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
This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Amazon Kinesis Source Connector for Confluent Platform.
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 position:
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.
- For connector limitations, see Amazon Kinesis 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¶
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
- Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud Platform (GCP).
- The Confluent CLI installed and configured for the cluster. See Install and Configure the Confluent CLI.
- For networking considerations, see Networking, DNS, and service endpoints. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
- An AWS account configured with Access Keys. You use these access keys when setting up the connector.
- An available Amazon Kinesis Data Stream.
- 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 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 way you want to provide Kafka Cluster credentials. You can
choose one of the following options:
- Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
- Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
- Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
- Click Continue.
- In the AWS Access Key ID field, enter the Amazon Access Key used to connect to Kinesis.
- Enter the Amazon Secret Key used to connect to Kinesis in the AWS Secret Key field.
- In the Kinesis stream name field, enter the Kinesis stream name.
- Click Continue.
Note
- The connector does not convert Kinesis base64-encoded data before storing the data in Kafka.
- For all property values and definitions, see Configuration Properties.
In the Kinesis region field, enter the AWS region for the Kinesis stream.
Show advanced configurations
Stream offset position: The position in the stream to reset to if no offsets are stored.
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
.Number of records to read per poll: The number of records to read in each poll of the Kinesis shard.
Number of ms to backoff when throughput is exceeded: The number of milliseconds to backoff when a throughput exceeded exception is thrown.
Number of ms to backoff when stream is empty: The number of milliseconds to backoff when the stream is empty.
For information about transforms and predicates, see the Single Message Transforms (SMT) documentation for details. See Unsupported transformations for a list of SMTs that are not supported with this connector.
See Configuration Properties for all property values and definitions.
Click Continue.
Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.
- To change the number of tasks, use the Range Slider to select the desired number of tasks.
- Click Continue.
Verify the connection details by previewing the running configuration.
Once you’ve validated that the properties are configured to your satisfaction, click Launch.
Tip
For information about previewing your connector output, see Confluent Cloud Connector Data Previews.
The status for the connector should go from Provisioning to Running.
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
orKAFKA_API_KEY
(the default). To use an API key and secret, specify the configuration propertieskafka.api.key
andkafka.api.secret
, as shown in the example configuration (above). To use a service account, specify the Resource ID in the propertykafka.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 areus-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 whenAT_TIMESTAMP
is selected. Allowed formats are the simple date-time formatyyyy-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.