Why data format affects performance in Apache Spark - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a Parquet file into a Spark DataFrame.

Apache Spark

df = spark.read.[1]("data.parquet")

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

Aparquet

Bcsv

Cjson

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv or json to read a parquet file causes errors or slow performance.

Using text format reads data as plain text, losing structure.

Parquet is a columnar storage format optimized for Spark. Using parquet reads the file correctly and efficiently.

2fill in blank

medium

Complete the code to write a DataFrame in an efficient columnar format.

Apache Spark

df.write.[1]("output_path")

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

Aparquet

Bcsv

Cjson

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Writing as csv or json can slow down later reads.

Text format loses data structure and is inefficient.

Writing data in Parquet format stores it in a columnar way, improving read and query speed.

3fill in blank

hard

Fix the error in the code to read a JSON file with Spark.

Apache Spark

df = spark.read.[1]("data.json")

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

Aparquet

Bcsv

Ctext

Djson

Attempts:

3 left

💡 Hint

Common Mistakes

Using parquet or csv to read JSON files causes errors.

Using text reads raw strings, losing JSON structure.

To read a JSON file, use spark.read.json(). Using other formats causes errors or wrong parsing.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Apache Spark

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using <= instead of > in the condition.

The dictionary maps each word to its length using len(word). The condition filters words longer than 3 with >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than zero.

Apache Spark

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase.

Using <= or < instead of > in the condition.

Swapping keys and values.

The dictionary uses uppercase keys with k.upper(), values are v, and filters values greater than zero with >.

Practice

1. Why does using a columnar data format like Parquet improve performance in Apache Spark?

easy

A. Because it converts all data into JSON format for easy parsing.

B. Because it compresses data into a single large file without structure.

C. Because it stores data by columns, allowing faster reads of specific columns.

D. Because it stores data as plain text for better compatibility.

Solution

1. Step 1: Understand columnar storage

   Columnar formats store data by columns, not rows, so Spark reads only needed columns.

2. Step 2: Impact on performance

   Reading fewer columns reduces disk I/O and speeds up queries.

3. Final Answer:

   Because it stores data by columns, allowing faster reads of specific columns. -> Option C

4. Quick Check:

   Columnar storage = faster column reads [OK]

Hint: Columnar = faster column access, less data read [OK]

Common Mistakes:

• Thinking all formats compress data the same way
• Confusing row-based with column-based storage
• Assuming JSON is always faster due to readability

2. Which of the following is the correct way to read a Parquet file in Apache Spark using PySpark?

easy

A. spark.read.parquet('data.parquet')

B. spark.read.csv('data.parquet')

C. spark.load('data.parquet')

D. spark.read.json('data.parquet')

Solution

1. Step 1: Recall Spark read syntax

   Spark uses spark.read.format() or shortcut methods like parquet() to read files.

2. Step 2: Identify correct method for Parquet

   spark.read.parquet() is the correct method to read Parquet files.

3. Final Answer:

   spark.read.parquet('data.parquet') -> Option A

4. Quick Check:

   Use spark.read.parquet() for Parquet files [OK]

Hint: Use spark.read.parquet() to read Parquet files [OK]

Common Mistakes:

• Using csv() or json() methods for Parquet files
• Trying to use spark.load() which is invalid
• Confusing file format with method name

3. Given the following PySpark code, what will be the output type of df.count() when reading a Parquet file?

df = spark.read.parquet('data.parquet')
count = df.count()

medium

A. A DataFrame containing the count

B. An integer representing the number of rows

C. A string describing the count

D. A list of counts per column

Solution

1. Step 1: Understand df.count() method

   The count() method returns the total number of rows as an integer.

2. Step 2: Confirm output type

   Since count() returns a number, the output is an integer, not a DataFrame or list.

3. Final Answer:

   An integer representing the number of rows -> Option B

4. Quick Check:

   df.count() returns integer row count [OK]

Hint: count() returns integer row count [OK]

Common Mistakes:

• Expecting count() to return a DataFrame
• Thinking count() returns a string or list
• Confusing count() with collect()

4. You try to read a JSON file with spark.read.parquet('data.json') and get an error. What is the most likely cause?

medium

A. Spark cannot read any file formats other than Parquet

B. The file path is incorrect

C. The JSON file is empty

D. You are using the wrong read method for the file format

Solution

1. Step 1: Identify file format mismatch

   The code uses read.parquet() on a JSON file, which causes a format error.

2. Step 2: Correct method for JSON files

   Use spark.read.json() to read JSON files correctly.

3. Final Answer:

   You are using the wrong read method for the file format -> Option D

4. Quick Check:

   Use matching read method for file format [OK]

Hint: Match read method to file format (json, parquet, csv) [OK]

Common Mistakes:

• Assuming file path is always the problem
• Thinking Spark only reads Parquet files
• Ignoring file format when reading data

5. You have a large dataset stored as CSV and want to improve query speed in Spark. Which approach best improves performance while minimizing storage size?

hard

A. Convert CSV to Parquet format and use Spark SQL queries

B. Keep CSV but compress files with gzip

C. Convert CSV to JSON format for better readability

D. Split CSV into many small files without changing format

Solution

1. Step 1: Understand format impact on performance

   Parquet is columnar and compressed, improving query speed and reducing storage.

2. Step 2: Compare alternatives

   Compressing CSV helps storage but not query speed; JSON is slower; many small files hurt performance.

3. Final Answer:

   Convert CSV to Parquet format and use Spark SQL queries -> Option A

4. Quick Check:

   Parquet = faster queries + smaller size [OK]

Hint: Convert to Parquet for speed and compression [OK]

Common Mistakes:

• Thinking compression alone speeds queries
• Choosing JSON for performance
• Splitting files without format change improves speed