Amazon Redshift Sink Connector for Confluent Cloud¶

The fully-managed 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.

Note

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

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 Managed and Custom Connectors section.

Limitations¶

Be sure to review the following information.

For connector limitations, see Amazon Redshift Sink 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 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 a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

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 Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector¶

Click the 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.

Enter the connection details:
- AWS Redshift domain: The domain leader node for the cluster. The Redshift domain entered must be in the form: <cluster-name>.<cluster-id>.<region>.redshift.amazonaws.com.
- AWS Redshift port: The port number for incoming connections to the leader.
- Connection user: The username to authenticate with the database.
- Connection password: The password to authenticate with the database.
- Database name: The name of the database on the cluster.
Click Continue.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
- Table name format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field.
- Database timezone: Name of the JDBC timezone that should be sed in the connector when inserting time-based values.
- Batch size: Specifies how many records to attempt to batch together for insertion into the destination table.
- Auto create table: Whether to automatically create the destination table if it is missing.
- Auto add columns: Whether to automatically add columns in the table if they are missing.
- For Transforms and Predicates, see the Single Message Transforms (SMT) documentation for details.
See Configuration Properties for all property values and definitions.
Click Continue.

Step 5: Check the results in Redshift¶

From the AWS Management Console, go to your Redshift project.
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 Managed and Custom Connectors section.

Tip

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

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 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-name>.<cluster-id>.<region>.redshift.amazonaws.com",
    "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. The Redshift domain entered must be in the form: <cluster-name>.<cluster-id>.<region>.redshift.amazonaws.com.
"db.timezone": (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 cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file 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 cluster list

Example output:

ID          |       Name              | Status  | Type
+-----------+-------------------------+---------+------+
lcc-ix4dl   | redshift-sink-connector | RUNNING | sink

Step 6: Check the results in Redshift.¶

From the AWS Management Console, go to your Redshift project.
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 Managed and Custom Connectors section.

Tip

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

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

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

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

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 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. The domain entered must be in the form: <cluster-name>.<cluster-id>.<region>.redshift.amazonaws.com

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

Consumer configuration¶

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000]
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,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

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.