Amazon Redshift Connector 1.0 Example - Mule 4

This example shows you how to use Anypoint Connector for Amazon Redshift (Amazon Redshift Connector) to load data from Amazon S3 to Amazon Redshift.


  • Java 8 or 11

  • Anypoint Studio 7.5 and later

  • Mule runtime engine (Mule) 4.3.0 and later

  • DataWeave

  • Access to Amazon S3 and Amazon Redshift

  • Amazon S3 and Amazon Redshift Credentials

Create the Mule Project

  1. In Studio, select File > New > Mule Project.

  2. Enter redshift-demo as the name for your Mule project and click Finish.

  3. Open the redshift-demo.xml file in src/main/mule folder.

  4. In the Mule Palette view, click (X) Search in Exchange.

  5. In Add Modules to Project, type redshift in the search field.

  6. Click Amazon Redshift Connector in Available modules.

  7. Click Add.

  8. Click Finish.

  9. Repeat the previous steps to also add the Amazon S3 Connector (search for amazon s3) and File Connector (search for file) to the Mule project.

XML Code for This Example

Overwrite the contents of redshift-demo.xml file with the following XML snippet:

<?xml version="1.0" encoding="UTF-8"?>

<mule xmlns:ee=""

	<http:listener-config name="HTTP_Listener_config" doc:name="HTTP Listener config" basePath="/">
		<http:listener-connection host="" port="8081" />

	<redshift:config name="Amazon_Redshift_Connector_Config" doc:name="Amazon Redshift Connector Config">

	<s3:config name="Amazon_S3_Configuration" doc:name="Amazon S3 Configuration">
		<s3:basic-connection accessKey="${s3.access_key}" secretKey="${s3.secret_key}" region="${s3.region}"/>

	<file:config name="File_Config" doc:name="File Config">
		<file:connection workingDir="${mule.home}/apps/${}/" />

	<configuration-properties doc:name="Configuration properties" file="" />

	<flow name="Create-Redshift-Table-S3-Bucket-S3-Object-Flow">
		<http:listener doc:name="Listener" config-ref="HTTP_Listener_config" path="/init"/>
		<set-variable value="username-bucket" doc:name="Bucket Name" variableName="bucket" />
		<redshift:execute-ddl doc:name="Execute DDL" config-ref="Amazon_Redshift_Connector_Config">
			<redshift:sql ><![CDATA[CREATE TABLE username (Username VARCHAR(50), Identifier INTEGER, First_Name VARCHAR(50), Last_Name VARCHAR(50));]]></redshift:sql>
		<s3:create-bucket doc:name="Create bucket" config-ref="Amazon_S3_Configuration" bucketName="#[vars.bucket]"/>
		<file:read doc:name="Read" config-ref="File_Config" path="username.csv"/>
		<s3:create-object doc:name="Create object" config-ref="Amazon_S3_Configuration" bucketName="#[vars.bucket]" key="username.csv" contentType="text/csv"/>
		<ee:transform doc:name="Transform Message">
			<ee:message >
				<ee:set-payload ><![CDATA[%dw 2.0
output application/json
	success: true,
	redshiftTable: "username",
	s3bucket: vars.bucket,
	s3object: "username.csv"

	<flow name="Execute-Copy-Command-Flow">
		<http:listener doc:name="Listener" config-ref="HTTP_Listener_config" path="/execute"/>
		<set-variable value="#['s3://username-bucket/username.csv']" doc:name="Bucket Name" variableName="bucketNameEndpoint"/>
		<set-variable value="${copy.access_key}" doc:name="Access Key" variableName="access_key"/>
		<set-variable value="${copy.secret_key}" doc:name="Secret Key" variableName="secret_key"/>
		<redshift:execute-script doc:name="Execute script" config-ref="Amazon_Redshift_Connector_Config">
			<redshift:sql ><![CDATA[#["copy username from " ++ "'" ++ vars.bucketNameEndpoint ++ "'" ++ " access_key_id " ++ "'" ++ vars.access_key ++ "'" ++ " secret_access_key " ++ "'" ++ vars.secret_key ++ "'" ++ " delimiter ';' IGNOREHEADER 1 IGNOREBLANKLINES"]]]></redshift:sql>
		<ee:transform doc:name="Transform Message">
			<ee:message >
				<ee:set-payload ><![CDATA[%dw 2.0
output application/json
	"success": true

	<flow name="Delete-Redshift-Table-S3-Bucket-Flow">
		<http:listener doc:name="Listener" config-ref="HTTP_Listener_config" path="/deleteAll"/>
		<set-variable value="username-bucket" doc:name="Set Variable" variableName="bucketDelete"/>
		<redshift:execute-ddl doc:name="Execute DDL" config-ref="Amazon_Redshift_Connector_Config">
			<redshift:sql ><![CDATA[DROP TABLE username;]]></redshift:sql>
		<s3:delete-bucket doc:name="Delete bucket" config-ref="Amazon_S3_Configuration" force="true" bucketName='#[vars.bucketDelete]'/>
		<ee:transform doc:name="Transform Message">
			<ee:message >
				<ee:set-payload ><![CDATA[%dw 2.0
output application/json
	"success": true


Configure Global Elements

  1. Create a file named in the src/main/resources/ folder.

  2. Add the following properties in the file and assign the correct values:


    • You must have Amazon S3 credentials to create and delete buckets and to create an object.

    • Amazon Redshift credentials are required to establish a connection to the database.

    • This example is using a COPY command to load a table in parallel from a data file on Amazon S3.

      To use the COPY command, you must authenticate using your IAM user credentials with the correct policies attached to it, therefore, it is best to use the Amazon S3 Read only policy for the copy.access_key and copy.secret_key properties.

    • Alternatively, it is possible to use an IAM Role instead of the Access and Secret key pair.

      To do this, you must ensure the role has the correct policies attached to it. It is best to use the Amazon S3 Read only policy for this purpose.

    • To create an IAM role to allow your Redshift cluster to communicate with the Amazon S3 service on your behalf, follow the steps in this Amazon Redshift tutorial.

  3. Open the Amazon Redshift configuration, scroll down, and configure the JDBC driver in the Required libraries section. In this example, choose the Add recommended library option. If this doesn’t work, add the Amazon Maven repository to your pom.xml file and try again:


The following images show the Amazon S3 and Amazon Redshift configurations:

Amazon S3 configuration
Figure 1. Amazon S3 Configuration
Amazon Redshift configuration
Figure 2. Amazon Redshift Configuration

Prepare the Data File

Prepare a data file to upload to Amazon S3. This data file will then be used as the data set in the COPY command for the Amazon Redshift table.

  1. Create a file named username.csv in the src/main/resources/ folder in your Mule project.

  2. Populate the username.csv file with the following sample data:

Username; Identifier;First name;Last name

Flows in This Example

The following screenshots show the Anypoint Studio app flows for this example:

  • This flow creates the Amazon Redshift table, Amazon S3 bucket and object:

    Create the Redshift table Amazon S3 bucket and Amazon S3 object flow
  • This flow executes the COPY command, which leverages the Amazon Redshift massively parallel processing (MPP) architecture to load data in parallel from a file in an Amazon S3 bucket:

    Execute the Copy command flow
  • This flow deletes the Amazon Redshift table and Amazon S3 bucket:

    Delete the Amazon Redshift table and Amazon S3 bucket flow

Run the Example

  1. Right-click in the project’s canvas and select Run project redshift-demo.

  2. Open localhost:8081/init in a web browser and wait until it returns a response containing success:true with the created table, bucket, and an object.

  3. Verify that the new Amazon S3 bucket username-bucket was created in your Amazon S3 instance.

  4. Verify that the new Redshift table username was created in your Amazon Redshift instance.

  5. Open localhost:8081/execute in a web browser and wait until it returns a response containing success:true.

  6. Verify that the username table contains data from the username.csv file you added to the Mule project in Prepare the Data File.

  7. Open localhost:8081/delete in a web browser and wait until it returns a response containing success:true.

  8. Verify that both the username Amazon Redshift table and username-bucket Amazon S3 bucket were deleted.