WalzoneInterview Prep
📞 Interviewing soon? Practice with a realistic AI mock phone interview — it calls you, then scores you. First 15 min FREE →

Hadoop & Big Data · Introduction to YARN · question 44 of 120

What is the difference between MapReduce and YARN?

📕 Buy this interview preparation book: 120 Hadoop & Big Data questions & answers — PDF + EPUB for $5

MapReduce and YARN are both essential components of the Hadoop ecosystem, but they serve different purposes and have distinct functionalities.

MapReduce is a programming model and a data processing framework used to process large datasets in parallel across a Hadoop cluster. It has two main components: the Map function and the Reduce function.

- Map function: This takes input data, processes it, and generates intermediate key-value pairs.

- Reduce function: This takes intermediate key-value pairs generated by the Map function, processes them, and generates the final output.

YARN (Yet Another Resource Negotiator) is a resource management layer of Hadoop that was introduced in Hadoop 2.0. The primary goal of YARN is to allocate resources and manage the scheduling of applications running on top of Hadoop. YARN is responsible for managing resources such as CPU, memory, and storage across the cluster nodes, and it makes these resources available to different distributed applications running on Hadoop. YARN has the following main components:

- ResourceManager: This component is responsible for managing the resources in the cluster and allocating them to different applications.

- NodeManager: NodeManagers run on each node in the Hadoop cluster and are responsible for monitoring resource usage on individual machines and reporting this information to the ResourceManager.

- ApplicationMaster: Each application running on the Hadoop cluster has its ApplicationMaster, which is responsible for requesting resources from the ResourceManager and managing the execution of the application-specific tasks.

In summary, here’s the difference between MapReduce and YARN:

MapReduce:

- A programming model for parallel processing of large datasets

- Consists of ‘Map‘ and ‘Reduce‘ functions

- Processes data in key-value pairs

YARN:

- A resource management layer in Hadoop

- Allocates resources (CPU, memory, storage) to distributed applications on Hadoop

- Consists of ResourceManager, NodeManager, and ApplicationMaster

It’s important to note that YARN allows for running various distributed applications, such as Spark, Flink, and MapReduce, on the same Hadoop cluster. MapReduce is just one of the possible application frameworks that can be executed on a Hadoop cluster managed by YARN.

Reading is step one. Saying it out loud is the interview. Our AI interviewer calls your phone and runs a realistic Hadoop & Big Data interview — then scores it.
📞 Practice Hadoop & Big Data — free 15 min
📕 Buy this interview preparation book: 120 Hadoop & Big Data questions & answers — PDF + EPUB for $5

All 120 Hadoop & Big Data questions · All topics