Introduction To MapReduce Interview Questions And Answers
MapReduce is a simple parallel data programming model designed for scalability and fault-tolerance. We can say that MapReduce is a framework, that uses the concept of nodes to parallelize the problems that occur in large data sets, if they are local network it uses the same hardware and if MapReduce is geographically distributed it uses different hardware respectively. MapReduce is essentially composed of Map() function and Reduce () function. It was made popular by the open-source Hadoop project.
If you are looking for a job that is related to MapReduce, you need to prepare for the 2020 MapReduce Interview Questions. Though every MapReduce interview is different and the scope of a job is also different, we can help you out with the top MapReduce Interview Questions with answers, which will help you take the leap and get your success in your interview.
Below are the 9 important 2020 MapReduce Interview Questions and Answers. These questions are divided into two parts are as follows:
Part 1 – MapReduce Interview Questions (Basic)
This first part covers basic Interview Questions And Answers.
1. What is MapReduce?
Answer:
MapReduce is a simple parallel data programming model designed for scalability and fault-tolerance. In other words, it is a framework which processes parallelizable problems in large data sets using the concept of nodes (the number of computers) which are in turn classified as clusters if it is a local network and uses the same hardware or grids if they are geographically distributed and use different hardware. The MapReduce essentially comprises of a Map () function and a Reduce () function. It was pioneered by Google and processes many petabytes of data every day. It was made popular by the open-source Hadoop project and is used at Yahoo, Facebook and Amazon to name a few.
2. What is MapReduce used for-By Company?
Answer:
Google
Construction of Index for Google Search
The process of constructing a positional or nonpositional index is called index construction or indexing. The role of MapReduce is Index Construction and is designed for large computer clusters. The purpose of the cluster is to solve computational problems for nodes or computers that are built with standard parts rather than a supercomputer.
•Article Clustering for Google News
For article clustering, the pages are first classified according to whether they are needed for clustering. Pages include a lot of information that is not needed for the clustering. Then the article is brought to its vector form based on keywords and the weightage it is given. Then they are clustered using algorithms.
•Statistical Machine Translation
The translation of bilingual text corpora by analysis generates statistical models that translate one language to another using weights and is reduced to the most likely translation.
Yahoo
•“Web map” powering Yahoo! Search
Similar to the article clustering for Google News, MapReduce is used for clustering search outputs on the Yahoo! Platform.
•Spam Detection for Yahoo! Mail
Facebook
•Data Mining
The recent trend of data explosion has resulted in the need for sophisticated methods to divide the data into chunks that can be used easily for the next step of analyzing.
•d Optimizing
•Spam Detection
Let us move to the next MapReduce Interview Questions.
3. What are the MapReduce Design Goals
Answer:
•Scalability to large data Volumes
Since MapReduce is a framework that is aimed at working with parallelizable data using the concept of nodes which are the number of computers either as clusters or grids, it is scalable to n number of computer machines. So one prominent design goal of MapReduce is that it is scalable to 1000’s of machines and so 10,000’s of disks.
•Cost-Efficiency
As MapReduce works with parallelizing data at the nodes or number of computers, the following are the reasons which make it cost-efficient:
-Cheap commodity machines instead of a supercomputer. Though cheap they are unreliable.
-Commodity Network
-Automatic fault-tolerance i.e. fewer administrators are required.
-It is easy to use i.e. it requires fewer programmers.
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4. What are the challenges of MapReduce?
Answer:
This is the common MapReduce Interview questions asked in an interview. The main challenges of MapReduce are as follows:
-Cheap Nodes fail, especially if you have many
The mean time between failures for 1 node is equal to 3 years. The mean time between failures for 1000 nodes is equal to 1 day. The solution is to build fault-tolerance into the system itself.
-Commodity network is equal to or implies low bandwidth
The solution for a low bandwidth is to push computation to the data.
-Programming distributed systems are hard
The solution for this is that according to the data-parallel programming model, users write “map” and “reduce” functions. The system distributes the work and handles the faults.
5. What is the MapReduce programming model?
Answer:
MapReduce programming model is based on a concept called key-value records. It also provides paradigms for parallel data processing. For processing the data in MapReduce, both the Input data and Output needs to be mapped into the format of multiple key-value pairs. The single key-value pair is also referred to as a record. The MapReduce programming model consists of a Map () function and a Reduce function. The model for these is as follows.
Map () function:(K in, V in) list (K inter, V inter)
Reduce () function:(K inter, list (V inter)) list (K out, V out)
Part 2 – MapReduce Interview Questions (Advanced)
Let us now have a look at the advanced Interview Questions.
6. What are the MapReduce Execution Details?
Answer:
In the case of MapReduce execution, a single master controls job execution on multiple slaves. The mappers are preferred to be placed on the same node or same rack as their input block so that it minimizes network usage. Also, mappers save outputs to local disk before serving them to reducers. This allows recovery if a reducer crashes and allows more reducers than nodes.
7. What is a combiner?
Answer:
The combiner which is also known as the semi-reducer operates by accepting inputs from the Map class and passing the output key-value pairs to the Reducer class. The main function of a combiner is to summarize map output records with the same key. In other words, a combiner is a local aggregation function for repeated keys produced by the same map. It works for associative functions like SUM, COUNT, and MAX. It decreases the size of the intermediate data as it is a summary of the aggregation of values for all the repetitive keys.
Let us move to the next MapReduce Interview Questions.
8.Why Pig? Why not MapReduce?
Answer:
•MapReduce allows the programmer to carry out a map function followed by a reduced function, but working on how to fit your data processing into this pattern, which often requires multiple MapReduce stages, can be a challenge.
•With Pig, the data structures are much richer, as they are multivalued and nested, and the set of transformations you can apply to the data are much more powerful. For example, they include joins which are not possible in MapReduce.
•Also, Pig is one program that turns the transformation into a series of MapReduce Jobs.
9.MapReduce Criticism
Answer:
One prominent criticism of MapReduce is that the development cycle is very long. Writing the mappers and reducers, compiling and packaging the code, submitting the job and retrieving the results is time-consuming. Even with streaming, which removes the compile and package step, the experience is still taking a long time.
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