Hadoop MapReduce is developed for the programmers to write easily for the application in which the process is in huge amounts of data or in the cluster level of the data. In this software, there are no chances of coming out of error. The MapReduce job does the work to split the input data into the independent files, which have been processed by the map tasks in a completely parallel manner. The framework gives the output of the data in the maps, which then is reduced and then the file size reduces. In this case, both the input and output of the job are being stored in a different file in the computer system. The framework takes care of the scheduling tasks, then after monitoring them and then retry them if it is failed.
The computer nodes and the storage nodes are the same, and the MapReduce framework and the Hadoop Distributed File System are running in the same set. The configuration is very difficult to understand by any human mind. The configuration allows the framework to effectively schedule the task on any of the nodes where the data is already present before and which results in the high aggregate bandwidth across the cluster in the framework. The framework consists of a single master that is JobTracker and with that TaskTracker for each cluster node. The master is mainly responsible for scheduling the jobs and making the jobs complete within the time limit. The slaves are bound to do the work that has been directed by the master. The application must specify the input and the output locations and the supply map and the reduce functions through the appropriate interface and abstract classes. The Big Data Hadoop Training job has the clients of the JobTracker, and their work is to configure the framework perfectly without any issues.
It is a collection of a large dataset, and it cannot be processed with the old traditional methods and old technology. Big data consists of the various tools and techniques which are important to the framework. Big data produces different types of devices and applications.
Black Box data- It is a component of the helicopter, aeroplanes and jets. It helps to capture the voice of the flight crew, recording the microphones and earphones, and it also records the performance of the aircraft.
Social media data- There are different types of social media presence that hold the personal information of any individual. Posting pictures or any other media can be viewed by any of the people behind the phone.
Stock Exchange Data- The powerful data of different stocks of the different companies are stored here only. It is very much confidential, and it can create any problem if it goes under the wrong person. People can buy and sell their stocks through it.
Transport Data- Different types of transport vehicles are there in the market. It contains the list of the models, their brand, their logo, their engine number, their production date and every important thing that is mentioned in it.
Important Questions and Answers
Q1. What are the major configuration parameters required in a MapReduce program?
Ans- We have the following configuration parameters:
-Input location of the job in HDFS.
-Output location of the job in HDFS.
-Input and output formats.
-Classes containing a map with the code and reduce function.
-JAR file for mapper, reducer and driver classes for the code.
Q2.What is the role of the Output Committer class in the Map Reduce job?
Ans- Map Reduce relies on the Output Committer for the following:
-Set up the job initialization.
-Setting the temporary output
-Checking the task, whether it is completed or not.
-Showing the results of the task.
Q3. Explain the process of Spilling in MapReduce.
Ans- The process in which the data is copied from the memory to the disk when the memory reaches its maximum limit. It is happening at a time when there is not enough memory to store the mapper output. The thread starts spilling the content from memory to the disk at the exact 80% of its limit is fulfilled.
Q4. Can we write the output of MapReduce in different formats?
Ans- Hadoop supports various input and output formats, which are in the following:
- Text Output Format- It is the default format, and it is recorded as the line of the text.
- Sequence File Output Format- it is used when the file is written in the sequence files when the output needs to be fed to the other MapReduce.
- Map File Output Format- it is used to write in the map files and to get the output.
Q5. What are the different schedulers available in YARN?
Ans- The different schedulers available in YARN are in the following:
FIFO schedule- It is in the top list, and it runs the others in the order of submission. It is not desirable as long-running work might block the small running application.
Capacity scheduler- It is a separated dedicated queue that allows the small job to start as soon as it is submitted. The larger files take more time compared to the FIFO scheduler.
Fair scheduler- There is no need to start reserve a set of capacity since it will balance between the resources of the running works, whether it is big or small.