Parquet format and columnar storage in Apache Spark

Introduction

Parquet format stores data by columns, making it faster and smaller to read and write. It helps analyze big data efficiently.

When you want to save large datasets for fast reading later.

When you only need to read a few columns from a big table.

When you want to reduce storage space for your data.

When working with big data tools like Apache Spark or Hadoop.

When you want to improve query speed on large datasets.

Syntax

Apache Spark

df.write.parquet("path/to/save")
df = spark.read.parquet("path/to/save")

Use write.parquet() to save a DataFrame in Parquet format.

Use read.parquet() to load Parquet files back into a DataFrame.

Examples

Saves the DataFrame df as a Parquet file in the folder data/output.parquet.

Apache Spark

df.write.parquet("data/output.parquet")

Loads the Parquet file back into a new DataFrame df2.

Apache Spark

df2 = spark.read.parquet("data/output.parquet")

Saves only the name and age columns to Parquet, saving space and time.

Apache Spark

df.select("name", "age").write.parquet("data/names_ages.parquet")

Sample Program

This program creates a small DataFrame with people data, saves it as a Parquet file, then reads it back and shows the content. It demonstrates how Parquet stores data efficiently and can be reused easily.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ParquetExample").getOrCreate()

# Create a simple DataFrame
data = [(1, "Alice", 29), (2, "Bob", 31), (3, "Cathy", 25)]
columns = ["id", "name", "age"]
df = spark.createDataFrame(data, columns)

# Save DataFrame as Parquet
parquet_path = "./people.parquet"
df.write.mode("overwrite").parquet(parquet_path)

# Read the Parquet file back
df_parquet = spark.read.parquet(parquet_path)

# Show the loaded data
print("Data loaded from Parquet:")
df_parquet.show()

Important Notes

Parquet files store data by columns, so reading only needed columns is faster.

Parquet compresses data automatically, saving disk space.

Always use mode("overwrite") if you want to replace existing Parquet files.

Summary

Parquet format stores data column-wise for faster and smaller storage.

Use write.parquet() and read.parquet() in Spark to save and load data.

Parquet is great for big data and improves query speed and storage efficiency.

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