What is MapReduce in Hadoop?
MapReduce is defined as the framework of Hadoop, which is used to process a huge amount of data parallelly on large clusters of commodity hardware in a reliable manner. It allows the application to store the data in the distributed form and process large dataset across groups of computers using simple programming models, so that’s why we can call MapReduce as a programming model used for processing huge amount of data distributed over the number 0f clusters using the different steps like Input splits, Map, Shuffle and Reduce.
The Apache Hadoop project contains several subprojects:
- Hadoop Common: The Hadoop Common having utilities that support the other Hadoop subprojects.
- Hadoop Distributed File System (HDFS): Hadoop Distributed File System provides to access the distributed file to application data.
- Hadoop MapReduce: It is a software framework for processing large distributed data sets on compute clusters.
- Hadoop YARN: Hadoop YARN is a framework for resource management and scheduling job.
How does MapReduce in Hadoop make working so easy?
The MapReduce make it easy to scale up data processing over hundreds or thousands of cluster machines. The MapReduce model actually works in two steps called map and reduce, and the processing called mapper and reducer, respectively. Once we write MapReduce for an application, scaling up to run over multiples or even multiple of thousand clusters is merely a configuration change. This feature of the MapReduce model has attracted many programmers to use it.
How MapReduce in Hadoop works?
The MapReduce program executes mainly in Four Steps :
- Input splits
- Map
- Shuffle
- Reduce
Now we will see each step how they work.
1. Map step
This step is the combination of the input splits step and the Map step. In the Map step, the source file is passed as line by line. Before input pass to the Map function job, the input is divided into the small fixed-size called Input splits. The Input split is a chunk of the information which a single map could consume. In the Map step, each split data is passed to the mapper function, then the mapper function processes the data and then output values. Generally, the map or mapper’s job input data is in the form of a file or directory stored in the Hadoop file system (HDFS).
2. Reduce step
This step is the combination of the Shuffle step and the Reduce. The reduce function or Reducer’s job takes the data, which results from the map function, after processing by reducing the role new set of effect produces, which again store back into the HDFS.
A Hadoop framework is not sure that each cluster performs which job, either Map or Reduce or both Map and Reduce. So, the Map and Reduce tasks’ request should be sent to the appropriate servers in the cluster. The Hadoop framework itself manages all the tasks of issuing, verifying completion of work, fetching data from HDFS, copying data to the nodes’ group, and so all. In Hadoop, mostly the computing takes place on nodes and data in nodes itself which reduces the network traffic.
So the MapReduce framework is conducive to the Hadoop framework.
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Advantages of MapReduce
The Advantages areas listed below.
- Scalability: The MapReduce making Hadoop highly scalable because it makes it possible to store large data sets in distributed form across multiple servers. As it is spread across multiple so can operate in parallel.
- Cost-effective solution: MapReduce provides a very cost-effective solution for businesses that need to store the growing data and process the data in a very cost-effective manner, which is today’s business need.
- Flexibility: The MapReduce makes Hadoop very flexible for different data sources and even for different types of data such as structured or unstructured data. So it makes it very flexible to access structured or unstructured data and process them.
- Fast: As Hadoop storage data in the distributed file system, by which storing the data on the local disk of a cluster and the MapReduce programs are also generally located in the very same servers, which allows for faster processing of data as no need of accessing the data from other servers.
- Parallel processing: As Hadoop storage data in the distributed file system and the MapReduce program’s working, it divides tasks task map and reduce and that could execute in parallel. And again, because of the parallel execution, it reduces the entire run time.
Skills
Required skills for MapReduce in Hadoop are having good programming knowledge of Java (mandatory), operating System Linux and knowledge of SQL Queries.
Scope
It is a fast-growing field as the big data field is growing. Hence, the scope of MapReduce in Hadoop is very promising in the future as the amount of structured and unstructured data is increasing exponentially day by day. Social media platforms generate a lot of unstructured data that can be mined to get real insights into different domains.
Conclusion
- It is a Hadoop framework used to process parallel huge amounts of data on large clusters of commodity hardware reliably.
- The Apache Hadoop project contains many subprojects as Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Hadoop YARN.
- In the map step, each split data is passed to the mapper function, then the mapper function processes the data and then output values.
- The reduce function or Reducer’s job takes the data, which results from the map function.
- The MapReduce advantages as listed as Scalability, Cost-effective solution, Flexibility, Fast, Parallel processing.
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