Datagen Source Connector for Confluent Cloud

The fully-managed Confluent Cloud Datagen Source connector for Confluent Cloud is used to generate mock data for development and testing. The connector supports Avro, JSON Schema, Protobuf, and JSON (schemaless) output formats. The mock source data is provided through GitHub from datagen resources. This connector is not suitable for production use.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Note

• This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Datagen Source Connector for Confluent Platform.
• 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.

Limitations

Be sure to review the following information.

• For connector limitations, see Datagen Source Connector limitations.
• If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
• If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Quick start options

There are several ways to start using the Datagen Source connector in Confluent Cloud. You can get started quickly using a tutorial or you can manually configure the connector. Note that the tutorials are available only if your user account has OrganizationAdmin RBAC role privileges. The following descriptions provide additional information about how to start using the Datagen Source connector.

• Start the Produce sample data quick start tutorial from the Confluent Cloud home page after you first launch Confluent Cloud. Using this tutorial, you can create a Kafka topic and configure the Datagen Source connector to produce sample records in the topic. This tutorial is available to you after you log into your new Confluent Cloud cluster and may be launched later using the following UI tile.

  

• Start the Launch Sample Data quick start tutorial from the Datagen Source connector tile. This quick start automatically creates a sample_data Kafka topic and launches the Datagen Source connector using the quick start template you select. Note that this connector quick start is unavailable if you have already started the Produce sample data tutorial.

  

• Manually configure and launch the Datagen Source connector using the quick start instructions provided in this document.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Datagen source connector. The quick start provides the basics of selecting the connector and configuring it to use for testing and development. This connector is not suitable for production use.

Prerequisites

• Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
• Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). See Schema Registry Enabled Environments for additional information.

• 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 Datagen Source connector card. Note that you may see a Launch Sample Data tutorial when you click this tile. For more information, see Quick start options.

Step 4: Enter the connector details

Note

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

At the Add Datagen Source Connector screen, complete the following:

Select a topicKafka credentialsConfigurationSizingReview and Launch

Select the topic you want to send data to from the Topics list. To create a new topic, click +Add new topic.

Step 5: Check the Kafka topic

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Using the Confluent CLI

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
    "name" : "<datagen-connector-name>",
    "connector.class": "DatagenSource",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "kafka.topic" : "topic1, topic2",
    "output.data.format" : "JSON",
    "quickstart" : "PAGEVIEWS",
    "tasks.max" : "1"
}

Note the following property definitions:

• "name": Sets a name for your new connector.
• "connector.class": Identifies the connector plugin class.

• "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
  

• "kafka.topic": Enter one topic or multiple comma-separated topics.

• "output.data.format": Sets the output Kafka record value format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON (schemaless). You must have Confluent Cloud Schema Registry configured if using a schema-based format (for example, Avro).

The following two configuration properties are available to create sample source data:

• "schema.string": Provide a custom JSON-encoded Avro schema. The length of the schema string cannot exceed 10000 characters. This option cannot be used with the quickstart property. For schema string examples, see datagen resources. For supported annotations, see annotation types. Note the following current annotation type limitations:

  • The options annotation type object file variant is not supported. The JSON array variant is supported.
  • The regex annotation type is not supported.

• "quickstart": Enter one of the listed Quick Start schema names. This property cannot be used with the schema.string property. To view the sample data and schema specifications, see datagen resources.

  Show quickstart schema list

  • CLICKSTREAM_CODES

  • CLICKSTREAM

  • CLICKSTREAM_USERS

  • ORDERS

  • RATINGS

  • USERS

  • USERS_ARRAY

  • PAGEVIEWS

  • STOCK_TRADES

  • INVENTORY

  • PRODUCT

  • PURCHASES

  • TRANSACTIONS

  • STORES

  • CREDIT_CARDS

  • CAMPAIGN_FINANCE

  • FLEET_MGMT_DESCRIPTION

  • FLEET_MGMT_LOCATION

  • FLEET_MGMT_SENSORS

  • PIZZA_ORDERS

  • PIZZA_ORDERS_COMPLETED

  • PIZZA_ORDERS_CANCELLED

  • INSURANCE_OFFERS

  • INSURANCE_CUSTOMERS

  • INSURANCE_CUSTOMER_ACTIVITY

  • GAMING_GAMES

  • GAMING_PLAYERS

  • GAMING_PLAYER_ACTIVITY

  • PAYROLL_EMPLOYEE

  • PAYROLL_EMPLOYEE_LOCATION

  • PAYROLL_BONUS

  • SYSLOG_LOGS

  • DEVICE_INFORMATION

  • SIEM_LOGS

  • SHOES

  • SHOE_CUSTOMERS

  • SHOE_ORDERS

  • SHOE_CLICKSTREAM

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 configuration 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 datagen-source-config.json

Example output:

Created connector confluent-datagen-source 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   | confluent-datagen-source | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Example

Follow the steps in the Quick Start for Confluent Cloud to stream sample data to Kafka using the Datagen Source connector for Confluent Cloud.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

• Type: string
• Valid Values: A string at most 64 characters long
• Importance: high

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

Which topic do you want to send data to?

kafka.topic

Identifies the topic name to write the data to.

• Type: string
• 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

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. 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

json.output.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

Datagen Details

quickstart

Select from built-in quickstart schema specifications. Cannot be used with schema.string. Refer to kafka-connect-datagen on Github for additional information.

• Type: string
• Default: “”
• Importance: high

schema.string

The literal JSON-encoded Avro schema to use. Cannot be set with quickstart.

• Type: string
• Default: “”
• Valid Values: A string at most 10000 characters long
• Importance: medium

schema.keyfield

Name of the field to use as message key. It’s optional when using quickstart.

• Type: string
• Default: “”
• Importance: medium

max.interval

Set the maximum interval (in milliseconds) between each message.

• Type: int
• Default: 1000
• Valid Values: [10,…] for non-dedicated clusters and [5,…] for dedicated clusters
• Importance: high

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.

Suggested Reading

Blog post: Creating a Serverless Environment for Testing Your Apache Kafka Applications