Parquet format and columnar storage in Apache Spark - Time & Space Complexity

Time Complexity: Parquet format and columnar storage

O(n)

Understanding Time Complexity

We want to understand how reading data stored in Parquet format grows as the data size increases.

How does the columnar storage affect the time it takes to read specific columns?

Scenario Under Consideration

Analyze the time complexity of the following Apache Spark code reading Parquet data.

df = spark.read.parquet("/data/events.parquet")
selected_df = df.select("user_id", "event_type")
result = selected_df.filter(selected_df.event_type == "click").collect()

This code reads a Parquet file, selects two columns, filters rows where event_type is "click", and collects the results.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Scanning rows in the Parquet file to filter and select columns.
• How many times: Once for each row in the dataset.

How Execution Grows With Input

Reading and filtering grows roughly in proportion to the number of rows because each row is checked once.

Input Size (n rows)	Approx. Operations
10	About 10 row checks
100	About 100 row checks
1000	About 1000 row checks

Pattern observation: The work grows linearly as the number of rows increases.

Final Time Complexity

Time Complexity: O(n)

This means the time to read and filter grows directly with the number of rows in the data.

Common Mistake

[X] Wrong: "Because Parquet is columnar, reading any column is always constant time regardless of data size."

[OK] Correct: Even though Parquet stores columns separately, reading a column still requires scanning all rows in that column, so time grows with the number of rows.

Interview Connect

Understanding how columnar storage affects data reading helps you explain performance in real data tasks clearly and confidently.

Self-Check

"What if we only read a small subset of columns from a very wide Parquet file? How would the time complexity change?"

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