Window functions in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the window function module in PySpark.

Apache Spark

from pyspark.sql import [1]

Drag options to blanks, or click blank then click option'

Asql

Bwindow

Cfunctions

DWindow

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'functions' instead of 'Window' for window specifications.

Importing 'window' in lowercase which does not exist.

The correct import for window functions in PySpark is 'Window' from pyspark.sql.

2fill in blank

medium

Complete the code to create a window specification partitioned by 'department'.

Apache Spark

from pyspark.sql import Window
windowSpec = Window.partitionBy([1])

Drag options to blanks, or click blank then click option'

A'department'

B'salary'

C'age'

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a numeric column like 'salary' instead of a categorical one.

Not using quotes around the column name.

Partitioning by 'department' groups rows by department for window calculations.

3fill in blank

hard

Fix the error in the code to calculate the row number over the window specification.

Apache Spark

from pyspark.sql.functions import row_number
result = df.withColumn('row_num', row_number().over([1]))

Drag options to blanks, or click blank then click option'

Awindow

BwindowSpec

CWindow

Dwindow_spec

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Window' class instead of the window specification instance.

Using lowercase 'window' which is undefined.

The window specification variable is named 'windowSpec' and must be passed to 'over()'.

4fill in blank

hard

Fill both blanks to create a window specification partitioned by 'department' and ordered by 'salary' descending.

Apache Spark

windowSpec = Window.partitionBy([1]).orderBy(col([2]).desc())

Drag options to blanks, or click blank then click option'

A'department'

B'salary'

C'age'

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering by a non-numeric column like 'name'.

Not using the correct column names as strings.

Partition by 'department' and order by 'salary' descending to rank salaries within each department.

5fill in blank

hard

Fill all three blanks to calculate the cumulative sum of 'sales' partitioned by 'region' and ordered by 'date'.

Apache Spark

from pyspark.sql.functions import sum
windowSpec = Window.partitionBy([1]).orderBy([2])
cum_sum = sum([3]).over(windowSpec)

Drag options to blanks, or click blank then click option'

A'region'

B'date'

C'sales'

D'profit'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'profit' instead of 'sales' for the sum.

Mixing up partition and order columns.

Partition by 'region', order by 'date', and sum 'sales' cumulatively over the window.

Practice

1. What is the main purpose of window functions in Apache Spark?

easy

A. To perform calculations across rows related to the current row without collapsing the data

B. To group data and reduce the number of rows

C. To filter rows based on a condition

D. To join two datasets together

Solution

1. Step 1: Understand window function behavior

   Window functions calculate values across related rows but keep all original rows intact.

2. Step 2: Compare with grouping

   Grouping reduces rows by aggregation, but window functions add columns without reducing rows.

3. Final Answer:

   To perform calculations across rows related to the current row without collapsing the data -> Option A

4. Quick Check:

   Window functions add calculations without grouping ≠ A [OK]

Hint: Window functions add columns, grouping reduces rows [OK]

Common Mistakes:

• Confusing window functions with groupBy aggregations
• Thinking window functions filter rows
• Assuming window functions join datasets

2. Which of the following is the correct way to define a window specification in PySpark to partition by 'department' and order by 'salary'?

easy

A. Window.groupBy('department').sortBy('salary')

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

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

D. Window.partition('department').order('salary')

Solution

1. Step 1: Recall PySpark window spec syntax

   The correct methods are partitionBy() and orderBy() chained on Window.

2. Step 2: Check method order and names

   partitionBy comes first, then orderBy; groupBy and sortBy are incorrect here.

3. Final Answer:

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

4. Quick Check:

   Use partitionBy then orderBy methods [OK]

Hint: Use Window.partitionBy().orderBy() exactly [OK]

Common Mistakes:

• Using groupBy instead of partitionBy
• Using sortBy instead of orderBy
• Using incorrect method names like partition or order

3. Given the following PySpark code, what will be the output of the 'rank' column?

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

spark = SparkSession.builder.getOrCreate()
data = [("Alice", "Sales", 5000), ("Bob", "Sales", 4000), ("Charlie", "Sales", 5000)]
df = spark.createDataFrame(data, ["name", "department", "salary"])
windowSpec = Window.partitionBy("department").orderBy("salary")
df = df.withColumn("rank", rank().over(windowSpec))
df.orderBy("salary").show()

medium

A. [ ("Bob", 4000, 1), ("Alice", 5000, 2), ("Charlie", 5000, 2) ]

B. [ ("Bob", 4000, 1), ("Alice", 5000, 3), ("Charlie", 5000, 3) ]

C. [ ("Bob", 4000, 1), ("Alice", 5000, 2), ("Charlie", 5000, 3) ]

D. [ ("Bob", 4000, 1), ("Alice", 5000, 1), ("Charlie", 5000, 1) ]

Solution

1. Step 1: Understand rank() behavior

   Rank assigns the same rank to ties and skips ranks after ties.

2. Step 2: Apply rank over salary ascending

   Bob has salary 4000 ranked 1; Alice and Charlie both have 5000, so both get rank 2.

3. Final Answer:

   [ ("Bob", 4000, 1), ("Alice", 5000, 2), ("Charlie", 5000, 2) ] -> Option A

4. Quick Check:

   rank() ties share rank, next rank skips [OK]

Hint: rank() gives same rank to ties, skips next ranks [OK]

Common Mistakes:

• Confusing rank() with dense_rank()
• Assigning different ranks to tied values
• Not ordering data before applying rank

4. Identify the error in this PySpark code snippet using window functions:

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

windowSpec = Window.partitionBy('department')
df = df.withColumn('row_num', row_number().over(windowSpec.orderBy('salary')))

medium

A. Missing import for Window functions causes error

B. windowSpec.orderBy('salary') is valid; no error in code

C. row_number() cannot be used with partitionBy

D. windowSpec.orderBy('salary') is invalid; orderBy should be called on Window, not on windowSpec

Solution

1. Step 1: Check windowSpec usage

   windowSpec is a WindowSpec object; calling orderBy on it is valid and returns a new WindowSpec.

2. Step 2: Confirm chaining

   Chaining orderBy on windowSpec is allowed to refine the window specification.

3. Final Answer:

   windowSpec.orderBy('salary') is valid; no error in code -> Option B

4. Quick Check:

   WindowSpec supports chaining orderBy to refine window [OK]

Hint: WindowSpec supports chaining orderBy to refine window [OK]

Common Mistakes:

• Calling orderBy on windowSpec instead of Window
• Assuming row_number can't be used with partitionBy
• Forgetting to import Window or functions

5. You have a sales dataset with columns: 'store', 'date', and 'sales'. You want to add a column showing the 3-day moving average of sales per store ordered by date. Which PySpark window function setup and code will achieve this?

hard

A. Use Window.partitionBy('store').orderBy('date').rowsBetween(0, 2) and avg('sales').over(windowSpec)

B. Use Window.partitionBy('store').orderBy('date').rangeBetween(-2, 0) and avg('sales').over(windowSpec)

C. Use Window.orderBy('date').rowsBetween(-3, 0) and avg('sales').over(windowSpec)

D. Use Window.partitionBy('store').orderBy('date').rowsBetween(-2, 0) and avg('sales').over(windowSpec)

Solution

1. Step 1: Define window for 3-day moving average per store

   Partition by 'store' to separate stores, order by 'date' for time sequence, use rowsBetween(-2, 0) for current and previous 2 rows.

2. Step 2: Apply avg() over this window

   Use avg('sales').over(windowSpec) to compute moving average over the defined window.

3. Final Answer:

   Use Window.partitionBy('store').orderBy('date').rowsBetween(-2, 0) and avg('sales').over(windowSpec) -> Option D

4. Quick Check:

   rowsBetween(-2,0) for 3-day window, partition by store [OK]

Hint: Use rowsBetween(-2,0) for 3-row moving average [OK]

Common Mistakes:

• Using rangeBetween instead of rowsBetween for row count
• Not partitioning by store
• Using wrong rowsBetween range (like 0 to 2)