Exploring Hadoop MapReduce Framework

What is the primary purpose of the Hadoop MapReduce framework?

• To perform machine learning tasks on small datasets.

• To process large datasets in a distributed computing environment.

• To visualize data in real-time dashboards.

• To store data in a centralized database.

Explain the role of the Mapper in the MapReduce process.

• The Mapper sorts the output data into final results.

• The Mapper combines data from multiple sources into one output.

• The Mapper processes input data and produces intermediate key-value pairs.

• The Mapper is responsible for storing the final output in the database.

What is the function of the Reducer in MapReduce?

• The Reducer formats the final output for user display.

• The Reducer splits data into smaller chunks for the Mapper.

• The Reducer is responsible for loading data into the database.

• The Reducer aggregates and processes intermediate key-value pairs from the Mapper.

How does data flow from the Mapper to the Reducer?

• Data is sent directly from the Mapper to the Reducer without any processing.

• Data is aggregated in the Mapper before being sent to the Reducer.

• Data flows from the Reducer back to the Mapper for final processing.

• Data flows from the Mapper to the Reducer via shuffling and sorting of key-value pairs.

What are the key components of a MapReduce job?

• Mapper, Reducer, DataFormat, Job Execution

• Mapper, Reducer, Task Tracker, Job Monitor

• Mapper, Combiner, InputReader, OutputWriter

• Mapper, Reducer, InputFormat, OutputFormat, Job Configuration

Define the term 'input split' in the context of MapReduce.

• An 'input split' refers to the final output generated by the reduce tasks in MapReduce.

• An 'input split' is a method for combining multiple map tasks into one.

• An 'input split' is a logical representation of a portion of the input data processed by a single map task in MapReduce.

• An 'input split' is a physical representation of the entire input data in MapReduce.

What is the significance of the 'combiner' in MapReduce?

• The combiner reduces data transfer and improves performance by processing intermediate outputs from mappers.

• The combiner is responsible for sorting the final output.

• The combiner only runs after the reducers complete their tasks.

• The combiner is used to initiate the MapReduce job.

How does Hadoop handle failures during a MapReduce job?

• Hadoop requires manual intervention to handle failures.

• Hadoop reassigns failed tasks to other TaskTrackers and can restart from checkpoints.

• Hadoop automatically shuts down the job upon failure.

• Hadoop ignores failed tasks and continues processing.

What programming languages can be used to write MapReduce applications?

• Java, Python, Ruby

• JavaScript

• C++

• Swift

Describe the process of writing a MapReduce program in Java.

• Using Python instead of Java for MapReduce

• Creating only a Mapper class without a Reducer

• Submitting the job without configuring it first

• The process involves creating Mapper and Reducer classes, configuring the job in the main class, and submitting it to Hadoop.