Spark vs Hadoop MapReduce in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Spark RDD from a text file.

Apache Spark

rdd = sparkContext.[1]("data.txt")

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

Aread

BtextFile

Cload

Dopen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'read' instead of 'textFile' causes an error.

Using 'load' is for DataFrames, not RDDs.

The textFile method is used to create an RDD from a text file in Spark.

2fill in blank

medium

Complete the code to perform a map operation on an RDD to square each number.

Apache Spark

squared_rdd = numbers_rdd.[1](lambda x: x * x)

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

Areduce

Bfilter

CflatMap

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'reduce' tries to combine all elements into one value.

Using 'filter' removes elements instead of transforming them.

The map transformation applies a function to each element of the RDD.

3fill in blank

hard

Fix the error in the code to count the number of elements in an RDD.

Apache Spark

count = rdd.[1]()

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

Acount

Bsize

Clen

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'len' causes a Python error because RDDs do not support it.

Using 'size' or 'length' are not valid RDD methods.

The count() action returns the number of elements in an RDD.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts using Spark RDD transformations.

Apache Spark

word_counts = rdd.[1](lambda line: line.split()).[2](lambda word: (word, 1)).reduceByKey(lambda a, b: a + b).collectAsMap()

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

AflatMap

Bmap

Cfilter

DgroupBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'map' instead of 'flatMap' for splitting lines results in nested lists.

Using 'filter' or 'groupBy' in wrong places breaks the logic.

flatMap splits lines into words, then map creates (word, 1) pairs for counting.

5fill in blank

hard

Fill all three blanks to filter an RDD for words longer than 3 characters, convert to uppercase, and collect results.

Apache Spark

result = rdd.[1](lambda word: len(word) [2] 3).[3](lambda word: word.upper()).collect()

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

Afilter

B>

Cmap

DflatMap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'flatMap' instead of 'map' for uppercase conversion.

Using '<' instead of '>' in the length comparison.

filter selects words longer than 3, map converts them to uppercase.

Practice

1. Which of the following is a key advantage of Apache Spark compared to Hadoop MapReduce?

easy

A. It cannot handle big data workloads.

B. It always uses disk storage for processing data.

C. It is slower but more stable than Spark.

D. It processes data faster by using memory instead of disk.

Solution

1. Step 1: Understand Spark's processing method

   Spark uses in-memory computation which speeds up data processing compared to disk-based methods.

2. Step 2: Compare with Hadoop MapReduce

   Hadoop MapReduce writes intermediate data to disk, making it slower but stable for large batch jobs.

3. Final Answer:

   It processes data faster by using memory instead of disk. -> Option D

4. Quick Check:

   Spark uses memory = Faster processing [OK]

Hint: Spark uses memory for speed; MapReduce uses disk [OK]

Common Mistakes:

• Thinking MapReduce is faster than Spark
• Assuming Spark always uses disk
• Believing Spark cannot handle big data

2. Which syntax correctly initializes a SparkContext in Python?

easy

A. sc = SparkContext.new()

B. sc = SparkContext()

C. sc = SparkContext.create()

D. sc = new SparkContext()

Solution

1. Step 1: Recall SparkContext initialization in PySpark

   The correct way is to call SparkContext() directly to create a new context.

2. Step 2: Check each option

   Options B and C use non-existent methods; D uses Java-like syntax not valid in Python.

3. Final Answer:

   sc = SparkContext() -> Option B

4. Quick Check:

   Correct Python syntax = sc = SparkContext() [OK]

Hint: Use SparkContext() directly in Python [OK]

Common Mistakes:

• Using Java syntax in Python
• Calling non-existent methods like new() or create()
• Forgetting to import SparkContext before use

3. Given the following code snippet in Spark, what will be the output?

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

medium

A. [1, 2, 3, 4]

B. [4, 8, 12, 16]

C. [2, 4, 6, 8]

D. [0, 2, 4, 6]

Solution

1. Step 1: Understand the map transformation

   The map applies the function x * 2 to each element in the RDD: 1*2=2, 2*2=4, 3*2=6, 4*2=8.

2. Step 2: Collect the results

   collect() gathers all transformed elements into a list: [2, 4, 6, 8].

3. Final Answer:

   [2, 4, 6, 8] -> Option C

4. Quick Check:

   Each element doubled = [2,4,6,8] [OK]

Hint: map applies function to each element [OK]

Common Mistakes:

• Confusing map with filter
• Forgetting to call collect() to get results
• Multiplying elements incorrectly

4. Identify the error in this Hadoop MapReduce pseudo-code snippet:

map(key, value):
  emit(value, key)

reduce(key, values):
  sum = 0
  for v in values:
    sum += v
  emit(key, sum)

medium

A. The reduce function sums values but values are keys from map output.

B. The map function emits key and value in wrong order.

C. The reduce function tries to sum keys instead of values.

D. There is no error; code is correct.

Solution

1. Step 1: Analyze map output

   Map emits (value, key), so keys in reduce are original values, and values are original keys.

2. Step 2: Check reduce summation

   Reduce sums 'values', but these are original keys, likely non-numeric or not intended for summation.

3. Final Answer:

   The reduce function sums values but values are keys from map output. -> Option A

4. Quick Check:

   Reduce sums wrong data = error [OK]

Hint: Check what map emits vs what reduce receives [OK]

Common Mistakes:

• Assuming map output keys and values are unchanged
• Summing keys instead of values
• Ignoring data types of reduce inputs

5. You have a large dataset to process repeatedly with iterative algorithms. Which framework is better and why?

hard

A. Apache Spark, because it caches data in memory for faster iterative processing.

B. Apache Spark, because it uses disk storage to handle iterative jobs.

C. Hadoop MapReduce, because it is easier to program for iterative tasks.

D. Hadoop MapReduce, because it writes intermediate results to disk for reliability.

Solution

1. Step 1: Understand iterative algorithm needs

   Iterative algorithms reuse data multiple times, so fast access is important.

2. Step 2: Compare Spark and MapReduce for iteration

   Spark caches data in memory, speeding up repeated access; MapReduce writes to disk each time, slowing down iteration.

3. Final Answer:

   Apache Spark, because it caches data in memory for faster iterative processing. -> Option A

4. Quick Check:

   Memory caching = faster iteration [OK]

Hint: Use Spark for fast iterative jobs with memory caching [OK]

Common Mistakes:

• Choosing MapReduce for iterative speed
• Confusing disk use with speed
• Thinking Spark uses disk for iteration