<a id="cc-azure-functions-sink"></a>

# Azure Functions Sink Connector for Confluent Cloud

The fully managed Azure Functions Sink connector for Confluent Cloud consumes records
from Apache Kafka® topics and executes Azure Functions. For more information about
creating an Azure function, see [Create your first function](https://docs.microsoft.com/en-us/azure/azure-functions/functions-create-first-azure-function).
Each request sent to Azure Functions can contain up to the `max.batch.size`
number of records.

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 Functions Sink
  connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-functions/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 Functions Sink connector provides the following features:

* Results from Azure Functions are stored in the following topics:
  - `success-<connector-id>`
  - `error-<connector-id>`
* Input data formats supported are Bytes, AVRO, JSON_SR (JSON Schema), JSON (Schemaless) and PROTOBUF. If no schema is defined, values are encoded as plain strings. For example,  `"name": "Kimberley Human"` is encoded as `name=Kimberley Human`.

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 Functions Sink Connector](limits.md#azure-functions-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 Functions sink
connector. The quick start provides the basics of selecting the connector and
configuring it to stream events to a target Azure Function.

<a id="cc-azure-functions-sink-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Microsoft Azure.
  - Access to an Azure function. For basic information about functions, see [Create your first function](https://docs.microsoft.com/en-us/azure/azure-functions/functions-create-first-azure-function).
  - 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).
  - The target Azure function and the Kafka cluster should be in the same region.
  <br/>
  - 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 Functions Sink** connector card.

![Azure Functions Sink Connector Card](images/ccloud-azure-functions-sink-icon.png)

<a id="cc-azure-functions-sink-setup-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-azure-functions-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

At the **Add Azure Functions 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:
   - **Function URL**: Enter the Azure Function URL to invoke a predefined Azure function in
     the **Function URL** field. For example:
     `https://myfunctionapp-devtest.azurewebsites.net/api/HttpTrigger1`.
   - **Function Key**: In the **Function Key** field, enter the Azure Function Key to invoke a
     predefined Azure function.
2. Click **Continue**.

### Configuration

#### NOTE
Configuration properties that are not shown in the
Cloud Console use the default values.  See
[Configuration Properties](#cc-azure-functions-sink-config-properties) for all property
values and definitions.

- **Input Kafka record value format**: Select an input Kafka record value format (data coming from the
  Kafka topic). Valid entries are `AVRO`, `JSON_SR`, `PROTOBUF`, `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.

### **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).
- **Max Batch Size**: The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1.
- **Max Pending Requests**: The maximum number of pending requests
  that can be made to Azure Functions concurrently.
- **Request Timeout**: The maximum time, in milliseconds, that the connector attempts to request Azure Functions before timing out (socket timeout).
- **Retry Timeout**: The total amount of time, in milliseconds,
  that the connector will exponentially backoff and retry failed
  requests (that is–on throttling). Response codes that are retried
  are `HTTP 429 Too Busy` and `HTTP 502 Bad Gateway`. A value of
  `-1` indicates indefinite retrying.
- **Behavior on Error**: The connector’s behavior if the called
  Azure function returns an error. Valid options are `log` and
  `fail`. `log` logs the error message and continues processing
  and `fail` stops the connector in case of an error.

**Additional Configs**

- **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.
- **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.
- **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`.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **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 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 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 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.
- **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).

See [Configuration Properties](#cc-azure-functions-sink-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 number of tasks.

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.
2. Click **Continue**.

### Review and Launch

1. Verify the connection details.
2. Click **Launch**.

   The status for the connector should go from **Provisioning** to
   **Running**.

#### Step 5: Check for records

Verify that records are being produced.

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-functions-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 <connector-plugin-name>
```

The command output shows the required and optional configuration properties.

#### Step 3: Create the connector configuration file

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

```none
{
  "topics":"pageviews",
  "input.data.format": "AVRO",
  "connector.class": "AzureFunctionsSink",
  "name": "AzureFunctionsSinkConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "function.url": "https://myfunctionapp-dev.azurewebsites.net/api/HttpTrigger1",
  "function.key": "***************",
  "tasks.max": "1"
}
```

Note the following property definitions:

* `"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).
* `"connector.class"`: Identifies the connector plugin name.
* `"name"`: Sets a name for your new connector.

* `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above).  To use a [service account](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=<service-account-resource-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
  ```

* `"function.url"`: The URL for your predefined Azure function.
* `"function.key"`: The key for your predefined Azure function.

*Optional:*

* `"behavior.on.error"`: Sets the error handling behavior of the connector in case the configured Azure function returns an error during processing of records. Defaults to `log`. Valid options are `log` and `fail`. `log` logs the error message in `error-<connector-id>` and continues processing and `fail` stops the connector in case of an error.
* `"max.batch.size"`: The maximum number of records to combine when invoking a single Azure function. Defaults to `1` (batching disabled). Accepts values from `1` to `1000`. If you are seeing duplicates hitting Azure Function, it could be because connector consumer is taking long time to process the records polled from kafka topic. Try increasing batch size to enable the connector to process the polled records quickly. Note that Azure Functions can only receive 100MB per request and large batch size may fail as a result.
* `"max.pending.requests"`: The maximum number of pending requests that can be made to Azure functions concurrently. Defaults to `1`. If you are seeing duplicates hitting Azure Function, it could be because connector consumer is taking long time to process the records polled from kafka topic. Try increasing max pending requests to enable more concurrent requests to Azure Function, in order to enable connector to process the polled records quickly. Try with increased max batch size before tuning this parameter.
* `"request.timeout"`: The maximum time in milliseconds that the connector will attempt a request to Azure Functions before timing out (i.e., socket timeout). Defaults to `300000` ms (5 minutes).
* `"retry.timeout"`: The total amount of time, in milliseconds (ms), that the connector will exponentially backoff and retry failed requests (i.e., throttling). Response codes that are retried are `HTTP 429 Too Busy` and `HTTP 502 Bad Gateway`. Defaults to `300000` ms (5 minutes). Enter `-1` to configure this property for indefinite retries.

**SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation.

See [Configuration Properties](#cc-azure-functions-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 <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file azure-functions-sink-config.json
```

Example output:

```none
Created connector AzureFunctionsSinkConnector_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   | AzureFunctionsSinkConnector_0 | RUNNING | sink
```

#### Step 6: Check for records.

Verify that records are being produced.

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.

<a id="cc-azure-functions-sink-config-properties"></a>

## 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.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

`reporter.result.topic.name`
: The name of the topic to produce records to after successfully processing a sink record. Defaults to ‘success-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: success-${connector}
  * Importance: low

`reporter.error.topic.name`
: The name of the topic to produce records to after each unsuccessful record sink attempt. Defaults to ‘error-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: error-${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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: JSON
  * Importance: high

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * Type: password
  * Importance: high

### How should we connect to your functions

`function.url`
: Azure Function URL to invoke a predefined Azure function
  <br/>
  * Type: string
  * Importance: high

`function.key`
: Azure Function Key to invoke a predefined Azure function
  <br/>
  * Type: password
  * Default: [hidden]
  * Importance: medium

### Function Details

`max.batch.size`
: The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1
  <br/>
  * Type: int
  * Default: 1
  * Valid Values: [1,…]
  * Importance: high

`max.pending.requests`
: The maximum number of pending requests that can be made to Azure Functions concurrently.
  <br/>
  * Type: int
  * Default: 1
  * Valid Values: [1,…,128]
  * Importance: medium

`request.timeout`
: The maximum time, in milliseconds, that the connector attempts to request Azure Functions before timing out (socket timeout)
  <br/>
  * Type: int
  * Default: 300000
  * Valid Values: [1,…]
  * Importance: low

`retry.timeout`
: The total amount of time, in milliseconds, that the connector will exponentially backoff and retry failed requests i.e on throttling. Response codes that are retried are HTTP 429 Too Busy and HTTP 502 Bad Gateway. A value of -1 indicates indefinite retrying.
  <br/>
  * Type: int
  * Default: 300000
  * Valid Values: [-1,…]
  * Importance: low

### How should we handle errors?

`behavior.on.error`
: The connector’s behavior if the called Azure function returns an error. Valid options are ‘log’ and ‘fail’. ‘log’ logs the error message and continues processing and ‘fail’ stops the connector in case of an error.
  <br/>
  * Type: string
  * Default: log
  * Importance: low

### 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).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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)
  <br/>
  * 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)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

## FAQ

Find answers to frequently asked questions about the Azure Functions Sink connector.

### Why does the connector fail with `4xx` or `5xx` status response errors?

```text
Task has received 4xx-5xx status response from configured azure function while trying to process records.
The connector has failed because 'behavior.on.error' config is set to 'FAIL'.
```

Common causes include:

* **Azure Function errors**: The function code threw an exception, has misconfigured bindings, missing dependencies, or timed out before returning a response.
* **Request formatting issues**: Records from Kafka are not compatible with the expected schema of the Azure Function, or payloads exceed the 100MB request size limit.
* **Authentication problems**: The `function.key` is invalid, expired, or does not match the configured function.
* **Network or throttling issues**: Requests are being throttled (HTTP 429) or encountering network errors (HTTP 502/503).

To troubleshoot:

1. Check the Azure Function logs in the Azure Portal for errors, exceptions, or timeout messages.
2. Verify the `function.url` and `function.key` are correct and the function is accessible.
3. Test the function independently with sample payloads to ensure it processes data correctly.
4. Review the connector’s error topic (`error-<connector-id>`) for details about which records failed and why. By default, the error topic contains basic metadata (topic, partition, offset); to include full error details, modify your Azure Function to return them in the response body.
5. Consider setting `behavior.on.error` to `log` instead of `fail` to log errors without stopping the connector.
6. For timeout issues, increase `request.timeout` beyond the default of `300000` ms (five minutes).

### Why is the connector experiencing high latency or delays in processing?

The connector might experience delays between Kafka message timestamps and Azure Function invocations for low-throughput topics.
Common causes include:

* The connector waits to accumulate records up to `max.batch.size` before invoking the function.
* Low concurrency settings limit parallel processing.
* The Azure Function takes too long to process each batch.
* The `tasks.max` configuration doesn’t match the topic partition count.

To improve performance:

* Set `max.batch.size` to `1` for low-throughput topics to avoid waiting for a full batch.
* Increase `max.pending.requests` from the default `1` to enable more concurrent requests to Azure Functions.
* Increase `tasks.max` to match the number of topic partitions for better parallelization.
* Tune `request.timeout` to allow sufficient time for function processing without excessive waits.
* Monitor Azure Function execution times and resource limits. Azure Functions have a 230-second timeout and 100MB request size limit.

#### NOTE
If you see duplicate records reaching the Azure Function, the connector consumer might be taking too long to process polled records.
Try increasing `max.batch.size` and `max.pending.requests` to enable faster processing.

### Why does the connector fail with `Unknown magic byte` or schema deserialization errors?

These errors occur when there’s a mismatch between the `input.data.format` configuration and the actual data in the topic:

```text
Unknown magic byte!
Failed to deserialize data for topic ...
```

To resolve:

* For schema-based formats (AVRO, JSON_SR, PROTOBUF), ensure the topic has a registered schema in Confluent Cloud Schema Registry.
* For schemaless JSON, note that the connector encodes values as plain strings. For example, `"name": "Kimberley Human"` becomes `name=Kimberley Human`.
* To preserve JSON structure, use `JSON_SR` with JSON Schema or `AVRO` instead of schemaless `JSON`.
* Verify your producer serializes data in the format specified in the `input.data.format` configuration.

### Why does connector validation fail even though the function URL and key are correct?

Validation errors can occur due to regional or network restrictions:

```text
function.url: Could not validate region
```

Check the following:

* Ensure the Azure Function and Kafka cluster are in the **same region**. This is required for Confluent Cloud.
* Verify the function URL is publicly accessible. Functions in a VNET or with private IPs are not supported.
* Confirm the `function.url` is in the correct format: `https://<function-app>.azurewebsites.net/api/<function-name>`
* Confirm that the `function.key` matches the key shown in the Azure Portal.

### Can the connector use a proxy to reach Azure Functions?

The connector does not support proxy configuration at the connector level. Proxy settings must be configured at the Kafka Connect worker JVM level
using Java system properties such as `-Dhttps.proxyHost` and `-Dhttps.proxyPort`.

This is currently a known limitation. For Confluent Cloud, contact [Confluent Support](https://support.confluent.io/) if you require proxy support for your deployment.

### Why aren’t failed records automatically retried?

The connector has specific retry behavior:

* **Retried errors**: Only HTTP 429 Too Many Requests and HTTP 502 Bad Gateway are automatically retried for up to `retry.timeout`, which defaults to `300000` ms or five minutes.
* **Not retried**: HTTP 500/501 errors, including timeouts, are **not** automatically retried. Affected records are written to the error topic `error-<connector-id>`.
* **Error topic**: Contains the error message and record metadata. You must consume and reprocess these records manually.

To reprocess failed records:

1. Consume records from the `error-<connector-id>` topic.
2. Transform the data back to the original topic format.
3. Replay the records through a new connector or processing job such as ksqlDB or Flink.

#### NOTE
The error topic does not have a schema by default, so you may need to define one for ksqlDB or Flink processing.

If you want the connector to stop on errors to allow manual intervention and reprocessing, set `behavior.on.error` to `fail`.
Be aware this might cause duplicate processing if the connector is restarted.

## 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)