YARN vs MapReduce

MapReduce

MapReduce is a programming model and framework introduced in Hadoop 1.0. It was a primary processing framework in Hadoop until the introduction of YARN in Hadoop 2.0. 

We use MapReduce to process large datasets across multiple nodes in a Hadoop cluster. MapReduce breaks down a large dataset into smaller chunks, then processes it independently.

The “Map” phase involves processing each chunk of data independently to generate key-value pairs. The “Reduce” phase aggregates the key-value pairs to generate the final output.

Architecture of MapReduce

MapReduce simplifies the process of performing large computations into two steps: the map phase and the reduced phase.

Map Phase 

• In the map phase we divide the data into smaller chunks and process the independently.
   
• Each map performs a specific computational task and transformation on them.
   
• The output of the map phase is a collection of key-value pairs.
   
• Once the map phase gets over the key-value pairs are grouped to prepare for their reduced phase.
   

Reduced Phase

• In the reduced phase the key value pairs are processed by the reduce tasks in parallel.
   
• Each reduced task performs some operations on the key value pair.
   
• The output of the reduced phase is a set of key-value pairs.

Advantages of MapReduce

• MapReduce enables large scale data by distributing work across multiple machines in a cluster.
   
• It handles node failures. It is applicable to a wide range of data processing tasks including batch processing.
   
• It has cost-efficient distributed processing without the need for specialized infrastructure.

Limitations of MapReduce  

• The MapReduce is not suitable for processing real-time data. We generally use it for processing large datasets.
   
• The MapReduce can be complex and difficult to program for specific workloads.
   
• MapReduce takes a long job completion time for small and medium-sized datasets.
   

YARN vs MapReduce

Now we will look at the difference between YARN and MapReduce in more detail.

Which one to use?

• YARN is used when we need a resource management framework that can handle a wide range of applications. It can run different workloads like batch processing and real time screening.
   
• YARN offers advanced features such as dynamic resource allocation. It utilizes fair allocation between resources.
   
• MapReduce is particularly useful for large-scale batch processing tasks, such as processing massive amounts of data.
   
• MapReduce manages task failures by reassigning tasks to other available nodes, minimizing the impact of failures.

Frequently Asked Questions 

What does YARN stand for?

YARN stands for Yet Another Resource Negotiator.

What is the purpose of the YARN container?

YARN container provides resources for running a specific application.

What is the default scheduler used by YARN?

The default scheduler used by YARN is the capacity scheduler.

What is the maximum number of reducers to use in a MapReduce job?

The number of available cluster nodes limits the maximum number of reducers.

What is a YARN node manager?

YARN node manager manages the resources such as memory and CPU on that node.

Conclusion

In this article, we discussed the difference between YARN and MapReduce. You can also read the article difference between NPM and YARN to improve your knowledge about List and Set.

To learn more, check out our articles:

• Managing Applications and Resources with Hadoop Yarn
• Difference between NPM and YARN
• Hadoop MapReduce
• Clean Architecture
• IEnumerable vs IQueryable
   

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