Step 3: Create and run a Managed Service for Apache Flink application - Managed Service for Apache Flink
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Amazon Managed Service for Apache Flink was previously known as Amazon Kinesis Data Analytics for Apache Flink.

Step 3: Create and run a Managed Service for Apache Flink application

In this step, you create a Managed Service for Apache Flink application with Kinesis data streams as a source and a sink.

Create dependent resources

Before you create a Managed Service for Apache Flink application for this exercise, you create the following dependent resources:

  • Two Kinesis data streams for input and output

  • An Amazon S3 bucket to store the application's code

    Note

    This tutorial assumes that you are deploying your application in the us-east-1 US East (N. Virginia) Region. If you use another Region, adapt all steps accordingly.

Create two Amazon Kinesis data streams

Before you create a Managed Service for Apache Flink application for this exercise, create two Kinesis data streams (ExampleInputStream and ExampleOutputStream). Your application uses these streams for the application source and destination streams.

You can create these streams using either the Amazon Kinesis console or the following Amazon CLI command. For console instructions, see Creating and Updating Data Streams in the Amazon Kinesis Data Streams Developer Guide. To create the streams using the Amazon CLI, use the followig commands, adjusting to the Region you use for your application.

To create the data streams (Amazon CLI)
  1. To create the first stream (ExampleInputStream), use the following Amazon Kinesis create-stream Amazon CLI command:

    $ aws kinesis create-stream \ --stream-name ExampleInputStream \ --shard-count 1 \ --region us-east-1 \
  2. To create the second stream that the application uses to write output, run the same command, changing the stream name to ExampleOutputStream:

    $ aws kinesis create-stream \ --stream-name ExampleOutputStream \ --shard-count 1 \ --region us-east-1 \

Create an Amazon S3 bucket for the application code

You can create the Amazon S3 bucket using the console. To learn how to create an Amazon S3 bucket using the console, see Creating a bucket in the Amazon S3 User Guide. Name the Amazon S3 bucket using a globally unique name, for example by appending your login name.

Note

Make sure that you create the bucket in the Region you use for this tutorial (us-east-1).

Other resources

When you create your application, Managed Service for Apache Flink automatically creates the following Amazon CloudWatch resources if they don't already exist:

  • A log group called /AWS/KinesisAnalytics-java/<my-application>

  • A log stream called kinesis-analytics-log-stream

Set up your local development environment

For development and debugging, you can run the Apache Flink application on your machine directly from your IDE of choice. Any Apache Flink dependencies are handled like regular Java dependencies using Apache Maven.

Note

On your development machine, you must have Java JDK 11, Maven, and Git installed. We recommend that you use a development environment such as Eclipse Java Neon or IntelliJ IDEA. To verify that you meet all prerequisites, see Fulfill the prerequisites for completing the exercises. You do not need to install an Apache Flink cluster on your machine.

Authenticate your Amazon session

The application uses Kinesis data streams to publish data. When running locally, you must have a valid Amazon authenticated session with permissions to write to the Kinesis data stream. Use the following steps to authenticate your session:

  1. If you don't have the Amazon CLI and a named profile with valid credential configured, see Step 2: Set up the Amazon Command Line Interface (Amazon CLI).

  2. Verify that your Amazon CLI is correctly configured and your users have permissions to write to the Kinesis data stream by publishing the following test record:

    $ aws kinesis put-record --stream-name ExampleOutputStream --data TEST --partition-key TEST
  3. If your IDE has a plugin to integrate with Amazon, you can use it to pass the credentials to the application running in the IDE. For more information see Amazon Toolkit for IntelliJ IDEA and Amazon Toolkit for Eclipse.

Download and examine the Apache Flink streaming Java code

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

  1. Clone the remote repository using the following command:

    git clone https://github.com/aws-samples/amazon-managed-service-for-apache-flink-examples.git
  2. Navigate to the amazon-managed-service-for-apache-flink-examples/tree/main/java/GettingStarted directory.

Review application components

The application is entirely implemented in the com.amazonaws.services.msf.BasicStreamingJob class. The main() method defines the data flow to process the streaming data and to run it.

Note

For the best developer experience, the application is designed to run without any code changes both on Amazon Managed Service for Apache Flink and locally, for development in your IDE.

  • To read the runtime configuration so it will work when running in Amazon Managed Service for Apache Flink and in your IDE, the application automatically detects if it's running standalone locally in the IDE. In that case, the application loads the runtime configuration differently:

    1. When the application detects that it's running in standalone mode in your IDE, form the application_properties.json file included in the resources folder of the project. The content of the file follows.

    2. When the application runs in Amazon Managed Service for Apache Flink, the default behavior loads the application configuration from the runtime properties you will define in the Amazon Managed Service for Apache Flink application. See Create and configure the Managed Service for Apache Flink application.

      private static Map<String, Properties> loadApplicationProperties(StreamExecutionEnvironment env) throws IOException { if (env instanceof LocalStreamEnvironment) { LOGGER.info("Loading application properties from '{}'", LOCAL_APPLICATION_PROPERTIES_RESOURCE); return KinesisAnalyticsRuntime.getApplicationProperties( BasicStreamingJob.class.getClassLoader() .getResource(LOCAL_APPLICATION_PROPERTIES_RESOURCE).getPath()); } else { LOGGER.info("Loading application properties from Amazon Managed Service for Apache Flink"); return KinesisAnalyticsRuntime.getApplicationProperties(); } }
  • The main() method defines the application data flow and runs it.

    • Initializes the default streaming environments. In this example, we show how to create both the StreamExecutionEnvironment to be used with the DataSteam API and the StreamTableEnvironment to be used with SQL and the Table API. The two environment objects are two separate references to the same runtime environment, to use different APIs.

      StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    • Load the application configuration parameters. This will automatically load them from the correct place, depending on where the application is running:

      Map<String, Properties> applicationParameters = loadApplicationProperties(env);
    • The application defines a source using the Kinesis Consumer connector to read data from the input stream. The configuration of the input stream is defined in the PropertyGroupId=InputStream0. The name and Region of the stream are in the properties named stream.name and aws.region respectively. For simplicity, this source reads the records as a string.

      private static FlinkKinesisConsumer<String> createSource(Properties inputProperties) { String inputStreamName = inputProperties.getProperty("stream.name"); return new FlinkKinesisConsumer<>(inputStreamName, new SimpleStringSchema(), inputProperties); } ... public static void main(String[] args) throws Exception { ... SourceFunction<String> source = createSource(applicationParameters.get("InputStream0")); DataStream<String> input = env.addSource(source, "Kinesis Source"); ... }
    • The application then defines a sink using the Kinesis Streams Sink connector to send data to the output stream. Output stream name and Region are defined in the PropertyGroupId=OutputStream0, similarly to the input stream. The sink is connected directly to the internal DataStream that is getting data from the source. In a real application, you have some transformation between source and sink.

      private static KinesisStreamsSink<String> createSink(Properties outputProperties) { String outputStreamName = outputProperties.getProperty("stream.name"); return KinesisStreamsSink.<String>builder() .setKinesisClientProperties(outputProperties) .setSerializationSchema(new SimpleStringSchema()) .setStreamName(outputStreamName) .setPartitionKeyGenerator(element -> String.valueOf(element.hashCode())) .build(); } ... public static void main(String[] args) throws Exception { ... Sink<String> sink = createSink(applicationParameters.get("OutputStream0")); input.sinkTo(sink); ... }
    • Finally, you run the data flow that you just defined. This must be the last instruction of the main() method, after you defined all the operators the data flow requires:

      env.execute("Flink streaming Java API skeleton");

Use the pom.xml file

The pom.xml file defines all dependencies required by the application and sets up the Maven Shade plugin to build the fat-jar that contains all dependencies required by Flink.

  • Some dependencies have provided scope. These dependencies are automatically available when the application runs in Amazon Managed Service for Apache Flink. They are required for compiling the application or to run the application locally in your IDE. For more information, see Run your application locally. Make sure that you are using the same Flink version as the runtime you will use in Amazon Managed Service for Apache Flink.

    <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-clients</artifactId> <version>${flink.version}</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java</artifactId> <version>${flink.version}</version> <scope>provided</scope> </dependency>
  • You must add additional Apache Flink dependencies to the pom with the default scope, such as the Kinesis connector used by this application. For more information, see Apache Flink connectors. You can also add any additional Java dependencies required by your application.

    <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kinesis</artifactId> <version>${aws.connector.version}</version> </dependency>
  • The Maven Java Compiler plugin makes sure that the code is compiled against Java 11, the JDK version currently supported by Apache Flink.

  • The Maven Shade plugin packages the fat-jar, excluding some libraries that are provided by the runtime. It also specifies two transformers: ServicesResourceTransformer and ManifestResourceTransformer. The latter configures the class containing the main method to start the application. If you rename the main class, don't forget to update this transformer.

  • <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-shade-plugin</artifactId> ... <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"> <mainClass>com.amazonaws.services.msf.BasicStreamingJob</mainClass> </transformer> ... </plugin>

Write sample records to the input stream

In this section, you will send sample records to the stream for the application to process. You have two options for generating sample data, either using a Python script or the Kinesis Data Generator.

Generate sample data using a Python script

You can use a Python script to send sample records to the stream.

Note

To run this Python script, you must use Python 3.x and have the Amazon SDK for Python (Boto) library installed.

To start sending test data to the Kinesis input stream:

  1. Download the data generator stock.py Python script from the Data generator GitHub repository.

  2. Run the stock.py script:

    $ python stock.py

Keep the script running while you complete the rest of the tutorial. You can now run your Apache Flink application.

Generate sample data using Kinesis Data Generator

Alternatively to using the Python script, you can use Kinesis Data Generator, also available in a hosted version, to send random sample data to the stream. Kinesis Data Generator runs in your browser, and you don't need to install anything on your machine.

To set up and run Kinesis Data Generator:

  1. Follow the instructions in the Kinesis Data Generator documentation to set up access to the tool. You will run an Amazon CloudFormation template that sets up a user and password.

  2. Access Kinesis Data Generator through the URL generated by the CloudFormation template. You can find the URL in the Output tab after the CloudFormation template is completed.

  3. Configure the data generator:

    • Region: Select the Region you are using for this tutorial: us-east-1

    • Stream/delivery stream: Select the input stream the application will use: ExampleInputStream

    • Records per second: 100

    • Record template: Copy and paste the following template:

      { "event_time" : "{{date.now("YYYY-MM-DDTkk:mm:ss.SSSSS")}}, "ticker" : "{{random.arrayElement( ["AAPL", "AMZN", "MSFT", "INTC", "TBV"] )}}", "price" : {{random.number(100)}} }
  4. Test the template: Choose Test template and verify that the generated record is similar to the following:

    { "event_time" : "2024-06-12T15:08:32.04800, "ticker" : "INTC", "price" : 7 }
  5. Start the data generator: Choose Select Send Data.

Kinesis Data Generator is now sending data to the ExampleInputStream.

Run your application locally

You can run and debug your Flink application locally in your IDE.

Note

Before you continue, verify that the input and output streams are available. See Create two Amazon Kinesis data streams. Also, verify that you have permission to read and write from both streams. See Authenticate your Amazon session.

Setting up the local development environment requires Java 11 JDK, Apache Maven, and and IDE for Java development. Verify you meet the required prerequisites. See Fulfill the prerequisites for completing the exercises.

Import the Java project into your IDE

To start working on the application in your IDE, you must import it as a Java project.

The repository you cloned contains multiple examples. Each example is a separate project. For this tutorial, import the content in the ./jave/GettingStarted subdirectory into your IDE .

Insert the code as existing Java project using Maven.

Note

The exact process to import a new Java project varies depending on the IDE you are using.

Check the local application configuration

When running locally, the application uses the configuration in the application_properties.json file in the resources folder of the project under ./src/main/resources. You can edit this file to use different Kinesis stream names or Regions.

[ { "PropertyGroupId": "InputStream0", "PropertyMap": { "stream.name": "ExampleInputStream", "flink.stream.initpos": "LATEST", "aws.region": "us-east-1" } }, { "PropertyGroupId": "OutputStream0", "PropertyMap": { "stream.name": "ExampleOutputStream", "aws.region": "us-east-1" } } ]

Set up your IDE run configuration

You can run and debug the Flink application from your IDE directly by running the main class com.amazonaws.services.msf.BasicStreamingJob, as you would run any Java application. Before running the application, you must set up the Run configuration. The setup depends on the IDE you are using. For example, see Run/debug configurations in the IntelliJ IDEA documentation. In particular, you must set up the following:

  1. Add the provided dependencies to the classpath. This is required to make sure that the dependencies with provided scope are passed to the application when running locally. Without this set up, the application displays a class not found error immediately.

  2. Pass the Amazon credentials to access the Kinesis streams to the application. The fastest way is to use Amazon Toolkit for IntelliJ IDEA. Using this IDE plugin in the Run configuration, you can select a specific Amazon profile. Amazon authentication happens using this profile. You don't need to pass Amazon credentials directly.

  3. Verify that the IDE runs the application using JDK 11.

Run the application in your IDE

After you set up the Run configuration for the BasicStreamingJob, you can run or debug it like a regular Java application.

Note

You can't run the fat-jar generated by Maven directly with java -jar ... from the command line. This jar does not contain the Flink core dependencies required to run the application standalone.

When the application starts successfully, it logs some information about the standalone minicluster and the initialization of the connectors. This is followed by a number of INFO and some WARN logs that Flink normally emits when the application starts.

13:43:31,405 INFO com.amazonaws.services.msf.BasicStreamingJob [] - Loading application properties from 'flink-application-properties-dev.json' 13:43:31,549 INFO org.apache.flink.streaming.connectors.kinesis.FlinkKinesisConsumer [] - Flink Kinesis Consumer is going to read the following streams: ExampleInputStream, 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.cpu.cores required for local execution is not set, setting it to the maximal possible value. 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.memory.task.heap.size required for local execution is not set, setting it to the maximal possible value. 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.memory.task.off-heap.size required for local execution is not set, setting it to the maximal possible value. 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.memory.network.min required for local execution is not set, setting it to its default value 64 mb. 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.memory.network.max required for local execution is not set, setting it to its default value 64 mb. 13:43:31,676 INFO org.apache.flink.runtime.taskexecutor.TaskExecutorResourceUtils [] - The configuration option taskmanager.memory.managed.size required for local execution is not set, setting it to its default value 128 mb. 13:43:31,677 INFO org.apache.flink.runtime.minicluster.MiniCluster [] - Starting Flink Mini Cluster ....

After the initialization is complete, the application doesn't emit any further log entries. While data is flowing, no log is emitted.

To verify if the application is correctly processing data, you can inspect the input and output Kinesis streams, as described in the following section.

Note

Not emitting logs about flowing data is the normal behavior for a Flink application. Emitting logs on every record might be convenient for debugging, but can add considerable overhead when running in production.

Observe input and output data in Kinesis streams

You can observe records sent to the input stream by the (generating sample python) or the Kinesis Data Generator (link) by using the Data Viewer in the Amazon Kinesis console.

To observe records:

Stop your application running locally

Stop the application running in your IDE. The IDE usually provides a "stop" option. The exact location and method depends on the IDE.

Compile and package your application code

In this section, you use Apache Maven to compile the Java code and package it into a JAR file. You can compile and package your code using the Maven command line tool or your IDE.

To compile and package using the Maven command line:

Move to the directory containing the Java GettingStarted project and run the following command:

$ mvn package

To compile and package using your IDE:

Run mvn package from your IDE Maven integration.

In both cases, the following JAR file is created: target/amazon-msf-java-stream-app-1.0.jar.

Note

Runninga "build project" from your IDE might not create the JAR file.

Upload the application code JAR file

In this section, you upload the JAR file you created in the previous section to the Amazon Simple Storage Service (Amazon S3) bucket you created at the beginning of this tutorial. If you have not completed this step, see (link).

To upload the application code JAR file
  1. Open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. Choose the bucket you previously created for the application code.

  3. Choose Upload.

  4. Choose Add files.

  5. Navigate to the JAR file generated in the previous step: target/amazon-msf-java-stream-app-1.0.jar.

  6. Choose Upload without changing any other settings.

Warning

Make sure that youselect the correct JAR file in <repo-dir>/java/GettingStarted/target/amazon-msf-java-stream-app-1.0.jar.

The target directory also contains other JAR files that you don't need to upload.

Create and configure the Managed Service for Apache Flink application

You can create and run a Managed Service for Apache Flink application using either the console or the Amazon CLI. For this tutorial, you will use the console.

Note

When you create the application using the console, your Amazon Identity and Access Management (IAM) and Amazon CloudWatch Logs resources are created for you. When you create the application using the Amazon CLI, you create these resources separately.

Create the application

To create the application
  1. Open the Managed Service for Apache Flink console at https://console.aws.amazon.com/flink

  2. Verify that the correct Region is selected: us-east-1 US East (N. Virginia)

  3. Open the menu on the right and choose Apache Flink applications and then Create streaming application. Alternatively, choose Create streaming application in the Get started container of the initial page.

  4. On the Create streaming application page:

    • Choose a method to set up the stream processing application: choose Create from scratch.

    • Apache Flink configuration, Application Flink version: choose Apache Flink 1.19.

  5. Configure your application

    • Application name: enter MyApplication.

    • Description: enter My java test app.

    • Access to application resources: choose Create / update IAM role kinesis-analytics-MyApplication-us-east-1 with required policies.

  6. Configure your Template for application settings

    • Templates: choose Development.

  7. Choose Create streaming application at the bottom of the page.

Note

When you create a Managed Service for Apache Flink 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-east-1

  • Role: kinesisanalytics-MyApplication-us-east-1

Amazon Managed Service for Apache Flink was formerly know as Kinesis Data Analytics. The name of the resources that are automatically created are prefixeed with kinesis-analytics- for backward compatibility.

Edit the IAM policy

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

To edit the policy
  1. Open the IAM console at https://console.amazonaws.cn/iam/.

  2. Choose Policies. Choose the kinesis-analytics-service-MyApplication-us-east-1 policy that the console created for you in the previous section.

  3. Choose Edit and then 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", "s3:GetObjectVersion" ], "Resource": [ "arn:aws:s3:::my-bucket/kinesis-analytics-placeholder-s3-object" ] }, { "Sid": "ListCloudwatchLogGroups", "Effect": "Allow", "Action": [ "logs:DescribeLogGroups" ], "Resource": [ "arn:aws:logs:us-east-1:012345678901:log-group:*" ] }, { "Sid": "ListCloudwatchLogStreams", "Effect": "Allow", "Action": [ "logs:DescribeLogStreams" ], "Resource": [ "arn:aws:logs:us-east-1:012345678901:log-group:/aws/kinesis-analytics/MyApplication:log-stream:*" ] }, { "Sid": "PutCloudwatchLogs", "Effect": "Allow", "Action": [ "logs:PutLogEvents" ], "Resource": [ "arn:aws:logs:us-east-1:012345678901:log-group:/aws/kinesis-analytics/MyApplication:log-stream:kinesis-analytics-log-stream" ] }, { "Sid": "ReadInputStream", "Effect": "Allow", "Action": "kinesis:*", "Resource": "arn:aws:kinesis:us-east-1:012345678901:stream/ExampleInputStream" }, { "Sid": "WriteOutputStream", "Effect": "Allow", "Action": "kinesis:*", "Resource": "arn:aws:kinesis:us-east-1:012345678901:stream/ExampleOutputStream" } ] }
  5. Choose Next at the bottom of the page and then choose Save changes.

Configure the application

Edit the application configuration to set the application code artifact.

To edit the configuration
  1. On the MyApplication page, choose Configure.

  2. In the Application code location section:

    • For Amazon S3 bucket, select the bucket you previously created for the application code. Choose Browse and select the correct bucket, and then select Choose. Do not click on the bucket name.

    • For Path to Amazon S3 object, enter amazon-msf-java-stream-app-1.0.jar.

  3. For Access permissions, choose Create / update IAM role kinesis-analytics-MyApplication-us-east-1 with required policies.

  4. In the Runtime properties section, add the following properties.

  5. Choose Add new item and add each of the following parameters:

    Group ID Key Value
    InputStream0 stream.name ExampleInputStream
    InputStream0 aws.region us-east-1
    OutputStream0 stream.name ExampleOutputStream
    OutputStream0 aws.region us-east-1
  6. Do not modify any of the other sections.

  7. Choose Save changes.

Note

When you choose to enable Amazon CloudWatch logging, Managed Service for Apache Flink 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

Run the application

The application is now configured and ready to run.

To run the application
  1. On the console for Amazon Managed Service for Apache Flink, choose My Application and choose Run.

  2. On the next page, the Application restore configuration page, choose Run with latest snapshot and then choose Run.

    The Status in Application details transitions from Ready to Starting and then to Running when the application has started.

When the application is in the Running status, you can now open the Flink dashboard.

To open the dashboard
  1. Choose Open Apache Flink dashboard. The dashboard opens on a new page.

  2. In the Runing jobs list, choose the single job that you can see.

    Note

    If you set the Runtime properties or edited the IAM policies incorrectly, the application status might turn into Running, but the Flink dashboard shows that the jobs is continuously restarting. This is a common failure scenario if the application is misconfigured or lacks permissions to access the external resources.

    When this happens, check the Exceptions tab in the Flink dashboard to see the cause of the problem.

Observe the metrics of the running application

On the MyApplication page, in the Amazon CloudWatch metrics section, you can see some of the fundamental metrics from the running application.

To view the metrics
  1. Next to the Refresh button, select 10 seconds from the dropdown.

  2. When the application is running and healthy, you can see the uptime metric continuously increasing.

  3. The fullrestarts metric should be zero. If it is increasing, the configuration might have issues. Review the Exceptions tab on the Flink dashboard to investigate the issue.

  4. The Number of failed checkpoints metric should be zero in an healthy application.

    Note

    This dashboard displays a fixed set of metrics with a granularity of 5 minutes. You can create a custom application dashboard with any metrics in the CloudWatch dashboard.

Observe output data in Kinesis streams

Make sure you are still publishing data to the input, either using the Python script or the Kinesis Data Generator.

You can now observe the output of the application running on Managed Service for Apache Flink by using the Data Viewer in the https://console.amazonaws.cn/kinesis/, similarly to what you already did earlier.

To view the output
  1. Open the Kinesis console at https://console.amazonaws.cn/kinesis.

  2. Verify that the Region is the same as the one you are using to run this tutorial. By default, it is us-east-1US East (N. Virginia). Change the Region if necessary.

  3. Choose Data Streams.

  4. Select the stream you want to observe. For this tutorial, use ExampleOutputStream.

  5. Choose the Data viewer tab.

  6. Select any Shard, keep Latest as Starting position, and then choose Get records. You might see a "no record found for this request" error. If so, choose Retry getting records. The newest records published to the stream display.

  7. Select the value in the Data column to inspect the content of the record in JSON format.

Stop the application

To stop the applicatio, go to the console page of the Managed Service for Apache Flink application named MyApplication.

To stop the application
  1. From the Action dropdown menu, choose Stop.

  2. The Status in Application details transitions from Running to Stopping, and then to Ready when the application is completely stopped.

    Note

    Don't forget to also stop sending data to the input stream from the Python script or the Kinesis Data Generator.

Next step

Step 4: Clean up Amazon resources