What is Hadoop MapReduce?
When it comes to handling a large amount of data from social media, companies, sports, research, medical assistance or any other relevant source, the big data analysis is the most favorable option. Technologies such as Hadoop, Yarn, NoSQL, Hive, Spark, etc., are increasing throughout the digital lake to look for useful ideas hidden within the data. In this tutorial, we will discover the operation of the Hadoop core, that is, the MapReduce. We are going to do that.
What is MapReduce in Hadoop?
MapReduce is a programming model suitable for the processing of huge data. Hadoop is capable of running MapReduce programs written in several languages: Java, Ruby, Python and C ++. MapReduce programs are parallel in nature, so they are very useful for performing large-scale data analysis using several machines in the cluster.
Terminologies in the MapReduce process are:
- MasterNode – Place where JobTracker runs and which accepts work requests from clients
- Slave node – It is the place where the allocation and reduction programs are executed
- JobTracker – is the entity that schedules the tasks and tracks the tasks assigned by the Task
- TaskTracker – is the entity that actually tracks the tasks and provides the status of the report to JobTracker.
- Job – A MapReduce job is the execution of the Mapper & Reducer program in a data set
- Task – the execution of the Mapper & Reducer program in a section of specific dataTaskTttempt – An attempt to execute specific tasks in a slave node
MapReduce programs work in two phases:
The work of the map or the mapper is to process the input data. In general, the input data is in the file or directory format and is stored in the Hadoop file system (HDFS). The input file is passed to the line mapper function per line. The allocator processes the data and creates several pieces of data.
The reducing phase can consist of multiple processes. In the shuffling process, the data is transferred from the allocator to the reducer. Without the successful sweep of the data, there would be no entry into the reduction phase. But the shuffling process can begin even before the allocation process is completed. Then, the data is being classified to reduce the time used to reduce the data.
The classification really helps the reduction process by providing a suggestion when the next key in the classified input data is different from the previous one. The reduction task requires a couple of specific key values to call the reduction function that uses the key value as input. The output of the reducer can be implemented directly to be stored in the HDFS.
- During a MapReduce task, Hadoop sends the Assign and Reduce tasks to the appropriate servers in the cluster.
- The structure manages all the details of the data entry, such as the emission of tasks, the verification of the completion of the task and the copying of data
- The majority of the computation occurs in nodes with data in local disks that reduce the network traffic.
- Once the determined tasks are completed, the cluster collects and reduces the data to form an appropriate result and sends them back to the Hadoop server.
How Map and Reduce work Together?
The input data supplied to the dispatcher is processed by the user-defined function stored in the dispatcher. All the necessary complex business logic is implemented at the allocator level, so that heavy processing is done by the allocator in parallel, since the number of allocators is much greater than the number of reducers. The dispatcher generates an output that is an intermediate die and this output serves as an input to the reducer. This intermediate result is then processed by the function defined by the user-written on the reducer and the final output is generated. Normally, in the reducer very light processing is done.
Advantages of Hadoop MapReduce
In MapReduce, we are dividing the work between several nodes and each node works with a part of the work simultaneously. Thus, MapReduce is based on the Divide and Conquer paradigm, which helps us process data using different machines. As the data is processed by several machines instead of a single machine in parallel, the time it takes to process the data is reduced by a huge amount
Rather than moving information to the preparing unit, we are moving the handling unit to the information in the MapReduce Framework. In the conventional framework, we used to carry information to the preparing unit and procedure it. However, as the information developed and turned out to be exceptionally huge, carrying that colossal measure of information to the handling unit exhibited the accompanying issues:
- Transferring huge data for processing is expensive and impairs network performance.
- The master node may be overloaded and may fail.