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Cluster Resource Management

Managing Hadoop/YARN cluster resources

Learn how to manage resources effectively on a Hadoop/YARN cluster, including advanced scheduling techniques and deployment strategies.

5 minutes 5 Questions

Cluster Resource Management in Big Data engineering involves efficiently allocating and monitoring computational resources across distributed systems to optimize performance and cost. It's essential for handling large-scale data processing workloads. At its core, Cluster Resource Management system…

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Big Data Engineer - Cluster Resource Management Example Questions

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Question 1

What is the difference between a block and a container in Hadoop?

A block is a physical set of data stored in HDFS, while a container is a logical entity managed by the NameNode to allocate resources to YARN applications. A block is a physical set of data stored in HDFS, while a container is a physical entity managed by the ResourceManager to allocate resources to Hadoop jobs. A block is a logical set of data stored in HDFS, while a container is a logical entity managed by the ResourceManager to allocate resources to YARN applications. A block is a logical set of data stored in HDFS, while a container is a physical entity managed by the NameNode to allocate resources to Hadoop jobs. A block and a container are the same thing in Hadoop. A block is a physical set of data stored in HDFS, while a container is a logical entity managed by the NodeManager to allocate resources to YARN applications.

Correct Answer: A block is a physical set of data stored in HDFS, while a container is a logical entity managed by the NodeManager to allocate resources to YARN applications.

A block is a physical set of data stored in HDFS, typically with a default size of 128 MB or 256MB, while a container is a logical entity managed by the NodeManager to allocate resources to YARN applications running on a specific node, such as memory and CPU cores.

Question 2

What is the purpose of the TaskTracker in Hadoop MapReduce?

To run tasks and report status information to the JobTracker To manage resources in the Hadoop cluster To allocate resources to applications in YARN To manage storage on DataNodes To execute queries in Hive

Correct Answer: To run tasks and report status information to the JobTracker

TaskTracker is responsible for running tasks of a job and reporting status information to the JobTracker. DataNodes store data blocks in HDFS, Hive is used for data warehousing, YARN is responsible for resource allocation, ResourceManager manages resources in the Hadoop cluster and NodeManager manages resources for a single node in the cluster.

Question 3

What is the maximum number of inputs that can be specified for a Hadoop MapReduce job?

10000 1000 10 No limit 100

Correct Answer: No limit

There is no limit on the number of inputs that can be specified for a Hadoop MapReduce job. A job can have any number of input paths represented as comma-separated path strings or glob patterns.

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