Introduction to YARN (Yet Another Resource Negotiator)

What is the Yarn (Yet Another Resource Negotiator)? IT Glossary.

In the world of big data, managing resources efficiently is crucial for the success of any organization. This is where YARN, or Yet Another Resource Negotiator, comes into play. YARN is a key component of Apache Hadoop, an open-source framework that allows for the distributed processing of large datasets across clusters of computers.

YARN was introduced in Hadoop 2.0 as a replacement for the previous resource management system, known as MapReduce. While MapReduce was primarily designed for batch processing, YARN provides a more flexible and scalable solution that can handle a wide range of workloads.

At its core, YARN is responsible for managing and allocating resources in a Hadoop cluster. It acts as a middle layer between the applications and the cluster’s resources, ensuring that each Application gets the necessary resources to run efficiently. This resource management is achieved through a two-level architecture consisting of a global ResourceManager and per-application ApplicationMaster.

The global ResourceManager is the central authority in the cluster that manages the allocation of resources. It keeps track of available resources in the cluster and assigns them to different applications based on their requirements. The ResourceManager also monitors the health of the cluster and handles failover scenarios to ensure uninterrupted operation.

On the other hand, the ApplicationMaster is responsible for managing the resources allocated to a specific application. It negotiates with the ResourceManager to obtain the necessary resources and then works with the NodeManagers, which are responsible for managing resources on individual nodes, to execute the application tasks. The ApplicationMaster also monitors the progress of the application and reports back to the ResourceManager.

One of the key advantages of YARN is its ability to support multiple types of workloads. While MapReduce was primarily designed for batch processing, YARN can handle a variety of workloads, including real-time streaming, interactive queries, and graph processing. This flexibility allows organizations to use Hadoop for a wide range of applications, making it a more versatile and valuable tool.

Another important feature of YARN is its scalability. YARN can handle thousands of nodes and tens of thousands of applications, making it suitable for large-scale deployments. It also supports dynamic resource allocation, allowing resources to be allocated and deallocated based on the workload requirements. This dynamic allocation ensures that resources are utilized efficiently and that applications can scale up or down as needed.

In conclusion, YARN is a critical component of Apache Hadoop that provides efficient resource management for big data processing. Its two-level architecture, consisting of a global ResourceManager and per-application ApplicationMaster, ensures that resources are allocated and managed effectively. With its support for multiple workloads and scalability, YARN enables organizations to harness the power of Hadoop for a wide range of applications.

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