If you are into big data, you already know about the popularity of MapReduce. There is a huge demand for the MapReduce professionals in the market. It doesn’t matter if you are a beginner or looking to re-apply for a new job position, going through the 10 most popular MapReduce interview questions and answers can help you get prepared for the MapReduce interview. So, without any delay, let’s jump into the questions.
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Also Read : 7 Most Popular Big Data Interview Questions
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10 Most Popular MapReduce Interview Questions are –
1. What is MapReduce?
Answer: MapReduce is at the core of Hadoop. It is a framework that enables Hadoop to scale across multiple clusters while working on big data.
The term “MapReduce” is derived from the two important task in the programming paradigm. The first one is “map” which converts one set of data into another. The conversion is done such that the output is in a simple format of key/value pairs. The reduce function, on the other hand, takes the input produced by “map” and create smaller data tuples combining the previously created ones.
2. Compare Spark and MapReduce.
Answer: Apache Spark and Hadoop MapReduce are both popular tools to work on big data. Below are some of the main differences between these two.
| Criteria | Spark | MapReduce |
| Speed |
Spark is up to 100x faster in memory and 10x faster in drive |
MapReduce is comparatively slower than Spark |
| Security | Spark only supports secret password authentication. | Hadoop in addition to secret password authentication also supports ACLs which offers better security compared to Spark. |
| Dependability | Spark can work on its own without the need for any other software. | Hadoop is required for MapReduce to work |
| Ease of Usability | Spark is easy to use, learn, and implement, thanks to the APIs available in Java, Python, and Scala. | MapReduce is harder to learn and implement as it requires the developer to learn extensive Java and Scala programming language |
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3. Discuss the main components of MapReduce job.
Answer: There are three main components of a MapReduce job which are as follows:
- Map Driver Class: it provides the necessary parameters for job configuration.
- Mapper Class: The mapper class provides map() method. It extends org.apache.hadoop.mapreduce.Mapper class.
- Reducer Class: The reducer class provides reduce() method. It extents org.apache.hadoop.mapreduce.Reducer class.
4. What are the main configuration parameters specified in MapReduce?
Answer: To work properly, MapReduce needs some configuration parameters to be set correctly. Without them set correctly, the map and reduce jobs will not function properly. The configuration parameters that need to be set correctly are as follows:
- Job’s input location in HDFS.
- Job’s output location in HDFS.
- Input and Output format.
- Classes that contain the map and reduce functions.
- Last, but not the least, .jar file for reducer, mapper and driver classes.
5. Explain the basic parameters of mapper and reducer function.
Answer: The basic parameters of the mapper function are as below:
- Input – Text, and LongWritable.
- Intermediate Output – Text and IntWritable.
Also, the basic parameters of reducer function are
- Final Output – Text, IntWritable
- Intermediate Output – Text, IntWritable
6. How would you split data into Hadoop?
Answer: Splits are created with the help of the InputFormat. Once the splits are created, the number of mappers is decided based on the total number of splits. The splits are created according to the programming logic defined within the getSplits() method of InputFormat, and it is not bound to the HDFS block size.
The split size is calculated according to the following formula.
Split size = input file size/ number of map tasks
7. What is distributed Cache in MapReduce Framework? Explain.
Answer: Distributed cache is an important part of the MapReduce framework. It is used to cache files across operations during the time of execution and ensures that tasks are performed faster. The framework uses the distributed cache to store important file that is frequently required to execute tasks at that particular node.
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Also Read: 5 Best Apache Spark Certification To Boost Your Career
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8. What is heartbeat in HDFS? Explain.
Answer: A heartbeat in HDFS is a signal mechanic used to signal if it is active or not. For example, a DataNode and NameNode use heartbeat to convey if they are active or not. Similarly, JobTracker and NameNode also use heartbeat to do the same.
9. What happens when a DataNode fails?
Answer: As big data processing is data and time sensitive, there are backup processes if DataNode fails. Once a DataNode fails, a new replication pipeline is created. The pipeline takes over the write process and resumes from where it failed. The whole process is governed by NameNode which constantly observes if any of the blocks is under-replicated or not.
10. Can you tell us how many daemon processes run on a Hadoop system?
Answer: There are five separate daemon processes on a Hadoop system. Each of the daemon processes has its JVM. Out of the five daemon processes, three runs on the master node whereas two runs on the slave nodes. They are as below.
Master Nodes
- NameNode- maintains and store data in HDFS
- Secondary NameNode – Works for NameNode and performs housekeeping functions.
- JobTracker – Take care of the main MapReduce jobs. Also takes care of distributing tasks to machines listed under task tracker.
Slave Nodes
- DataNode – manages HDFS data blocks.
- TaskTracker – manages the individual Reduce and Map tasks.
These MapReduce interview questions will help you get started with the MapReduce interview preparation. Notice that you need to read more questions and answers to get truly prepared for the job interview as this article only covers the 10 most popular MapReduce interview questions. If you have any questions regarding MapReduce or MapReduce interview, you can easily ask us using the comments section below!
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