Spark vs Hadoop MapReduce in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark RDD from a text file.

Apache Spark

rdd = sparkContext.[1]("data.txt")

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Aread

BtextFile

Cload

Dopen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'read' instead of 'textFile' causes an error.

Using 'load' is for DataFrames, not RDDs.

The textFile method is used to create an RDD from a text file in Spark.

2fill in blank

medium

Complete the code to perform a map operation on an RDD to square each number.

Apache Spark

squared_rdd = numbers_rdd.[1](lambda x: x * x)

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Areduce

Bfilter

CflatMap

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'reduce' tries to combine all elements into one value.

Using 'filter' removes elements instead of transforming them.

The map transformation applies a function to each element of the RDD.

3fill in blank

hard

Fix the error in the code to count the number of elements in an RDD.

Apache Spark

count = rdd.[1]()

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Acount

Bsize

Clen

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len' causes a Python error because RDDs do not support it.

Using 'size' or 'length' are not valid RDD methods.

The count() action returns the number of elements in an RDD.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts using Spark RDD transformations.

Apache Spark

word_counts = rdd.[1](lambda line: line.split()).[2](lambda word: (word, 1)).reduceByKey(lambda a, b: a + b).collectAsMap()

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AflatMap

Bmap

Cfilter

DgroupBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'map' instead of 'flatMap' for splitting lines results in nested lists.

Using 'filter' or 'groupBy' in wrong places breaks the logic.

flatMap splits lines into words, then map creates (word, 1) pairs for counting.

5fill in blank

hard

Fill all three blanks to filter an RDD for words longer than 3 characters, convert to uppercase, and collect results.

Apache Spark

result = rdd.[1](lambda word: len(word) [2] 3).[3](lambda word: word.upper()).collect()

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Afilter

B>

Cmap

DflatMap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'flatMap' instead of 'map' for uppercase conversion.

Using '<' instead of '>' in the length comparison.

filter selects words longer than 3, map converts them to uppercase.