Amazon Kinesis Source Connector for Confluent Cloud

Note

If you are installing the connector locally for Confluent Platform, see Amazon Kinesis Source Connector for Confluent Platform.

The Kafka Connect Amazon Kinesis Source connector for Confluent Cloud is used to pull data from Amazon Kinesis and persist the data to an Apache Kafka® topic.

Features

The Amazon Kinesis Source connector provides the following features:

  • Topics created automatically: The connector can automatically create Kafka topics.
  • Fetches records from all shards in one Kinesis stream.
  • Select configuration properties:
    • Offset position:
      • AT_TIMESTAMP
      • LATEST
      • TRIM_HORIZON
      • kinesis.shard.timestamp.ms
    • Other properties:
      • kinesis.region
      • kinesis.record.limit
      • kinesis.throughput.exceeded.backoff.ms

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

Limitations

Be sure to review the following information.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Kinesis source connector. The quick start shows how to select the connector and configure it to pull data from Amazon Kinesis and persist the data to an Apache Kafka® topic. It then monitors and records all subsequent row-level changes.

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 Apache Kafka using Confluent Cloud for installation instructions.

Step 2: Add a connector.

In the left navigation menu, click Data integration, and then click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector.

Click the Amazon Kinesis Source connector icon.

Amazon Kinesis Source Connector Icon

Step 4: Enter the connector details.

Note

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

At the Add Amazon Kinesis Source Connector screen, complete the following:

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 section.

See also

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.

../_images/topology.png

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.
  • The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Important

You must create topic names before before creating and launching this connector. For this Quick Start example, the database table being sourced is named kinesis-testing. Before starting these steps, make sure you create a Kafka topic named kinesis-testing using the command below:

confluent kafka topic create kinesis-testing

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe KinesisSource

Example output:

Following are the required configs:
connector.class
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
kafka.topic
aws.access.key.id
kinesis.region
aws.secret.key.id
kinesis.stream
kinesis.position
tasks.max

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" : "confluent-kinesis-source",
    "connector.class": "KinesisSource",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret" : "<my-kafka-api-secret>",
    "kafka.topic" : "kinesis-testing",
    "aws.access.key.id" : "<my-aws-access-key>",
    "aws.secret.key.id": "<my-aws-access-key-secret>",
    "kinesis.stream": "my-kinesis-stream",
    "kinesis.region" : "us-west-2",
    "kinesis.position": "AT_TIMESTAMP",
    "kinesis.shard.timestamp.ms": "1590692978237"
    "tasks.max" : "1"
}

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, 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
    
  • "kinesis.region": Identifies the AWS region where the Kinesis data stream is located. Examples are us-west-2, us-east-2, ap-northeast-1, eu-central-1, and so on.

  • (Optional) "kinesis.position": Identifies the stream offset position. This is where messages start being consumed from the Kinesis stream. Available offset positions are:

    • AT_TIMESTAMP: Get records starting at a point in time. Used with the timestamp format below.
    • LATEST: Start with the most recent record.
    • TRIM_HORIZON (default): Start with the last untrimmed record (the oldest record).
  • (Optional) "kinesis.shard.timestamp.ms": The timestamp format to use when AT_TIMESTAMP is selected. Allowed formats are the simple date-time format yyyy-MM-dd’T’HH:mm:ss.SSSXXX or epoch time in milliseconds.

  • "tasks.max": The maximum number of connector tasks.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs. See Unsupported transformations for a list of SMTs that are not supported with this connector.

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 create --config <file-name>.json

For example:

confluent connect create --config kinesis-source.json

Example output:

Created connector confluent-kinesis-source lcc-ix4dl

Step 5: Check the connector status.

Enter the following command to check the connector status:

confluent connect list

Example output:

ID          |           Name           | Status  |  Type
+-----------+--------------------------+---------+--------+
lcc-ix4dl   | confluent-kinesis-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 section.

Configuration Properties

Use the following configuration properties with this connector.

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
  • 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
  • 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

AWS Credentials

aws.access.key.id

The Amazon Access Key used to connect to Kinesis.

  • Type: password
  • Importance: high
aws.secret.key.id

The Amazon Secret Key used to connect to Kinesis.

  • Type: password
  • Importance: high

Kinesis details

kinesis.region

The AWS region for the Kinesis stream.

  • Type: string
  • Default: us-west-2
  • Importance: high
kinesis.stream

The Kinesis stream to read from.

  • Type: string
  • Importance: high
kinesis.shard.timestamp

Timestamp (the Unix epoch date with precision in milliseconds) after which to start reading records from. To be used only in combination with kinesis.shard.position=AT_TIMESTAMP. Allowed formats: yyyy-MM-dd’T’HH:mm:ss.SSSXXX or epoch time in ms. Note: this will apply to every specified shard in the stream.

  • Type: string
  • Importance: low
kinesis.position

The position in the stream to reset to if no offsets are stored.

  • Type: string
  • Default: TRIM_HORIZON
  • Importance: low

Connection details

kinesis.record.limit

The number of records to read in each poll of the Kinesis shard.

  • Type: int
  • Default: 500
  • Valid Values: [1,…,10000]
  • Importance: low
kinesis.throughput.exceeded.backoff.ms

The number of milliseconds to backoff when a throughput exceeded exception is thrown.

  • Type: long
  • Default: 10000 (10 seconds)
  • Valid Values: [500,…]
  • Importance: low
kinesis.empty.records.backoff.ms

The number of milliseconds to backoff when the stream is empty.

  • Type: long
  • Default: 5000 (5 seconds)
  • Valid Values: [500,…]
  • Importance: low

Number of tasks for this connector

tasks.max
  • Type: int
  • Valid Values: [1,…]
  • Importance: high

Suggested Reading

The following blog post includes steps to set up an example pipeline to get a mock payments stream from Amazon Kinesis into Confluent Cloud using the Confluent Cloud Amazon Kinesis Source connector.

Blog post: How Merging Companies Will Give Rise to Unified Data Streams

Next Steps

See also

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.

../_images/topology.png