Example: Using Apache Beam - Amazon Kinesis Data Analytics

Example: Using Apache Beam

In this exercise, you create a Kinesis Data Analytics application that transforms data using Apache Beam. Apache Beam is a programming model for processing streaming data.


To set up required prerequisites for this exercise, first complete the Getting Started exercise.

Create Dependent Resources

Before you create a Kinesis Data Analytics application for this exercise, you create the following dependent resources:

  • Two Kinesis data streams (ExampleInputStream and ExampleOutputStream)

  • An Amazon S3 bucket to store the application's code (ka-app-code-<username>)

You can create the Kinesis streams and Amazon S3 bucket using the console. For instructions for creating these resources, see the following topics:

  • Creating and Updating Data Streams in the Amazon Kinesis Data Streams Developer Guide. Name your data streams ExampleInputStream and ExampleOutputStream.

  • How Do I Create an S3 Bucket? in the Amazon Simple Storage Service Developer Guide. Give the Amazon S3 bucket a globally unique name by appending your login name, such as ka-app-code-<username>.

Write Sample Records to the Input Stream

In this section, you use a Python script to write random strings to the stream for the application to process.


This section requires the AWS SDK for Python (Boto).

  1. Create a file named ping.py with the following contents:

    import json import boto3 import random kinesis = boto3.client('kinesis') while True: data = random.choice(['ping', 'telnet', 'ftp', 'tracert', 'netstat']) print(data) kinesis.put_record( StreamName="ExampleInputStream", Data=data, PartitionKey="partitionkey")
  2. Run the ping.py script:

    $ python ping.py

    Keep the script running while completing the rest of the tutorial.

Download and Examine the Application Code

The Java application code for this example is available from GitHub. To download the application code, do the following:

  1. Install the Git client if you haven't already. For more information, see Installing Git.

  2. Clone the remote repository with the following command:

    git clone https://github.com/aws-samples/amazon-kinesis-data-analytics-java-examples
  3. Navigate to the amazon-kinesis-data-analytics-java-examples/Beam directory.

The application code is located in the BasicBeamStreamingJob.java file. Note the following about the application code:

  • The application uses the Apache Beam ParDo to process incoming records by invoking a custom transform function called PingPongFn.

    The code to invoke the PingPongFn function is as follows:

    .apply("Pong transform", ParDo.of(new PingPongFn())
  • Kinesis Data Analytics applications that use Apache Beam require the following components. If you don't include these components and versions in your pom.xml, your application loads the incorrect versions from the environment dependencies, and since the versions do not match, your application crashes at runtime.

    <jackson.version>2.10.2</jackson.version> ... <dependency> <groupId>com.fasterxml.jackson.module</groupId> <artifactId>jackson-module-jaxb-annotations</artifactId> <version>2.10.2</version> </dependency>
  • The PingPongFn transform function passes the input data into the output stream, unless the input data is ping, in which case it emits the string pong\n to the output stream.

    The code of the transform function is as follows:

    private static class PingPongFn extends DoFn<KinesisRecord, byte[]> { private static final Logger LOG = LoggerFactory.getLogger(PingPongFn.class); @ProcessElement public void processElement(ProcessContext c) { String content = new String(c.element().getDataAsBytes(), StandardCharsets.UTF_8); if (content.trim().equalsIgnoreCase("ping")) { LOG.info("Ponged!"); c.output("pong\n".getBytes(StandardCharsets.UTF_8)); } else { LOG.info("No action for: " + content); c.output(c.element().getDataAsBytes()); } } }

Compile the Application Code

To compile the application, do the following:

  1. Install Java and Maven if you haven't already. For more information, see Prerequisites in the Getting Started tutorial.

  2. In order to use the Kinesis connector for the following application, you need to download, build, and install Apache Maven. For more information, see Using the Apache Flink Kinesis Streams Connector.

  3. Compile the application with the following command:

    mvn package -Dflink.version=1.8.2 -Dflink.version.minor=1.8

    The provided source code relies on libraries from Java 1.8. If you are using a development environment, ensure that your project's Java version is 1.8.

Compiling the application creates the application JAR file (target/basic-beam-app-1.0.jar).

Upload the Apache Flink Streaming Java Code

In this section, you upload your application code to the Amazon S3 bucket you created in the Create Dependent Resources section.

  1. In the Amazon S3 console, choose the ka-app-code-<username> bucket, and choose Upload.

  2. In the Select files step, choose Add files. Navigate to the basic-beam-app-1.0.jar file that you created in the previous step.

  3. You don't need to change any of the settings for the object, so choose Upload.

Your application code is now stored in an Amazon S3 bucket where your application can access it.

Create and Run the Kinesis Data Analytics Application

Follow these steps to create, configure, update, and run the application using the console.

Create the Application

  1. Open the Kinesis Data Analytics console at https://console.aws.amazon.com/kinesisanalytics.

  2. On the Kinesis Data Analytics dashboard, choose Create analytics application.

  3. On the Kinesis Analytics - Create application page, provide the application details as follows:

    • For Application name, enter MyApplication.

    • For Runtime, choose Apache Flink.


      Kinesis Data Analytics uses Apache Flink version 1.8.2.

    • Leave the version pulldown as Apache Flink 1.8 (Recommended Version).

  4. For Access permissions, choose Create / update IAM role kinesis-analytics-MyApplication-us-west-2.

                                Console screenshot showing the settings on the create
                                    application page.
  5. Choose Create application.


When you create a Kinesis Data Analytics application using the console, you have the option of having an IAM role and policy created for your application. Your application uses this role and policy to access its dependent resources. These IAM resources are named using your application name and Region as follows:

  • Policy: kinesis-analytics-service-MyApplication-us-west-2

  • Role: kinesis-analytics-MyApplication-us-west-2

Edit the IAM Policy

Edit the IAM policy to add permissions to access the Kinesis data streams.

  1. Open the IAM console at https://console.aws.amazon.com/iam/.

  2. Choose Policies. Choose the kinesis-analytics-service-MyApplication-us-west-2 policy that the console created for you in the previous section.

  3. On the Summary page, choose Edit policy. Choose the JSON tab.

  4. Add the highlighted section of the following policy example to the policy. Replace the sample account IDs (012345678901) with your account ID.

    { "Version": "2012-10-17", "Statement": [ { "Sid": "ReadCode", "Effect": "Allow", "Action": [ "s3:GetObject", "logs:DescribeLogGroups", "s3:GetObjectVersion" ], "Resource": [ "arn:aws:logs:us-west-2:012345678901:log-group:*", "arn:aws:s3:::ka-app-code-<username>/basic-beam-app-1.0.jar" ] }, { "Sid": "DescribeLogStreams", "Effect": "Allow", "Action": "logs:DescribeLogStreams", "Resource": "arn:aws:logs:us-west-2:012345678901:log-group:/aws/kinesis-analytics/MyApplication:log-stream:*" }, { "Sid": "PutLogEvents", "Effect": "Allow", "Action": "logs:PutLogEvents", "Resource": "arn:aws:logs:us-west-2:012345678901:log-group:/aws/kinesis-analytics/MyApplication:log-stream:kinesis-analytics-log-stream" }, { "Sid": "ListCloudwatchLogGroups", "Effect": "Allow", "Action": [ "logs:DescribeLogGroups" ], "Resource": [ "arn:aws:logs:us-west-2:012345678901:log-group:*" ] }, { "Sid": "ReadInputStream", "Effect": "Allow", "Action": "kinesis:*", "Resource": "arn:aws:kinesis:us-west-2:012345678901:stream/ExampleInputStream" }, { "Sid": "WriteOutputStream", "Effect": "Allow", "Action": "kinesis:*", "Resource": "arn:aws:kinesis:us-west-2:012345678901:stream/ExampleOutputStream" } ] }

Configure the Application

  1. On the MyApplication page, choose Configure.

                                Screenshot showing the MyApplication page and the
                                    configure and run buttons.
  2. On the Configure application page, provide the Code location:

    • For Amazon S3 bucket, enter ka-app-code-<username>.

    • For Path to Amazon S3 object, enter basic-beam-app-1.0.jar.

  3. Under Access to application resources, for Access permissions, choose Create / update IAM role kinesis-analytics-MyApplication-us-west-2.

  4. Under Properties, for Group ID, enter BeamApplicationProperties.

  5. Enter the following application properties and values:

    Key Value
    InputStreamName ExampleInputStream
    OutputStreamName ExampleOutputStream
    AwsRegion us-west-2
  6. Under Monitoring, ensure that the Monitoring metrics level is set to Application.

  7. For CloudWatch logging, select the Enable check box.

  8. Choose Update.


When you choose to enable CloudWatch logging, Kinesis Data Analytics creates a log group and log stream for you. The names of these resources are as follows:

  • Log group: /aws/kinesis-analytics/MyApplication

  • Log stream: kinesis-analytics-log-stream

This log stream is used to monitor the application. This is not the same log stream that the application uses to send results.

Run the Application

  1. On the MyApplication page, choose Run. Confirm the action.

                                Screenshot of the MyApplication page and the run
  2. When the application is running, refresh the page. The console shows the Application graph.

                        Screenshot of the Application graph.

You can check the Kinesis Data Analytics metrics on the CloudWatch console to verify that the application is working.

Clean Up AWS Resources

This section includes procedures for cleaning up AWS resources created in the Tumbling Window tutorial.

Delete Your Kinesis Data Analytics Application

  1. Open the Kinesis Data Analytics console at https://console.aws.amazon.com/kinesisanalytics.

  2. in the Kinesis Data Analytics panel, choose MyApplication.

  3. Choose Configure.

  4. In the Snapshots section, choose Disable and then choose Update.

  5. In the application's page, choose Delete and then confirm the deletion.

Delete Your Kinesis Data Streams

  1. Open the Kinesis console at https://console.aws.amazon.com/kinesis.

  2. In the Kinesis Data Streams panel, choose ExampleInputStream.

  3. In the ExampleInputStream page, choose Delete Kinesis Stream and then confirm the deletion.

  4. In the Kinesis streams page, choose the ExampleOutputStream, choose Actions, choose Delete, and then confirm the deletion.

Delete Your Amazon S3 Object and Bucket

  1. Open the Amazon S3 console at https://console.aws.amazon.com/s3/.

  2. Choose the ka-app-code-<username> bucket.

  3. Choose Delete and then enter the bucket name to confirm deletion.

Delete Your IAM Resources

  1. Open the IAM console at https://console.aws.amazon.com/iam/.

  2. In the navigation bar, choose Policies.

  3. In the filter control, enter kinesis.

  4. Choose the kinesis-analytics-service-MyApplication-<your-region> policy.

  5. Choose Policy Actions and then choose Delete.

  6. In the navigation bar, choose Roles.

  7. Choose the kinesis-analytics-MyApplication-<your-region> role.

  8. Choose Delete role and then confirm the deletion.

Delete Your CloudWatch Resources

  1. Open the CloudWatch console at https://console.aws.amazon.com/cloudwatch/.

  2. In the navigation bar, choose Logs.

  3. Choose the /aws/kinesis-analytics/MyApplication log group.

  4. Choose Delete Log Group and then confirm the deletion.