Create and run a Managed Service for Apache Flink application - Managed Service for Apache Flink
Create dependent resourcesSet up your local development environmentDownload and examine the Apache Flink streaming Java codeRun your application locallyObserve the application writing data to an S3 bucketStop your application running locallyCompile and package your application codeUpload the application code JAR fileCreate and configure the Managed Service for Apache Flink application
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.

Create and run a Managed Service for Apache Flink application

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

Create dependent resources

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

  • An Amazon S3 bucket to store the application's code and to write the application output.

    Note

    This tutorial assumes that you are deploying your application in the us-east-1 Region. If you use another Region, you must adapt all steps accordingly.

Create an Amazon S3 bucket

You can create the Amazon S3 bucket using the console. For instructions for creating this resource, see the following topics:

  • How Do I Create an S3 Bucket? in the Amazon Simple Storage Service User Guide. Give the Amazon S3 bucket a globally unique name by appending your login name.

    Note

    Make sure that you create the bucket in the Region you use for this tutorial. The default for the tutorial is us-east-1.

Other resources

When you create your application, Managed Service for Apache Flink 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 as normal Java dependencies using 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 Set up the Amazon Command Line Interface (Amazon CLI).

  2. 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 compiling the application or running Eclipse.

Download and examine the Apache Flink streaming Java code

The application code for this example is available from GitHub.

To download the Java application code

  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 ./java/GettingStartedTable directory.

Review application components

The application is entirely implemented in the com.amazonaws.services.msf.BasicTableJob class. The main() method defines sources, transformations, and sinks. The execution is initiated by an execution statement at the end of this method.

Note

For an optimal 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 that 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 use with the DataStream API, and the StreamTableEnvironment to use 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();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, EnvironmentSettings.newInstance().build());

    • 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 FileSystem sink connector that the application uses to write results to Amazon S3 output files when Flink completes a checkpoint. You must enable checkpoints to write files to the destination. When the application is running in Amazon Managed Service for Apache Flink, the application configuration controls the checkpoint and enables it by default. Conversely, when running locally, checkpoints are disabled by default. The application detects that it runs locally and configures checkpointing every 5,000 ms. 

       if (env instanceof LocalStreamEnvironment) {
    env.enableCheckpointing(5000);
 }

    • This application does not receive data from an actual external source. It generates random data to process through the DataGen connector. This connector is available for DataStream API, SQL, and Table API. To demonstrate the integration between APIs, the application uses the DataStram API version because it provides more flexibility. Each record is generated by a generator function called StockPriceGeneratorFunction in this case, where you can put custom logic. 

      DataGeneratorSource<StockPrice> source = new DataGeneratorSource<>(
        new StockPriceGeneratorFunction(),
        Long.MAX_VALUE,
        RateLimiterStrategy.perSecond(recordPerSecond),
        TypeInformation.of(StockPrice.class));

    • In the DataStream API, records can have custom classes. Classes must follow specific rules so that Flink can use them as record. For more information, see Supported Data Types. In this example, the StockPrice class is a POJO.

    • The source is then attached to the execution environment, generating a DataStream of StockPrice. This application doesn't use event-time semantics and doesn't generate a watermark. Run the DataGenerator source with a parallelism of 1, independent of the parallelism of the rest of the application.

      DataStream<StockPrice> stockPrices = env.fromSource(
        source, 
        WatermarkStrategy.noWatermarks(),
        "data-generator"
    ).setParallelism(1);

    • What follows in the data processing flow is defined using the Table API and SQL. To do so, we convert the DataStream of StockPrices into a table. The schema of the table is automatically inferred from the StockPrice class. 

      Table stockPricesTable = tableEnv.fromDataStream(stockPrices);

    • The following snippet of code shows how to define a view and a query using the programmatic Table API:

      Table filteredStockPricesTable = stockPricesTable.
        select(
                $("eventTime").as("event_time"),
                $("ticker"),
                $("price"),
                dateFormat($("eventTime"), "yyyy-MM-dd").as("dt"),
                dateFormat($("eventTime"), "HH").as("hr")
        ).where($("price").isGreater(50));
        
tableEnv.createTemporaryView("filtered_stock_prices", filteredStockPricesTable);

    • A sink table is defined to write the results to an Amazon S3 bucket as JSON files. To illustrate the difference with defining a view programmatically, with the Table API the sink table is defined using SQL.

      tableEnv.executeSql("CREATE TABLE s3_sink (" +
            "eventTime TIMESTAMP(3)," +
            "ticker STRING," +
            "price DOUBLE," +
            "dt STRING," +
            "hr STRING" +
        ") PARTITIONED BY ( dt, hr ) WITH (" +
        "'connector' = 'filesystem'," +
        "'fmat' = 'json'," +
        "'path' = 's3a://"  + s3Path + "'" +
        ")");

    • The last step of the is an executeInsert() that inserts the filtered stock prices view into the sink table. This method initiates the execution of the data flow we have defined so far. 

      filteredStockPricesTable.executeInsert("s3_sink");

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 application or to the application locally in your IDE. For more information, see (update to TableAPI) 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. To use the TableAPI and SQL, you must include the flink-table-planner-loader and flink-table-runtime-dependencies, both with provided scope. 

    <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java</artifactId>
    <version>${flink.version}</version>
    <scope>provided</scope>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-clients</artifactId>
    <version>${flink.version}</version>
    <scope>provided</scope>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-table-planner-loader</artifactId>
    <version>${flink.version}</version>
    <scope>provided</scope>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-table-runtime</artifactId>
    <version>${flink.version}</version>
    <scope>provided</scope>
</dependency>

  • You must add additional Apache Flink dependencies to the pom with the default scope. For example, the DataGen connector, the FileSystem SQL connector, and the JSON format. 

    <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-datagen</artifactId>
    <version>${flink.version}</version>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-files</artifactId>
    <version>${flink.version}</version>
</dependency>
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-json</artifactId>
    <version>${flink.version}</version>
</dependency>

  • To write to Amazon S3 when running locally, the S3 Hadoop File System is also included wit provided scope.

    <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-s3-fs-hadoop</artifactId>
    <version>${flink.version}</version>
    <scope>provided</scope>
</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 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>

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 an 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/GettingStartedTable subdirectory into your IDE .

Insert the code as an existing Java project using Maven.

Note

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

Modify 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. For this tutorial application, the configuration parameters are the name of the bucket and the path where the data will be written.

Edit the configuration and modify the name of the Amazon S3 bucket to match the bucket that you created at the beginning of this tutorial.

[
 {
 "PropertyGroupId": "bucket",
 "PropertyMap": {
 "name": "<bucket-name>",
 "path": "output"
 }
 }
]
Note

The configuration property name must contain only the bucket name, for example my-bucket-name. Don't include any prefix such as s3:// or a trailing slash.

If you modify the path, omit any leading or trailing slashes.

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.BasicTableJob, as you would run any Java application. Before running the application, you must set up the Run configuration. The setup depends on the IDE that 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 BasicTableJob, 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.

21:28:34,982 INFO  com.amazonaws.services.msf.BasicTableJob                     
                [] - Loading application properties from 'flink-application-properties-dev.json'
21:28:35,149 INFO  com.amazonaws.services.msf.BasicTableJob                     
[] - s3Path is ExampleBucket/my-output-bucket
...

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 content of the output bucket, 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 the application writing data to an S3 bucket

This example application generates random data internally and writes this data to the destination S3 bucket you configured. Unless you modified the default configuration path, the data will be written to the output path followed by data and hour partitioning, in the format ./output/<yyyy-MM-dd>/<HH>.

The FileSystem sink connector creates new files on the Flink checkpoint. When running locally, the application runs a checkpoint every 5 seconds (5,000 milliseconds), as specified in the code. 

 if (env instanceof LocalStreamEnvironment) {
    env.enableCheckpointing(5000);
 }
To browse the S3 bucket and observe the file written by the application

    1. Open the Amazon S3 console at https://console.amazonaws.cn/s3/.

  2. Choose the bucket you previously created.

  3. Navigate to the output path, and then to the date and hour folders that correspond to the current time in the UTC time zone.

  4. Periodically refresh to observe new files appearing every 5 seconds.

  5. Select and download one file to observe the content.

    Note

    By default, the files have no extensions. The content is formatted as JSON. You can open the files with any text editor to inspect the content.

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 that contains the Jave 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 JAR file target/amazon-msf-java-table-app-1.0.jar is created.

Note

Running a 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 S3 bucket you created at the beginning of this tutorial. If you have done it yet, complete Create an Amazon S3 bucket.

To upload the application code

  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 field.

  4. Choose Add files.

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

  6. Choose Upload without changing any other settings.

    Warning

    Make sure that you select the correct JAR file in <repo-dir>/java/GettingStarted/target/amazon/msf-java-table-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 configure 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 must create these resources separately.

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 (N. Virginia) us-east-1.

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

  4. On the Create streaming application page, complete the following:

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

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

    • In the Application configuration section, complete the following:

      • For Application name, enter MyApplication.

      • For Description, enter My Java Table API test app.

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

    • In Template for application settings, complete the following:

      • For Templates, choose Develoment.

  5. Choose Create streaming application.

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

Edit the IAM policy

Edit the IAM policy to add permissions to access the Amazon S3 bucket.

To edit the IAM policy to add S3 bucket permissions

  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 ID (012345678901) with your account ID and <bucket-name> with the name of the S3 bucket that you created.

    {
    "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": "WriteOutputBucket", 
            "Effect": "Allow", 
            "Action": "s3:*", 
            Resource": [
                "arn:aws:s3:::my-bucket"
            ]
       }
    ]
}

  5. Choose Next and then choose Save changes.

Configure the application

Edit the application to set the application code artifact.

To configure the application

  1. On the MyApplication page, choose Configure.

  2. In the Aplication code location section, choose Configure.

    • For Amazon S3 bucket, select the bucket you previously created for the application code. Choose Browse and select the correct bucket, and then choose Choose. Don't click on the bucket name.

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

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

  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
    bucket name your-bucket-name
    bucket path output

  6. Don't modify any other setting.

  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. Return to the console page in Amazon Managed Service for Apache Flink and choose MyApplication.

  2. Choose Run to start the application.

  3. On the Application restore configuration, choose Run with latest snapshot.

  4. Choose Run.

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

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

To open the dashboard and view the job

  1. Choose Open Apache Flink dashbard. The dashboard opens in a new page.

  2. In the Running Jobs list, choose the single job you can see.

    Note

    If you set the runtime properties or edited the IAM policies incorrectly, the application status might change to Running, but the Flink dashboard shows the job continuously restarting. This is a common failure scenario when the application is misconfigured or lacks the permissions to access the external resources.

    When this happens, check the Exceptions tab in the Flink dashboard to investigate 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 list.

  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 a 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 the application writing data to the destination bucket

You can now observe the application running in Amazon Managed Service for Apache Flink writing files to Amazon S3.

To observe the files, follow the same process you used to check the files being written when the application was running locally. See Observe the application writing data to an S3 bucket.

Remember that the application writes new files on the Flink checkpoint. When running on Amazon Managed Service for Apache Flink, checkpoints are enabled by default and run every 60 seconds. The application creates new files approximately every 1 minute.

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 list, 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.