# Azure Blob Storage Sink Connector for Confluent Cloud You can use the fully managed Azure Blob Storage Sink connector for Confluent Cloud to export Avro, JSON Schema, Protobuf, JSON (schemaless), or Bytes data from Apache Kafka® topics to Azure storage in Avro, JSON, or Bytes format. Additionally, for certain data layouts, the connector exports data by guaranteeing exactly-once delivery semantics to consumers of the objects it produces. The Azure Blob Storage Sink connector periodically polls data from Kafka and then uploads the data to Azure Blob Storage. The data of every Kafka partition will be split into chunks, where each chunk is represented as an Azure Blob Storage object, using the configured partitioner. The size of each data chunk is determined by the number of records written to Azure Blob Storage and by schema compatibility. 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 Sink connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-blob-storage-sink/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 Sink connector provides the following features: * **Exactly Once Delivery**: Records that are exported using a deterministic partitioner are delivered with exactly-once semantics regardless of the eventual consistency of Azure Blob Storage. * **Data formats with or without a schema:** The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) input data formats and Avro, JSON, and Bytes output formats. [Schema Registry](../../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). * **Schema Evolution**: `schema.compatibility` is set to `NONE`. * **Partitioner**: The connector supports three classes for partitioning data: * `TimeBasedPartitioner`: Partitions data based on the Kafka class `TimeStamp`. Time-based partitioning options are daily or hourly. * `FieldPartitioner`: Partitions data based on the value of a specified field. This creates Azure Blob Storage object paths that reflect the field’s name and value, such as `//=/++.`. * `DefaultPartitioner`: Creates a single partition per Kafka topic partition. This results in Azure Blob Storage object paths using the format `//partition=/++.`. * **Scheduled Rotation and Rotation Interval**: The connector supports a regularly scheduled interval for closing and uploading files to storage. See [Scheduled Rotation](#az-storage-scheduled-rotation) for details. * **Flush size:** Defaults to 1000. The value can be increased if needed. The value can be lowered (1 minimum) if you are running a [Dedicated Confluent Cloud cluster](../../clusters/cluster-types.md#dedicated-cluster). The minimum value is 1000 for non-dedicated clusters. * **Provider integration support**: The connector supports Microsoft’s native identity authentication using Confluent Provider Integration. For more information about provider integration setup, see the [connector authentication](#cc-azure-blob-sink-setup-connection). The following scenarios describe a couple of ways records may be flushed to storage: * You use the default setting of 1000 and your topic has six partitions. Files start to be created in storage after more than 1000 records exist in each partition. * You use the default setting of 1000 and the partitioner is set to Hourly. 500 records arrive at one partition from 2:00pm to 3:00pm. At 3:00pm, an additional 5 records arrive at the partition. You will see 500 records in storage at 3:00pm. #### NOTE The properties `rotate.schedule.interval.ms` and `rotate.interval.ms` can be used with `flush.size` to determine when files are created in storage. These parameters kick in and files are stored based on which condition is met first. For FieldPartitioner in fully managed connectors, an additional automatic rotation occurs when the number of open files reaches 50. This uploads the current batch of files to storage and creates a new batch. For example: You have one topic partition. You set `flush.size=1000` and `rotate.schedule.interval.ms=600000` (10 minutes). 500 records arrive at the topic partition from 12:01 to 12:10. 500 additional records arrive from 12:11 to 12:20. You will see two files in the storage bucket with 500 records in each file. This is because the 10 minute `rotate.schedule.interval.ms` condition tripped before the `flush.size=1000` condition was met. * **Secret manager integration**: The connector supports secret manager integration. For `Storage Account Key` based authentication, the connector can retrieve the following configurations from an integrated secret manager at runtime as needed. | **Secret manager managed configuration** | **Type** | |--------------------------------------------|------------| | `azblob.account.name` | `STRING` | | `azblob.account.key` | `PASSWORD` | For more information, see [Create a secret manager integration in Confluent Cloud](../secret-manager-integration/overview.md#cloud-secret-manager-quickstart). 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 Sink Connector](../limits.md#azure-blob-sink-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). ## Quick Start Use this quick start to get up and running with the Confluent Cloud Azure Blob Storage Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to Azure storage. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Microsoft Azure. - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - [Schema Registry](../../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). - An [Azure Blob Storage Container](https://docs.microsoft.com/en-us/cli/azure/storage/container?view=azure-cli-latest#az-storage-container-create) created in the same region as your Confluent Cloud cluster. Provisioning the connector in a different region from the one where the storage container is located is unsupported. If you need to use Confluent Cloud and Azure Blob storage in different regions contact your Confluent representative. - An Azure [block blob storage account](https://docs.microsoft.com/en-gb/azure/storage/blobs/storage-blob-create-account-block-blob). - An Azure [storage account access key](https://docs.microsoft.com/en-us/azure/storage/common/storage-account-manage).
- 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 Sink** connector card. ![Azure Blob Storage Sink Connector Card](images/ccloud-azure-blob-sink-icon.png) #### Step 4: Set up the connection. Complete the following and click **Continue**. #### NOTE * Make sure you have all your [prerequisites](#cc-azure-blob-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Azure Blob Storage Sink Connector** screen, complete the following: ### Topic selection If you’ve already populated your Kafka topics, select the topics you want to connect from the **Topics** list. To create a new topic, click **+Add new topic**. ### Kafka 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: **Authentication method** - **Authentication method**: How Confluent Cloud authenticates with Azure. Allowed values - `Storage Account Key` and `Microsoft Entra ID application`. * If you select **Storage Account Key**, enter the Azure Blob Storage Account key. 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&session_ref=direct&url_ref=https%3A%2F%2Fdocs.confluent.io%2Fcloud%2Fcurrent%2Fconnectors%2Fcc-azure-blob-sink%2Fcc-azure-blob-sink.html). * If you select **Microsoft Entra ID application**, select an existing integration name under the Provider integration dropdown that has access to your Azure resource required to run this connector. For more information, see [Manage an Microsoft Azure Provider Integration](../provider-integration.md#connector-az-pi). - **Use secret manager**: Fetch sensitive configuration values from a secret manager. **Azure credentials** - **Provider Integration**: Select an integration that has access to your Azure resource required to run this connector if you select **Microsoft Entra ID** as your authentication method. - **Azure Blob Storage Account Key**: Provide the Storage account key in the **Azure Blob Storage Account Key** field if you choose **Storage Account Key** as your authentication method. 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). **Secret manager configuration** - **Secret manager**: Select the secret manager to use for retrieving sensitive data. - **Configurations from Secret manager**: Select the configurations whose values Confluent Cloud should fetch from the secret manager. - **Provider Integration**: Select an integration that has access to your Azure resource required to run this connector if you select **Microsoft Entra ID** as your authentication method. **Azure Blob Storage details** - **Azure Blob Storage Account Name**: Your storage account name in the **Azure Blob Storage Name** field. Must be between 3-23 alphanumeric characters. For more information, see [Storage Accounts](https://portal.azure.com/?session_ref=direct&url_ref=https%3A%2F%2Fdocs.confluent.io%2Fcloud%2Fcurrent%2Fconnectors%2Fcc-azure-blob-sink%2Fcc-azure-blob-sink.html#blade/HubsExtension/BrowseResource/resourceType/Microsoft.Storage%2FStorageAccounts) in the Azure portal. **Azure Blob Storage Container name** - **Container name**: Specify the Azure Blob Storage container in the **Container name** field. For more information, see [View or create Azure Bob Storage containers](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-portal?session_ref=direct&url_ref=https%3A%2F%2Fdocs.confluent.io%2Fcloud%2Fcurrent%2Fconnectors%2Fcc-azure-blob-sink%2Fcc-azure-blob-sink.html#create-a-container) in the Azure portal. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-azure-blob-sink-config-properties) for all property values and definitions. - **Input Kafka record value format**: Select the input Kafka record value format (data coming from the Kafka topic). Valid entires are AVRO, JSON_SR (JSON Schema), PROTOBUF, JSON (schemaless), 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 (for example, Avro, JSON_SR, or Protobuf). #### NOTE Input format JSON to output format AVRO does not work for the connector. - **Output message format**: Select an output message format (data coming from the connector). Valid entries are AVRO, JSON, 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 (for example, Avro). - **Partitioner Class**: Select the Partitioner class that sets the partitioner to use for the connector: * `TimeBasedPartitioner`: Groups messages into folders based on time. When selected, you can configure the **Time interval**, **Path format**, **Timezone**, and **Locale** settings. * `FieldPartitioner`: Groups messages into folders based on specified record field values. When selected, you must specify the **Partitioner Field Name** (required field, up to 5 values). * `DefaultPartitioner`: Groups messages by Kafka topic partition. This creates a single partition folder per topic partition, using the format `partition=`. - **Locale**: (`TimeBasedPartitioner` only) Formats dates and times. For example, you can use `en-US` for English (US), `en-GB` for English (UK), `en-IN` for English (India), or `fr-FR` for French (France). Defaults to `en`. For a list of locale IDs, see [Java locales](https://www.localeplanet.com/java/). - **Flush size**: Enter the **Flush size**. Defaults to 1000. The value can be increased if needed. The value can be lowered (1 minimum) if you are running a [Dedicated Confluent Cloud cluster](../../clusters/cluster-types.md#dedicated-cluster). The minimum value is 1000 for non-dedicated clusters. The following scenarios describe a couple of ways records may be flushed to storage: * You use the default setting of 1000 and your topic has six partitions. Files start to be created in storage after more than 1000 records exist in each partition. * You use the default setting of 1000 and the partitioner is set to Hourly. 500 records arrive at one partition from 2:00pm to 3:00pm. At 3:00pm, an additional 5 records arrive at the partition. You will see 500 records in storage at 3:00pm. #### NOTE The properties `rotate.schedule.interval.ms` and `rotate.interval.ms` can be used with `flush.size` to determine when files are created in storage. These parameters kick in and files are stored based on which condition is met first. For `FieldPartitioner` in fully managed connectors, an additional automatic rotation occurs when the number of open files reaches 50. This uploads the current batch of files to storage and creates a new batch. For example: You have one topic partition. You set `flush.size=1000` and `rotate.schedule.interval.ms=600000` (10 minutes). 500 records arrive at the topic partition from 12:01 to 12:10. 500 additional records arrive from 12:11 to 12:20. You will see two files in the storage bucket with 500 records in each file. This is because the 10 minute `rotate.schedule.interval.ms` condition tripped before the `flush.size=1000` condition was met. - **Timezone**: (`TimeBasedPartitioner` only) Uses a [valid timezone](https://docs.oracle.com/middleware/12212/wcs/tag-ref/MISC/TimeZones.html). For example, you can use `EST`, `PST`, `WET`, or `UTC`. Defaults to `UTC` if not used. - **Path format**: (`TimeBasedPartitioner` only) Configures the time-based partitioning path created in Azure Blob Storage. The property converts the UNIX timestamp to a date format string. If not used, this property defaults to `'year'=YYYY/'month'=MM/'day'=dd/'hour'=HH` if an Hourly **Time interval** was selected or `'year'=YYYY/'month'=MM/'day'=dd` if a Daily Time interval was selected. - **Partition Field Name**: (`FieldPartitioner` only) Specifies the record field names to use for partitioning. This is a required field with a maximum of 5 values. The specified field values will be used to create Azure Blob Storage folder structures. - **Time interval**: (`TimeBasedPartitioner` only) Sets how your messages are grouped in the Azure Blob Storage container. For example, if you select **Hourly**, messages are grouped into folders for each hour data is streamed to the container. ### **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). - **Topics Directory**: Configures the top-level directory to store the data ingested from Kafka. Defaults to `topics` if not used. If you want to organize files like `https://.blob.core.windows.net//json_logs/daily//dt=2020-02-06/hr=09/`, put `topics.dir=json_logs/daily` and `time.interval=HOURLY`. - **Maximum span of record time (in ms) before scheduled rotation**: Field to configure a regular schedule for when files are closed and uploaded to storage. The default value is -1 (disabled). When this is set for 600000 ms, you will see files available in the storage bucket at least every 10 minutes. See [Scheduled Rotation](../cc-gcs-sink.md#gcs-storage-scheduled-rotation) for details about Scheduled rotation properties. - **Compression Type**: The type of compression to use when the connector writes files to Azure. Compression is applied for JSON and BYTES output message formats. - **Maximum span of record time (in ms) before rotation**: Field to configure the maximum time span (in milliseconds) that a file can remain open for additional records. When using this property, the time span interval for the file starts with the timestamp of the first record added to the file. The connector closes and uploads the file to storage when the timestamp of a subsequent record falls outside the time span set by the first file’s timestamp. This property defaults to the interval set by the `time.interval` property. See [Scheduled Rotation](../cc-gcs-sink.md#gcs-storage-scheduled-rotation) for details about **Scheduled rotation** properties. - **File Delimiter Pattern**: Character used to separate fields that make up the file name. This property defaults to `+`. - **Behavior on null values**: How to handle records with null values (for example, Kafka tombstone records). Defaults to `ignore`. #### NOTE When using Parquet, only compression types `PARQUET - none`, `PARQUET - gzip`, and `PARQUET - snappy` are supported. - **Timestamp field name**: The record field used for the timestamp, which is used with the time-base partitioner. If not used, this defaults to the timestamp when the Kafka record was produced or stored by the Kafka broker. **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 Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers. - **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`. - **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **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. - **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers. - **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. - **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. - **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. - **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`. **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. **Consumer configuration** - **Max poll interval(ms)**: Sets the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes). - **Max poll records**: Sets the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records. **Transforms** - **Single Message 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). - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. One task can handle up to 100 partitions (but is limited to 1 partition when using FieldPartitioner). 1. To change the number of recommended tasks, enter the number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks** field. For help with sizing your connector, click **How many tasks do I need?**. 2. Click **Continue**. #### NOTE For details, see [Single Message Transformations](../single-message-transforms.md#cc-single-message-transforms). For a list of SMTs that are not supported with this connector, see [Unsupported transformations](../single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). ### Review and Launch Review the configuration summary and verify the following: 1. Ensure your data is going to the correct bucket. 2. Check that the last directory in the path shown is using the **Time Interval** you entered earlier. ![Launch the connector](images/ccloud-azure-blob-launch-connector.png) The status for the connector should go from **Provisioning** to **Running**. ![Check the status](images/ccloud-azure-blob-status.png) 3. Click **Launch**. #### Step 5: Check the Azure storage container 1. From the Azure portal, go to the container in your Azure storage account. 2. Open each folder until you see your messages displayed. ![Check the storage container](images/ccloud-azure-blob-container-details.png) 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-sink-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 examples show the required connector properties for both partitioner types. **Example configuration for TimeBasedPartitioner:** ```none { "name" : "confluent-azure-blob-sink-time", "connector.class" : "AzureBlobSink", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key" : "", "kafka.api.secret" : "", "topics" : "pageviews", "input.data.format" : "AVRO", "azblob.account.name" : "", "azblob.account.key" : "", "azblob.container.name" : "", "output.data.format" : "AVRO", "topics.dir" : "json_logs/daily", "partitioner.class": "TimeBasedPartitioner", "locale": "en", "timezone": "UTC", "time.interval" : "DAILY", "flush.size": "1000", "tasks.max" : "1" } ``` **Example configuration for FieldPartitioner:** ```none { "name" : "confluent-azure-blob-sink-field", "connector.class" : "AzureBlobSink", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key" : "", "kafka.api.secret" : "", "topics" : ", ", "input.data.format" : "AVRO", "azblob.account.name" : "", "azblob.account.key" : "", "azblob.container.name" : "", "output.data.format" : "AVRO", "partitioner.class": "FieldPartitioner", "partition.field.name": ",", "flush.size": "1000", "tasks.max" : "2" } ``` Note the following property definitions: * `"name"`: Sets a name for your new connector. * `"connector.class"`: Identifies the connector plugin name. * `"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 ``` * `"topics"`: Identifies the topic name or a comma-separated list of topic names. * `"input.data.format"`: Sets the input Kafka record value format. Valid entries are **AVRO**, **JSON_SR**, **PROTOBUF**, **JSON**, or **BYTES**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). #### NOTE Input format JSON to output format AVRO does not work for the connector. * `"output.data.format"`: Sets the output Kafka record value format (data coming from the connector). Valid entries are **AVRO**, **JSON**, or **BYTES**. You must have Confluent Cloud Schema Registry configured if using a schema-based output format (for example, Avro). * `"partitioner.class"`: Sets the partitioner class to use. Valid entries are: * `"TimeBasedPartitioner"` (default) * `"FieldPartitioner"` * `"DefaultPartitioner"` * `"locale"`: (TimeBasedPartitioner only) The locale to use with the time-based partitioner. Used to format dates and times. For example, you can use `en-US` for English (USA), `en-GB` for English (UK), `en-IN` for English (India), or `fr-FR` for French (France). Defaults to `en`. For a list of locale IDs, see [Java locales](https://www.localeplanet.com/java/). * `"timezone"`: (TimeBasedPartitioner only) A [valid timezone](https://docs.oracle.com/middleware/12212/wcs/tag-ref/MISC/TimeZones.html). For example, you can use `EST`, `PST`, `WET`, or `UTC`. Defaults to `UTC` if not used. * `"path.format"`: (TimeBasedPartitioner only) Configures the time-based partitioning path created in Azure Blob Storage. The property converts the UNIX timestamp to a date format string. If not used, this property defaults to `'year'=YYYY/'month'=MM/'day'=dd/'hour'=HH` if an Hourly `time.interval` was selected or `'year'=YYYY/'month'=MM/'day'=dd` if a Daily Time interval was selected. * `"time.interval"`: (TimeBasedPartitioner only) Sets how your messages are grouped in the Azure Blob Storage container. Valid entries are **DAILY** or **HOURLY**. * `"partition.field.name"`: (FieldPartitioner only) Specifies the record field names to use for partitioning. This property is required when using the FieldPartitioner. You can specify up to five field names in a comma-separated list (for example, `","`). The property supports nested field paths using dot-separated notation which allows the partitioner to traverse hierarchical `STRUCT` records to locate values within the record schema. The property has the following limitations: * The partitioner does not support fields that contain a literal dot (`.`) in their name, as the dot is strictly interpreted as a path separator. * The partitioner does not support partitioning for fields located within or under an `ARRAY` element. The partitioner can only traverse nested `STRUCT` types. * (Optional) `flush.size`: Defaults to 1000. The value can be increased if needed. The value can be lowered (1 minimum) if you are running a [Dedicated Confluent Cloud cluster](../../clusters/cluster-types.md#dedicated-cluster). The minimum value is 1000 for non-dedicated clusters. The following scenarios describe a couple of ways records may be flushed to storage: * You use the default setting of 1000 and your topic has six partitions. Files start to be created in storage after more than 1000 records exist in each partition. * You use the default setting of 1000 and the partitioner is set to Hourly. 500 records arrive at one partition from 2:00pm to 3:00pm. At 3:00pm, an additional 5 records arrive at the partition. You will see 500 records in storage at 3:00pm. #### NOTE The properties `rotate.schedule.interval.ms` and `rotate.interval.ms` can be used with `flush.size` to determine when files are created in storage. These parameters kick in and files are stored based on which condition is met first. For FieldPartitioner in fully managed connectors, an additional automatic rotation occurs when the number of open files reaches 50. This uploads the current batch of files to storage and creates a new batch. For example: You have one topic partition. You set `flush.size=1000` and `rotate.schedule.interval.ms=600000` (10 minutes). 500 records arrive at the topic partition from 12:01 to 12:10. 500 additional records arrive from 12:11 to 12:20. You will see two files in the storage bucket with 500 records in each file. This is because the 10 minute `rotate.schedule.interval.ms` condition tripped before the `flush.size=1000` condition was met. * `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. * `"topics"`: Enter the topic name or a comma-separated list of topic names. * `"topics.dir"`: A top-level directory path to use for stored data. Defaults to `topics` if not used. * `rotate.schedule.interval.ms` and `rotate.interval.ms`: See [Scheduled Rotation](#az-storage-scheduled-rotation) for details about using these properties. **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](../single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](../single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). See [Configuration Properties](#cc-azure-blob-sink-config-properties) for all property values and definitions. #### 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-sink-config.json ``` Example output: ```none Created connector confluent-azure-blob-sink 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 | confluent-azure-blob-sink | RUNNING | sink ``` #### Step 6: Check the Azure storage container. 1. From the Azure portal, go to the container in your Azure storage account. 2. Open each folder until you see your messages displayed. ![image](images/ccloud-azure-blob-container-details.png) 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. ## Scheduled Rotation Two optional properties are available that enable you to set up a rotation schedule. These properties are provided in the Cloud Console (shown below) and in the Confluent CLI. ![Rotate Schedule and Rotate Interval](images/ccloud-storage-connector-rotation-schedule.png) * `rotate.schedule.interval.ms` (Scheduled rotation): This property allows you to configure a regular schedule for when files are closed and uploaded to storage. The default value is `-1` (disabled). For example, when this is set for 600000 ms, you will see files available in the storage bucket at least every 10 minutes. `rotate.schedule.interval.ms` does not require a continuous stream of data. #### NOTE Using the `rotate.schedule.interval.ms` property results in a non-deterministic environment and invalidates exactly-once guarantees. * `rotate.interval.ms` (Rotation interval): This property allows you to specify the maximum time span (in milliseconds) that a file can remain open for additional records. When using this property, the time span interval for the file starts with the timestamp of the first record added to the file. The connector closes and uploads the file to storage when the timestamp of a subsequent record falls outside the time span set by the first file’s timestamp. This property defaults to the interval set by the `time.interval` property. `rotate.interval.ms` requires a continuous stream of data. #### IMPORTANT The start and end of the time span interval is determined using file timestamps. For this reason, a file could potentially remain open for a long time if a record does not arrive with a timestamp falling outside the time span set by the first file’s timestamp. ## 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). ### Which topics do you want to get data from? `topics.regex` : A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.
* Type: string * Importance: low `topics` : Identifies the topic name or a comma-separated list of topic names.
* Type: list * Importance: high `errors.deadletterqueue.topic.name` : The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
* Type: string * Default: dlq-${connector} * Importance: low ### Schema Config `schema.context.name` : Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
* Type: string * Default: default * Importance: medium ### Input messages `input.data.format` : Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
* Type: string * Default: JSON * Importance: high ### Authentication method `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 `secret.manager.enabled` : Fetch sensitive configuration values from a secret manager.
* Type: boolean * Default: false * Importance: high ### Secret manager configuration `secret.manager` : Select the secret manager to use for retrieving sensitive data.
* Type: string * Importance: high `secret.manager.managed.configs` : Select the configurations to fetch their values from the secret manager.
* Type: list * Importance: high `secret.manager.provider.integration.id` : Select an existing provider integration that has access to your secret manager.
* Type: string * Importance: high ### Azure credentials `provider.integration.id` : Azure provider-integration to mint Microsoft Entra ID application tokens.
* Type: string * Importance: high `azblob.account.key` : The Azure Storage account key.
* Type: password * Importance: high ### How should we connect to your data? `name` : Sets a name for your connector.
* Type: string * Valid Values: A string at most 64 characters long * Importance: high ### Kafka Cluster credentials `kafka.auth.mode` : Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, 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 Blob Storage details `azblob.account.name` : Must be between 3-23 alphanumeric characters.
* Type: password * Importance: high `azblob.container.name` : An Azure Blob Storage Container should be in the same region as your Confluent Cloud cluster. If you use a different region, be aware that you may incur additional data transfer charges. Contact Confluent support if you need to use Confluent Cloud and Azure Blob storage in different regions.
* Type: string * Importance: high ### Output messages `output.data.format` : Set the output message format for values. 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. Note that the output message format defaults to the value in the Input Message Format field. If either PROTOBUF or JSON_SR is selected as the input message format, you should select one explicitly. If no value for this property is provided, the value specified for the ‘input.data.format’ property is used.
* Type: string * Importance: high ### Organize my data by… `partitioner.class` : The partitioner to use when writing data to the Object store
* Type: string * Default: TimeBasedPartitioner * Valid Values: DefaultPartitioner, FieldPartitioner, TimeBasedPartitioner * Importance: high `locale` : Sets the locale to use with TimeBasedPartitioner.
* Type: string * Default: en * Importance: high `timezone` : Sets the timezone used by the TimeBasedPartitioner.
* Type: string * Default: UTC * Importance: high `topics.dir` : Configures the directory to store the data ingested from Kafka. If you want to organize files like the following example, [https:/](https:/)/.blob.core.windows.net//json_logs/daily//dt=2020-02-06/hr=09/, please put topics.dir=json_logs/daily, and time.interval=HOURLY.
* Type: string * Default: topics * Importance: low `rotate.schedule.interval.ms` : Scheduled rotation uses rotate.schedule.interval.ms to close the file and upload to storage on a regular basis using the current time, rather than the record time. Setting rotate.schedule.interval.ms is nondeterministic and will invalidate exactly-once guarantees.
* Type: int * Default: -1 * Importance: medium `az.compression.type` : Compression type for file written to Azure. Applied when using JsonFormat or ByteArrayFormat.
* Type: string * Default: none * Valid Values: gzip, none * Importance: low `rotate.interval.ms` : The connector’s rotation interval specifies the maximum timespan (in milliseconds) a file can remain open and ready for additional records. In other words, when using rotate.interval.ms, the timestamp for each file starts with the timestamp of the first record inserted in the file. The connector closes and uploads a file to the blob store when the next record’s timestamp does not fit into the file’s rotate.interval time span from the first record’s timestamp. If the connector has no more records to process, the connector may keep the file open until the connector can process another record (which can be a long time). If no value for this property is provided, the value specified for the ‘time.interval’ property is used.
* Type: int * Importance: high `path.format` : This configuration is used to set the format of the data directories when partitioning with TimeBasedPartitioner. The format set in this configuration converts the Unix timestamp to a valid directory string. To organize files like this example, [https:/](https:/)/.blob.core.windows.net//json_logs/daily//dt=2020-02-06/hr=09/, use the properties: topics.dir=json_logs/daily, and time.interval=HOURLY.
* Type: string * Default: ‘year’=YYYY/’month’=MM/’day’=dd/’hour’=HH * Importance: high `file.delim` : File delimiter pattern.
* Type: string * Default: + * Importance: low `partition.field.name` : The partition field name to use when partitioning with FieldPartitioner
* Type: list * Importance: high `flush.size` : Number of records written to storage before invoking file commits.
* Type: int * Default: 1000 * Valid Values: [1000,…] for non-dedicated clusters and [1,…] for dedicated clusters * Importance: high `behavior.on.null.values` : How to handle records with a null value (i.e. Kafka tombstone records).
* Type: string * Default: ignore * Valid Values: fail, ignore * Importance: low `timestamp.field` : Sets the field that contains the timestamp used for the TimeBasedPartitioner
* Type: string * Default: “” * Importance: high `time.interval` : Partitioning interval of data, according to the time ingested to storage.
* Type: string * Valid Values: DAILY, HOURLY * Importance: high ### Consumer configuration `max.poll.interval.ms` : The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
* Type: long * Default: 300000 (5 minutes) * Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters * Importance: low `max.poll.records` : The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.
* Type: long * Default: 500 * Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters * Importance: low ### Number of tasks for this connector `tasks.max` : Maximum number of tasks for the connector.
* Type: int * Valid Values: [1,…] * Importance: high ### Additional Configs `consumer.override.auto.offset.reset` : Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
* Type: string * Importance: low `consumer.override.isolation.level` : Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level)
* Type: string * Importance: low `header.converter` : The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
* Type: string * Importance: low `key.converter.use.schema.guid` : The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string * Importance: low `key.converter.use.schema.id` : The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int * 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.use.schema.guid` : The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: string * Importance: low `value.converter.use.schema.id` : The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
* Type: int * 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: all * Importance: low `key.converter.key.schema.id.deserializer` : The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer * 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.deserializer` : The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
* Type: string * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer * 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 Sink connector for Confluent Cloud. ### Why do I see `Unable to connect to configured blob container` errors? 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). ### Can I use egress IP addresses with the connector? When your Confluent Cloud cluster is running on Microsoft Azure, the connector does not support public egress IP addresses for IP allowlisting. You can: * **Public endpoint**: Allow public inbound traffic access (`0.0.0.0/0`) to your Azure storage account. This is required if you are connecting through the public endpoint. * **Private endpoint**: Use Azure Private Link with an Egress Private Link Endpoint configured from Confluent Cloud to your Azure storage account. This avoids opening your storage account to the public internet. #### NOTE This limitation applies only when both your Confluent Cloud cluster and Azure storage account are on Azure. See [Azure Blob Storage Sink Connector](../limits.md#azure-blob-sink-limits) for complete networking requirements. ### Why is my connector failing with schema compatibility errors? Schema compatibility errors typically occur when: * The schema in Confluent Cloud Schema Registry has evolved in an incompatible way. * Multiple producers are writing data with different schemas to the same topic. * The connector’s `schema.compatibility` setting conflicts with your schema evolution. To resolve schema issues: * Check that all producers are using compatible schemas when writing to the topic. * Ensure Confluent Cloud Schema Registry is properly configured and accessible by the connector. ### Why aren’t files being written to storage even though I have data in my topics? Files are written to Azure Blob Storage when one of the following conditions is met: * The number of records buffered reaches the `flush.size` value. * The time interval specified by `rotate.interval.ms` is exceeded. * The scheduled interval specified by `rotate.schedule.interval.ms` is reached. If files are not appearing in storage: * Check that enough records have been produced to reach `flush.size`. The connector buffers records until this threshold is met. * If using `rotate.interval.ms`, ensure data is continuously flowing. This property checks rotation based on record timestamps and requires new records to trigger the rotation check. * Consider using `rotate.schedule.interval.ms` if you have intermittent data flow. This property rotates files on a fixed schedule independent of data flow. * Verify the connector status shows `RUNNING` and check connector logs for any errors. ### Can I customize the file naming convention? The connector uses a fixed file naming format that encodes the topic name, Kafka partition number, and starting offset. Therefore, you cannot change this filename logic or inject custom identifiers into the filename. However, you can customize the directory structure using: * `topics.dir` to set a top-level directory path (defaults to `topics`). * `partitioner.class` and related properties to control how data is organized into subdirectories. * `path.format` with `TimeBasedPartitioner` to customize the time-based directory structure. ### Why am I seeing small files in Azure Blob Storage? Small files are typically created when one of the rotation conditions is met before `flush.size` records are buffered. This can happen when: * `flush.size` is set too low relative to your data volume. * `rotate.schedule.interval.ms` is set and triggers rotation before enough records accumulate. * `rotate.interval.ms` is set and the time span is reached with fewer than `flush.size` records. * Schema changes occur, causing the connector to rotate files to maintain schema compatibility. To reduce small files: * Increase `flush.size` to buffer more records before rotation. * Ensure consistent data flow to your topics. * Minimize schema changes or plan schema evolution during low-traffic periods. ## 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)