MapReduce Interview Questions

Are you preparing for a MapReduce Interview? This curated list of MapReduce Interview Questions covers technical expertise and analytical thinking.

1. What is MapReduce?

MapReduce is a distributed programming model (popularized by Hadoop) for processing large datasets in parallel across clusters. It splits tasks into two phases:

1. Map: Processes input data into key-value pairs.
2. Reduce: Aggregates results from mappers.
   It handles fault tolerance, scalability, and batch processing, but is less common now due to frameworks like Apache Spark. (60 words)

2. MapReduce Interview Topics

• Core: Map/Reduce phases, JobTracker/TaskTracker, Input/OutputFormats (Text, SequenceFile), Shuffle & Sort.
• Optimization: Combiner functions, data locality, partitioners, handling data skew.
• Failure Handling: Speculative execution, task/node failures.
• Use Cases: Word count, log analysis, joins (reduce-side/map-side).
• Advanced: YARN integration, counters, debugging (logs, counters).
• Comparisons: Spark vs. MapReduce (performance, APIs).
• Scenario: Design a MapReduce job for inverted indexing or trend analysis.

MapReduce Interview Questions

1. Compare MapReduce and Spark.

There are 4 criteria to be followed to compare MR with Spark:

• Processing speed
• Standalone mode
• Ease of use
• Versatility

MapReduce	Spark
Processing speed is good.	It is exceptional.
Standalone mode needs Hadoop.	It can work independently.
It is an optimized real-time machine-learning applications.	API for Python, Scala & Java.
Not optimized for real-time & ML applications.	It is an optimized real time machine-learning applications.

2. What is MapReduce?

MapReduce is a framework/a programming model that is used for processing large data sets over a cluster of computers using parallel programming.

3. State the reason why we can’t perform aggregation in the mapper. Why do we need a reducer for this?

We cannot perform aggregation (addition) in the mapper because sorting does not occur in the mapper function, as sorting occurs only on reducer.
During “aggregation”, we need the output of all the mapper functions which may not be possible to collect in the map phase as mappers may be running on the different machine.

4. What is the RecordReader in Hadoop?

The RecordReader class loads the data from its source and converts it into (key, value) pairs suitable for reading by the Mapper task.

5. Explain the distributed cache in the MapReduce Framework.

Distributed Cache is a dedicated service of the Hadoop MapReduce framework, which is used to cache the files whenever required by the applications.
This can cache read-only text files, archives, jar files, among others, which can be accessed and read later on each data node where map/reduce tasks are running.

6. How do reducers communicate with each other?

The MapReduce programming model does not allow reducers to communicate with each other.

7. What does the MapReduce partitioner do?

A MapReduce Partitioner makes sure that all the values of a single key go to the same “reducer”, thus allowing even distribution of the map output over the “reducers”.
It redirects the “mapper” output to the “reducer” by determining which “reducer” is responsible for the particular key.

8. How will you write a custom partitioner?

A custom partitioner can be written in the following ways-

• Create a new class that extends the Partitioner Class.
• Override method – getPartition, in the wrapper that runs in the MapReduce.
• Add the custom partitioner to the job by using the method setPartitioner.

9. What is a combiner?

A Combiner is a mini-reducer that performs the local reduce task. It receives the input from the mapper on a particular node and sends the output to the reducer.

10. What are the main components of MapReduce?

• Main Driver Class: providing job configuration parameters.
• Mapper Class: must extend org.apache.hadoop.mapreduce. The Mapper class performs execution of the map() method.
• Reducer Class: must extend org.apache.hadoop.mapreduce.Reducer class.

11. What is Shuffling and Sorting in MapReduce?

Shuffling and Sorting are two major processes operating simultaneously during the work of the mapper and the reducer. The process of transferring data from Mapper to the reducer is called Shuffling.
In MapReduce, the output key-value pairs between the map and reduce phases (after the mapper) are automatically sorted before moving to the Reducer.

12. What are identity mapper and Chain mapper?

• Identity Mapper: Identity Mapper is the default Mapper class provided by Hadoop. It only writes the input data into the output and does not perform computations and calculations on the input data.
• Chain mapper: Chain Mapper is the implementation of a simple Mapper class through chain operations across a set of Mapper classes, within a single map task. In this, the output from the first mapper becomes the input for the second mapper

13. What main configuration parameters are specified in MapReduce?

Following configuration parameters to perform the map and reduce jobs:

• The input location of the job in HDFs.
• The output location of the job in HDFS.
• The input and output formats.
• The classes contain map and reduce functions, respectively.
• The .jar file for mapper, reducer, and driver classes.

14. Name the Job control options specified by MapReduce?

Since this framework supports chained operations wherein an input of one map job serves as the output for another. The various job control options are:

• Job.submit(): to submit the job to the cluster and immediately return.
• Job.waitforCompletion(boolean): to submit the job to the cluster and wait for its completion

15. What is inputFormat in Hadoop?

inputformat defines the input specifications for a job. It performs the following instructions-

• Validates the input specifications of the job.
• Split the input file(s) into logical instances called InputSplit.
• Provides implementation of RecordReader to extract input records from the above instances for further Mapper processing.

16. What is the difference between an HDFS block and inputsplit?

An HDFS block splits data into physical divisions, while InputSplit in MapReduce splits input files logically.

17. What is the text inputformat?

TextInputFormat, files are broken into lines, wherein the key is a position in the file, and the value refers to the line of text.
Programmers can write their own InputFormat.

18. What is the role of Job Tracker?

The primary function of the JobTracker is resource management, which essentially means managing the TaskTrackers.
Apart from this, JobTracker also tracks resource availability and handles task life cycle management.

19. Explain jobconf in MapReduce?

jobconf is a primary interface to define a MapReduce job in Hadoop for job execution.
JobConf specifies mapper, Combiner, partitioner, Reducer, InputFormat, and OutputFormat implementations

20. What is outputCommitter?

outputCommitter describes the commit of the MapReduce task.
FileOutputCommitter is the default available class for OutputCommitter in MapReduce.

21. What is a map in Hadoop?

In Hadoop, a map is a phase in HDFS query solving.
A map reads data from an input location and outputs a key-value pair according to the input type.

22. What is a reducer in Hadoop?

In Hadoop, a reducer collects the output generated by the mapper, processes it, and creates a final output of its own.

23. What are the parameters of mappers and reducers?

The four parameters for mappers are:

• LongWritable (input)
• Text (input)
• Text (intermediate output)
• IntWritable (intermediate output)

The four parameters for reducers are:

• Text (intermediate output)
• IntWritable (intermediate output)
• Text (final output)
• IntWritable (final output)

24. What is partitioning?

Partitioning is a process to identify the reducer instance that would be used to supply the mappers’ output.
Before the mapper emits the data (Key Value) pair to the reducer, the mapper identifies the reducer as a recipient of the mapper output.

25. What is MapReduce used for by the company?

• Construction of an index for Google search: The process of constructing a positional or non-positional index is called index construction or indexing.
• Article clustering for Google News: For article clustering, the pages are first classified according to whether they are needed for clustering.
• Statistical machine translation.

26. What are the MapReduce design goals?

• Scalability to large data volumes.
• Cost-efficiency.

27. What are the challenges of MapReduce?

• Cheap node fails, especially if you have many.
• A commodity network is equal to.
• Programming distributed systems is hard.

28. What is the MapReduce programming model?

The MapReduce programming model is based on a concept called key-value records. It also provides paradigms for parallel data processing.

29. What are the MapReduce execution details?

In the case of MapReduce execution, a single master controls job execution on multiple slaves.

30. Mention the benefits of MapReduce.

• Highly scalable
• cost-effective
• secure.

31. Is renaming the output file possible?

Yes, the implementation of a multiple format output class makes it possible to rename the output file.