What is an RDD (Resilient Distributed Dataset) in Apache Spark - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an RDD from a list in Spark.

Apache Spark

rdd = sparkContext.[1]([1, 2, 3, 4])

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

Aparallelize

Bcollect

Cmap

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'collect' which gathers data back to the driver, not creating an RDD.

Using 'map' or 'filter' which are transformations, not creation methods.

The parallelize method creates an RDD from a local collection like a list.

2fill in blank

medium

Complete the code to apply a transformation that doubles each number in the RDD.

Apache Spark

doubled_rdd = rdd.[1](lambda x: x * 2)

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

Amap

Bfilter

Creduce

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filter' which removes elements instead of transforming them.

Using 'reduce' which combines elements into one value.

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

3fill in blank

hard

Fix the error in the code to collect the results from the RDD.

Apache Spark

results = rdd.[1]()

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

Areduce

Bcollect

Cfilter

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'map' which returns a new RDD, not the data itself.

Using 'reduce' which combines elements into a single value.

The collect action returns all elements of the RDD to the driver as a list.

4fill in blank

hard

Fill both blanks to create an RDD and filter out numbers less than 5.

Apache Spark

filtered_rdd = sparkContext.[1]([3, 6, 1, 8]).[2](lambda x: x >= 5)

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

Aparallelize

Bmap

Cfilter

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'map' instead of 'filter' to remove elements.

Using 'collect' too early which brings data back to the driver.

First, parallelize creates the RDD. Then, filter keeps only numbers 5 or greater.

5fill in blank

hard

Fill all three blanks to create an RDD, transform it by adding 10, and collect the results.

Apache Spark

result = sparkContext.[1]([1, 2, 3]).[2](lambda x: x + 10).[3]()

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

Aparallelize

Bmap

Ccollect

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filter' instead of 'map' for transformation.

Forgetting to collect results to see output.

First, parallelize creates the RDD. Then, map adds 10 to each element. Finally, collect gathers the results to the driver.

Practice

1. What is an RDD in Apache Spark?

easy

A. A distributed collection of data that can be processed in parallel

B. A type of database used for storing structured data

C. A tool for visualizing data in charts and graphs

D. A programming language for big data analysis

Solution

1. Step 1: Understand the definition of RDD

   An RDD is a core data structure in Apache Spark that represents a distributed collection of objects.

2. Step 2: Identify the correct description

   Among the options, only A distributed collection of data that can be processed in parallel correctly describes RDD as a distributed collection processed in parallel.

3. Final Answer:

   A distributed collection of data that can be processed in parallel -> Option A

4. Quick Check:

   RDD = distributed data collection [OK]

Hint: RDDs are distributed data collections for parallel processing [OK]

Common Mistakes:

• Confusing RDD with databases
• Thinking RDD is a visualization tool
• Assuming RDD is a programming language

2. Which of the following is the correct way to create an RDD from a list in PySpark?

easy

A. rdd = sc.createRDD([1, 2, 3, 4])

B. rdd = SparkContext.create([1, 2, 3, 4])

C. rdd = sc.parallelize([1, 2, 3, 4])

D. rdd = spark.parallelize([1, 2, 3, 4])

Solution

1. Step 1: Recall the method to create RDD from a list

   In PySpark, the SparkContext object (usually named sc) has a method called parallelize to create an RDD from a Python list.

2. Step 2: Check each option for correctness

   rdd = sc.parallelize([1, 2, 3, 4]) uses sc.parallelize correctly. Options B and C use incorrect method names. rdd = spark.parallelize([1, 2, 3, 4]) uses spark, which is a SparkSession, not SparkContext, so parallelize is not directly available there.

3. Final Answer:

   rdd = sc.parallelize([1, 2, 3, 4]) -> Option C

4. Quick Check:

   Use sc.parallelize to create RDD from list [OK]

Hint: Use sc.parallelize(list) to create an RDD [OK]

Common Mistakes:

• Using SparkSession instead of SparkContext
• Wrong method names like create or createRDD
• Confusing RDD creation with DataFrame creation

3. What will be the output of the following PySpark code?

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

medium

A. [1, 2, 3, 4]

B. [2, 4, 6, 8]

C. [0, 2, 4, 6]

D. Error: map function not found

Solution

1. Step 1: Understand the map transformation on RDD

   The map function applies the given lambda function to each element of the RDD. Here, each element is multiplied by 2.

2. Step 2: Calculate the result of map and collect

   Input list is [1, 2, 3, 4]. Multiplying each by 2 gives [2, 4, 6, 8]. collect() gathers the results into a list.

3. Final Answer:

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

4. Quick Check:

   map doubles values, collect returns list [OK]

Hint: map applies function to each element, collect returns list [OK]

Common Mistakes:

• Thinking map returns original list
• Confusing map with filter
• Expecting map to modify RDD in place

4. Identify the error in the following PySpark code snippet:

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

medium

A. The map function syntax is incorrect

B. The filter function is used incorrectly

C. The collect function cannot be used here

D. No error, code runs fine

Solution

1. Step 1: Check the filter function usage

   The filter function is correctly used with a lambda to keep elements greater than 2.

2. Step 2: Check the map function syntax

   map expects a function as argument. Here, x * 2 is an expression, not a function. It should be lambda x: x * 2.

3. Final Answer:

   The map function syntax is incorrect -> Option A

4. Quick Check:

   map needs a function, not an expression [OK]

Hint: map requires a function like lambda x: x * 2 [OK]

Common Mistakes:

• Passing expression instead of function to map
• Misusing filter syntax
• Thinking collect causes error

5. You have an RDD of numbers and want to create a new RDD containing only the squares of even numbers. Which PySpark code correctly achieves this?

hard

A. rdd.map(lambda x: x % 2 == 0).filter(lambda x: x ** 2)

B. rdd.map(lambda x: x ** 2).filter(lambda x: x % 2 != 0)

C. rdd.filter(lambda x: x ** 2 % 2 != 0).map(lambda x: x ** 2)

D. rdd.filter(lambda x: x % 2 == 0).map(lambda x: x ** 2)

Solution

1. Step 1: Filter even numbers from the RDD

   Use filter with lambda x: x % 2 == 0 to keep only even numbers.

2. Step 2: Map the filtered RDD to squares

   Apply map with lambda x: x ** 2 to square each even number.

3. Final Answer:

   rdd.filter(lambda x: x % 2 == 0).map(lambda x: x ** 2) -> Option D

4. Quick Check:

   Filter evens first, then square [OK]

Hint: Filter evens first, then map to square [OK]

Common Mistakes:

• Mapping before filtering changes logic
• Filtering on squared values instead of original
• Using incorrect lambda expressions