Windowed aggregations in Apache Spark - Mini Project: Build & Apply

Windowed aggregations

📖 Scenario: You work for a retail company that tracks daily sales of products. You want to analyze sales trends by calculating the total sales for each product over a moving 3-day window.

🎯 Goal: Build a Spark program that uses windowed aggregations to calculate the 3-day rolling sum of sales for each product.

📋 What You'll Learn

Create a Spark DataFrame with sales data for products over several days

Define a window specification partitioned by product and ordered by date

Use a window function to calculate the rolling 3-day sum of sales

Display the final DataFrame with the rolling sums

💡 Why This Matters

🌍 Real World

Windowed aggregations help businesses analyze trends over time, like moving averages or rolling sums, which are useful for sales forecasting and inventory management.

💼 Career

Data scientists and analysts use window functions in Spark to efficiently compute time-based metrics on large datasets, a common task in many data-driven roles.

1

Create the sales DataFrame

Create a Spark DataFrame called sales_df with these exact rows: ("2024-01-01", "A", 10), ("2024-01-02", "A", 20), ("2024-01-03", "A", 30), ("2024-01-01", "B", 5), ("2024-01-02", "B", 15), ("2024-01-03", "B", 25). The columns must be named date (string), product (string), and sales (integer).

Apache Spark

from pyspark.sql import SparkSession

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

# Create the sales_df DataFrame with the specified rows and columns
# Your code here

Hint

Use spark.createDataFrame() with a list of tuples and specify the column names as a list.

2

Define the window specification

Create a window specification called window_spec that partitions data by product and orders by date. The window frame should include the current row and the two previous rows (3-day window). Use Window.partitionBy("product").orderBy("date").rowsBetween(-2, 0).

Apache Spark

from pyspark.sql import Window

# Define the window_spec with partitionBy product, orderBy date, and rowsBetween -2 and 0
# Your code here

Hint

Import Window from pyspark.sql and chain partitionBy, orderBy, and rowsBetween.

3

Calculate the rolling 3-day sum

Create a new DataFrame called result_df by adding a column named rolling_sum to sales_df. Use the sum function over the window_spec on the sales column.

Apache Spark

from pyspark.sql.functions import sum

# Add a column rolling_sum to sales_df using sum over window_spec
# Your code here

Hint

Use withColumn on sales_df and apply sum("sales").over(window_spec).

4

Display the rolling sums

Use print to display the contents of result_df by calling result_df.show().

Apache Spark

# Display the result_df DataFrame
# Your code here

Hint

Call result_df.show() to print the DataFrame to the console.

Practice

1. What is the main purpose of using windowed aggregations in Apache Spark?

easy

A. To perform calculations over a group of rows without collapsing them into a single result

B. To join two DataFrames based on a key column

C. To filter rows based on a condition

D. To sort the entire DataFrame globally

Solution

1. Step 1: Understand windowed aggregation concept

   Windowed aggregations allow calculations like sums or averages over a set of rows related to the current row, without reducing the number of rows.

2. Step 2: Compare with other operations

   Joins combine tables, filters remove rows, and sorting orders rows globally, but do not perform grouped calculations without collapsing rows.

3. Final Answer:

   To perform calculations over a group of rows without collapsing them into a single result -> Option A

4. Quick Check:

   Windowed aggregation = grouped calculation without row collapse [OK]

Hint: Windowed means grouped but keeps all rows [OK]

Common Mistakes:

• Confusing windowed aggregation with join operations
• Thinking windowed aggregation filters rows
• Assuming windowed aggregation sorts globally

2. Which of the following is the correct way to define a window specification that partitions data by 'department' and orders by 'salary' in ascending order?

easy

A. Window.orderBy('department').partitionBy('salary')

B. Window.partitionBy('department').orderBy('salary')

C. Window.partitionBy('salary').orderBy('department')

D. Window.orderBy('salary').partitionBy('department')

Solution

1. Step 1: Recall window specification syntax

   In Spark, window specs use partitionBy first to group, then orderBy to sort within groups.

2. Step 2: Check option order and columns

   Window.partitionBy('department').orderBy('salary') correctly partitions by 'department' and orders by 'salary'. Other options reverse or swap columns incorrectly.

3. Final Answer:

   Window.partitionBy('department').orderBy('salary') -> Option B

4. Quick Check:

   partitionBy then orderBy = correct window spec [OK]

Hint: partitionBy groups first, orderBy sorts inside groups [OK]

Common Mistakes:

• Swapping partitionBy and orderBy order
• Using wrong columns in partitionBy or orderBy
• Confusing partitionBy with filter

3. Given the following Spark code, what will be the output for the 'running_total' column?

from pyspark.sql import SparkSession
from pyspark.sql.window import Window
from pyspark.sql.functions import sum

spark = SparkSession.builder.getOrCreate()
data = [(1, 'A', 10), (2, 'A', 20), (3, 'A', 30), (4, 'B', 40)]
df = spark.createDataFrame(data, ['id', 'group', 'value'])

windowSpec = Window.partitionBy('group').orderBy('id').rowsBetween(Window.unboundedPreceding, Window.currentRow)
df = df.withColumn('running_total', sum('value').over(windowSpec))
df.orderBy('id').show()

medium

A. [10, 30, 60, 100]

B. [10, 20, 30, 40]

C. [10, 30, 60, 40]

D. [10, 20, 50, 40]

Solution

1. Step 1: Understand window specification

   The window partitions by 'group' and orders by 'id'. The frame includes all rows from the start to the current row in that group.

2. Step 2: Calculate running totals per group

   For group 'A': id=1 value=10 running_total=10; id=2 value=20 running_total=10+20=30; id=3 value=30 running_total=10+20+30=60. For group 'B': id=4 value=40 running_total=40.

3. Final Answer:

   [10, 30, 60, 40] -> Option C

4. Quick Check:

   Running total sums values up to current row per group [OK]

Hint: Running total sums values up to current row in partition [OK]

Common Mistakes:

• Not partitioning by group, summing all rows
• Misunderstanding rowsBetween frame
• Mixing order of rows in calculation

4. Identify the error in the following Spark code snippet that tries to calculate a moving average over the last 2 rows including the current row:

from pyspark.sql.window import Window
from pyspark.sql.functions import avg

windowSpec = Window.partitionBy('category').orderBy('date').rowsBetween(-2, 0)
df = df.withColumn('moving_avg', avg('sales').over(windowSpec))

medium

A. partitionBy should be orderBy for moving average

B. orderBy column 'date' must be included in select

C. avg function cannot be used with window functions

D. rowsBetween frame is incorrect; it should be rowsBetween(-1, 0) for last 2 rows

Solution

1. Step 1: Understand rowsBetween frame

   rowsBetween(-2, 0) includes current row and two preceding rows, totaling 3 rows, not 2.

2. Step 2: Correct frame for last 2 rows

   To include current and previous row (2 rows total), use rowsBetween(-1, 0).

3. Final Answer:

   rowsBetween frame is incorrect; it should be rowsBetween(-1, 0) for last 2 rows -> Option D

4. Quick Check:

   rowsBetween(-1,0) = last 2 rows including current [OK]

Hint: rowsBetween counts rows including current, so -1 means one before [OK]

Common Mistakes:

• Using wrong rowsBetween range for desired window size
• Confusing partitionBy and orderBy roles
• Thinking avg can't be used with window

5. You have a sales DataFrame with columns: 'store', 'date', and 'sales'. You want to calculate a 7-day moving average of sales per store, but some dates are missing for certain stores. Which approach correctly handles missing dates to compute the moving average using windowed aggregations?

hard

A. Fill missing dates with zero sales before applying a window partitioned by 'store' and ordered by 'date' with rowsBetween(-6, 0)

B. Use partitionBy('store') and orderBy('date') with rowsBetween(-7, 0) directly on original data without filling missing dates

C. Apply a global window without partitioning and order by 'date' only

D. Calculate moving average using groupBy('store', 'date') and avg('sales') without window functions

Solution

1. Step 1: Understand missing dates impact

   Missing dates cause gaps in ordering, so rowsBetween(-6, 0) won't cover full 7 days if dates are missing.

2. Step 2: Fill missing dates with zero sales

   Filling missing dates ensures continuous date sequence per store, so window frame covers exact 7 days.

3. Step 3: Apply window with partition and order

   Partition by 'store', order by 'date', and use rowsBetween(-6, 0) to get 7-day moving average correctly.

4. Final Answer:

   Fill missing dates with zero sales before applying a window partitioned by 'store' and ordered by 'date' with rowsBetween(-6, 0) -> Option A

5. Quick Check:

   Fill gaps first, then window for accurate moving average [OK]

Hint: Fill missing dates to avoid gaps in window frame [OK]

Common Mistakes:

• Ignoring missing dates causing wrong window frames
• Using global window ignoring store partitions
• Using groupBy instead of window for moving average