Amazon Redshift Sink Connector for Confluent Cloud

Note

This is a Quick Start for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Amazon Redshift Sink Connector for Confluent Platform.

The Kafka Connect Amazon Redshift Sink connector for Confluent Cloud allows you to export Avro, JSON Schema, or Protobuf data from Apache Kafka® topics to Amazon Redshift. The connector polls data from Kafka and writes this data to an Amazon Redshift database. Polling data is based on subscribed topics. Auto-creation of tables and limited auto-evolution are supported.

Features

  • The Amazon Redshift Sink connector inserts Kafka records into an Amazon Redshift database.
  • The connector supports Avro, JSON Schema, or Protobuf input data formats. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
  • auto.create and auto-evolve are supported. If tables or columns are missing, they can be created automatically.
  • There is no primary key support.

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 Amazon Redshift Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to Amazon Redshift.

Prerequisites
  • Authorized access to a Confluent Cloud cluster on Amazon Web Services.

  • 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_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

  • The Amazon Redshift database must be in the same region as your Confluent Cloud cluster.

  • For networking considerations, see Networking, DNS, and service endpoints. To use static egress IPs, see Static Egress IP Addresses.

  • The connector configuration requires a Redshift user (and password) with Redshift database privileges. For example:

    CREATE DATABASE <DB_NAME>;
    
    CREATE USER <DB_USER> PASSWORD '<DB_PASSWORD>';
    
    GRANT USAGE ON SCHEMA public TO <DB_USER>;
    GRANT CREATE ON SCHEMA public TO <DB_USER>;
    GRANT SELECT ON ALL TABLES IN SCHEMA public TO <DB_USER>;
    GRANT ALL ON SCHEMA public TO <DB_USER>;
    
    GRANT CREATE ON DATABASE <DB_NAME> TO <DB_USER>;
    

    For additional information, see the Redshift docs.

  • 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 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 Redshift Sink connector card.

Amazon Redshift 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 Amazon Redshift Sink Connector screen, complete the following:

If you’ve already populated your Kafka topics, select the topic(s) you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Step 5: Check the results in Redshift.

  1. From the AWS Management Console, go to your Redshift project.
  2. Verify that new records are being added to the database.

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

Tip

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

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.

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 RedshiftSink

Example output:

Following are the required configs:
connector.class: RedshiftSink
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
topics
input.data.format
aws.redshift.domain
aws.redshift.user
aws.redshift.password
aws.redshift.database
tasks.max

Step 3: Create the connector configuration file.

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

{
    "name": "redshift-sink-connector",
    "connector.class": "RedshiftSink",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "topics": "<topic-name>",
    "input.data.format": "AVRO",
    "aws.redshift.domain": "<cluster-leader-path>",
    "aws.redshift.port": "5439",
    "aws.redshift.user": "<redshift-username>",
    "aws.redshift.password": "<redshift-user-password>",
    "aws.redshift.database": "<redshift-database-name>",
    "db.timezone": "UTC",
    "auto.create": "true",
    "auto.evolve": "true",
    "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
    
  • "topics": Identifies the topic name or a comma-separated list of topic names.

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

  • aws.redshift.domain: This is the domain path to the cluster leader node. For example: redshift-cluster-1.cf3bzixkdiebh.us-east-1.redshift.amazonaws.com.

  • "db.timesone": (Optional) The timezone for the database. Defaults to UTC. For a list of valid entries, see tz database time zones.

  • "auto.create" (tables) and "auto-evolve" (columns): (Optional) Sets whether to automatically create tables or columns if they are missing relative to the input record schema. If not entered in the configuration, both default to "false".

  • "tasks.max": Maximum number of tasks the connector can run. See Confluent Cloud connector limitations for additional task information.

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 create --config <file-name>.json

For example:

confluent connect create --config redshift-sink-config.json

Example output:

Created connector redshift-sink-connector 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   | redshift-sink-connector | RUNNING | sink

Step 6: Check the results in Redshift.

  1. From the AWS Management Console, go to your Redshift project.
  2. Verify that new records are being added to the database.

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

Tip

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

Configuration Properties

Use the following configuration properties with this connector.

Note

These are properties for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Amazon Redshift Sink Connector 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

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR and PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured

  • Type: string
  • 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 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

How should we connect to your Redshift?

aws.redshift.domain

The domain leader node for the cluster.

  • Type: string
  • Importance: high
aws.redshift.port

Port number for incoming connections to the leader.

  • Type: int
  • Default: 5439
  • Valid Values: [0,…,65535]
  • Importance: high
aws.redshift.user

Username to authenticate with the database.

  • Type: string
  • Importance: high
aws.redshift.password

Password to authenticate with the database.

  • Type: password
  • Importance: high
aws.redshift.database

Name of the database on the cluster.

  • Type: string
  • Importance: high

Database details

table.name.format

A format string for the destination table name, which may contain ‘${topic}’ as a placeholder for the originating topic name.

For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.

  • Type: string
  • Default: ${topic}
  • Importance: medium
db.timezone

Name of the JDBC timezone that should be used in the connector when inserting time-based values. Defaults to UTC.

  • Type: string
  • Default: UTC
  • Importance: medium

Connection details

batch.size

Specifies how many records to attempt to batch together for insertion into the destination table.

  • Type: int
  • Default: 3000
  • Valid Values: [1,…,5000]
  • Importance: medium

SQL/DDL Support

auto.create

Whether to automatically create the destination table if it is missing.

  • Type: boolean
  • Default: false
  • Importance: medium
auto.evolve

Whether to automatically add columns in the table if they are missing.

  • Type: boolean
  • Default: false
  • Importance: medium

Number of tasks for this connector

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

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