Hadoop vs Spark comparison - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the main difference between Hadoop and Spark.

Hadoop

print("Hadoop processes data using [1] computing.")

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

Astreaming

Breal-time

Cbatch

Dinteractive

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'streaming' because Spark supports streaming, but Hadoop mainly uses batch.

Confusing real-time with batch processing.

Hadoop processes data mainly using batch computing, which means it handles large data sets in chunks.

2fill in blank

medium

Complete the code to show which framework is faster for iterative tasks.

Hadoop

print("Spark is generally [1] than Hadoop for iterative algorithms.")

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

Afaster

Bslower

Cequal

Dless efficient

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'slower' because Hadoop is older.

Confusing speed with efficiency.

Spark is faster than Hadoop for iterative tasks because it keeps data in memory.

3fill in blank

hard

Fix the error in the code to correctly compare data processing models.

Hadoop

model = "Spark uses [1] processing, Hadoop uses batch processing."

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

Abatch

Bstreaming

Creal-time

Doffline

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'batch' which is Hadoop's model, not Spark's.

Confusing 'real-time' with 'streaming' in this context.

Spark supports streaming processing, which allows real-time data handling, unlike Hadoop's batch model.

4fill in blank

hard

Fill both blanks to create a dictionary comparing Hadoop and Spark features.

Hadoop

comparison = {"Hadoop": "[1] processing", "Spark": "[2] processing"}

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

Abatch

Bstreaming

Cinteractive

Doffline

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up batch and streaming between Hadoop and Spark.

Using 'interactive' which is not the main model for either.

Hadoop uses batch processing, while Spark supports streaming processing for real-time data.

5fill in blank

hard

Fill all three blanks to create a summary dictionary with key features.

Hadoop

summary = {"Speed": "[1]", "Data Model": "[2]", "Memory Usage": "[3]"}

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

ASpark is faster

BHadoop uses MapReduce

CSpark uses in-memory

DHadoop uses in-memory

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing which framework uses in-memory processing.

Mixing up data models between Hadoop and Spark.

Spark is faster due to in-memory computing, Hadoop uses MapReduce as its data model and does not use in-memory processing.

Practice

1. Which of the following best describes the main difference between Hadoop and Spark?

easy

A. Spark requires more disk space than Hadoop.

B. Hadoop is only for small data; Spark is only for big data.

C. Hadoop uses disk storage for processing; Spark uses in-memory computing.

D. Hadoop is faster than Spark for all tasks.

Solution

1. Step 1: Understand Hadoop's processing method

   Hadoop processes data using MapReduce which reads and writes data to disk between steps.

2. Step 2: Understand Spark's processing method

   Spark processes data in memory, which makes it faster for many tasks.

3. Final Answer:

   Hadoop uses disk storage for processing; Spark uses in-memory computing. -> Option C

4. Quick Check:

   Disk vs memory processing = A [OK]

Hint: Remember: Hadoop = disk, Spark = memory speed [OK]

Common Mistakes:

• Thinking Spark always uses more disk space
• Believing Hadoop is faster for all tasks
• Confusing data size limits for each tool

2. Which syntax correctly describes how Spark handles data compared to Hadoop?

easy

A. Spark.read().cache() stores data in memory; Hadoop writes to disk.

B. Hadoop.cache() stores data in memory; Spark writes to disk.

C. Spark.write().disk() stores data on disk; Hadoop caches in memory.

D. Hadoop.read().cache() stores data in memory; Spark writes to disk.

Solution

1. Step 1: Identify Spark's caching method

   Spark uses cache() to keep data in memory for faster access.

2. Step 2: Identify Hadoop's data handling

   Hadoop writes intermediate data to disk during MapReduce jobs.

3. Final Answer:

   Spark.read().cache() stores data in memory; Hadoop writes to disk. -> Option A

4. Quick Check:

   Spark cache = memory, Hadoop = disk [OK]

Hint: Spark.cache() means in-memory storage [OK]

Common Mistakes:

• Confusing which tool uses cache()
• Assuming Hadoop has cache() method
• Mixing read and write operations

3. Given the following code snippets, which one will likely run faster for iterative machine learning tasks?

medium

A. Spark job caching data in memory and reusing it across iterations.

B. Hadoop MapReduce job reading and writing data to disk each iteration.

C. Hadoop job using in-memory caching for iterations.

D. Spark job writing intermediate results to disk every iteration.

Solution

1. Step 1: Analyze Hadoop's iterative processing

   Hadoop MapReduce writes data to disk after each iteration, slowing down iterative tasks.

2. Step 2: Analyze Spark's iterative processing

   Spark caches data in memory, avoiding repeated disk I/O, speeding up iterations.

3. Final Answer:

   Spark job caching data in memory and reusing it across iterations. -> Option A

4. Quick Check:

   In-memory caching speeds iterations = B [OK]

Hint: Iterative tasks run faster with Spark's memory caching [OK]

Common Mistakes:

• Assuming Hadoop can cache in memory like Spark
• Thinking disk writes are faster than memory
• Confusing Spark's disk writes with Hadoop's

4. Identify the error in this Spark code snippet meant to cache data for faster processing:

val data = spark.read.text("file.txt")
data.cache
val count = data.count()

medium

A. Using read.text instead of read.csv.

B. Missing parentheses after cache method call.

C. Calling count before caching data.

D. No error; code is correct.

Solution

1. Step 1: Check method call syntax

   The cache method requires parentheses: cache().

2. Step 2: Verify method order

   Calling count() after caching is correct to trigger caching.

3. Final Answer:

   Missing parentheses after cache method call. -> Option B

4. Quick Check:

   cache() needs () to execute [OK]

Hint: Remember: methods need () to run in Scala [OK]

Common Mistakes:

• Ignoring missing parentheses on cache
• Thinking read.text is wrong for text files
• Believing count must come before cache

5. You have a large batch job that processes petabytes of data once a day and requires fault tolerance. Which tool is better and why?

hard

A. Hadoop, because it cannot handle large data but is faster.

B. Spark, because in-memory processing is always better for large batch jobs.

C. Spark, because it does not support fault tolerance but is faster.

D. Hadoop, because it writes intermediate data to disk ensuring fault tolerance.

Solution

1. Step 1: Understand batch job needs

   Large batch jobs need fault tolerance to recover from failures without losing data.

2. Step 2: Compare Hadoop and Spark fault tolerance

   Hadoop writes intermediate data to disk, which helps recover from failures. Spark relies on memory and lineage but may lose data if memory fails.

3. Final Answer:

   Hadoop, because it writes intermediate data to disk ensuring fault tolerance. -> Option D

4. Quick Check:

   Disk storage = fault tolerance = Hadoop [OK]

Hint: For big, stable batch jobs, choose Hadoop [OK]

Common Mistakes:

• Assuming Spark is always better for big data
• Ignoring fault tolerance importance
• Confusing speed with reliability