Flink on Yarn: A Step-by-Step Guide

Apache Flink is a distributed streaming data processing engine that enables real-time analysis and processing of data streams. Apache Yarn is a cluster resource management system that provides a framework for scheduling and managing compute resources in a Hadoop cluster. Integrating Flink with Yarn allows you to leverage the resources of your Hadoop cluster to run Flink jobs, providing scalability and fault tolerance. In this article, we will guide you through the process of setting up and running Flink on Yarn.
Step 1: Installing Apache Flink
To get started, you need to have Apache Flink installed on your system. You can download the latest version of Flink from the official Flink website or use your package manager to install it. Once you have downloaded the package, follow the installation instructions provided in the Flink documentation.
Step 2: Configuring Apache Yarn
Next, you need to configure Apache Yarn to support Flink jobs. Open the Yarn configuration file (通常是yarn-site.xml在Hadoop安装目录下), and add the following properties:

• yarn.resourcemanager.scheduler.class设置为org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler或org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler，选择适合你的集群的调度器。
• yarn.resourcemanager.resource-tracker.class设置为org.apache.hadoop.yarn.server.resourcemanager.resourcechecker.ResourceTrackerService。
• yarn.nodemanager.aux-services添加mapreduce_shuffle和flink_shuffle。
  Step 3: Configuring Apache Flink
  Now, you need to configure Flink to run on Yarn. Open the Flink configuration file (通常是flink-conf.yaml在Flink安装目录下), and add the following properties:
• jobmanager.rpc.address设置为Yarn ResourceManager的主机名或IP地址。
• jobmanager.rpc.port设置为Yarn ResourceManager的RPC端口，默认为8032。
• taskmanager.numberOfTaskSlots设置为每个TaskManager的槽位数，根据你集群的配置进行设置。
• parallelism.default设置为默认的并行度。
  Step 4: Running Flink on Yarn
  Once you have completed the configuration, you can run Flink on Yarn by submitting your Flink job to Yarn’s ResourceManager using the following command:

  flink submit --target yarn --jar your-flink-job.jar

  This command will submit your Flink job to Yarn’s ResourceManager for execution on the cluster.
  Step 5: Using Flink CDC with Flink on Yarn
  Flink CDC (Change Data Capture) allows you to capture and process changes in data streams from external databases in real-time. To use Flink CDC with Flink on Yarn, you need to include the appropriate CDC connector in your Flink job and configure it accordingly.
  For example, if you want to capture changes from a MySQL database, you can use the MySQL CDC connector provided by Debezium. You can add the connector as a dependency in your Flink job’s pom.xml file or include it in your job JAR file.
  Once you have included the CDC connector, you can create a Flink datastream from the CDC source and apply any desired transformations or processing logic.
  Remember to set up any required database authentication credentials and other relevant configurations in your Flink job.
  That’s it! You have successfully set up and run Apache Flink on Apache Yarn with Flink CDC integration.
  In this article, we walked through the steps of installing, configuring, and running Flink on Yarn, as well as demonstrating how to use Flink CDC with Flink on Yarn.
  Now that you have completed these steps, you can start developing and deploying your own Flink jobs on your Hadoop cluster using Yarn as the resource management system.
  Feel free to explore more advanced concepts such as fault tolerance, state