Why MapReduce parallelizes data processing in Hadoop - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the MapReduce phase that processes input data in parallel.

Hadoop

job.setMapperClass([1].class);

Drag options to blanks, or click blank then click option'

AReducer

BCombiner

CMapper

DInputFormat

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing Mapper with Reducer

Using Combiner instead of Mapper

The Mapper class handles the parallel processing of input data in MapReduce.

2fill in blank

medium

Complete the code to set the number of reduce tasks for parallel processing.

Hadoop

job.setNumReduceTasks([1]);

Drag options to blanks, or click blank then click option'

A0

Bnull

C-1

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Setting reduce tasks to zero disables reduce phase

Using negative numbers or null causes errors

Setting the number of reduce tasks to a positive number enables parallel reduce processing.

3fill in blank

hard

Fix the error in the code that sets the input format for parallel data splitting.

Hadoop

job.setInputFormatClass([1]);

Drag options to blanks, or click blank then click option'

ATextOutputFormat.class

BTextInputFormat.class

CFileOutputFormat.class

DNullOutputFormat.class

Attempts:

3 left

💡 Hint

Common Mistakes

Using OutputFormat classes for input

Choosing NullOutputFormat which does nothing

TextInputFormat splits input data for parallel processing in MapReduce.

4fill in blank

hard

Fill both blanks to create a MapReduce job that processes data in parallel and writes output.

Hadoop

job.setMapperClass([1].class);
job.setOutputFormatClass([2].class);

Drag options to blanks, or click blank then click option'

AMapper

BTextInputFormat

CTextOutputFormat

DReducer

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing input and output format classes

Using Reducer where Mapper is needed

The Mapper processes data in parallel, and TextOutputFormat writes the output files.

5fill in blank

hard

Fill all three blanks to configure a MapReduce job with parallel map, reduce, and output format classes.

Hadoop

job.setMapperClass([1].class);
job.setReducerClass([2].class);
job.setOutputFormatClass([3].class);

Drag options to blanks, or click blank then click option'

AMapper

BReducer

CTextOutputFormat

DTextInputFormat

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing input and output format classes

Omitting the Reducer class

Mapper and Reducer classes handle parallel processing phases, and TextOutputFormat writes the results.