Hadoop vs Spark comparison - Quick Revision & Key Differences

Recall & Review

beginner

What is Hadoop primarily used for?

Hadoop is mainly used for storing and processing large data sets using a distributed file system called HDFS and batch processing with MapReduce.

beginner

How does Spark process data differently from Hadoop MapReduce?

Spark processes data in-memory, which makes it faster for iterative tasks and real-time processing compared to Hadoop MapReduce that reads and writes data to disk between steps.

beginner

Which system supports real-time data processing better: Hadoop or Spark?

Spark supports real-time data processing better because it can handle streaming data and perform computations quickly using in-memory processing.

beginner

What is the main storage system used by Hadoop?

Hadoop uses HDFS (Hadoop Distributed File System) to store data across many machines in a cluster.

intermediate

Why might Spark be preferred over Hadoop for machine learning tasks?

Spark is preferred because it can quickly process data in-memory and has built-in libraries like MLlib for machine learning, making it faster and easier to use for iterative algorithms.

Which of the following is a key feature of Spark compared to Hadoop?

A Only batch processing

B Uses MapReduce only

C In-memory data processing

D No support for streaming data

Spark processes data in-memory, which makes it faster than Hadoop's disk-based MapReduce.

What does Hadoop primarily use to store data?

A Spark SQL

B In-memory cache

C NoSQL databases

D HDFS

Hadoop stores data in HDFS, a distributed file system.

Which system is better suited for real-time data processing?

A Hadoop MapReduce

B Spark

C Both are equal

D Neither supports real-time

Spark supports real-time streaming and in-memory processing, making it better for real-time tasks.

Which processing model does Hadoop MapReduce follow?

A Batch processing

B Streaming processing

C In-memory processing

D Graph processing

Hadoop MapReduce processes data in batches, reading and writing to disk between steps.

Why is Spark faster than Hadoop MapReduce for iterative algorithms?

A Because it processes data in-memory

B Because it uses disk storage

C Because it uses MapReduce

D Because it does not support machine learning

Spark keeps data in memory during processing, speeding up iterative tasks like machine learning.

Explain the main differences between Hadoop and Spark in terms of data processing and speed.

Think about how each system handles data during processing.

Describe scenarios where you would choose Hadoop over Spark and vice versa.

Consider the type of data processing and speed requirements.

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