{"id":43474,"date":"2017-11-16T14:58:16","date_gmt":"2017-11-16T09:28:16","guid":{"rendered":"https:\/\/www.whizlabs.com\/?p=43474"},"modified":"2019-04-11T06:37:03","modified_gmt":"2019-04-11T06:37:03","slug":"mapreduce-interview-questions","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/mapreduce-interview-questions\/","title":{"rendered":"10 Most Popular MapReduce Interview Questions"},"content":{"rendered":"

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\u2019t 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\u2019s jump into the questions.<\/p>\n

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Also Read : 7 Most Popular Big Data Interview Questions<\/a><\/p><\/blockquote>\n

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10 Most Popular MapReduce Interview Questions are –<\/h2>\n

1. What is MapReduce?<\/h4>\n

Answer:<\/b>\u00a0MapReduce is at the core of Hadoop. It is a framework that enables Hadoop to scale across multiple clusters while working on big data.<\/p>\n

The term \u201cMapReduce\u201d is derived from the two important task in the programming paradigm. The first one is \u201cmap\u201d 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 \u201cmap\u201d and create smaller data tuples combining the previously created ones.<\/p>\n

2. Compare Spark and MapReduce.<\/h4>\n

Answer:<\/b>\u00a0Apache Spark and Hadoop MapReduce are both popular tools to work on big data. Below are some of the main differences between these two.<\/p>\n

 <\/p>\n\n\n\n\n\n\n\n
Criteria<\/strong><\/td>\nSpark<\/strong><\/td>\nMapReduce<\/strong><\/td>\n<\/tr>\n
Speed<\/strong><\/td>\n\n

Spark is up to 100x faster in<\/p>\n

memory and 10x faster in drive<\/p>\n<\/td>\n

MapReduce is comparatively slower than Spark<\/td>\n<\/tr>\n
Security<\/strong><\/td>\nSpark only supports secret password authentication.<\/td>\nHadoop in addition to secret password authentication also supports ACLs which offers better security compared to Spark.<\/td>\n<\/tr>\n
Dependability<\/strong><\/td>\nSpark can work on its own without the need for any other software.<\/td>\nHadoop is required for MapReduce to work<\/td>\n<\/tr>\n
Ease of Usability<\/strong><\/td>\nSpark is easy to use, learn, and implement, thanks to the APIs available in Java, Python, and Scala.<\/td>\nMapReduce is harder to learn and implement as it requires the developer to learn extensive Java and Scala programming language<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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\"Spark<\/a><\/p>\n

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3. Discuss the main components of MapReduce job.<\/h4>\n

Answer:<\/b>\u00a0There are three main components of a MapReduce job which are as follows:<\/p>\n