Lambda architecture (batch + streaming) in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read data from HDFS using Hadoop streaming.

Hadoop

hadoop fs -cat [1]/input.txt

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

A/tmp

B/data

C/user/hadoop

D/input

Attempts:

3 left

💡 Hint

Common Mistakes

Using a local file system path instead of HDFS path.

Choosing a directory that does not contain the input file.

The correct HDFS path to the input data is /data. This command reads the file input.txt from that directory.

2fill in blank

medium

Complete the code to define a mapper function in Hadoop streaming that converts input text to lowercase.

Hadoop

"#!/bin/bash
while read line; do
  echo [1]
done"

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

Aecho $line

Becho $line | tr '[:lower:]' '[:upper:]'

Cecho $line | tr '[:upper:]' '[:lower:]'

Decho $line | rev

Attempts:

3 left

💡 Hint

Common Mistakes

Reversing the translation and converting lowercase to uppercase.

Not using the tr command.

The mapper converts each input line to lowercase using tr '[:upper:]' '[:lower:]'.

3fill in blank

hard

Fix the error in the reducer code that sums counts from the mapper output.

Hadoop

"#!/usr/bin/env python3
import sys

current_word = None
current_count = 0

for line in sys.stdin:
    word, count = line.strip().split('\t')
    count = int(count)
    if word == [1]:
        current_count += count
    else:
        if current_word:
            print(f"{current_word}\t{current_count}")
        current_word = word
        current_count = count

if current_word == [1]:
    print(f"{current_word}\t{current_count}")
"

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

Acurrent_word

Bword

Ccount

Dline

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing with the new word variable instead of the current word.

Using the count variable in the comparison.

The reducer compares the current word with current_word to accumulate counts correctly.

4fill in blank

hard

Fill both blanks to create a batch layer job that reads from HDFS and writes output to a directory.

Hadoop

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \
  -input [1] \
  -output [2] \
  -mapper mapper.sh \
  -reducer reducer.py

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

A/data/batch_input

B/user/hadoop/batch_output

C/tmp/input

D/output

Attempts:

3 left

💡 Hint

Common Mistakes

Using local file system paths instead of HDFS paths.

Writing output to an input directory.

The batch job reads input from /data/batch_input and writes output to /user/hadoop/batch_output.

5fill in blank

hard

Fill all three blanks to create a streaming layer command that reads from Kafka and writes to HDFS.

Hadoop

kafka-console-consumer.sh --bootstrap-server [1] --topic [2] --from-beginning | \
  hadoop fs -put - [3]/streaming_output/data.txt

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

Alocalhost:9092

Buser_events

C/user/hadoop

Dlocalhost:2181

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing Kafka ports 9092 and 2181.

Using local file system paths instead of HDFS paths.

The Kafka server is localhost:9092, the topic is user_events, and the HDFS output directory is /user/hadoop.

Practice

1. What is the main purpose of the batch layer in Lambda architecture?

easy

A. To process all data for accurate and complete results

B. To handle real-time data streams quickly

C. To store raw data without processing

D. To visualize data for end users

Solution

1. Step 1: Understand batch layer role

   The batch layer processes all accumulated data to provide accurate and comprehensive views.

2. Step 2: Compare with other layers

   The speed layer handles real-time data, not the batch layer.

3. Final Answer:

   To process all data for accurate and complete results -> Option A

4. Quick Check:

   Batch layer = accurate full data processing [OK]

Hint: Batch layer = full data, accurate results [OK]

Common Mistakes:

• Confusing batch layer with speed layer
• Thinking batch layer processes data in real-time
• Assuming batch layer only stores raw data

2. Which of the following is the correct component of Lambda architecture for handling real-time data?

easy

A. Batch layer

B. Speed layer

C. Serving layer

D. Storage layer

Solution

1. Step 1: Identify real-time processing component

   The speed layer is designed to process data streams in real-time for quick insights.

2. Step 2: Eliminate other options

   Batch layer processes full data, serving layer provides query results, storage layer is not a standard Lambda component.

3. Final Answer:

   Speed layer -> Option B

4. Quick Check:

   Speed layer = real-time data processing [OK]

Hint: Speed layer = real-time data [OK]

Common Mistakes:

• Choosing batch layer for real-time data
• Confusing serving layer with speed layer
• Thinking storage layer is a Lambda component

3. Consider this simplified pseudocode for Lambda architecture layers:

batch_result = batch_layer(data)
speed_result = speed_layer(stream)
final_result = merge(batch_result, speed_result)
print(final_result)

If batch_layer returns {'count': 100} and speed_layer returns {'count': 5}, what will final_result be after merging by summing counts?

medium

A. {'count': 105}

B. {'count': 100}

C. {'count': 5}

D. Error: Cannot merge dictionaries

Solution

1. Step 1: Understand merging logic

   Merging sums the 'count' values from batch and speed results: 100 + 5 = 105.

2. Step 2: Confirm final result

   The final dictionary after merging is {'count': 105}.

3. Final Answer:

   {'count': 105} -> Option A

4. Quick Check:

   100 + 5 = 105 [OK]

Hint: Sum batch and speed counts for final result [OK]

Common Mistakes:

• Ignoring speed layer result
• Assuming merge replaces batch with speed
• Thinking merge causes error

4. This code snippet tries to implement the speed layer but has an error:

def speed_layer(stream):
    for record in stream:
        process(record)
    return processed_data

stream = get_stream()
result = speed_layer(stream)
print(result)

What is the likely error here?

medium

A. process() function is missing

B. stream is not iterable

C. processed_data is not defined before return

D. print() syntax error

Solution

1. Step 1: Check variable initialization

   The variable processed_data is returned but never defined or updated inside the function.

2. Step 2: Verify other parts

   Assuming stream is iterable and process() exists, the main error is missing processed_data definition.

3. Final Answer:

   processed_data is not defined before return -> Option C

4. Quick Check:

   Undefined variable causes error [OK]

Hint: Check all variables are defined before return [OK]

Common Mistakes:

• Assuming process() is missing without checking
• Thinking stream is not iterable
• Ignoring variable initialization

5. In a Lambda architecture, if the batch layer updates data every 24 hours and the speed layer processes data every second, what is the best way to ensure the serving layer shows the most accurate and up-to-date results?

hard

A. Use the speed layer results immediately and replace them with batch results after 24 hours

B. Only show batch layer results to avoid inconsistency

C. Ignore speed layer and rely on batch layer for all queries

D. Merge speed and batch results continuously, updating serving layer with combined data

Solution

1. Step 1: Understand serving layer role

   The serving layer combines batch and speed layer outputs to provide accurate and timely results.

2. Step 2: Evaluate options

   Merge speed and batch results continuously, updating serving layer with combined data merges both results continuously, ensuring the serving layer is both up-to-date and accurate.

3. Final Answer:

   Merge speed and batch results continuously, updating serving layer with combined data -> Option D

4. Quick Check:

   Serving layer = combined batch + speed data [OK]

Hint: Serving layer merges batch and speed data continuously [OK]

Common Mistakes:

• Using only speed or batch layer results alone
• Replacing speed results abruptly without merging
• Ignoring serving layer's combining role