Amazon Kinesis Source Connector for Confluent Cloud

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

Note

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
  • Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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.

Manage custom offsets

You can manage the offsets for this connector. Offsets provide information on the point in the system from which the connector is accessing data. For more information, see Manage Offsets for Fully-Managed Connectors in Confluent Cloud.

To manage offsets:

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud

Response:

Successful calls return HTTP 200 with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
       {
          "partition": {
              "kinesis.shard.id": "shardId-123400000000",
              "kinesis.stream.name": "my-kinesis-stream123"
          },
          "offset": {
              "kinesis.sequence.number": "4965198826755595916031282174506905389407012937123456789",
              "kinesis.subsequence.number": 0
          }
       }
    ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

  • The position of latest offset.
  • The observed time of the offset in the metadata portion of the payload. The observed_at time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use observed_at to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling GET repeatedly will fetch more recently observed offsets.
  • Information about the connector.
  • In these examples, the curly braces around “{connector_name}” indicate a replaceable value.

JSON payload

The table below offers a description of the unique fields in the JSON payload for managing offsets of the TODO: {NAME} connector.

Field Definition Required/Optional
kinesis.shard.id The ID of the Kinesis shard. Required
kinesis.stream.name The name of the Kinesis stream. Required
kinesis.sequence.number This is the sequence identifier that AWS assigns to the record when it gets persisted in AWS Kinesis data shard. Required
kinesis.subsequence.number At times, multiple user generated records get combined into a single Kinesis record due to aggregation by AWS Kinesis Library. This identifier is used to distinguish between such individual user records that have been pushed as a single record in Kinesis Data Stream. Optional

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 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 Amazon Kinesis Source connector card.

Amazon Kinesis Source 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 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 Managed and Custom Connectors 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.

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: 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" : "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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file 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 cluster 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 Managed and Custom Connectors section.

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

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
kinesis.record.deaggregation.enable

Set this value as true if you want to de-aggregate individual Kinesis Record (aggregated using KPL) into separate Source Record(s)

  • Type: boolean
  • Default: false
  • 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

Maximum number of tasks for the connector.

  • 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

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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