Parquet Format in PySpark: What It Is and How It Works

In PySpark, Parquet is a columnar storage file format that stores data efficiently by organizing it by columns instead of rows. It supports compression and encoding schemes, making data reading and writing faster and saving storage space.

How It Works

Imagine you have a big spreadsheet with many rows and columns. Normally, data is stored row by row, like reading a book line by line. Parquet changes this by storing data column by column, like reading all the first words of each line together, then all the second words, and so on. This helps when you only need some columns because you can skip reading the others.

Parquet also compresses data and uses smart encoding to reduce file size. This means it takes less space on disk and loads faster when you process it with PySpark. Because of its design, Parquet is great for big data tasks where speed and storage efficiency matter.

Example

This example shows how to save a PySpark DataFrame as a Parquet file and then read it back.

python

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)

# Write DataFrame to Parquet format
parquet_path = '/tmp/people.parquet'
df.write.mode('overwrite').parquet(parquet_path)

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

Output

+---+-----+---+
| id| name|age|
+---+-----+---+
|  1|Alice| 29|
|  2|  Bob| 31|
|  3|Cathy| 25|
+---+-----+---+

When to Use

Use Parquet in PySpark when working with large datasets that need fast read and write operations. It is especially useful for analytics and reporting where you often select only a few columns from a big table.

Parquet is ideal for data lakes, ETL pipelines, and machine learning workflows because it reduces storage costs and speeds up processing. If you want to save data efficiently and load it quickly later, Parquet is a great choice.

Key Points

• Parquet stores data by columns, not rows, improving speed for column queries.
• It supports compression and encoding to save storage space.
• Parquet files are compatible with many big data tools, including PySpark.
• It is best for large datasets and analytical workloads.

Key Takeaways

Parquet is a columnar storage format that improves speed and storage efficiency in PySpark.

It compresses data and uses encoding to reduce file size.

Parquet is ideal for big data analytics and selective column queries.

Use Parquet for faster data processing and lower storage costs in PySpark workflows.