Spark vs Hadoop MapReduce in Apache Spark - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key Difference in Data Processing Models

Which statement best describes the main difference between Apache Spark and Hadoop MapReduce in how they process data?

A Spark processes data in-memory for faster computation, while Hadoop MapReduce writes intermediate results to disk.

B Hadoop MapReduce processes data in-memory, while Spark writes intermediate results to disk.

C Both Spark and Hadoop MapReduce process data only on disk without using memory.

D Spark and Hadoop MapReduce both use in-memory processing exclusively.

💡 Hint

Think about how each framework handles intermediate data during processing.

Spark keeps data in memory between steps, making it faster. Hadoop MapReduce writes data to disk after each step, which slows it down.

❓ Predict Output

intermediate

Output of Spark RDD Transformation

What will be the output of this Spark code snippet?

Apache Spark

rdd = sc.parallelize([1, 2, 3, 4])
result = rdd.map(lambda x: x * 2).filter(lambda x: x > 4).collect()
print(result)

A [4, 6, 8]

B [2, 4]

C [6, 8]

D [8]

💡 Hint

First multiply each number by 2, then keep only those greater than 4.

Mapping doubles each number: [2,4,6,8]. Filtering keeps numbers >4: [6,8].

❓ data_output

advanced

Memory Usage Comparison

Given a large dataset, which framework will typically use less disk I/O during iterative machine learning tasks?

A Apache Spark, because it caches data in memory across iterations.

B Hadoop MapReduce, because it writes intermediate results to disk after each iteration.

C Both use the same amount of disk I/O regardless of the task.

D Neither uses disk I/O during iterative tasks.

💡 Hint

Consider how iterative tasks benefit from caching.

Spark caches data in memory, reducing disk reads and writes during iterations. Hadoop MapReduce writes to disk each time, increasing disk I/O.

🔧 Debug

advanced

Identifying Cause of Slow Job

A Spark job is running slower than expected compared to a similar Hadoop MapReduce job. Which of the following is the most likely cause?

A The Spark job is using too much disk space for intermediate data.

B The Spark job is not caching data and repeatedly recomputes RDDs.

C Spark always runs slower than Hadoop MapReduce for all jobs.

D The Hadoop job is using in-memory caching, causing overhead.

💡 Hint

Think about how Spark handles repeated computations without caching.

If Spark does not cache data, it recomputes RDDs each time they are used, causing slowdowns. Hadoop MapReduce writes to disk but does not recompute.

🚀 Application

expert

Choosing Framework for Real-Time Data Processing

You need to process streaming data with low latency for real-time analytics. Which framework is best suited for this task?

A Hadoop MapReduce, because it processes batch data efficiently.

B Hadoop MapReduce, because it supports real-time stream processing natively.

C Neither, because both are designed only for batch processing.

D Apache Spark, because it supports in-memory stream processing with Spark Streaming.

💡 Hint

Consider which framework supports streaming and low latency.

Spark Streaming processes data in near real-time using in-memory computations. Hadoop MapReduce is designed for batch jobs and is not suitable for real-time streaming.