Reduce phase explained in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the reduce function that sums values for each key.

Hadoop

def reduce(key, values):
    result = 0
    for value in values:
        result += [1]
    return (key, result)

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

Akey

Bvalues

Cvalue

Dresult

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'key' instead of 'value' inside the loop.

Adding 'values' instead of individual 'value'.

The reduce function sums each value in the list of values for a given key, so we add 'value' to the result.

2fill in blank

medium

Complete the code to emit the final key-value pair from the reduce function.

Hadoop

def reduce(key, values):
    total = sum(values)
    [1](key, total)

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

Aprint

Boutput

Creturn

Demit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'print' instead of 'emit'.

Using 'return' which does not output to Hadoop.

In Hadoop's reduce phase, the 'emit' function outputs the final key and its aggregated value.

3fill in blank

hard

Fix the error in the reduce function to correctly sum values.

Hadoop

def reduce(key, values):
    total = 0
    for val in values:
        total = total [1] val
    emit(key, total)

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

A+

B/

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' which subtracts values.

Using '*' or '/' which multiply or divide.

To sum values, use the '+' operator to add each val to total.

4fill in blank

hard

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

Hadoop

word_counts = {word: [1] for word in words if len(word) [2] 3}

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Awords.count(word)

Blen(word)

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len(word)' as count instead of 'words.count(word)'.

Using '<=' instead of '>' for filtering.

We count occurrences of each word with 'words.count(word)' and filter words longer than 3 with '>'.

5fill in blank

hard

Fill all three blanks to create a reduce function that filters and sums values.

Hadoop

def reduce(key, values):
    filtered = [v for v in values if v [1] 10]
    total = sum(filtered)
    if total [2] 0:
        [3](key, total)

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

A>

B>=

Cemit

Dprint

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Using 'print' instead of 'emit' to output.

Filter values greater than 10 with '>', check if total is >= 0, then emit the result.