How to integrate Hadoop Resource Manager with other Hadoop components

Introduction

Hadoop Resource Manager is a crucial component in the Hadoop ecosystem, responsible for managing and allocating resources across the cluster. This tutorial will guide you through the process of integrating Hadoop Resource Manager with YARN and other Hadoop components, ensuring efficient resource utilization and job scheduling in your big data environment.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL hadoop(("`Hadoop`")) -.-> hadoop/HadoopYARNGroup(["`Hadoop YARN`"]) hadoop(("`Hadoop`")) -.-> hadoop/HadoopHiveGroup(["`Hadoop Hive`"]) hadoop/HadoopYARNGroup -.-> hadoop/yarn_setup("`Hadoop YARN Basic Setup`") hadoop/HadoopYARNGroup -.-> hadoop/apply_scheduler("`Applying Scheduler`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_app("`Yarn Commands application`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_container("`Yarn Commands container`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_log("`Yarn Commands log`") hadoop/HadoopYARNGroup -.-> hadoop/yarn_jar("`Yarn Commands jar`") hadoop/HadoopYARNGroup -.-> hadoop/resource_manager("`Resource Manager`") hadoop/HadoopYARNGroup -.-> hadoop/node_manager("`Node Manager`") hadoop/HadoopHiveGroup -.-> hadoop/integration("`Integration with HDFS and MapReduce`") subgraph Lab Skills hadoop/yarn_setup -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/apply_scheduler -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/yarn_app -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/yarn_container -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/yarn_log -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/yarn_jar -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/resource_manager -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/node_manager -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} hadoop/integration -.-> lab-414986{{"`How to integrate Hadoop Resource Manager with other Hadoop components`"}} end

Hadoop Resource Manager Overview

The Hadoop Resource Manager is a key component of the Hadoop ecosystem that is responsible for managing and allocating resources across the Hadoop cluster. It is the central authority that manages the life cycle of applications, scheduling resources, and monitoring the overall health of the cluster.

The Resource Manager is part of the YARN (Yet Another Resource Negotiator) architecture, which is the resource management and job scheduling system in Hadoop 2.x and later versions. YARN separates the resource management and job scheduling/monitoring functions of the JobTracker in Hadoop 1.x, allowing Hadoop to support a wide variety of processing engines, such as Apache Spark, Apache Tez, and others, in addition to the traditional MapReduce.

The key responsibilities of the Hadoop Resource Manager include:

Resource Allocation: The Resource Manager is responsible for allocating resources (CPU, memory, disk, and network) to applications running on the cluster. It uses a pluggable scheduling algorithm to determine how to best utilize the available resources.
Application Lifecycle Management: The Resource Manager manages the life cycle of applications running on the cluster, including accepting application submissions, negotiating the execution containers, and monitoring the progress of the applications.
Cluster Monitoring: The Resource Manager monitors the overall health of the cluster, including the status of nodes, the utilization of resources, and the performance of running applications.
Security and Access Control: The Resource Manager enforces security and access control policies, ensuring that only authorized users and applications can access the cluster resources.

To interact with the Hadoop Resource Manager, you can use the YARN command-line interface (CLI) or the YARN web UI. The YARN CLI provides a set of commands for submitting, monitoring, and managing applications running on the Hadoop cluster.

Here's an example of how to submit a MapReduce job using the YARN CLI:

yarn jar /path/to/hadoop-mapreduce-examples.jar wordcount /input/path /output/path

This command submits a WordCount MapReduce job to the Hadoop cluster, with the input data located at /input/path and the output data to be written to /output/path.

The Hadoop Resource Manager plays a crucial role in the overall Hadoop ecosystem, providing a centralized and efficient way to manage and utilize the resources of the Hadoop cluster.

Integrating Hadoop Resource Manager with YARN

The Hadoop Resource Manager is tightly integrated with YARN, the resource management and job scheduling system in Hadoop 2.x and later versions. YARN provides the necessary infrastructure and APIs for the Resource Manager to effectively manage and allocate resources across the Hadoop cluster.

YARN Architecture

YARN follows a master-slave architecture, where the Resource Manager acts as the central authority for resource management and application scheduling, while the Node Managers running on each worker node are responsible for managing the resources and executing tasks on their respective nodes.

graph LR subgraph YARN RM[Resource Manager] -- Manages resources and schedules applications --> NM[Node Manager] NM -- Manages resources and executes tasks on its node --> AM[Application Master] AM -- Requests resources from RM and coordinates task execution --> Containers end

Integrating Resource Manager with YARN

The integration between the Hadoop Resource Manager and YARN is achieved through the following key components and processes:

Resource Allocation: The Resource Manager is responsible for allocating resources (CPU, memory, disk, and network) to the applications running on the cluster. It uses a pluggable scheduling algorithm, such as the default Fair Scheduler or the Capacity Scheduler, to determine the best way to utilize the available resources.
Application Submission and Lifecycle Management: When an application is submitted to the Hadoop cluster, the Resource Manager is responsible for accepting the application, negotiating the execution containers with the Node Managers, and monitoring the progress of the application.
Cluster Monitoring and Health Management: The Resource Manager continuously monitors the overall health of the cluster, including the status of nodes, the utilization of resources, and the performance of running applications. It uses this information to make informed decisions about resource allocation and application scheduling.
Security and Access Control: The Resource Manager enforces security and access control policies, ensuring that only authorized users and applications can access the cluster resources.

To configure the integration between the Hadoop Resource Manager and YARN, you can modify the relevant configuration files, such as yarn-site.xml, and set the appropriate properties, such as the Resource Manager address, the scheduling algorithm, and the resource allocation policies.

Here's an example of how to configure the Resource Manager address in the yarn-site.xml file:

<configuration>
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>resource-manager.example.com</value>
  </property>
</configuration>

By integrating the Hadoop Resource Manager with YARN, you can leverage the powerful resource management and job scheduling capabilities of YARN to efficiently utilize the resources of your Hadoop cluster and run a wide variety of applications, including MapReduce, Spark, Tez, and more.

Integrating Hadoop Resource Manager with Other Components

The Hadoop Resource Manager is not only integrated with YARN, but it also interacts with other key components in the Hadoop ecosystem to provide a comprehensive and efficient resource management solution.

Integration with Apache Spark

The Hadoop Resource Manager can be integrated with Apache Spark, a popular data processing engine, to manage the resources for Spark applications running on the Hadoop cluster. This integration allows Spark applications to leverage the resource allocation and scheduling capabilities of the Resource Manager, ensuring efficient utilization of cluster resources.

To integrate the Hadoop Resource Manager with Spark, you can configure the Spark application to use the YARN cluster manager. This can be done by setting the following properties in the spark-defaults.conf file:

spark.master                     yarn
spark.submit.deployMode          cluster
spark.yarn.resourceManager       resource-manager.example.com:8032

These settings will instruct Spark to submit its applications to the Hadoop cluster managed by the Resource Manager.

Integration with Apache Hive

The Hadoop Resource Manager can also be integrated with Apache Hive, a data warehouse infrastructure built on top of Hadoop. When Hive queries are executed, the Resource Manager can manage the resources allocated to the Hive tasks, ensuring that they are executed efficiently and without resource contention.

To integrate the Hadoop Resource Manager with Hive, you can configure the Hive execution engine to use the YARN cluster manager. This can be done by setting the following properties in the hive-site.xml file:

<property>
  <name>hive.execution.engine</name>
  <value>mr</value>
</property>
<property>
  <name>mapreduce.framework.name</name>
  <value>yarn</value>
</property>

These settings will instruct Hive to use the YARN cluster manager, which is integrated with the Hadoop Resource Manager, for executing Hive queries.

Integration with Other Components

The Hadoop Resource Manager can also be integrated with other components in the Hadoop ecosystem, such as:

Apache Kafka: The Resource Manager can manage the resources for Kafka-based applications running on the Hadoop cluster.
Apache HBase: The Resource Manager can manage the resources for HBase tables and regions.
Apache Flink: The Resource Manager can manage the resources for Flink jobs running on the Hadoop cluster.

By integrating the Hadoop Resource Manager with these and other Hadoop components, you can ensure a cohesive and efficient resource management solution for your entire Hadoop ecosystem, enabling you to run a wide variety of applications and workloads on the Hadoop cluster.

Summary

By the end of this tutorial, you will have a comprehensive understanding of how to integrate Hadoop Resource Manager with YARN and other Hadoop components, enabling you to optimize resource management and job scheduling in your Hadoop-based big data infrastructure.