Word count as MapReduce example in Hadoop - Mini Project: Build & Apply

Word count as MapReduce example

📖 Scenario: Imagine you have a large text document and you want to find out how many times each word appears. This is useful for understanding which words are most common in a book, article, or any text data.

🎯 Goal: You will build a simple MapReduce program that counts the occurrences of each word in a given text. This program will split the text into words, count each word, and then sum the counts to get the total for each word.

📋 What You'll Learn

Create a mapper function that splits lines into words and outputs each word with a count of 1

Create a reducer function that sums counts for each word

Use a configuration variable to set the input text

Print the final word counts as output

💡 Why This Matters

🌍 Real World

Counting words helps analyze text data like customer reviews, social media posts, or books to find popular topics or keywords.

💼 Career

Understanding MapReduce and word counting is a foundational skill for big data processing jobs, especially when working with Hadoop or similar frameworks.

1

DATA SETUP: Create the input text variable

Create a variable called input_text and set it to the string: "hello world hello hadoop"

Hadoop

# Create a variable called input_text with the exact string
# Your code here

Hint

Use a simple string assignment like input_text = "hello world hello hadoop".

2

CONFIGURATION: Create a list of lines from the input text

Create a variable called lines that splits input_text by spaces into a list of words

Hadoop

input_text = "hello world hello hadoop"
# Create a list called lines by splitting input_text by spaces
# Your code here

Hint

Use the split() method on input_text to get words.

3

CORE LOGIC: Create a mapper and reducer to count words

Create a dictionary called word_counts. Use a for loop with variable word to iterate over lines. For each word, add 1 to its count in word_counts (initialize to 0 if not present).

Hadoop

input_text = "hello world hello hadoop"
lines = input_text.split()
# Create a dictionary word_counts and count each word in lines
# Your code here

Hint

Use word_counts.get(word, 0) + 1 to update counts safely.

4

OUTPUT: Print the final word counts

Write a print statement to display the word_counts dictionary.

Hadoop

input_text = "hello world hello hadoop"
lines = input_text.split()
word_counts = {}
for word in lines:
    word_counts[word] = word_counts.get(word, 0) + 1
# Print the word_counts dictionary
# Your code here

Hint

Use print(word_counts) to show the result.

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