `tombstones.on.delete` : Controls whether a tombstone event should be generated after a delete event.
`true` - a delete operation is represented by a delete event and a subsequent tombstone event.
`false` - only a delete event is emitted.
Emitting tombstone events (the default behavior) allows Kafka to completely delete all events pertaining to the key of the deleted row when log compaction is enabled for the topic.
* Type: boolean * Default: true * Importance: medium `heartbeat.interval.ms` : The interval at which heartbeat messages are sent, in milliseconds. Must be between 100 and 300000.
* Type: int * Default: 300000 (5 minutes) * Valid Values: [100,…,300000] * Importance: low `connector.spanner.max.missed.heartbeats` : The maximum number of missed heartbeats before the connector identifies that a partition is stuck.
* Type: int * Default: 10 * Valid Values: [1,…] * Importance: low `schema.name.adjustment.mode` : Specifies how schema names should be adjusted for compatibility with the message converter used by the connector. Possible settings are `none`, `avro`, and `avro_unicode`.
`none` does not apply any adjustment.
`avro` replaces the characters that cannot be used in the AVRO type name with an underscore.
`avro_unicode` replaces the underscore or characters that cannot be used in the AVRO type name with corresponding unicode like \_uxxxx.
Note that `_` is an escape sequence like backslash in Java.
* Type: string * Default: none * Valid Values: avro, avro_unicode, none * Importance: medium `field.name.adjustment.mode` : Specifies how field names should be adjusted for compatibility with the message converter used by the connector. Possible settings are `none`, `avro`, and `avro_unicode`.
`none` does not apply any adjustment.
`avro` replaces the characters that cannot be used in the AVRO type name with an underscore.
`avro_unicode` replaces the underscore or characters that cannot be used in the AVRO type name with corresponding unicode like `_uxxxx`.
Note that `_` is an escape sequence like backslash in Java.
* Type: string * Default: none * Valid Values: avro, avro_unicode, none * Importance: medium `gcp.spanner.low-watermark.enabled` : Enables low-watermark tracking for monitoring connector progress.
* Type: boolean * Default: false * Importance: low `gcp.spanner.low-watermark.update-period.ms` : The low-watermark update period for each task, in milliseconds.
* Type: long * Default: 1000 (1 second) * Valid Values: [1000,…] * Importance: low `gcp.spanner.low-watermark.stamp.interval` : The interval, in milliseconds, at which the connector periodically sends low-watermark stamp messages to all table topics and Kafka partitions.
* Type: int * Default: 10000 * Valid Values: [5000,…] * Importance: low ### Sync topic configuration `connector.spanner.sync.cleanup.policy` : The sync topic cleanup policy.
* Type: string * Default: delete * Valid Values: compact, delete * Importance: low `connector.spanner.sync.retention.ms` : The sync topic retention, in milliseconds.
* Type: int * Default: 86400000 (1 day) * Importance: low `connector.spanner.sync.segment.ms` : The sync topic segment time, in milliseconds.
* Type: int * Default: 43200000 (12 hours) * Importance: low ### Number of tasks for this connector `tasks.max` : Maximum number of tasks for the connector.
* Type: int * Valid Values: [1,…] * 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 ### 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 `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string * Importance: low `producer.override.compression.type` : The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.
* Type: string * Importance: low `producer.override.linger.ms` : The producer groups together any records that arrive in between request transmissions into a single batched request. More details can be found in the documentation: [https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms](https://docs.confluent.io/platform/current/installation/configuration/producer-configs.html#linger-ms).
* Type: long * Valid Values: [100,…,1000] * 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.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: none * Importance: low `key.converter.key.schema.id.serializer` : The class name of the schema ID serializer for keys. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * 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.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.serializer` : The class name of the schema ID serializer for values. This is used to serialize schema IDs in the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.PrefixSchemaIdSerializer * 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 ## Frequently asked questions ### What happens if I set a start time outside the change stream’s retention window? The connector fails with an `OUT_OF_RANGE` error. The start time must fall within the change stream’s retention window, which defaults to one day. You can configure it up to 30 days using Spanner DDL. ### Do I need to create the change stream before configuring the connector? Yes. The connector does not create change streams automatically. Create the change stream using Spanner DDL before configuring the connector. For example: ```none CREATE CHANGE STREAM my_stream FOR ALL ``` ### What timestamp format does the connector accept for start and end time? ISO 8601 Instant format with a `Z` suffix. For example: `2024-01-15T10:00:00Z`. Timezone offsets like `+05:30` are not supported. ### Can I get duplicate records from this connector? Yes. The connector provides at-least-once delivery semantics. If a task restarts between writing records to Kafka and committing offsets, the connector might replay records. Design your consumers to handle duplicates. ### How does the connector detect stuck partitions? The connector uses heartbeat messages. If a change stream partition misses more than **Max missed heartbeats** consecutive heartbeats, the connector considers the partition stuck and takes corrective action. The detection window is `heartbeat.interval.ms × max.missed.heartbeats`. With defaults (300,000 ms × 10), this is approximately 50 minutes. ### What is the low watermark and when should I enable it? The low watermark provides a completeness guarantee: a timestamp at which the connector has streamed all events with an earlier commit timestamp to Kafka. Enable it if your downstream consumers need to know all events up to time `T` have arrived, for example, when building consistent materialized views. When enabled, the connector embeds a `low_watermark` field in every record’s source struct, and low-watermark stamp records are periodically published to all data topics. ### What happens if the connector is stopped longer than the sync topic retention period? The connector might lose its prior state. On restart, it might re-read changes from the change stream’s start, potentially causing duplicate records. Increase the **Sync topic retention** value if you expect extended downtime. The default is 24 hours. ### How do I reset the connector to re-read from a specific point in time? Delete the internal sync topic, then restart the connector with the desired **Start time** configured. Updating Kafka Connect offsets alone does not reset the connector’s read position. ## 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. [![Topology diagram for the Confluent Cloud ETL demo showing connectors, Apache Kafka topics, and Apache Flink processing.](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)