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MapReduce

Hadoop data processing model

5 minutes 5 Questions

MapReduce is a programming paradigm and processing model designed for distributed computing on large datasets across clusters of computers. Developed by Google, it enables parallel processing by breaking down complex computations into manageable pieces. At its core, MapReduce consists of two prima…

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Big Data Engineer - MapReduce Example Questions

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Question 1

What is the purpose of a distributed cache in a Hadoop MapReduce job?

To store output data generated by the Reduce phase before writing it to the HDFS To store configuration files specific to each node in a cluster To store intermediate data generated by the Map phase before sending it to the Reduce phase To store read-only data that can be used across all nodes in a cluster To store large datasets that cannot fit in memory for faster processing To store temporary files generated during the MapReduce job

Correct Answer: To store read-only data that can be used across all nodes in a cluster

The distributed cache is used to store read-only data, such as lookup tables or small configuration files, that may be used by all nodes in the cluster for faster processing. This helps avoid the overhead of repeatedly transferring this data over the network.

Question 2

What is a block in Hadoop and how big is a block?

A block is a container used to hold intermediate data in MapReduce and the default size is 32 MB A block is a unit of time used in Hadoop and the default duration is 1 minute A block is a data structure used to store metadata about files in HDFS and the default size is 16 MB A block is a chunk of data that is stored in HDFS and the default size is 64 MB A block is a type of compression algorithm used in Hadoop and the default compression ratio is 2:1 A block is a unit of memory used in MapReduce and the default size is 128 MB

Correct Answer: A block is a chunk of data that is stored in HDFS and the default size is 64 MB

A block is a chunk of data that is stored in HDFS and the default size is 64 MB. The size of the block can be configured based on the specific use case and data type to optimize data transfer and processing.

Question 3

What is the difference between Mapper and Reducer in a MapReduce job?

Mapper combines the input data and Reducer processes it. Mapper and Reducer both create the intermediate key-value pairs. Mapper and Reducer are interchangeable terms for the same process. Mapper is used for real-time processing and Reducer for batch processing. Mapper and Reducer are programming languages. Mapper processes the input data and generates intermediate key-value pairs, and Reducer takes the intermediate results and combines them into a final output.

Correct Answer: Mapper processes the input data and generates intermediate key-value pairs, and Reducer takes the intermediate results and combines them into a final output.

Mapper processes the input data and generates intermediate key-value pairs, and Reducer takes the intermediate results and combines them into a final output.

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