Amazon Redshift Sink Connector for Confluent Platform

The Kafka Connect Amazon Redshift Sink Connector allows you to export 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.


The Amazon Redshift Sink Connector for Confluent Platform includes the following features:

At least once delivery

This connector guarantees that records from the Kafka topic are delivered at least once.

Dead Letter Queue

This connector supports the Dead Letter Queue (DLQ) functionality. For information about accessing and using the DLQ, see Confluent Platform Dead Letter Queue.

Multiple tasks

The Amazon Redshift Sink Connector supports running one or more tasks. You can specify the number of tasks in the tasks.max configuration parameter. This can lead to performance gains when multiple files need to be parsed.

Install the Amazon Redshift Connector

You can install this connector by using the Confluent Hub client installation instructions or by manually downloading the ZIP file.


  • You must install the connector on every machine where Connect will run.
  • If you want to install the connector using Confluent Hub, you must install the Confluent Hub Client. This is installed by default with Confluent Enterprise.
  • Confluent Platform 3.3.0 or later, or Kafka 0.11.0 or later
  • Java 1.8
  • At minimum, INSERT access privilege is required for this connector. See Amazon Redshift Grant. If delete.enabled=true, DELETE access privilege is required.

Install the connector using Confluent Hub

To install the latest connector version using Confluent Hub Client, navigate to your Confluent Platform installation directory and run the following command:

confluent-hub install confluentinc/kafka-connect-aws-redshift:latest

You can install a specific version by replacing latest with a version number as shown in the following example:

confluent-hub install confluentinc/kafka-connect-aws-redshift:1.2.2

If you are running a multi-node Connect cluster, the Redshift connector and JDBC driver JARs must be installed on every Connect worker in the cluster. See below for details.

Install the connector manually

Download and extract the ZIP file for your connector and then follow the manual connector installation instructions.


You can use this connector for a 30-day trial period without a license key.

After 30 days, you must purchase a connector subscription which includes Confluent enterprise license keys to subscribers, along with enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, please contact Confluent Support at for more information.

See Confluent Platform license for license properties and License topic configuration for information about the license topic.

Configuration Properties

For a complete list of configuration properties for this connector, see Amazon Redshift Sink Connector Configuration Properties.

Quick Start

To see the basic functionality of the connector, we’ll be copying Avro data from a single topic to a Redshift instance.


For an example of how to get Kafka Connect connected to Confluent Cloud, see Distributed Cluster.


  • Confluent Platform is installed and services are running by using the Confluent CLI Confluent CLI commands.


    This quick start assumes that you are using the Confluent CLI commands, but standalone installations are also supported. By default ZooKeeper, Kafka, Schema Registry, Kafka Connect REST API, and Kafka Connect are started with the confluent local services start command.

  • Kafka and Schema Registry are running locally on the default ports.

Create an Amazon Redshift instance

  1. Log into your AWS Management Console.

  2. Navigate to Redshift.


    Your account needs permission to create and administer Redshift instances. If you see User <you> is not authorized to describe clusters, then you will need to contact your account administrator to set up your Redshift cluster.

  3. Navigate to Clusters.

  4. Click “Quick Launch Cluster”.

  5. Set the “Master User Password”. Remember this password for a later step.

  6. Click “Launch Cluster” to complete the setup.

  7. Wait for your cluster to be in the “available” state (approximately 5 minutes)


    You will need the information in the Cluster Configuration screen to complete the connector configuration.

Load the Amazon Redshift Sink Connector

  1. Create a properties file for your Redshift Sink Connector.

    aws.redshift.domain=< Required Configuration >
    aws.redshift.port=< Required Configuration >
    aws.redshift.database=< Required Configuration >
    aws.redshift.user=< Required Configuration >
    aws.redshift.password=< Required Configuration >

    Fill in the configuration parameters of your cluster as they appear in your Cluster Details.

  2. Load the redshift-sink connector:


    You must include a double dash (--) between the topic name and your flag. For more information, see this post.


    The command syntax for the Confluent CLI development commands changed in 5.3.0. These commands have been moved to confluent local. For example, the syntax for confluent start is now confluent local services start. For more information, see confluent local.

    confluent local services connect connector load redshift-sink --config

    Your output should resemble:

      "name": "redshift-sink",
      "config": {
        "confluent.topic.bootstrap.servers": "localhost:9092",
        "connector.class": "",
        "tasks.max": "1",
        "topics": "orders",
        "aws.redshift.domain": "",
        "aws.redshift.port": "5439",
        "aws.redshift.database": "dev",
        "aws.redshift.user": "awsuser",
        "aws.redshift.password": "your-password",
        "auto.create": "true",
        "pk.mode": "kafka",
        "name": "redshift-sink"
      "tasks": [],
      "type": "sink"


    For non-CLI users, you can load the Redshift Sink connector with the command below:

    <path-to-confluent>/bin/connect-standalone \
    <path-to-confluent>/etc/schema-registry/ \

Produce a record in Kafka

  1. Produce a record into the orders topic.

    ./bin/kafka-avro-console-producer \
    --broker-list localhost:9092 --topic orders \
    --property value.schema='{"type":"record","name":"myrecord","fields":[{"name":"id","type":"int"},{"name":"product", "type": "string"}, {"name":"quantity", "type": "int"}, {"name":"price", "type": "float"}]}'

    The console producer waits for input.

  2. Copy and paste the following record into the terminal and press Enter:

    {"id": 999, "product": "foo", "quantity": 100, "price": 50}
  3. Open the Query Editor and execute the following query

    SELECT * from orders;


Data mapping

The sink connector requires knowledge of schemas, so you should use a suitable converter e.g. the Avro converter that comes with Schema Registry, or the JSON converter with schemas enabled. Kafka record keys, if present, can be primitive types or a Connect struct, and the record value must be a Connect struct. Fields being selected from Connect structs must be of primitive types. If the data in the topic is not of a compatible format, implementing a custom Converter or using Single Message Transforms (SMTs) may be necessary.

Key handling

The default is for primary keys to not be extracted with pk.mode set to none, which is not suitable for advanced usage such as upsert semantics and when the connector is responsible for auto-creating the destination table. There are different modes that enable to use fields from the Kafka record key, the Kafka record value, or the Kafka coordinates for the record.

Refer to primary key configuration options for further detail.

Delete mode

The connector can delete rows in a database table when it consumes a tombstone record, which is a Kafka record that has a non-null key and a null value. This behavior is disabled by default, meaning that any tombstone records will result in a failure of the connector, making it easy to upgrade the JDBC connector and keep prior behavior.

Deletes can be enabled with delete.enabled=true, but only when the pk.mode is set to record_key. This is because deleting a row from the table requires the primary key be used as criteria.

Enabling delete mode does not affect the insert.mode.

Auto-creation and auto-evolution


Make sure the JDBC user has the appropriate permissions for DDL.

If auto.create is enabled, the connector can CREATE the destination table if it is found to be missing. The creation takes place online with records being consumed from the topic, since the connector uses the record schema as a basis for the table definition. Primary keys are specified based on the key configuration settings.

If auto.evolve is enabled, the connector can perform limited auto-evolution by issuing ALTER on the destination table when it encounters a record for which a column is found to be missing. Since data-type changes and removal of columns can be dangerous, the connector does not attempt to perform such evolutions on the table. Addition of primary key constraints is also not attempted.

For both auto-creation and auto-evolution, the nullability of a column is based on the optionality of the corresponding field in the schema, and default values are also specified based on the default value of the corresponding field if applicable. We use the following mapping from Connect schema types to database types:

Schema Type Redshift
‘Decimal’ DECIMAL
‘Date’ DATE
‘Time’ TIME
‘Timestamp’ TIMESTAMP
BYTES Not supported
‘Struct’ Not supported
‘Map’ Not supported
‘Array’ Not supported


For backwards-compatible table schema evolution, new fields in record schemas must be optional or have a default value. If you need to delete a field, the table schema should be manually altered to either drop the corresponding column, assign it a default value, or make it nullable.