Word count as MapReduce example in Hadoop

Introduction

Word count helps us find how many times each word appears in a big text. It shows how MapReduce breaks big tasks into small parts and then combines results.

Counting words in a large book or document collection.

Analyzing common words in social media posts.

Finding popular search terms from logs.

Summarizing text data for reports.

Learning how big data tools process information.

Syntax

Hadoop

map(key, value):
    for word in value.split():
        emit(word, 1)

reduce(key, values):
    total = sum(values)
    emit(key, total)

map processes each line and outputs (word, 1) pairs.

reduce adds all counts for each word to get total occurrences.

Examples

The map function splits the line into words and emits each word with count 1.

Hadoop

map(1, "hello world hello")
# emits ('hello', 1), ('world', 1), ('hello', 1)

The reduce function sums the counts for 'hello' to get 2.

Hadoop

reduce('hello', [1, 1])
# emits ('hello', 2)

Sample Program

This code simulates MapReduce word count: it maps words to counts, groups them, then reduces by summing counts.

Hadoop

from collections import defaultdict

# Sample input: list of lines
input_data = [
    "hello world",
    "hello hadoop",
    "hello mapreduce world"
]

# Map step
mapped = []
for line in input_data:
    for word in line.split():
        mapped.append((word, 1))

# Shuffle and sort step (group by word)
grouped = defaultdict(list)
for word, count in mapped:
    grouped[word].append(count)

# Reduce step
reduced = {}
for word, counts in grouped.items():
    reduced[word] = sum(counts)

# Print results
for word, total in sorted(reduced.items()):
    print(f"{word}: {total}")

Important Notes

MapReduce splits big data into small pieces to process in parallel.

Shuffle step groups all same words together before reducing.

This example uses Python to simulate MapReduce logic simply.

Summary

Map step breaks text into (word, 1) pairs.

Reduce step sums counts for each word.

Word count shows how MapReduce handles big data tasks.

Practice

1. What is the main purpose of the Map step in the Word Count MapReduce example?

easy

A. To split input text into pairs of (word, 1)

B. To sum the counts of each word

C. To sort the words alphabetically

D. To output the final word counts

Solution

1. Step 1: Understand the Map step role

   The Map step processes input text and breaks it into pairs where each word is paired with the number 1. The Reduce step sums these counts, so the Map step does not sum or sort but only creates (word, 1) pairs.

2. Final Answer:

   To split input text into pairs of (word, 1) -> Option A

3. Quick Check:

   Map step = (word, 1) pairs [OK]

Hint: Map creates (word, 1) pairs, Reduce sums counts [OK]

Common Mistakes:

• Confusing Map with Reduce step
• Thinking Map sorts words
• Assuming Map sums counts

2. Which of the following is the correct syntax for the Mapper output in a Hadoop Word Count example?

easy

A. context.write(word, 1);

B. context.write(1, word);

C. write.context(word, 1);

D. context.write(word);

Solution

1. Step 1: Recall Mapper output syntax

   In Hadoop MapReduce, the Mapper outputs key-value pairs using context.write(key, value);. For word count, the key is the word and the value is 1, so context.write(word, 1); is correct.

2. Final Answer:

   context.write(word, 1); -> Option A

3. Quick Check:

   Mapper output = context.write(word, 1) [OK]

Hint: Mapper outputs (word, 1) using context.write(word, 1) [OK]

Common Mistakes:

• Swapping key and value order
• Using incorrect method name
• Omitting the value in output

3. Given the following input text: "cat dog cat bird dog cat", what will be the output of the Reduce step in the Word Count MapReduce example?

medium

A. [('cat', 3), ('dog', 3), ('bird', 1)]

B. [('cat', 3), ('dog', 2), ('bird', 1)]

C. [('cat', 2), ('dog', 3), ('bird', 1)]

D. [('cat', 1), ('dog', 1), ('bird', 1)]

Solution

1. Step 1: Count each word occurrences

   Input words: cat (3 times), dog (2 times), bird (1 time). The Reduce step sums all counts for each word, resulting in ('cat', 3), ('dog', 2), ('bird', 1).

2. Final Answer:

   [('cat', 3), ('dog', 2), ('bird', 1)] -> Option B

3. Quick Check:

   Word counts = [('cat', 3), ('dog', 2), ('bird', 1)] [OK]

Hint: Count each word's frequency, sum in Reduce step [OK]

Common Mistakes:

• Mixing counts of dog and cat
• Counting only unique words once
• Adding counts incorrectly

4. Identify the error in this Mapper code snippet for Word Count:

public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
  String[] words = value.toString().split(" ");
  for (String word : words) {
    context.write(word, new IntWritable(1));
  }
}

medium

A. The split method should use "," instead of space

B. The map method should return a value

C. IntWritable should be replaced with Integer

D. The key type in context.write should be Text, not String

Solution

1. Step 1: Check context.write parameter types

   In Hadoop, context.write expects key and value types matching Mapper output types, usually Text and IntWritable. The variable word is a String, but context.write requires a Text object as key, so it should be new Text(word).

2. Final Answer:

   The key type in context.write should be Text, not String -> Option D

3. Quick Check:

   Use Text for keys in context.write [OK]

Hint: Use Hadoop types (Text, IntWritable) in context.write [OK]

Common Mistakes:

• Passing String instead of Text as key
• Using wrong split delimiter
• Expecting map method to return a value

5. You want to modify the Word Count MapReduce job to ignore case (treat 'Cat' and 'cat' as the same word). Which change should you apply in the Mapper?

hard

A. Sort words alphabetically in the Mapper

B. Convert words to uppercase in the Reducer

C. Convert each word to lowercase before writing: context.write(new Text(word.toLowerCase()), new IntWritable(1));

D. Filter out words shorter than 3 characters in the Mapper

Solution

1. Step 1: Understand case normalization

   To treat 'Cat' and 'cat' as the same, convert all words to the same case before counting. Changing words to lowercase in the Mapper ensures all keys are uniform before Reduce sums counts.

2. Final Answer:

   Convert each word to lowercase before writing: context.write(new Text(word.toLowerCase()), new IntWritable(1)); -> Option C

3. Quick Check:

   Normalize case in Mapper for consistent keys [OK]

Hint: Lowercase words in Mapper to unify counts [OK]

Common Mistakes:

• Changing case in Reducer instead of Mapper
• Sorting words instead of normalizing case
• Filtering words unrelated to case