# Azure Blob Storage Source Connector for Confluent Cloud The fully managed Azure Storage Source connector for Confluent Cloud reads data from Azure Blob Storage and produces the data as records in Apache Kafka®. Confluent Cloud is available through [Azure Marketplace](https://azuremarketplace.microsoft.com/en/marketplace/apps/confluentinc.confluent-cloud-azure-prod?tab=Overview) or [directly from Confluent](https://www.confluent.io/get-started/). #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [Azure Blob Storage Source connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-blob-storage-source/current/). * If you require private networking for fully managed connectors, make sure to set up the proper networking beforehand. For more information, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ## Features The Azure Blob Storage Source connector provides the following features: * **At least once delivery**: The connector guarantees that records are delivered at least once. * **Supports multiple tasks**: The connector supports running one or more tasks. * **Data Format with or without a Schema**: The connector supports reading data from Azure Blob Storage in Avro, JSON, String, and Bytes format. Besides records with schemas, the connector supports importing schemaless JSON records in text files, one record per line. * **Client-side encryption (CSFLE and CSPE) support**: The connector supports CSFLE and CSPE for sensitive data. For more information about CSFLE or CSPE setup, see the [connector configuration](#cc-azure-blob-source-setup-connection). * **Offset management capabilities**: Supports offset management. For more information, see [Manage custom offsets](#cc-azure-blob-source-custom-offsets). * **Provider integration support**: The connector supports Microsoft Entra ID-based authentication using Confluent Provider Integration. For more information about provider integration setup, see the [connector authentication](#cc-azure-blob-source-setup-connection). For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ## Limitations Be sure to review the following information. * For connector limitations, see [Azure Blob Storage Source Connector](limits.md#azure-blob-source-limits) limitations. * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). ## 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 Azure Blob Storage Source connector. The quick start provides the basics of selecting the connector and configuring it to get data from an Azure Blob Storage container. 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). - An Azure [block blob storage account](https://docs.microsoft.com/en-gb/azure/storage/blobs/storage-blob-create-account-block-blob) and an Azure [storage account access key](https://docs.microsoft.com/en-us/azure/storage/common/storage-account-manage). - Confluent Cloud Schema Registry must be enabled for your cluster, if you are using a messaging schema (like Avro and JSON_SR). See [Work with schemas and data contracts](../sr/schemas-manage.md#cloud-schemas-manage) for additional information. - For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
- Kafka cluster credentials. The following lists the different ways you can provide credentials. - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID. - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). Some connectors have specific ACL requirements. - Create a Confluent Cloud API key and secret. To create a key and secret, you can use [confluent api-key create](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *or* you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector. ### Using the Confluent Cloud Console #### Step 1: Launch your Confluent Cloud cluster To create and launch a Kafka cluster in Confluent Cloud, see [Create a kafka cluster in Confluent Cloud](../get-started/index.md#cloud-create-kafka-cluster). #### Step 2: Add a connector In the left navigation menu, click **Connectors**. If you already have connectors in your cluster, click **+ Add connector**. #### Step 3: Select your connector Click the **Azure Blob Storage Source** connector card. ![Azure Blob Storage Source Card](images/ccloud-azure-blob-source-icon.png) #### Step 4: Enter the connector details #### NOTE * Be sure you have all your [prerequisites](#cc-azure-blob-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: **Azure credentials** - **Authentication method**: How Confluent Cloud authenticates with Azure. Allowed values are: * `Storage Account Key` * `Microsoft Entra ID application` - **Provider Integration**: The Azure provider integration Confluent Cloud uses to generate Microsoft Entra ID application tokens. - **Azure Blob Storage Account Key**: The Storage account key in the **Azure Blob Storage Account Key** field. For information about how to set these up, see [Manage storage account access keys](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal). **Azure Blob Storage details** - **Azure Blob Storage Account Name**: Your storage account name in the **Azure Blob Storage Name** field. Must be between 3-24 alphanumeric characters. - **Container name**: The Azure Blob Storage container in the **Container name** field. The name must be between three and 63 alphanumeric characters, and the character `-` can be used. - **Account Shared Access Signature**: Shared access signature (SAS) is a URI that grants restricted access rights to Azure Storage resources. Provide a shared access signature to clients who should not be trusted with your storage account key but whom you wish to delegate access to certain storage account resources. 2. Click **Continue**. ### Configuration - **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. **Input and output messages** - **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#azure-blob-source-limits) for additional information. **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. **Azure Blob Storage details** - **Azure Blob Storage Type of Retry**: The policy specifying the type of retry pattern to use. Should be either `Exponential` or `Fixed`. **Storage** - **Directory Delimiter Character**: The pattern to use as the delimiter character for directories. Defaults to `/`. - **Behavior on error**: Sets whether the connector fails the task on an error or continues to the next file. Defaults to `FAIL`. - **Byte Array Line Separator**: Sets the string inserted between records when using `ByteArrayFormat` is the 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**: 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**: A UNIX timestamp (in milliseconds) 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 (that is, the beginning of data in the container). **Data polling policy** - **Azure Blob poll interval (ms)**: The frequency at which 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. Defaults to `60000` ms (one minute). The minimum interval allowed is `1000` ms (one second). - **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 Azure Blob Storage 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-azure-blob-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. After 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. #### NOTE The Azure Blob Storage Source connector loads and filters all object names in the container before it starts sourcing records. When starting up, the connector may display `RUNNING` but not show any throughput. This is because container loading is not finished. For a container with a large amount of objects, 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. ### 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-azure-blob-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": "AzureBlobSource", "name": "AzureBlobSourceConnector_0", "topic.regex.list": "kafka-topic-for-json:*", "kafka.auth.mode": "SERVICE_ACCOUNT", "kafka.service.account.id": "", "azblob.account.name": "", "azblob.account.key": "", "azblob.container.name": "", "input.data.format": "JSON", "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. In case of a single file `abc.json`, path returned by Azure client is the absolute path, so the regex would be `special-topic:.*abc\.json`. * `"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 ``` * `"azblob.account.name"`: The storage account name to use for the connector. * `"azblob.account.key"`: The storage account key to use. For information about how to set these up, see [Manage storage account access keys](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?tabs=azure-portal). * `"azblob.container.name"`: The container name. Must be between three and 63 alphanumeric characters, and the character `-` can be used. * `"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#azure-blob-source-limits) for additional information. * `"tasks.max"`: The total number of tasks to run in parallel. More tasks may improve performance. * Transforms and Predicates: See the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation for details. #### NOTE To enable CSFLE or CSPE for data encryption, specify the following properties: * `csfle.enabled`: Flag to indicate whether the connector honors CSFLE or CSPE rules. * `sr.service.account.id`: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema. For more information on CSFLE or CSPE setup, see [Manage encryption for connectors](csfle.md#connect-csfle). For configuration property values and descriptions, see [Configuration Properties](#cc-azure-blob-source-config-properties). #### Step 4: Load the properties file and create the connector Enter the following command to load the configuration and start the connector: ```none confluent connect cluster create --config-file .json ``` For example: ```none confluent connect cluster create --config-file azure-blob-source-config.json ``` Example output: ```none Created connector AzureBlobSourceConnector_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 | AzureBlobSourceConnector_0 | RUNNING | source ``` #### Step 6. Check the Kafka topic. After the connector is running, verify records are populating the Kafka topic. #### NOTE The Azure Blob Storage Source connector loads and filters all object names in the container before it starts sourcing records. When starting up, the connector may display `RUNNING` but not show any throughput. This is because container loading is not finished. For a container with a large amount of objects, 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 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 ### Azure credentials `provider.integration.id` : Azure provider-integration to mint Microsoft Entra ID application tokens.
* Type: string * Importance: high `authentication.method` : How Confluent Cloud authenticates with Azure. Allowed values - `Storage Account Key` and `Microsoft Entra ID application`
* Type: string * Default: Storage Account Key * Valid Values: Microsoft Entra ID application, Storage Account Key * Importance: high `azblob.account.key` : The Azure Storage account key.
* Type: password * Importance: high ### Azure Blob Storage details `azblob.account.name` : Must be between 3-24 alphanumeric characters.
* Type: string * Valid Values: A string at most 24 characters long * Importance: high `azblob.container.name` : Please provide the Azure Blob Storage Container name. Must be between 3-63 alphanumeric and ‘-’ characters.
* Type: string * Importance: high `azblob.sas.token` : Shared access signature (SAS) is a URI that grants restricted access rights to Azure Storage resources. Please provide a shared access signature to clients who should not be trusted with your storage account key but whom you wish to delegate access to certain storage account resources.
* Type: password * Importance: medium `azblob.retry.type` : The policy specifying the type of retry pattern to use. Should be either Exponential or Fixed.
* Type: string * Default: EXPONENTIAL * Valid Values: EXPONENTIAL, FIXED * Importance: medium ### Input and output messages `input.data.format` : Sets the input message format. Valid entries are AVRO, JSON, BYTES or STRING. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO.
* Type: string * Valid Values: AVRO, BYTES, JSON, 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 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 `azblob.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 Azure Blob Storage 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 frequently asked questions about the fully managed Azure Blob Storage Source connector for Confluent Cloud. ### Why do I see `Unable to connect to configured blob container` error? This error typically occurs when the `azblob.account.name` is incorrect or unresolvable. Verify the following: * Ensure the account name (`azblob.account.name`) is correct. * If private networking is configured, verify that the FQDN (`.blob.core.windows.net`) is resolvable and reachable from your Confluent Cloud cluster. * For private networking troubleshooting, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ### Why does the connector fail to start after a successful authentication check? Authentication validation only verifies credentials. The connector may still fail if: * The storage account or container name is incorrect. * Network connectivity issues prevent access to the storage account. * The authenticated principal lacks sufficient permissions to read blob data. ### Why is my connector not processing new files or show `Task has no files assigned to it` in the logs? The most common causes are: * **Incorrect directory configuration**: Ensure files are located in the directory specified by `topics.dir` within the `azblob.container.name` container. To process files at the root level, set `topics.dir` to an empty string. * **File filter regex mismatch**: Verify that your `file.filter.regex` pattern matches your file names. For a single file like `abc.json`, the Azure client returns the absolute path, so use a regex like `.*abc\.json`. * **Files already processed**: Check the connector’s offset using the Confluent CLI: ```bash confluent connect offset describe ``` ### Why does the connector show `RUNNING` status but reports no throughput? The connector loads and filters all object names in the container before sourcing records. For containers with large numbers of objects, this initial loading can take several minutes. The connector displays `RUNNING` status during this time even though no data is flowing yet. This is an expected behavior. ### Why do I get an `Anything` schema when reading JSON files with AVRO converter? When reading JSON files with schema-aware converters like AVRO, the connector generates a generic `Anything` schema because it cannot infer the schema from plain JSON data. To resolve this: * **Use AVRO files**: Read AVRO files instead of JSON files. AVRO files contain embedded schemas that the connector uses directly. * **Use custom SMT**: Deploy the [Kafka Connect JSON Schema](https://www.confluent.io/hub/jcustenborder/kafka-connect-json-schema) SMT as a [custom SMT](configure-custom-single-message-transforms/quick-start-custom-smt.md#cc-custom-single-message-transforms) and configure it with your JSON schema. * **Use JSON format and transform downstream**: Set `input.data.format` to `JSON` and use Confluent Cloud for Apache Flink or ksqlDB to transform the data into schema-aware formats. ### Why are decimal values inconsistently converted to strings? When using JSON format, the connector may convert certain decimal values like `1.0`, `2.0`, `3.0` to strings while keeping others like `1.2`, `0.20` as doubles. This occurs due to JSON parsing behavior. To ensure consistent data types: * Use AVRO files with explicit schema definitions. * Apply schema transformations in Confluent Cloud for Apache Flink or ksqlDB after ingestion. * Configure appropriate SMTs to cast data types. ### Why do I get `Topic authorization failed` errors? This error occurs when the service account doesn’t have permission to create or write to topics. Ensure: * The service account specified in `kafka.service.account.id` has appropriate ACLs for the target topics. * The service account has topic creation permissions when using `topic.creation.groups`. * The topics specified in `topic.regex.list` exist or the service account can create them. This action will cause duplicate records in your Kafka topics. ### How can I improve connector throughput? To improve the throughput of your connector: * Increase the number of tasks (`tasks.max`). The connector supports running multiple tasks in parallel. * Ensure your Azure storage account is not rate-limited. * Use appropriate network connectivity options for your deployment (see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud)). ### How do I handle malformed or unparsable files? Set the `behavior.on.error` property to `IGNORE` to ignore unparsable files and proceed to the next one. ### How can I reprocess files that have already been read? To reprocess files, you must modify the connector’s offsets. The connector identifies which files to process based on their creation time relative to the offset’s timestamp. To reprocess files: 1. Stop the connector. The connector must be in a `STOPPED` state before offsets can be modified. 2. Update the connector offsets using the Confluent Cloud Console, Confluent CLI, or API: * To reprocess files from a specific point in time, modify the `earliestIncomplete` timestamp to a value earlier than the `creationTime` of the files you wish to reprocess. Note that the connector will reprocess all files created at or after this new timestamp. * To reprocess all files, use the `DELETE` offsets action to clear all progress. This causes the connector to behave as if it is brand new and scan the entire container from the beginning. 3. Restart the connector. ## 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)