Map phase explained in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the map function that processes input key-value pairs.

Hadoop

def map([1], value):
    # process input key and value
    pass

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Arecord

Bkey

Cdata

Dinput

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'input' or 'data' instead of 'key' as the first parameter.

The map function takes a key and a value as input parameters. The first parameter is usually called key.

2fill in blank

medium

Complete the code to emit intermediate key-value pairs from the map function.

Hadoop

def map(key, value):
    words = value.split()
    for word in words:
        [1](word, 1)

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Aemit

Bprint

Creturn

Doutput

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'print' or 'return' instead of 'emit'.

In the map phase, the function emit is used to output intermediate key-value pairs.

3fill in blank

hard

Fix the error in the map function to correctly split the input value into words.

Hadoop

def map(key, value):
    words = value.[1]()
    for word in words:
        emit(word, 1)

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Areplace

Bjoin

Cstrip

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'join' which combines strings instead of splitting.

The split() method breaks a string into a list of words, which is needed here.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that counts word lengths greater than 3.

Hadoop

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

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the condition.

The dictionary comprehension maps each word to its length if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension filtering words by length and mapping to uppercase keys.

Hadoop

result = { [1]: [2] for word in words if len(word) [3] 4 }

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Aword.upper()

Bword

C>

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' instead of '>' in the condition.

The comprehension creates keys as uppercase words, values as the original words, filtering words longer than 4 characters.

Practice

1. What is the main role of the Map phase in Hadoop's data processing?

easy

A. To store data permanently in HDFS

B. To combine all results into a final output

C. To process input data and produce intermediate key-value pairs

D. To clean and validate input data before processing

Solution

1. Step 1: Understand the Map phase purpose

   The Map phase reads input data and processes it piece by piece.

2. Step 2: Identify output of Map phase

   It produces intermediate key-value pairs for the next phase (Reduce) to process.

3. Final Answer:

   To process input data and produce intermediate key-value pairs -> Option C

4. Quick Check:

   Map phase = intermediate key-value pairs [OK]

Hint: Map phase outputs key-value pairs from input data [OK]

Common Mistakes:

• Confusing Map phase with Reduce phase
• Thinking Map phase stores final results
• Assuming Map phase cleans data

2. Which of the following is the correct syntax to define a Map function in Hadoop's MapReduce framework (Java)?

easy

A. public void map(Context context, LongWritable key, Text value)

B. public void reduce(Text key, Iterable values, Context context)

C. public void map(String key, String value)

D. public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException

Solution

1. Step 1: Recall Map function signature in Hadoop Java

   The Map method signature includes input key, input value, and context for output.

2. Step 2: Match correct parameters and exceptions

   public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException matches the correct signature with LongWritable key, Text value, Context context, and throws exceptions.

3. Final Answer:

   public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException -> Option D

4. Quick Check:

   Map method signature = public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException [OK]

Hint: Map method has key, value, context parameters [OK]

Common Mistakes:

• Mixing Map and Reduce method signatures
• Wrong parameter order or types
• Omitting exception declarations

3. Given the following Map function in Hadoop, what will be the intermediate output for input line "apple orange apple"?

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

medium

A. [('apple', 1), ('orange', 1), ('apple', 1)]

B. [('apple orange apple', 1)]

C. [('apple', 3), ('orange', 1)]

D. [('apple', 1), ('orange', 1)]

Solution

1. Step 1: Split input line into words

   The input "apple orange apple" splits into ["apple", "orange", "apple"].

2. Step 2: Emit each word with count 1

   The Map function outputs each word paired with 1: ('apple',1), ('orange',1), ('apple',1).

3. Final Answer:

   [('apple', 1), ('orange', 1), ('apple', 1)] -> Option A

4. Quick Check:

   Map outputs word counts as pairs [OK]

Hint: Map outputs each word with count 1 separately [OK]

Common Mistakes:

• Summing counts in Map phase (done in Reduce)
• Outputting entire line as one key
• Missing duplicate words in output

4. Identify the error in this Map function snippet:

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

medium

A. Missing throws IOException, InterruptedException in method signature

B. Using wrong data type for key parameter

C. Incorrect split delimiter in split method

D. Context object cannot be used inside map method

Solution

1. Step 1: Check method signature requirements

   Hadoop Map method must declare throwing IOException and InterruptedException.

2. Step 2: Verify method signature correctness

   The given method lacks "throws IOException, InterruptedException", causing a compile error.

3. Final Answer:

   Missing throws IOException, InterruptedException in method signature -> Option A

4. Quick Check:

   Map method must declare exceptions [OK]

Hint: Map method must declare IOException, InterruptedException [OK]

Common Mistakes:

• Ignoring required exceptions in method signature
• Assuming context cannot be used
• Thinking split delimiter is wrong

5. You want to count the frequency of words but only output words longer than 3 characters during the Map phase. Which modification to the Map function below achieves this?

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

hard

A. Use context.write only once after the loop with all words

B. Add an if condition: if (word.length() > 3) before context.write

C. Change split to split(".{3}") to split only words longer than 3

D. Filter words in the Reduce phase instead of Map phase

Solution

1. Step 1: Identify filtering requirement in Map phase

   We want to output only words longer than 3 characters during Map.

2. Step 2: Add condition to output only qualifying words

   Adding if (word.length() > 3) before context.write filters words correctly.

3. Final Answer:

   Add an if condition: if (word.length() > 3) before context.write -> Option B

4. Quick Check:

   Filter words by length in Map phase [OK]

Hint: Use if condition inside Map loop to filter words [OK]

Common Mistakes:

• Trying to filter by regex in split (wrong approach)
• Filtering only in Reduce phase (too late)
• Writing all words at once outside loop