Reduce and aggregate actions in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the sum of all numbers in the RDD.

Apache Spark

total_sum = numbers.[1](lambda x, y: x + y)

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Areduce

Bcollect

Cmap

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using collect returns all elements, not the sum.

Using map transforms elements but does not aggregate.

Using filter selects elements but does not combine.

The reduce action combines all elements using a function, here summing all numbers.

2fill in blank

medium

Complete the code to find the maximum value in the RDD.

Apache Spark

max_value = numbers.[1]()

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Amax

Breduce

Cmin

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using min returns the smallest element.

Using reduce without a function will cause an error.

Using count returns the number of elements, not the max.

The max action returns the largest element in the RDD.

3fill in blank

hard

Fix the error in the code to correctly compute the sum using reduce.

Apache Spark

total = numbers.reduce(lambda x, y: x [1] y)

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A/

B+

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication * will compute the product, not sum.

Using subtraction - or division / will give wrong results.

The reduce function needs to add elements to compute the sum, so use +.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts using reduceByKey.

Apache Spark

word_counts = words.[1](lambda a, b: a [2] b).collectAsMap()

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AreduceByKey

B+

C*

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using map does not aggregate values.

Using multiplication * will multiply counts incorrectly.

reduceByKey groups values by key and combines them using the function. Here, + sums counts.

5fill in blank

hard

Fill all three blanks to create a dictionary of word lengths for words longer than 3 characters.

Apache Spark

lengths = words.filter(lambda word: len(word) [3] 3).map(lambda word: (word[1](), [2])).collectAsMap()

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A.upper()

Blen(word)

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of > filters wrong words.

Not transforming the word to uppercase as key.

Using the word itself instead of its length as value.

This code creates keys as uppercase words and values as their lengths, filtering words longer than 3 characters.