Azure Functions Sink Connector for Confluent Cloud

The fully-managed Azure Functions Sink connector for Confluent Cloud integrates Apache Kafka® with Azure Functions. For more information about creating an Azure function, see Create your first function.

The connector consumes records from Kafka topics and executes an Azure Function. Each request sent to Azure Functions can contain up to the max.batch.size number of records.

Note

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 Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

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.

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

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

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

Step 4: Enter the connector details

Note

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

At the Add Azure Functions Sink Connector screen, complete the following:

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.

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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
  "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, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

    confluent iam service-account list

    For example:

    confluent iam service-account list

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

  • "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.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration 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:

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

For example:

confluent connect cluster create --config-file azure-functions-sink-config.json

Example output:

Created connector AzureFunctionsSinkConnector_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |       Name                    | Status  | Type
+-----------+-------------------------------+---------+------+
lcc-ix4dl   | 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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

Which topics do you want to get data from?

topics

Identifies the topic name or a comma-separated list of topic names.

  • Type: list
  • Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

  • Type: string
  • Default: default
  • Importance: medium

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
  • 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.

  • Type: string
  • Default: KAFKA_API_KEY
  • Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
  • Importance: high
kafka.api.key

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

  • Type: password
  • Importance: high
kafka.service.account.id

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

  • Type: string
  • Importance: high
kafka.api.secret

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

  • Type: password
  • Importance: high

How should we connect to your functions

function.url

Azure Function URL to invoke a predefined Azure function

  • Type: string
  • Importance: high
function.key

Azure Function Key to invoke a predefined Azure function

  • 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

  • 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.

  • 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)

  • 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.

  • 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.

  • 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).

  • 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

Next Steps

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

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