Can you explain the difference between HDFS and YARN, and how they work together in a Hadoop cluster?

Explaining the Difference Between HDFS and YARN in a Hadoop Cluster

When it comes to a Hadoop cluster, two key components that play essential roles in handling and processing big data are HDFS and YARN.

What is HDFS?

HDFS, which stands for Hadoop Distributed File System, is the primary storage system used in Hadoop clusters. It is designed to store large data sets across multiple nodes in a distributed manner. HDFS distributes data across the cluster, replicating it for fault tolerance and ensuring high availability.

What is YARN?

YARN, or Yet Another Resource Negotiator, is the resource management layer in Hadoop. YARN acts as the brain of the Hadoop cluster, managing resources and scheduling tasks on the cluster. It separates the processing engine from the resource management, allowing different processing frameworks to run on the same data stored in HDFS.

How HDFS and YARN Work Together

In a Hadoop cluster, HDFS is responsible for storing and managing the data, while YARN takes care of resource management and job scheduling. When a task is submitted to the cluster, YARN allocates the necessary resources and schedules the job to run on specific nodes. YARN also monitors the job's progress and resource utilization, ensuring efficient use of cluster resources.

Overall, the synergy between HDFS and YARN in a Hadoop cluster is vital for storing, processing, and analyzing big data effectively and efficiently.

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Focus Keyword: Hadoop cluster