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 or directly from Confluent.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Google Cloud Storage (GCS) Source Connector for Confluent Platform.

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.
Supports multiple tasks: The connector supports running one or more tasks.
Offset management capabilities: Supports offset management. For more information, see Manage 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 section.

Refer to Confluent Cloud connector limitations 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.

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.

To manage offsets:

Manage offsets using Confluent Cloud APIs. For more information, see Cluster API reference.

Get the current offset

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

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.

{
    "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.

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.

{
    "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.

 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.

{
  "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.

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.

{
   "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 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.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
An IAM policy allowing bucket access. See IAM Policy for GCS.
A Google Cloud service account. You download service account credentials as a JSON file. These credentials are used when setting up the connector configuration.

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

Confluent Cloud Schema Registry must be enabled for your cluster, if you are using a messaging schema (like Apache Avro). See Work with schemas and data contracts.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

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

Step 2: Add a connector

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

Step 3: Select your connector

Click the Google Cloud Storage Source connector card.

Step 4: Enter the connector details

Note

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

Kafka credentials

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note
Freight clusters support only service accounts for Kafka authentication.

Click Continue.

Authentication

Upload the GCP credentials file. You can download the credentials as a JSON file.
Enter the GCS bucket name.
Under GCP credentials, select how you want to authenticate with GCP:
- 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.
- 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.
Under the Google Cloud Storage details section, enter the GCS bucket name.
Click Continue.

Configuration

Note

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

Select an Input message format: Supports AVRO, JSON (schemaless), STRING, or BYTES. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations for additional information.
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 if using a schema-based format.
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.
Enter a Topics directory. This is a top-level directory name where data is stored in the bucket. Defaults to topics.
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?.
Auto-restart policy
- Enable Connector Auto-restart: Control the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.
- GCS Part Upload Retries. This is 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.
- 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.
- Enter a Directory Delimiter Character. The pattern to use as the delimiter character for directories. Defaults to /.
- Select the Behavior on error. Defaults to FAIL.
- 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.
- 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.
- 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).
- Enter an GCS poll interval in milliseconds (ms). Defaults to 60000 ms (one minute). The minimum interval allowed is 1000 ms (one second).
- 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.
Transforms
- Single Message Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.
See Configuration Properties for all property values and definitions.
Click Continue.

Sizing

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

Enter the maximum number of tasks. The connector supports running one or more tasks. More tasks can improve performance.
Click Continue.

Review and Launch

Verify the connection details by previewing the running configuration.
Once you’ve validated that the properties are configured to your satisfaction, click Continue.
Tip
For information about previewing your connector output, see Data Previews for Confluent Cloud Connectors.

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 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 completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
  "connector.class": "GcsSource",
  "name": "GcsSourceConnector_0",
  "topic.regex.list": "kafka-topic-for-json:.*",
  "kafka.auth.mode": "SERVICE_ACCOUNT",
  "kafka.service.account.id": "<service-account-resource-ID>",
  "input.data.format": "JSON",
  "output.data.format": "JSON",
  "gcs.credentials.json": "",
  "gcs.bucket.name": "<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, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```

"input.data.format": Supports Avro, JSON (schemaless), String, or Bytes. A valid schema must be available in Schema Registry to use a schema-based message format, like Avro. Refer to Confluent Cloud connector limitations 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 if using a schema-based format.
"gcs.credentials.json": This contains the contents of the downloaded JSON file. See Formatting Google Cloud credentials for details about how to format and use the contents of the downloaded credentials file.
"tasks.max": The total number of tasks to run in parallel. More tasks may improve performance.
Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

For configuration property values and descriptions, see Configuration 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.

Convert the JSON file contents into string format.

Add the escape character \ before all \n entries in the Private Key section so that each section begins with \\n (see the highlighted lines below). The example below has been formatted so that the \\n entries are easier to see. Most of the credentials key and other properties have been omitted.

Tip

A script is available that converts the credentials to a string and also adds additional \ escape characters where needed. See Stringify Google Cloud Credentials.

  {
      "connector.class": "GcsSource",
      "name": "GcsSourceConnector_0",
      "kafka.api.key": "<my-kafka-api-key>",
      "kafka.api.secret": "<my-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"
  }

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:

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

For example:

confluent connect cluster create --config-file gcs-source-config.json

Example output:

Created connector GcsSourceConnector_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |       Name             | Status  |  Type
+-----------+------------------------+---------+--------+
lcc-ix4dl   | 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 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.

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.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

GCP credentials

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

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.

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.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.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

Egress allowlist

connector.egress.whitelist

Type: string
Default: “”
Importance: high

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.