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Kafka Connect AWS Redshift Sink Connector¶

You can use the AWS Redshift Sink Connector to export data from Apache Kafka® topics to AWS Redshift.

The AWS Redshift Sink connector allows you to export data from Apache Kafka® topics to AWS Redshift. The connector polls data from Kafka to write to the database based on the topics subscription. Auto-creation of tables and limited auto-evolution are supported.

Prerequisites¶

The following are required to run the Kafka Connect AWS Redshift Sink Connector:

Confluent Platform 3.3.0 or above, or Kafka 0.11.0 or above
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 AWS Redshift Connector¶

Install the connector using Confluent Hub¶

Prerequisite: Confluent Hub Client must be installed. This is installed by default with Confluent Enterprise.

Navigate to your Confluent Platform installation directory and run this command to install the latest (latest) connector version. The connector must be installed on every machine where Connect will be run.

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

You can install a specific version by replacing latest with a version number. For example:

confluent-hub install confluentinc/kafka-connect-aws-redshift:1.0.0-preview

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

Install Connector Manually¶

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

Installing the Redshift JDBC Driver¶

The Redshift sink connector uses the Java Database Connectivity (JDBC) API, to connect to AWS Redshift. In order for this to work, the connector must have a JDBC Driver for Redshift.

Click Here to download Redshift JDBC Drivers
Find the latest JDBC 4.0 driver JAR file that comes with the AWS SDK.
Place this JAR file into the share/confluent-hub-components/confluentinc-kafka-connect-aws-redshift/lib directory in your Confluent Platform installation on each of the Connect worker nodes.
Restart all of the Connect worker nodes.

Note

Since this connector uses the Amazon Redshift JDBC driver for database authentication, you must have the AWS SDK for Java 1.11.118 or later in your Java class path. If you don’t have the AWS SDK for Java installed, you can use a driver that includes the AWS SDK. For more information, see Previous JDBC Driver Versions With the AWS SDK for Java.
The share/confluent-hub-components/confluentinc-kafka-connect-aws-redshift/lib directory mentioned above is for Confluent Platform when this connector is installed through Confluent Hub (“confluent-hub install confluentinc/kafka-connect-aws-redshift:latest”). If you are using a different installation, find the location where the Confluent connector JAR files are located, and place the JDBC driver JAR file into the same directory.
If the JDBC driver is not installed correctly, the Redshift connector will fail on startup. Typically, the system throws the error No suitable driver found. If this happens, install the JDBC driver again by following the instructions.

License¶

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

After 30 days, this connector is available under a Confluent enterprise license. Confluent issues enterprise license keys to subscribers, along with providing enterprise-level support for Confluent Platform and your connectors. If you are a subscriber, please contact Confluent Support at support@confluent.io for more information.

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

Quick Start¶

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

Prerequisites:

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

Note

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

Create an AWS Redshift instance¶

Log into your AWS Management Console
Navigate to Redshift

Warning

Your account needs permission to create and administrate 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.
Navigate to Clusters
Click “Quick Launch Cluster”.
Set the “Master User Password”. Remember this password for a later step.
Click “Launch Cluster” to complete the setup.
Wait for your cluster to be in the “avaliable” state (approximately 5 minutes)

Note

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

Load the AWS Redshift Sink Connector¶

Create a properties file for your Redshift Sink Connector

name=redshift-sink
confluent.topic.bootstrap.servers=localhost:9092
confluent.topic.replication.factor=1
connector.class=io.confluent.connect.aws.redshift.RedshiftSinkConnector
tasks.max=1
topics=orders
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 >
pk.mode=kafka
auto.create=true

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

Load the redshift-sink connector:

confluent load redshift-sink -d redshift-sink.properties

Your output should resemble:

{
  "name": "redshift-sink",
  "config": {
    "confluent.topic.bootstrap.servers": "localhost:9092",
    "connector.class": "io.confluent.connect.aws.redshift.RedshiftSinkConnector",
    "tasks.max": "1",
    "topics": "orders",
    "aws.redshift.domain": "cluster-name.cluster-id.region.redshift.amazonaws.com",
    "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"
}

Tip

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/connect-avro-standalone.properties \
redshift-sink.properties

Produce a Record in Kafka¶

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.

Copy and paste the following record into the terminal and press Enter:
```
{"id": 999, "product": "foo", "quantity": 100, "price": 50}
```
Open the Query Editor and execute the following query
```
SELECT * from orders;
```

Features¶

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.mode=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¶

Tip

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
INT8	SMALLINT
INT16	SMALLINT
INT32	INT
INT64	BIGINT
FLOAT32	REAL
FLOAT64	DOUBLE PRECISION
BOOLEAN	BOOLEAN
STRING	TEXT
‘Decimal’	DECIMAL
‘Date’	DATE
‘Time’	TIME
‘Timestamp’	TIMESTAMP
BYTES	Not Supported
‘Struct’	Not Supported
‘Map’	Not Supported
‘Array’	Not Supported