Creating RDDs from collections and files in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an RDD from a Python list.

Apache Spark

data = [1, 2, 3, 4, 5]
rdd = sc.[1](data)

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

Aparallelize

BtextFile

Cread

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using textFile instead of parallelize for a list.

Trying to use collect to create an RDD.

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

2fill in blank

medium

Complete the code to create an RDD by reading a text file.

Apache Spark

rdd = sc.[1]("/path/to/file.txt")

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

Aparallelize

BreadFile

Cload

DtextFile

Attempts:

3 left

💡 Hint

Common Mistakes

Using parallelize to read a file path string.

Using readFile which is not a Spark method.

The textFile method reads a text file and creates an RDD where each element is a line.

3fill in blank

hard

Fix the error in the code to create an RDD from a list of numbers.

Apache Spark

numbers = [10, 20, 30]
rdd = sc.[1](numbers).collect()

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

Aparallelize

BtextFile

Cread

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Using textFile on a list causes errors.

Trying to use read or load which are not valid here.

To create an RDD from a list, use parallelize. The other methods are for files or do not exist.

4fill in blank

hard

Fill both blanks to create an RDD from a list and filter even numbers.

Apache Spark

data = [1, 2, 3, 4, 5]
rdd = sc.[1](data).filter(lambda x: x [2] 2 == 0)

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

Aparallelize

B%

C//

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using textFile instead of parallelize.

Using // instead of % for modulus.

Use parallelize to create the RDD and the modulus operator % to check even numbers.

5fill in blank

hard

Fill all three blanks to create an RDD from a file, map each line to uppercase, and collect the results.

Apache Spark

rdd = sc.[1]("/path/to/file.txt")
upper_rdd = rdd.[2](lambda line: line.[3]())
result = upper_rdd.collect()

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

AtextFile

Bmap

Cupper

Dparallelize

Attempts:

3 left

💡 Hint

Common Mistakes

Using parallelize instead of textFile for reading files.

Using map incorrectly or missing it.

Using lower instead of upper for uppercase conversion.

Use textFile to read the file, map to apply a function to each line, and upper to convert text to uppercase.

Practice

1. What does the parallelize() method do in Apache Spark?

easy

A. Creates an RDD from a local collection like a list

B. Reads data from a text file into an RDD

C. Saves an RDD to a file

D. Filters elements in an RDD

Solution

1. Step 1: Understand the purpose of parallelize()

   This method takes a local collection such as a list and creates an RDD from it, enabling parallel processing.

2. Step 2: Differentiate from other methods

   textFile() reads from files, saveAsTextFile() saves data, and filter() filters data. These are different from parallelize().

3. Final Answer:

   Creates an RDD from a local collection like a list -> Option A

4. Quick Check:

   parallelize() = create RDD from list [OK]

Hint: parallelize() = local list to RDD [OK]

Common Mistakes:

• Confusing parallelize() with textFile()
• Thinking parallelize() reads files
• Mixing up saving and creating RDDs

2. Which of the following is the correct syntax to create an RDD from a list [1, 2, 3] using SparkContext sc?

easy

A. sc.load([1, 2, 3])

B. sc.parallelize([1, 2, 3])

C. sc.createRDD([1, 2, 3])

D. sc.textFile([1, 2, 3])

Solution

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

   The correct method is parallelize() called on SparkContext with the list as argument.

2. Step 2: Check syntax correctness

   sc.parallelize([1, 2, 3]) is valid syntax. Other options are invalid or do not exist.

3. Final Answer:

   sc.parallelize([1, 2, 3]) -> Option B

4. Quick Check:

   Use parallelize() with list [OK]

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

Common Mistakes:

• Using textFile() with a list argument
• Trying to use non-existent methods like createRDD()
• Passing list to load() which is for files

3. Given the following code snippet:

rdd = sc.parallelize(['apple', 'banana', 'cherry'])
result = rdd.collect()

What will be the value of result?

medium

A. A list of line numbers

B. An RDD object

C. ['apple', 'banana', 'cherry']

D. A text file path

Solution

1. Step 1: Understand what parallelize() does

   It creates an RDD from the list ['apple', 'banana', 'cherry'].

2. Step 2: Understand what collect() does

   collect() returns all elements of the RDD as a list to the driver program.

3. Final Answer:

   ['apple', 'banana', 'cherry'] -> Option C

4. Quick Check:

   collect() returns list of RDD elements [OK]

Hint: collect() returns all RDD elements as list [OK]

Common Mistakes:

• Thinking collect() returns an RDD
• Confusing collect() with saving to file
• Expecting line numbers instead of data

4. Identify the error in the following code snippet:

rdd = sc.textFile([1, 2, 3])
print(rdd.collect())

medium

A. textFile() expects a string path, not a list

B. collect() cannot be called on textFile RDD

C. sc.textFile() requires two arguments

D. print() cannot display RDD contents

Solution

1. Step 1: Check the argument type for textFile()

   textFile() expects a string path to a file, not a list of strings.

2. Step 2: Understand the error cause

   Passing a list causes a type error because Spark cannot interpret it as a file path.

3. Final Answer:

   textFile() expects a string path, not a list -> Option A

4. Quick Check:

   textFile() needs string path [OK]

Hint: Pass file path as string to textFile() [OK]

Common Mistakes:

• Passing list instead of string to textFile()
• Thinking collect() is invalid on textFile RDD
• Assuming textFile needs multiple arguments

5. You want to create an RDD from a text file named data.txt and then filter lines containing the word "error". Which code snippet correctly creates the RDD and applies the filter?

hard

A. rdd = sc.load('data.txt')
filtered = rdd.filter(lambda line: 'error' in line)

B. rdd = sc.parallelize('data.txt')
filtered = rdd.filter(lambda line: 'error' in line)

C. rdd = sc.textFile([1, 2, 3])
filtered = rdd.filter(lambda line: 'error' in line)

D. rdd = sc.textFile('data.txt')
filtered = rdd.filter(lambda line: 'error' in line)

Solution

1. Step 1: Choose correct method to create RDD from file

   sc.textFile('data.txt') reads the file line by line into an RDD. Passing a string path is required.

2. Step 2: Apply filter to select lines containing 'error'

   The filter() method with a lambda checks each line for the substring 'error'. This is the correct way to filter.

3. Final Answer:

   rdd = sc.textFile('data.txt') filtered = rdd.filter(lambda line: 'error' in line) -> Option D

4. Quick Check:

   Use textFile() with string path + filter() [OK]

Hint: Use textFile('file') then filter() with lambda [OK]

Common Mistakes:

• Using parallelize() to read files
• Passing list instead of string to textFile()
• Using non-existent sc.load() method