Parquet format and columnar storage in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of reading Parquet file with Spark

What will be the output of the following Spark code snippet when reading a Parquet file and showing the first 3 rows?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('Test').getOrCreate()
df = spark.read.parquet('data/sample.parquet')
df.show(3)

A Throws a SyntaxError due to incorrect method usage

B Displays first 3 rows of the DataFrame with all columns in tabular format

C Raises FileNotFoundError because 'data/sample.parquet' does not exist

D Prints schema of the DataFrame instead of data rows

💡 Hint

The show() method displays rows of the DataFrame in a table format.

The show(3) method prints the first 3 rows of the DataFrame read from the Parquet file. If the file exists and is readable, it outputs the data in tabular form.

🧠 Conceptual

intermediate

Why is Parquet format efficient for big data?

Which of the following best explains why Parquet format is efficient for big data processing?

A Parquet compresses data and stores it in a columnar format allowing faster reads of specific columns

B Parquet stores data in a row-based format making it faster for all queries

C Parquet requires no schema which speeds up data loading

D Parquet stores data as plain text making it easy to read

💡 Hint

Think about how columnar storage helps with reading only needed data.

Parquet stores data in a columnar format which allows reading only the columns needed for a query. It also compresses data efficiently, reducing storage and speeding up processing.

❓ data_output

advanced

Result of filtering Parquet data by column

Given a Parquet file with columns 'name', 'age', and 'city', what will be the output count after running this Spark code?

Apache Spark

df = spark.read.parquet('data/people.parquet')
filtered_df = df.filter(df.age > 30)
count = filtered_df.count()
print(count)

A Total number of rows in the DataFrame regardless of age

B Raises AnalysisException due to missing column 'age'

C Zero, because filter syntax is incorrect

D Number of rows where age is greater than 30

💡 Hint

Filter keeps only rows matching the condition.

The filter(df.age > 30) keeps rows where age is greater than 30. The count() returns how many such rows exist.

🔧 Debug

advanced

Identify the error in Parquet write code

What error will this Spark code produce when trying to write a DataFrame to Parquet?

Apache Spark

df = spark.createDataFrame([(1, 'Alice'), (2, 'Bob')], ['id', 'name'])
df.write.parquet('output/data.parquet', mode='overwrite', format='csv')

A TypeError because 'format' is not a valid argument for write.parquet()

B Writes the DataFrame successfully in CSV format

C AnalysisException due to missing output directory

D SyntaxError due to incorrect method chaining

💡 Hint

Check the method signature for write.parquet().

The write.parquet() method does not accept a 'format' argument. To write in CSV, use write.format('csv').save().

🚀 Application

expert

Optimizing query performance with Parquet and column pruning

You have a large Parquet dataset with many columns. You want to speed up a Spark query that only needs 'user_id' and 'purchase_amount'. Which approach will best improve performance?

A Read the entire Parquet file and then select 'user_id' and 'purchase_amount' columns

B Convert Parquet to CSV first, then read only needed columns

C Use Spark to read only 'user_id' and 'purchase_amount' columns from the Parquet file directly

D Load Parquet data into memory fully before filtering columns

💡 Hint

Think about how columnar storage helps avoid reading unnecessary data.

Parquet's columnar format allows Spark to read only the requested columns, reducing I/O and speeding up queries. Selecting columns after reading all data wastes resources.

Practice

1. What is the main advantage of storing data in Parquet format in Apache Spark?

easy

A. It compresses data by converting it to images.

B. It stores data as plain text for easy reading.

C. It stores data only in memory without saving to disk.

D. It stores data column-wise, making queries faster and storage smaller.

Solution

1. Step 1: Understand Parquet storage method

   Parquet stores data in a columnar format, meaning data is saved column by column instead of row by row.

2. Step 2: Benefits of columnar storage

   This column-wise storage allows faster queries on specific columns and reduces storage size due to better compression.

3. Final Answer:

   It stores data column-wise, making queries faster and storage smaller. -> Option D

4. Quick Check:

   Columnar storage = faster queries + smaller files [OK]

Hint: Remember: Parquet = column-wise storage for speed and size [OK]

Common Mistakes:

• Thinking Parquet stores data as plain text
• Confusing row-wise with column-wise storage
• Assuming Parquet only stores in memory

2. Which of the following is the correct way to save a Spark DataFrame named df as a Parquet file?

easy

A. df.saveAsParquet('path/to/file')

B. df.write.parquet('path/to/file')

C. df.write.save('path/to/file', format='csv')

D. df.save('path/to/file', format='parquet')

Solution

1. Step 1: Recall Spark DataFrame write syntax

   In Spark, to save a DataFrame as Parquet, use the write.parquet() method.

2. Step 2: Check each option

   df.write.parquet('path/to/file') uses df.write.parquet('path'), which is correct. Others use incorrect methods or formats.

3. Final Answer:

   df.write.parquet('path/to/file') -> Option B

4. Quick Check:

   Use write.parquet() to save DataFrame [OK]

Hint: Use df.write.parquet(path) to save DataFrame as Parquet [OK]

Common Mistakes:

• Using saveAsParquet which does not exist
• Saving with wrong format like CSV
• Using df.save() without write

3. Given the following Spark code, what will be the output schema of the loaded DataFrame?

df.write.parquet('data.parquet')
df2 = spark.read.parquet('data.parquet')
df2.printSchema()

medium

A. The schema will be the same as the original DataFrame df.

B. The schema will be empty because Parquet does not store schema.

C. The schema will only contain column names but no data types.

D. The schema will be reversed compared to df.

Solution

1. Step 1: Understand Parquet schema storage

   Parquet files store both data and schema (column names and types).

2. Step 2: Loading Parquet preserves schema

   When reading with spark.read.parquet(), the schema is restored exactly as saved.

3. Final Answer:

   The schema will be the same as the original DataFrame df. -> Option A

4. Quick Check:

   Parquet stores schema = schema preserved [OK]

Hint: Parquet files keep schema; reading restores it fully [OK]

Common Mistakes:

• Thinking Parquet files do not store schema
• Assuming schema is lost or incomplete
• Believing schema order changes on read

4. You try to read a Parquet file using spark.read.parquet('file_path') but get an error. Which of the following is the most likely cause?

medium

A. The file path is incorrect or the file does not exist.

B. Parquet files cannot be read by Spark.

C. You must use read.csv() to read Parquet files.

D. The DataFrame was not saved before reading.

Solution

1. Step 1: Check common read errors

   Errors reading Parquet usually happen if the file path is wrong or file is missing.

2. Step 2: Verify other options

   Parquet files are supported by Spark, so B and C are wrong. D is unrelated because reading does not depend on saving in the same session.

3. Final Answer:

   The file path is incorrect or the file does not exist. -> Option A

4. Quick Check:

   File not found = read error [OK]

Hint: Check file path and existence before reading Parquet [OK]

Common Mistakes:

• Assuming Spark cannot read Parquet
• Using wrong read method like read.csv
• Thinking saving is required in same session

5. You have a large Spark DataFrame with many columns but only need to query a few columns frequently. How does using Parquet format help improve performance?

hard

A. Parquet stores data row-wise, so all columns are always read together.

B. Parquet converts all columns to JSON strings for faster parsing.

C. Parquet stores data column-wise, so reading only needed columns is faster and uses less disk I/O.

D. Parquet compresses data by removing all null values automatically.

Solution

1. Step 1: Recall Parquet columnar storage benefits

   Parquet stores data column-wise, allowing queries to read only selected columns.

2. Step 2: Understand impact on performance

   Reading fewer columns reduces disk I/O and speeds up queries, especially on large datasets.

3. Final Answer:

   Parquet stores data column-wise, so reading only needed columns is faster and uses less disk I/O. -> Option C

4. Quick Check:

   Columnar storage = selective fast reads [OK]

Hint: Parquet reads only needed columns, saving time and I/O [OK]

Common Mistakes:

• Thinking Parquet stores data row-wise
• Believing Parquet converts data to JSON
• Assuming Parquet removes nulls automatically