# Google Cloud Storage Source Connector for Confluent Cloud The fully managed Google Cloud Storage (GCS) Source connector for Confluent Cloud can read data from any type of file naming convention listed under a GCS bucket (that is, the filenames in the bucket don’t have to be in a specific format). The connector can read file data in any of the supported formats (for example, JSON, Avro, and Byte Array). Confluent Cloud is available through [Google Cloud Marketplace](https://console.cloud.google.com/marketplace/product/confluent-prod/apache-kafka-on-confluent-cloud?inv=1&invt=Ab2Ryw) or [directly from Confluent](https://www.confluent.io/get-started/). #### 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 Storage (GCS) Source Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/gcs-source/current/). ## Features The GCS Source connector provides the following features: * **At least once delivery**: The connector guarantees that records are delivered at least once. * **Provider integration support**: The connector supports Google Cloud’s native identity authorization using Confluent Provider Integration. For more information about provider integration setup, see the [connector authentication](#cc-gcs-source-setup-connection). * **Supports multiple tasks**: The connector supports running one or more tasks. * **Offset management capabilities**: Supports offset management. For more information, see [Manage custom offsets](#cc-gcs-source-custom-offsets). 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. Refer to Confluent Cloud [connector limitations](limits.md#gcs-source-limits) for additional information. ## IAM Policy for GCS The following permissions are required for the GCS Source connector: - `storage.buckets.get` - `storage.objects.get` - `storage.objects.list` For more information, see [IAM permissions for Cloud Storage](https://cloud.google.com/storage/docs/access-control/iam-permissions). You may also grant a Service Account the following roles on the bucket: - Storage Object Viewer - Storage Legacy Bucket Reader ## Manage custom offsets You can manage the offsets for this connector. Offsets provide information on the point in the system from which the connector is accessing data. For more information, see [Manage Offsets for Fully Managed Connectors in Confluent Cloud](offsets.md#connect-custom-offsets). **To manage offsets**: - Manage offsets using Confluent Cloud APIs. For more information, see [Connect offsets API reference](https://docs.confluent.io/cloud/current/ccloud/offsets-connect-v-1/). ### Get the current offset To get the current offset, make a `GET` request that specifies the environment, Kafka cluster, and connector name. ```bash GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets Host: https://api.confluent.cloud ``` **Response:** Successful calls return HTTP `200` with a JSON payload that describes the offset. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "taskId": "lcc-example123-0-in_progress" }, "offset": { "earliestIncomplete": "2023-08-03T10:24:25Z", "completedFiles": "[{\"filePath\":\"topics/abc_0/partition=0/abc_0+0+00000.json\",\"creationTime\":\"2023-08-03T10:24:25Z\"},{\"filePath\":\"topics/abc_1/partition=0/abc_1+0+00000.json\",\"creationTime\":\"2023-08-03T10:34:56Z\"},{\"filePath\":\"topics/abc_3/partition=0/abc_3+0+00000.json\",\"creationTime\":\"2023-08-03T10:48:28Z\"}]", "recordNum": "98" } } { "partition": { "taskId": "lcc-example123-1" }, "offset": { "earliestIncomplete": "2023-08-03T10:24:25Z", "completedFiles": "[{\"filePath\":\"topics/babc_4/partition=0/babc_4+0+00000.json\",\"creationTime\":\"2023-08-03T10:33:04Z\"},{\"filePath\":\"topics/abc_2/partition=0/abc_2+0+00000.json\",\"creationTime\":\"2023-08-03T10:46:06Z\"},{\"filePath\":\"topics/weird/partition=0/weird+0+00000 copy.json\",\"creationTime\":\"2023-08-03T10:51:09Z\"}]", "recordNum": "99" } } { "partition": { "taskId": "lcc-example123-0" }, "offset": { "earliestIncomplete": "2023-08-03T10:24:25Z", "completedFiles": "[{\"filePath\":\"topics/abc_0/partition=0/abc_0+0+00000.json\",\"creationTime\":\"2023-08-03T10:24:25Z\"},{\"filePath\":\"topics/abc_1/partition=0/abc_1+0+00000.json\",\"creationTime\":\"2023-08-03T10:34:56Z\"},{\"filePath\":\"topics/abc_3/partition=0/abc_3+0+00000.json\",\"creationTime\":\"2023-08-03T10:48:28Z\"},{\"filePath\":\"topics/abc_5/partition=0/abc_5+0+00000.json\",\"creationTime\":\"2023-08-03T10:59:06Z\"}]", "recordNum": "99" } } { "partition": { "taskId": "lcc-example123-1-in_progress" }, "offset": { "earliestIncomplete": "2023-08-03T10:24:25Z", "completedFiles": "[{\"filePath\":\"topics/babc_4/partition=0/babc_4+0+00000.json\",\"creationTime\":\"2023-08-03T10:33:04Z\"},{\"filePath\":\"topics/abc_2/partition=0/abc_2+0+00000.json\",\"creationTime\":\"2023-08-03T10:46:06Z\"}]", "recordNum": "98" } } ], "metadata": { "observed_at": "2024-03-28T17:57:48.139635200Z" } } ``` Responses include the following information: - The position of latest offset. - The observed time of the offset in the metadata portion of the payload. The `observed_at` time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use `observed_at` to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling `GET` repeatedly will fetch more recently observed offsets. - Information about the connector. ### Update the offset You can approach offset updates in two ways: - Modify the `earliestIncomplete` time to reset the offsets so that next scan will source the files with `creationTime` equal to or after the new `earliestIncomplete`. If you use this approach, consider this: - If `earliestIncomplete` is set to a later time, the connector starts sourcing the files with `creationTime` equal to or after the `earliestIncomplete` and skips records. - If `earliestIncomplete` is set to an earlier time, the connector might produce duplicate records because it starts sourcing every record from files with a `creationTime` equal to or after the earlier time. - If you want to skip processing a file or files, add the files to `completedFiles`. To update the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies new offset and a patch type. ```bash POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request Host: https://api.confluent.cloud { "type": "PATCH", "offsets": [ { "partition": { "taskId": "lcc-devc3m1zkj-0" }, "offset": { "completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]", "earliestIncomplete": "2024-03-06T17:30:28.391Z", "recordNum": "0" } } ] } ``` Considerations: - You can only make one offset change at a time for a given connector. - This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see **Get the status of an offset request**. - For source connectors, the connector attempts to read from the position defined by the requested offsets. **Response:** Successful calls return HTTP `202 Accepted` with a JSON payload that describes the offset. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "taskId": "lcc-example123-0" }, "offset": { "completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]", "earliestIncomplete": "2024-03-06T17:30:28.391Z", "recordNum": "0" } } ], "requested_at": "2024-03-28T17:58:45.606796307Z", "type": "PATCH" } ``` Responses include the following information: - The requested position of the offsets in the source. - The time of the request to update the offset. - Information about the connector. ### Delete the offset To delete the offset, make a `POST` request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies the delete type. ```bash POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request Host: https://api.confluent.cloud { "type": "DELETE" } ``` Considerations: - Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector. - This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see **Get the status of an offset request**. - Do not issue delete and patch requests at the same time. - For source connectors, the connector attempts to read from the position defined in the base state. **Response**: Successful calls return HTTP `202 Accepted` with a JSON payload that describes the result. ```bash { "id": "lcc-example123", "name": "{connector_name}", "offsets": [], "requested_at": "2024-03-28T17:59:45.606796307Z", "type": "DELETE" } ``` Responses include the following information: - Empty offsets. - The time of the request to delete the offset. - Information about Kafka cluster and connector. - The type of request. ### Get the status of an offset request To get the status of a previous offset request, make a `GET` request that specifies the environment, Kafka cluster, and connector name. ```bash GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request/status Host: https://api.confluent.cloud ``` Considerations: - The status endpoint always shows the status of the most recent PATCH/DELETE operation. **Response**: Successful calls return HTTP `200` with a JSON payload that describes the result. The following is an example of an applied patch. ```bash { "request": { "id": "lcc-example123", "name": "{connector_name}", "offsets": [ { "partition": { "taskId": "lcc-example123-0" }, "offset": { "completedFiles": "[{\"filePath\":\"source/file_0\",\"creationTime\":\"2024-03-06T17:30:28.391Z\"},{\"filePath\":\"source/file_7\",\"creationTime\":\"2024-03-06T17:30:28.395Z\"},{\"filePath\":\"source/file_9\",\"creationTime\":\"2024-03-06T17:30:28.409Z\"},{\"filePath\":\"source/file_1\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_8\",\"creationTime\":\"2024-03-06T17:30:28.681Z\"},{\"filePath\":\"source/file_6\",\"creationTime\":\"2024-03-06T17:30:28.715Z\"},{\"filePath\":\"source/file_30\",\"creationTime\":\"2024-03-06T17:30:28.969Z\"},{\"filePath\":\"source/file_39\",\"creationTime\":\"2024-03-06T17:30:28.970Z\"},{\"filePath\":\"source/file_37\",\"creationTime\":\"2024-03-06T17:30:28.993Z\"},{\"filePath\":\"source/file_36\",\"creationTime\":\"2024-03-06T17:30:29.265Z\"},{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"}]", "earliestIncomplete": "2024-03-06T17:30:28.391Z", "recordNum": "0" } } ], "requested_at": "2024-03-28T17:58:45.606796307Z", "type": "PATCH" }, "status": { "phase": "APPLIED", "message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses." }, "previous_offsets": [ { "partition": { "taskId": "lcc-example123-0" }, "offset": { "completedFiles": "[{\"filePath\":\"source/file_31\",\"creationTime\":\"2024-03-06T17:30:29.268Z\"},{\"filePath\":\"source/file_38\",\"creationTime\":\"2024-03-06T17:30:29.278Z\"},{\"filePath\":\"source/file_25\",\"creationTime\":\"2024-03-06T17:30:29.549Z\"},{\"filePath\":\"source/file_22\",\"creationTime\":\"2024-03-06T17:30:29.551Z\"},{\"filePath\":\"source/file_13\",\"creationTime\":\"2024-03-06T17:30:29.552Z\"},{\"filePath\":\"source/file_47\",\"creationTime\":\"2024-03-06T17:30:30.015Z\"},{\"filePath\":\"source/file_14\",\"creationTime\":\"2024-03-06T17:30:30.020Z\"},{\"filePath\":\"source/file_40\",\"creationTime\":\"2024-03-06T17:30:30.028Z\"},{\"filePath\":\"source/file_15\",\"creationTime\":\"2024-03-06T17:30:30.305Z\"},{\"filePath\":\"source/file_49\",\"creationTime\":\"2024-03-06T17:30:30.313Z\"},{\"filePath\":\"source/file_12\",\"creationTime\":\"2024-03-06T17:30:30.326Z\"},{\"filePath\":\"source/file_23\",\"creationTime\":\"2024-03-06T17:30:30.600Z\"},{\"filePath\":\"source/file_24\",\"creationTime\":\"2024-03-06T17:30:30.613Z\"},{\"filePath\":\"source/file_48\",\"creationTime\":\"2024-03-06T17:30:30.639Z\"},{\"filePath\":\"source/file_46\",\"creationTime\":\"2024-03-06T17:30:30.899Z\"},{\"filePath\":\"source/file_41\",\"creationTime\":\"2024-03-06T17:30:30.926Z\"},{\"filePath\":\"source/file_3\",\"creationTime\":\"2024-03-06T17:30:30.927Z\"},{\"filePath\":\"source/file_4\",\"creationTime\":\"2024-03-06T17:30:31.198Z\"},{\"filePath\":\"source/file_5\",\"creationTime\":\"2024-03-06T17:30:31.220Z\"},{\"filePath\":\"source/file_2\",\"creationTime\":\"2024-03-06T17:30:31.225Z\"}]", "earliestIncomplete": "2024-03-06T17:30:29.268Z", "recordNum": "0" } } ], "applied_at": "2024-03-28T17:58:48.079141883Z" } ``` Responses include the following information: - The original request, including the time it was made. - The status of the request: applied, pending, or failed. - The time you issued the status request. - The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back. ### JSON payload The table below offers a description of the unique fields in the JSON payload for managing offsets of the object store connectors, including the following connectors: - Amazon S3 Source connector - Azure Blob Storage Source connector - Google Cloud Storage (GCS) Source connector | Field | Definition | Required/Optional | |----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------| | `taskId` | Represents the partition in the following format: `connector-name`-<`taskid`>[-`in-progress`]

- `connector-name` is the name of the connector.
- `taskid` is the task id.
- `in-progress` is conditional and only appears if a file is currently being sourced. After the file is processed, the file appears listed in `completedFiles`. | Required | | `earliestIncomplete` | The position of the latest offset. When a connectors starts or restarts, the connector reads the files
with a creation time equal to or after `earliestIncomplete` offset. These files are sorted by creation time then filename. | Required | | `completedFiles` | List of sourced files. | Required | | `recordNum` | Number of records sourced. | Required | ## Quick Start Use this quick start to get up and running with the Confluent Cloud GCS Source connector. The quick start provides the basics of selecting the connector and configuring it to get files from a GCS bucket. 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 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). - An IAM policy allowing bucket access. See [IAM Policy for GCS](#cc-gcs-bucket-policy). - A Google Cloud [service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts). You download service account [credentials as a JSON file](https://cloud.google.com/iam/docs/creating-managing-service-account-keys). 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](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.
- Confluent Cloud Schema Registry must be enabled for your cluster, if you are using a messaging schema (like [Apache Avro](https://avro.apache.org/docs/current/)). See [Work with schemas and data contracts](../sr/schemas-manage.md#cloud-schemas-manage). ### 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 Storage Source** connector card. ![Google Cloud Storage Source Card](images/ccloud-gcs-source-icon.png) #### Step 4: Enter the connector details #### NOTE * Be sure you have all your [prerequisites](#cc-gcs-connect-source-prereqs) completed. * An asterisk ( \* ) designates a required entry. ### 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: **GCP credentials** - **Authentication method**: Under **GCP credentials**, select one of the following authenticatation menthods with GCP: - **Google Cloud service account** - **Google service account impersonation** - **Provider Integration**: If you select **Google service account impersonation**, choose an existing integration name under **Provider integration name** dropdown that has access to your resource or create a new provider integration. For more information, see [Manage a Google Cloud Provider Integration](provider-integration.md#connector-gcp-pi). - **GCP credentials file**: If you select **Google Cloud service account**, upload your Google Cloud credentials JSON file. For information about how to set these up, see [Create credentials](https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account). **Google Cloud Storage details** - **GCS Bucket name.**: Enter the **GCS bucket name**. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-gcs-source-config-properties) for all property values and definitions. - **Input message format**: Select an **Input message format**. Supports AVRO, JSON (schemaless), STRING, or BYTES. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format, like Avro. Refer to Confluent Cloud [connector limitations](limits.md#gcs-source-limits) for additional information. - **Output Kafka record value format**: Select an **Output Kafka record value format**. Defaults to the file format selected for the input message format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) if using a schema-based format. **Which topic(s) do you want to send data to?** - **Topic Name Regex Patterns**: Enter the **Topic Name Regex Patterns**. A list of topics along with a regex expression of the files which are to be sent to that topic. For example, `"my-topic:.*"` sends all files to `"my-topic"`. The expression `"special-topic:.*\.json+*"`” sends only files ending with `".json"` to `"special-topic"`. The connector ignores (doesn’t source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file. **Storage** - **Topics directory**: Enter a **Topics directory**. This is a top-level directory name where data is stored in the bucket. Defaults to `topics`. **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). **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. **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. **Google Cloud Storage details** - **GCS Part Upload Retries**: The number of times the connector retries uploading a GCS part. Defaults to `3` retries. When set to `0`, the connector does not retry an upload that fails. - **Retry Backoff (ms)**: Enter the **Retry Backoff** time in milliseconds (ms). This sets how many ms to wait before attempting the first retry of a failed request. Upon a failure, this connector may wait up to twice as long as the previous wait, up to the maximum number of retries. This avoids retrying in a tight loop under failure scenarios. **Storage** - **Directory Delimiter Character**: Enter a **Directory Delimiter Character**. The pattern to use as the delimiter character for directories. Defaults to `/`. - **Behavior on error**: Select the **Behavior on error**. Defaults to `FAIL`. - **Byte Array Line Separator**: Select a **Byte Array Line Separator**. String inserted between records when using ByteArrayFormat as input.data.format. Defaults to `\\n` and may contain escape sequences like `\\n`. An input record that contains the line separator looks like multiple records in the storage object input. - **Task Batch Size**: Enter a **Task Batch Size**: The number of files assigned to each task at a time. Defaults to `10`. The maximum value supported is `2000` and the minimum value is `1`. - **File Discovery Starting Timestamp**: Enter a **File Discovery Starting Timestamp**. A UNIX timestamp (that is, seconds since Jan 1, 1970 UTC) that denotes where to start processing files. The connector ignores any file encountered having an earlier creation time. Defaults to `0`, which is Jan 1, 1970 (i.e., the beginning of data in the bucket). **Data polling policy** - **GCS poll interval (ms)**: Enter an **GCS poll interval** in milliseconds (ms). Defaults to `60000` ms (one minute). The minimum interval allowed is `1000` ms (one second). - **Max records per poll**: Set the **Max records per poll**. The maximum amount of records to return each time the connector polls storage. Defaults to `200`. The maximum value supported is `10000` and the minimum value is `1`. **Headers** - **Include file metadata in record headers**: When enabled, each produced Kafka record carries headers describing the source file: `file.name`, `file.path`, `file.last.modified` (TIMESTAMP), and `file.size` (LONG). Consumers that don’t read these headers are unaffected. Headers whose underlying metadata is unavailable from Google Cloud Storage bucket are omitted. Supported `header.converter` values are `SimpleHeaderConverter` (default), `StringConverter`, and `JsonConverter`. **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). **Processing position** - **Set offsets**: Click **Set offsets** to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see [Manage offsets](offsets.md#connect-custom-offsets). See [Configuration Properties](#cc-gcs-source-config-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 minimum number of tasks. 1. Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks). The connector supports running one or more tasks. More tasks can improve performance. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details by previewing the running configuration. 2. Once you’ve validated that the properties are configured to your satisfaction, click **Continue**. #### Step 5: Check the Kafka topic After the connector is running, verify that messages are populating your Kafka topic. 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 Complete the following steps to set up and run the connector using the Confluent CLI. #### NOTE Make sure you have all your [prerequisites](#cc-gcs-connect-source-prereqs) completed. #### 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 the required connector properties. ```json { "connector.class": "GcsSource", "name": "GcsSourceConnector_0", "topic.regex.list": "kafka-topic-for-json:.*", "kafka.auth.mode": "SERVICE_ACCOUNT", "kafka.service.account.id": "", "input.data.format": "JSON", "output.data.format": "JSON", "gcs.credentials.json": "", "gcs.bucket.name": "", "tasks.max": "1", } ``` Note the following required property definitions: * `"connector.class"`: Identifies the connector plugin name. * `"name"`: Sets a name for your new connector. * `"topic.regex.list"`: A list of topics along with a regex expression of the files which are to be sent to that topic. In the example above, `"kafka-topic-for-json:.*"` sends all files to `"kafka-topic-for-json"`. The expression `"special-topic:.*\.json+*"`” sends only files ending with `".json"` to `"special-topic"`. The connector ignores (doesn’t source) other files not matching any patterns. The connector sends files that match multiple mappings to the first topic in the list that maps the file. #### NOTE The `topic.regex.list` property matches the full GCS path (for example, `folder/file.txt`), not just the filename. Note the following regex pattern that must account for the full path to match correctly. - Correct: `topic:.*file.*` (includes `.*` at the beginning) - Incorrect: `topic:file.*` * `"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 ``` * `"input.data.format"`: Supports Avro, JSON (schemaless), String, or Bytes. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format, like Avro. Refer to Confluent Cloud [connector limitations](limits.md#gcs-source-limits) for additional information. * `"output.data.format"`: Defaults to the file format selected for the input message format. AVRO, BYTES, JSON, JSON_SR, PROTOBUF, and STRING. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) if using a schema-based format. * `"gcs.credentials.json"`: This contains the contents of the downloaded JSON file. See [Formatting Google Cloud credentials](#cc-gcs-source-json-config-format) for details about how to format and use the contents of the downloaded credentials file. * `"tasks.max"`: The total number of tasks to run in parallel. More tasks may improve performance. * Transforms and Predicates: See the [Single Message Transformation (SMT)](single-message-transforms.md#cc-single-message-transforms) documentation for details. For configuration property values and descriptions, see [Configuration Properties](#cc-gcs-source-config-properties). ##### 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. 1. Convert the JSON file contents into string format. 2. Add the escape character `\` before all `\n` entries in the Private Key section so that each section begins with `\\n` (see the highlighted lines below). The example below has been formatted so that the `\\n` entries are easier to see. Most of the credentials key and other properties have been omitted. ```json { "connector.class": "GcsSource", "name": "GcsSourceConnector_0", "kafka.api.key": "", "kafka.api.secret": "", ... omitted ... "gcs.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\"}", "tasks.max": "1" } ``` 3. Add all the converted string content to the `"gcs.credentials.json"` section of your configuration file as shown in the example above. #### 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 gcs-source-config.json ``` Example output: ```none Created connector GcsSourceConnector_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 | GcsSourceConnector_0 | RUNNING | source ``` #### Step 6. Check the Kafka topic. After the connector is running, verify records are populating the Kafka topic. #### NOTE The GCS Source connector loads and filters all object names in the bucket before it starts sourcing records. When starting up, the connector may display `RUNNING` but not show any throughput. This is because bucket loading is not finished. For buckets with a large amount of objects, bucket loading can take several minutes to complete. 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. For self-managed connector property definitions and other details, see the connector docs in [Self-managed connectors for Confluent Platform](/platform/current/connect/kafka_connectors.html). ### 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 ### Which topic(s) do you want to send data to? `topic.regex.list` : A list of topics along with a regex expression of the files which are to be sent to that topic. For example: “my-topic:.\*” will send all files to “my-topic”, while a list containing only the expression “special-topic:.\*.json” will send only files starting with “.json” to “special-topic”, and all other files not matching any patterns will be ignored and not sourced. Files that match multiple mappings will be sent to the first topic in the list that maps the file. The `topic.regex.list` property matches the full GCS path (for example, `folder/file.txt`), not just the filename.
* 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 ### 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 ### GCP credentials `provider.integration.id` : Select an existing integration that has access to your resource. In case you need to integrate a new Google Service Account, use provider integration
* Type: string * Importance: high `authentication.method` : Select how you want to authenticate with GCP.
* Type: string * Default: Google cloud service account * Valid Values: Google cloud service account, Google service account impersonation * Importance: high `gcs.credentials.json` : GCP service account JSON file with read permissions for Google Cloud Storage.
* Type: password * Importance: high ### Google Cloud Storage details `gcs.bucket.name` : The name of the GCS bucket.
* Type: string * Importance: high `gcs.part.retries` : Number of upload retries of a single GCS part. Zero means no retries
* Type: int * Default: 3 * Importance: medium `gcs.retry.backoff.ms` : How long to wait in milliseconds before attempting the first retry of a failed GCS request. Upon a failure, this connector may wait up to twice as long as the previous wait, up to the maximum number of retries. This avoids retrying in a tight loop under failure scenarios.
* Type: int * Default: 200 * Importance: medium ### Input and output messages `input.data.format` : Sets the input message format. Valid entries are AVRO, JSON, or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO.
* Type: string * Importance: high `output.data.format` : Set the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO. Note that the output message format defaults to the value in the Input Message Format field. If no value for this property is provided, the value specified for the ‘input.data.format’ property is used.
* Type: string * Importance: high ### Storage `topics.dir` : Top-level directory (in the GCS bucket) where data to be ingested is stored.
* Type: string * Default: topics * Importance: high `directory.delim` : Directory delimiter pattern.
* Type: string * Default: / * Importance: medium `behavior.on.error` : Should the task halt when it encounters an error or continue to the next file.
* Type: string * Default: FAIL * Importance: high `format.bytearray.separator` : String inserted between records for ByteArrayFormat. Defaults to n and may contain escape sequences like n. An input record that contains the line separator looks like multiple records in the storage object input.
* Type: string * Default: “” * Importance: medium `task.batch.size` : The number of files assigned to each task at a time
* Type: int * Default: 10 * Valid Values: [1,…,2000] * Importance: high `file.discovery.starting.timestamp` : A Unix timestamp (in epoch milliseconds since Jan 1, 1970 UTC) that denotes where to start processing files. The connector ignores any file with a creation time earlier than this timestamp. Note that the connector only uses this configuration property when no offsets are stored for a connector. This parameter allows new connectors to start from a specific timestamp instead of reading all files in a bucket.
* Type: long * Default: 0 * Importance: high ### Data polling policy `gcs.poll.interval.ms` : Frequency in milliseconds to poll for new or removed folders. This may result in updated task configurations starting to poll for data in added folders or stopping polling for data in removed folders
* Type: long * Default: 60000 (1 minute) * Valid Values: [1000,…] * Importance: medium `record.batch.max.size` : The maximum amount of records to return each time storage is polled.
* Type: int * Default: 200 * Valid Values: [1,…,10000] * Importance: medium ### Number of tasks for this connector `tasks.max` : The total number of tasks to run in parallel.
* Type: int * Valid Values: [1,…,1000] * Importance: high ### Headers `file.metadata.headers.enable` : When enabled, each produced Kafka record carries headers describing the source file: `file.name`, `file.path`, `file.last.modified` (TIMESTAMP), `file.creation.time` (TIMESTAMP), and `file.size` (LONG). Consumers that don’t read these headers are unaffected. Headers whose underlying metadata is unavailable from Google Cloud Storage bucket are omitted. Supported `header.converter` values are `SimpleHeaderConverter` (default), `StringConverter`, and `JsonConverter`.
* Type: boolean * Default: false * Importance: low ### 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 ### Auto-restart policy `auto.restart.on.user.error` : Enable connector to automatically restart on user-actionable errors.
* Type: boolean * Default: true * Importance: medium ## Frequently asked questions Find answers to common questions about the Google Cloud Storage Source connector for Confluent Cloud. ### How do I format the Google Cloud credentials JSON for configuration? You should convert the Google Cloud service account credentials to a properly escaped JSON string to be compatible with the connector configuration. For detailed instructions, see [Formatting Google Cloud credentials](#cc-gcs-source-json-config-format). Format the credentials as follows: * Convert the JSON file contents to a single-line string. * Escape all special characters. For example, update `\n` to `\\n` in the private key. * Use the [Stringify Google Cloud Credentials](https://github.com/NathanNam/stringify-gcp-credentials) script to automate the conversion and prevent manual formatting errors. ### Why is my `topic.regex.list` not matching any files? The `topic.regex.list` property tests the regex against the entire Google Cloud object path, not just the filename. Check the following: * If your files are nested in folders, ensure the regex matches the full path. * Ensure you’re using standard RE2-compatible regular expressions. For example, use `.*` for any characters or `\.` for literal dots. * Place your most specific patterns like `.json` or `.csv` at the beginning of the list as the connector sends files to the first matching topic in the list. Example configuration: ```json { "topic.regex.list": "json-topic:.*\\.json,csv-topic:.*\\.csv,default-topic:.*" } ``` This configuration routes `.json` files to `json-topic`, `.csv` files to `csv-topic`, and all other files to `default-topic`. ### How can I improve the connector performance? * **Increase task count**: Use `tasks.max` to process files in parallel. * **Optimize file size**: Large files take longer; consider splitting them into smaller objects. * **Simplify regex**: Avoid complex patterns in `topic.regex.list`. * **Check bucket density**: Buckets with millions of objects take longer to index during the initial load. ### Why is the connector running but not reading any files? If the connector is `RUNNING` but no data appears in Kafka, verify the following: 1. **Check the topics.dir prefix**: The connector only scans for objects within the directory path specified in `topics.dir`. 2. **Verify regex mappings**: Ensure your `topic.regex.list` patterns match your full object paths. Files that do not match any pattern are skipped. 3. **Confirm bucket access**: Verify that the service account has adequate permissions. Use `gsutil ls` with the same credentials to confirm the service account can see the objects under the expected prefix. ### Can the connector process very large files? While there is no hard maximum, processing single objects larger than 10 GB increases the risk of timeouts and memory overhead. Follow these best practices: * **Test**: Always validate large-file ingestion in a non-production environment first. * **Split large files**: Divide large files into smaller files for better throughput. ### Why is the Kafka message key always null? The Kafka message key is null when you use the `ExtractField$Key` SMT. The Google Cloud Source connector does not generate a key struct by default and since the SMT expects to find a field within a key struct that doesn’t exist, the result is null. Use ksqlDB or Flink to read the topic and use a `PARTITION BY` clause to promote a field from the value to the key. ### Why does the connector fail with the `Bucket is a requester pays bucket` error? This error occurs because the connector doesn’t support GCS buckets with [Requester Pays](https://cloud.google.com/storage/docs/requester-pays) enabled. To resolve this: 1. Disable Requester Pays on the bucket in the Google Cloud console. 2. Ensure you have the `storage.buckets.update` permission or include a billing project in your request to modify bucket settings. ### How do I handle invalid UTF-8 character errors? Errors like `invalid UTF-8 start byte 0x92` or similar encoding errors, indicate that the source files contain characters that are not valid UTF-8. To resolve this issue: * Verify that your source files are properly encoded in UTF-8 format. * If the files use a different encoding such as `Windows-1252` or `ISO-8859-1`, convert them to UTF-8 before uploading to Google Cloud. * Check for special characters, smart quotes, or other non-standard characters in your data that may cause encoding issues. * If you can’t control the source file encoding, consider pre-processing the files to clean or convert the encoding before they are read by the connector. ### Why am I seeing duplicate messages in my topic? The Google Cloud Source connector provides **at-least-once delivery** semantics, In certain scenarios, this can result in duplicate messages: * If a task restarts or a consumer group rebalances, the connector may re-process files from the last committed offset. * Manually resetting offsets can trigger a re-scan of previously processed files. To handle duplicates: * Use deduplication logic in your consumer applications. * Use ksqlDB or Flink with windowing and deduplication strategies to filter out redundant records. * Avoid manual offset overrides unless troubleshooting or performing a specific recovery. ### Can I add file metadata to message headers? Currently, the Google Cloud Source connector doesn’t support adding file metadata such as `file.path`, `file.name`, or `file.last.modified` to Kafka message headers. This feature is available in other connectors like the SFTP Source connector, but it is not yet available for the Google Cloud Source connector. To track which file a message came from: * Include the filename information within the message payload itself at the source. * Use an SMT to add custom headers based on other available fields. * Track file processing externally using the connector’s offset management API. ### Why does the connector take several minutes to start showing throughput? When the connector starts, it must first scan and index all objects in the bucket before it can begin processing files. During this initial bucket loading phase, the connector status may show `RUNNING` but display zero throughput. The time required for bucket loading depends on: * **Number of objects**: Buckets with millions of objects can take 5-10 minutes or longer to fully index. * **Bucket structure**: Deeply nested folder structures may take longer to scan. * **topic.regex.list complexity**: Complex regex patterns may increase scanning time. This is an expected behavior and not an error. The connector starts showing throughput after the initial bucket scan is complete. For buckets with a large number of objects, consider restructuring your bucket with more top-level folders to improve performance. ## 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. [![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)