Configuring Flink in Amazon EMR - Amazon EMR
Hive and GlueConfig fileMultiple primary nodesMemory process sizeLog output file sizeJava 11
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Configuring Flink in Amazon EMR

Amazon EMR releases 6.9.0 and higher support both Hive Metastore and Amazon Glue Catalog with the Apache Flink connector to Hive. This section outlines the steps required to configure Amazon Glue Catalog and Hive Metastore with Flink.

  1. Create an EMR cluster with release 6.9.0 or higher and at least two applications: Hive and Flink.

  2. Use script runner to execute the following script as a step function:

    hive-metastore-setup.sh

    sudo cp /usr/lib/hive/lib/antlr-runtime-3.5.2.jar /usr/lib/flink/lib 
sudo cp /usr/lib/hive/lib/hive-exec-3.1.3*.jar /lib/flink/lib 
sudo cp /usr/lib/hive/lib/libfb303-0.9.3.jar /lib/flink/lib 
sudo cp /usr/lib/flink/opt/flink-connector-hive_2.12-1.15.2.jar /lib/flink/lib
sudo chmod 755 /usr/lib/flink/lib/antlr-runtime-3.5.2.jar 
sudo chmod 755 /usr/lib/flink/lib/hive-exec-3.1.3*.jar 
sudo chmod 755 /usr/lib/flink/lib/libfb303-0.9.3.jar
sudo chmod 755 /usr/lib/flink/lib/flink-connector-hive_2.12-1.15.2.jar
    Form to add a Custom JAR step with fields for step type, name, JAR location, arguments, and failure action.

  1. Create an EMR cluster with release 6.9.0 or higher and at least two applications: Hive and Flink.

  2. Select Use for Hive table metadata in the Amazon Glue Data Catalog settings to enable Data Catalog in the cluster.

  3. Use script runner to execute the following script as a step function: Run commands and scripts on an Amazon EMR cluster:

    glue-catalog-setup.sh 

    sudo cp /usr/lib/hive/auxlib/aws-glue-datacatalog-hive3-client.jar /usr/lib/flink/lib 
sudo cp /usr/lib/hive/lib/antlr-runtime-3.5.2.jar /usr/lib/flink/lib 
sudo cp /usr/lib/hive/lib/hive-exec-3.1.3*.jar /lib/flink/lib 
sudo cp /usr/lib/hive/lib/libfb303-0.9.3.jar /lib/flink/lib 
sudo cp /usr/lib/flink/opt/flink-connector-hive_2.12-1.15.2.jar /lib/flink/lib
sudo chmod 755 /usr/lib/flink/lib/aws-glue-datacatalog-hive3-client.jar 
sudo chmod 755 /usr/lib/flink/lib/antlr-runtime-3.5.2.jar 
sudo chmod 755 /usr/lib/flink/lib/hive-exec-3.1.3*.jar 
sudo chmod 755 /usr/lib/flink/lib/libfb303-0.9.3.jar
sudo chmod 755 /usr/lib/flink/lib/flink-connector-hive_2.12-1.15.2.jar
    Form to add a Custom JAR step with fields for step type, name, JAR location, arguments, and failure action.

You can use the Amazon EMR configuration API to configure Flink with a configuration file. The files that are configurable within the API are:

  • flink-conf.yaml

  • log4j.properties

  • flink-log4j-session

  • log4j-cli.properties

The main configuration file for Flink is flink-conf.yaml.

To configure the number of task slots that are used for Flink from the Amazon CLI

  1. Create a file, configurations.json, with the following content:

    [
    {
      "Classification": "flink-conf",
      "Properties": {
        "taskmanager.numberOfTaskSlots":"2"
      }
    }
]

  2. Next, create a cluster with the following configuration:

    aws emr create-cluster --release-label emr-7.6.0 \
--applications Name=Flink \
--configurations file://./configurations.json \
--region us-east-1 \
--log-uri s3://myLogUri \
--instance-type m5.xlarge \
--instance-count 2 \
--service-role EMR_DefaultRole_V2 \ 
--ec2-attributes KeyName=YourKeyName,InstanceProfile=EMR_EC2_DefaultRole
Note

You can also change some configurations with the Flink API. For more information, see Concepts in the Flink documentation.

With Amazon EMR version 5.21.0 and later, you can override cluster configurations and specify additional configuration classifications for each instance group in a running cluster. You do this by using the Amazon EMR console, the Amazon Command Line Interface (Amazon CLI), or the Amazon SDK. For more information, see Supplying a Configuration for an Instance Group in a Running Cluster.

As the owner of your application, you know best what resources to assign to tasks within Flink. For the examples in this documentation, use the same number of tasks as the tasks instances that you use for the application. We generally recommend this for the initial level of parallelism, but you can also increase the granularity of parallelism with task slots, which should generally not exceed the number of virtual cores per instance. For more information about the Flink architecture, see Concepts in the Flink documentation.

The JobManager of Flink remains available during the primary node failover process in an Amazon EMR cluster with multiple primary nodes. Beginning with Amazon EMR 5.28.0, JobManager high availability is also enabled automatically. No manual configuration is needed.

With Amazon EMR versions 5.27.0 or earlier, the JobManager is a single point of failure. When the JobManager fails, it loses all job states and will not resume the running jobs. You can enable JobManager high availability by configuring application attempt count, checkpointing, and enabling ZooKeeper as state storage for Flink, as the following example demonstrates:

[
  {
    "Classification": "yarn-site",
    "Properties": {
      "yarn.resourcemanager.am.max-attempts": "10"
    }
  },
  {
    "Classification": "flink-conf",
    "Properties": {
        "yarn.application-attempts": "10",
        "high-availability": "zookeeper",
        "high-availability.zookeeper.quorum": "%{hiera('hadoop::zk')}",
        "high-availability.storageDir": "hdfs:///user/flink/recovery",
        "high-availability.zookeeper.path.root": "/flink"
    }
  }
]

You must configure both maximum application master attempts for YARN and application attempts for Flink. For more information, see Configuration of YARN cluster high availability. You may also want to configure Flink checkpointing to make restarted JobManager recover running jobs from previously completed checkpoints. For more information, see Flink checkpointing.

For Amazon EMR versions that use Flink 1.11.x, you must configure the total memory process size for both JobManager (jobmanager.memory.process.size) and TaskManager (taskmanager.memory.process.size) in flink-conf.yaml. You can set these values by either configuring the cluster with the configuration API or manually uncommenting these fields via SSH. Flink provides the following default values.

  • jobmanager.memory.process.size: 1600m

  • taskmanager.memory.process.size: 1728m

To exclude JVM metaspace and overhead, use the total Flink memory size (taskmanager.memory.flink.size) instead of taskmanager.memory.process.size. The default value for taskmanager.memory.process.size is 1280m. It's not recommended to set both taskmanager.memory.process.size and taskmanager.memory.process.size.

All Amazon EMR versions that use Flink 1.12.0 and later have the default values listed in the open-source set for Flink as the default values on Amazon EMR, so you don't need to configure them yourself.

Flink application containers create and write to three types of log files: .out files, .log files, and .err files. Only .err files are compressed and removed from the file system, while .log and .out log files remain in the file system. To ensure these output files remain manageable and the cluster remains stable, you can configure log rotation in log4j.properties to set a maximum number of files and limit their sizes.

Amazon EMR versions 5.30.0 and later

Starting with Amazon EMR 5.30.0, Flink uses the log4j2 logging framework with the configuration classification name flink-log4j. The following example configuration demonstrates the log4j2 format.

[
  {
    "Classification": "flink-log4j",
    "Properties": {
      "appender.main.name": "MainAppender",
      "appender.main.type": "RollingFile",
      "appender.main.append" : "false",
      "appender.main.fileName" : "${sys:log.file}",
      "appender.main.filePattern" : "${sys:log.file}.%i",
      "appender.main.layout.type" : "PatternLayout",
      "appender.main.layout.pattern" : "%d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n",
      "appender.main.policies.type" : "Policies",
      "appender.main.policies.size.type" : "SizeBasedTriggeringPolicy",
      "appender.main.policies.size.size" : "100MB",
      "appender.main.strategy.type" : "DefaultRolloverStrategy",
      "appender.main.strategy.max" : "10"
    },
  }
]

Amazon EMR versions 5.29.0 and earlier

With Amazon EMR versions 5.29.0 and earlier, Flink uses the log4j logging framework. The following example configuration demonstrates the log4j format.

[
  {
    "Classification": "flink-log4j",
    "Properties": {
      "log4j.appender.file": "org.apache.log4j.RollingFileAppender",
      "log4j.appender.file.append":"true",
      # keep up to 4 files and each file size is limited to 100MB
      "log4j.appender.file.MaxFileSize":"100MB",
      "log4j.appender.file.MaxBackupIndex":4,
      "log4j.appender.file.layout":"org.apache.log4j.PatternLayout",
      "log4j.appender.file.layout.ConversionPattern":"%d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n"
    },
  }
]

Amazon EMR releases 6.12.0 and higher provide Java 11 runtime support for Flink. The following sections describe how to configure the cluster to provide Java 11 runtime support for Flink.

Use the following steps to create an EMR cluster with Flink and Java 11 runtime. The configuration file where you add Java 11 runtime support is flink-conf.yaml.

Console
To create a cluster with Flink and Java 11 runtime in the console

  1. Sign in to the Amazon Web Services Management Console, and open the Amazon EMR console at https://console.amazonaws.cn/emr.

  2. Choose Clusters under EMR on EC2 in the navigation pane, and then Create cluster.

  3. Select Amazon EMR release 6.12.0 or higher, and choose to install the Flink application. Select any other applications that you want to install on your cluster.

  4. Continue setting up your cluster. In the optional Software settings section, use the default Enter configuration option and enter the following configuration:

    [
    {
      "Classification": "flink-conf",
      "Properties": {
        "containerized.taskmanager.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
        "containerized.master.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
        "env.java.home":"/usr/lib/jvm/jre-11"
      }
    }
]

  5. Continue to set up and launch your cluster.

Amazon CLI
To create a cluster with Flink and Java 11 runtime from the CLI

  1. Create a configuration file configurations.jsonthat configures Flink to use Java 11. 

    [
    {
      "Classification": "flink-conf",
      "Properties": {
        "containerized.taskmanager.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
        "containerized.master.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
        "env.java.home":"/usr/lib/jvm/jre-11"
      }
    }
]

  2. From the Amazon CLI, create a new EMR cluster with Amazon EMR release 6.12.0 or higher, and install the Flink application, as shown in the following example:

    aws emr create-cluster --release-label emr-6.12.0 \ 
--applications Name=Flink \ 
--configurations file://./configurations.json \ 
--region us-east-1 \ 
--log-uri s3://myLogUri \ 
--instance-type m5.xlarge \ 
--instance-count 2 \ 
--service-role EMR_DefaultRole_V2 \ 
--ec2-attributes KeyName=YourKeyName,InstanceProfile=EMR_EC2_DefaultRole

Use the following steps to update a running EMR cluster with Flink and Java 11 runtime. The configuration file where you add Java 11 runtime support is flink-conf.yaml.

Console
To update a running cluster with Flink and Java 11 runtime in the console

  1. Sign in to the Amazon Web Services Management Console, and open the Amazon EMR console at https://console.amazonaws.cn/emr.

  2. Choose Clusters under EMR on EC2 in the navigation pane, and then select the cluster that you want to update.

    Note

    The cluster must use Amazon EMR release 6.12.0 or higher to support Java 11.

  3. Select the Configurations tab.

  4. In the Instance group configurations section, select the Running instance group that you want to update and then choose Reconfigure from the list actions menu.

  5. Reconfigure the instance group with the Edit attributes option as follows. Select Add new configuration after each one.

    Classification Property Value

    flink-conf

    containerized.taskmanager.env.JAVA_HOME

    /usr/lib/jvm/jre-11

    flink-conf

    containerized.master.env.JAVA_HOME

    /usr/lib/jvm/jre-11

    flink-conf

    env.java.home

    /usr/lib/jvm/jre-11

  6. Select Save changes to add the configurations.

Amazon CLI
To update a running cluster to use Flink and Java 11 runtime from the CLI

Use the modify-instance-groups command to specify a new configuration for an instance group in a running cluster.

  1. First, create a configuration file configurations.jsonthat configures Flink to use Java 11. In the following example, replace ig-1xxxxxxx9 with the ID for the instance group that you want to reconfigure. Save the file in the same directory where you will run the modify-instance-groups command.

    [
   {
      "InstanceGroupId":"ig-1xxxxxxx9",
      "Configurations":[
         {
            "Classification":"flink-conf",
            "Properties":{
              "containerized.taskmanager.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
              "containerized.master.env.JAVA_HOME":"/usr/lib/jvm/jre-11",
              "env.java.home":"/usr/lib/jvm/jre-11"
            },
            "Configurations":[]
         }
      ]
   }
]

  2. From the Amazon CLI, run the following command. Replace the ID for the instance group that you want to reconfigure:

    aws emr modify-instance-groups --cluster-id j-2AL4XXXXXX5T9 \
--instance-groups file://configurations.json

To determine the Java runtime for a running cluster, log in to the primary node with SSH as described in Connect to the primary node with SSH. Then run the following command:

ps -ef | grep flink

The ps command with the -ef option lists all running processes on the system. You can filter that output with grep to find mentions of the string flink. Review the output for the Java Runtime Environment (JRE) value, jre-XX. In the following output, jre-11 indicates that Java 11 is picked up at runtime for Flink.

flink    19130     1  0 09:17 ?        00:00:15 /usr/lib/jvm/jre-11/bin/java -Djava.io.tmpdir=/mnt/tmp -Dlog.file=/usr/lib/flink/log/flink-flink-historyserver-0-ip-172-31-32-127.log -Dlog4j.configuration=file:/usr/lib/flink/conf/log4j.properties -Dlog4j.configurationFile=file:/usr/lib/flink/conf/log4j.properties -Dlogback.configurationFile=file:/usr/lib/flink/conf/logback.xml -classpath /usr/lib/flink/lib/flink-cep-1.17.0.jar:/usr/lib/flink/lib/flink-connector-files-1.17.0.jar:/usr/lib/flink/lib/flink-csv-1.17.0.jar:/usr/lib/flink/lib/flink-json-1.17.0.jar:/usr/lib/flink/lib/flink-scala_2.12-1.17.0.jar:/usr/lib/flink/lib/flink-table-api-java-uber-1.17.0.jar:/usr/lib/flink/lib/flink-table-api-scala-bridge_2.12-1.17.0.

Alternatively, log in to the primary node with SSH and start a Flink YARN session with command flink-yarn-session -d. The output shows the Java Virtual Machine (JVM) for Flink, java-11-amazon-corretto in the following example:

2023-05-29 10:38:14,129 INFO  org.apache.flink.configuration.GlobalConfiguration           [] - Loading configuration property: containerized.master.env.JAVA_HOME, /usr/lib/jvm/java-11-amazon-corretto.x86_64