Hadoop is an open-source software framework for storing and processing large datasets on clusters of commodity hardware. It includes a scheduler that manages the allocation of resources, such as CPU and memory, to the tasks running on the cluster.

There are two types of schedulers in Hadoop:

  1. Fair Scheduler: It allocates resources based on a fair sharing algorithm, which aims to balance the resource usage among all the running applications.
  2. Capacity Scheduler: It allocates resources based on a hierarchical queue structure, where each queue is associated with a certain capacity of resources, and the allocation of resources is done based on the capacity of the queue.

Both schedulers can be configured to support different resource allocation policies and can be used based on the specific requirements of the application and the cluster.

An example of how the Fair Scheduler and Capacity Scheduler might be used in a Hadoop cluster is as follows:

  • Fair Scheduler: Let's say you have a Hadoop cluster that is used by multiple teams for different projects. Each team has its own set of jobs that need to be run on the cluster. Using the Fair Scheduler, you can configure the cluster to allocate resources to each team's jobs based on a fair sharing algorithm. This ensures that each team gets a fair share of the resources, regardless of the size of their job or the number of jobs they have running.
  • Capacity Scheduler: Let's say you have a Hadoop cluster that is used by multiple departments in an organization. Each department has its own set of jobs that need to be run on the cluster. You can configure the cluster to use the Capacity Scheduler and create different queues for each department. Each queue is associated with a certain capacity of resources, and the allocation of resources is done based on the capacity of the queue. This ensures that each department gets the resources they need, while also ensuring that the resources are used efficiently, and no single department monopolizes the cluster.

Please note that these are examples, and that the actual configuration would be different depending on the specific requirements of the application and the cluster.

 

An example of how the Fair Scheduler and Capacity Scheduler might be configured in a Hadoop cluster is as follows:

  • Fair Scheduler: To configure the Fair Scheduler, you need to set the following parameters in the mapred-site.xml configuration file:
<property>
  <name>mapreduce.jobtracker.taskScheduler</name>
  <value>org.apache.hadoop.mapred.FairScheduler</value>
</property>
<property>
  <name>mapreduce.fairscheduler.allocation.file</name>
  <value>/etc/hadoop/fair-scheduler.xml</value>
</property>

The mapreduce.fairscheduler.allocation.file parameter specifies the location of the fair scheduler configuration file, where you can specify the pools and the allocation rules for each pool.

  • Capacity Scheduler: To configure the Capacity Scheduler, you need to set the following parameters in the mapred-site.xml configuration file:
<property>
  <name>mapreduce.jobtracker.taskScheduler</name>
  <value>org.apache.hadoop.mapred.CapacityTaskScheduler</value>
</property>
<property>
  <name>mapreduce.jobtracker.capacity-scheduler.config-file</name>
  <value>/etc/hadoop/capacity-scheduler.xml</value>
</property>

The mapreduce.jobtracker.capacity-scheduler.config-file parameter specifies the location of the capacity scheduler configuration file, where you can specify the queues and the capacity of each queue.

Please note that these are just examples, and the actual configuration of the scheduler will depend on the specific requirements of the application and the cluster. Also, you need to make sure that the Scheduler configuration is compatible with your Hadoop version and other configurations.

 

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