Mastering PySpark Window Functions: A Comprehensive Guide with Real-world Examples

Credit: Seaart.ai

Hey there, fellow data engineers! 👋 After spending years working with PySpark in production environments, I’ve come to appreciate the sheer power of window functions. Today, I’m excited to share my knowledge and practical experiences with you through this comprehensive guide.

Setting the Stage: Creating Our Dataset

First, let’s create a realistic sales dataset that we’ll use throughout this article. I’ve crafted a function that generates 1 million rows of sales data — something similar to what you might encounter in the real world.

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.window import Window
import random
from datetime import datetime, timedelta

def create_sales_dataset(spark):
    # Helper function to generate random dates
    def random_dates(start_date, end_date, n):
        time_between = end_date - start_date
        days_between = time_between.days
        return [start_date + timedelta(days=random.randint(0, days_between)) for _ in range(n)]

    # Generate 1 million records
    n_records = 1000000

    # Generate random data
    product_categories = ['Electronics', 'Clothing', 'Books', 'Home & Garden', 'Sports']
    regions = ['North', 'South', 'East', 'West', 'Central']…