Window function in PySpark — one stop to master it all

Sit patiently and and just follow along. Just reading will not help, copy paste the code first to get to know the syntax. Then start creating your own examples with sample data. There are 13 examples , please do one-by-one to get the basics and do the advance levels

Window functions in PySpark are powerful tools for performing operations like ranking, aggregations, and calculations across a subset of data within a defined "window." Unlike normal aggregations, window functions do not reduce the number of rows in the dataset.

Components of Window Functions

1. Partition: Divides the data into groups.
2. Order: Specifies the order of rows within a partition.
3. Window Specification: Defines the frame of rows over which the function operates.

Example Use Cases

1. Ranking rows within a partition (e.g., dense_rank, rank)
2. Calculating cumulative sums or averages
3. Fetching lead/lag values
4. Row numbering within partitions

Code Examples

Setup

from pyspark.sql import SparkSession
from pyspark.sql.window import Window
from pyspark.sql.functions import col, row_number, rank, dense_rank, sum, avg, lead, lag

# Create Spark session
spark = SparkSession.builder \\
    .appName("Window Functions Example") \\
    .getOrCreate()

# Sample data
data = [
    ("Alice", "HR", 1000),
    ("Bob", "HR", 1200),
    ("Cathy", "IT", 1500),
    ("David", "IT", 1700),
    ("Eve", "IT", 1600),
    ("Frank", "HR", 1100),
]

columns = ["Name", "Department", "Salary"]
df = spark.createDataFrame(data, columns)
df.show()

        

1. Row Number

Assign a unique row number within each partition.

window_spec = Window.partitionBy("Department").orderBy(col("Salary").desc())

df.withColumn("row_number", row_number().over(window_spec)).show()

        

Output:

+-----+----------+------+----------+
| Name|Department|Salary|row_number|
+-----+----------+------+----------+
|  Bob|        HR|  1200|         1|
|Frank|        HR|  1100|         2|
|Alice|        HR|  1000|         3|
|David|        IT|  1700|         1|
|  Eve|        IT|  1600|         2|
|Cathy|        IT|  1500|         3|
+-----+----------+------+----------+

        

2. Rank

Assign ranks to rows within a partition based on order, with gaps in ranking.

df.withColumn("rank", rank().over(window_spec)).show()

        

Output:

+-----+----------+------+----+
| Name|Department|Salary|rank|
+-----+----------+------+----+
|  Bob|        HR|  1200|   1|
|Frank|        HR|  1100|   2|
|Alice|        HR|  1000|   3|
|David|        IT|  1700|   1|
|  Eve|        IT|  1600|   2|
|Cathy|        IT|  1500|   3|
+-----+----------+------+----+

        

3. Dense Rank

Similar to rank() but without gaps in ranking.

df.withColumn("dense_rank", dense_rank().over(window_spec)).show()

        

4. Cumulative Sum

Calculate a running total of salaries within a department.

df.withColumn("cumulative_sum", sum("Salary").over(window_spec)).show()

        

5. Lead and Lag

Retrieve next (lead) or previous (lag) row's value in a partition.

df.withColumn("next_salary", lead("Salary").over(window_spec)) \\
  .withColumn("prev_salary", lag("Salary").over(window_spec)).show()

        

Output:

+-----+----------+------+-----------+-----------+
| Name|Department|Salary|next_salary|prev_salary|
+-----+----------+------+-----------+-----------+
|  Bob|        HR|  1200|       1100|       null|
|Frank|        HR|  1100|       1000|       1200|
|Alice|        HR|  1000|       null|       1100|
|David|        IT|  1700|       1600|       null|
|  Eve|        IT|  1600|       1500|       1700|
|Cathy|        IT|  1500|       null|       1600|
+-----+----------+------+-----------+-----------+

        

Summary

Window functions enable complex, row-wise calculations while retaining the original number of rows. Key functions include:

• Ranking: row_number, rank, dense_rank
• Aggregates: sum, avg, min, max
• Lag/Lead: lag, lead

6. Average (Moving Average or Rolling Average)

Calculating the average salary within each department.

df.withColumn("average_salary", avg("Salary").over(window_spec)).show()

        

Output:

+-----+----------+------+--------------+
| Name|Department|Salary|average_salary|
+-----+----------+------+--------------+
|  Bob|        HR|  1200|        1200.0|
|Frank|        HR|  1100|        1150.0|
|Alice|        HR|  1000|        1100.0|
|David|        IT|  1700|        1700.0|
|  Eve|        IT|  1600|        1650.0|
|Cathy|        IT|  1500|        1600.0|
+-----+----------+------+--------------+

        

7. Min and Max within Partitions

Finding the minimum and maximum salaries in each department.

from pyspark.sql.functions import min, max

df.withColumn("min_salary", min("Salary").over(Window.partitionBy("Department"))) \\
  .withColumn("max_salary", max("Salary").over(Window.partitionBy("Department"))).show()

        

Output:

+-----+----------+------+----------+----------+
| Name|Department|Salary|min_salary|max_salary|
+-----+----------+------+----------+----------+
|  Bob|        HR|  1200|      1000|      1200|
|Frank|        HR|  1100|      1000|      1200|
|Alice|        HR|  1000|      1000|      1200|
|David|        IT|  1700|      1500|      1700|
|  Eve|        IT|  1600|      1500|      1700|
|Cathy|        IT|  1500|      1500|      1700|
+-----+----------+------+----------+----------+

        

8. Percentage Rank

Calculating the percentage rank within a department.

from pyspark.sql.functions import percent_rank

df.withColumn("percent_rank", percent_rank().over(window_spec)).show()

        

Output:

+-----+----------+------+------------+
| Name|Department|Salary|percent_rank|
+-----+----------+------+------------+
|  Bob|        HR|  1200|         0.0|
|Frank|        HR|  1100|         0.5|
|Alice|        HR|  1000|         1.0|
|David|        IT|  1700|         0.0|
|  Eve|        IT|  1600|         0.5|
|Cathy|        IT|  1500|         1.0|
+-----+----------+------+------------+

        

9. Window Frame with Custom Range

Defining a custom window frame to calculate a rolling sum over the last two rows (including the current row).

rolling_window = Window.partitionBy("Department").orderBy("Salary").rowsBetween(-2, 0)

df.withColumn("rolling_sum", sum("Salary").over(rolling_window)).show()

        

Output:

+-----+----------+------+-----------+
| Name|Department|Salary|rolling_sum|
+-----+----------+------+-----------+
|Alice|        HR|  1000|       1000|
|Frank|        HR|  1100|       2100|
|  Bob|        HR|  1200|       3300|
|Cathy|        IT|  1500|       1500|
|  Eve|        IT|  1600|       3100|
|David|        IT|  1700|       4800|
+-----+----------+------+-----------+

        

10. Filtering Top N Rows within Each Partition

Selecting only the top 2 earners in each department.

top_n_window = Window.partitionBy("Department").orderBy(col("Salary").desc())

df.withColumn("rank", row_number().over(top_n_window)) \\
  .filter(col("rank") <= 2).drop("rank").show()

        

Output:

+-----+----------+------+
| Name|Department|Salary|
+-----+----------+------+
|  Bob|        HR|  1200|
|Frank|        HR|  1100|
|David|        IT|  1700|
|  Eve|        IT|  1600|
+-----+----------+------+

        

Key Notes

1. Window Frames: You can define custom window frames using: rowsBetween: A specific range of rows. rangeBetween: A specific range of values.
2. Performance: Window functions can be resource-intensive. Optimize partitions for large datasets.

In PySpark, unbounded window frames refer to frames that have no defined start or end boundary, effectively operating over all rows in the partition up to the current row or all rows in the partition entirely.

Unbounded frames are commonly used in cumulative operations, such as cumulative sums, averages, or ranks, where you want the calculation to include all rows from the beginning of the partition (or sometimes the end).

Types of Unbounded Windows

1. Unbounded Preceding: Includes all rows from the beginning of the partition to the current row.
2. Unbounded Following: Includes the current row to the end of the partition.
3. Unbounded Window: Includes all rows in the partition.

Examples with Unbounded Windows

1. Cumulative Sum Using Unbounded Preceding

Calculates the cumulative sum of salaries within each department.

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

# Define an unbounded window
unbounded_window = Window.partitionBy("Department").orderBy("Salary").rowsBetween(Window.unboundedPreceding, Window.currentRow)

# Apply cumulative sum
df.withColumn("cumulative_sum", sum("Salary").over(unbounded_window)).show()

        

Output:

+-----+----------+------+--------------+
| Name|Department|Salary|cumulative_sum|
+-----+----------+------+--------------+
|Alice|        HR|  1000|          1000|
|Frank|        HR|  1100|          2100|
|  Bob|        HR|  1200|          3300|
|Cathy|        IT|  1500|          1500|
|  Eve|        IT|  1600|          3100|
|David|        IT|  1700|          4800|
+-----+----------+------+--------------+

        

Here:

• Window.unboundedPreceding: Includes all rows from the beginning of the partition.
• Window.currentRow: Includes the current row in the calculation.

2. Unbounded Following

Calculates the cumulative sum from the current row to the end of the partition.

# Define unbounded following window
unbounded_following_window = Window.partitionBy("Department").orderBy("Salary").rowsBetween(Window.currentRow, Window.unboundedFollowing)

# Apply sum from current row to the end
df.withColumn("future_sum", sum("Salary").over(unbounded_following_window)).show()

        

Output:

+-----+----------+------+-----------+
| Name|Department|Salary|future_sum|
+-----+----------+------+-----------+
|Alice|        HR|  1000|       3300|
|Frank|        HR|  1100|       2300|
|  Bob|        HR|  1200|       1200|
|Cathy|        IT|  1500|       4800|
|  Eve|        IT|  1600|       3300|
|David|        IT|  1700|       1700|
+-----+----------+------+-----------+

        

Here:

• Window.currentRow: Starts the calculation from the current row.
• Window.unboundedFollowing: Includes all rows from the current row to the end of the partition.

3. Unbounded Window (Full Partition)

Applies an aggregate function to all rows in the partition without considering the current row position.

# Define an unbounded window
full_partition_window = Window.partitionBy("Department")

# Apply max salary across the partition
df.withColumn("max_salary", sum("Salary").over(full_partition_window)).show()

        

Output:

+-----+----------+------+-----------+
| Name|Department|Salary|max_salary|
+-----+----------+------+-----------+
|Alice|        HR|  1000|       3300|
|Frank|        HR|  1100|       3300|
|  Bob|        HR|  1200|       3300|
|Cathy|        IT|  1500|       4800|
|  Eve|        IT|  1600|       4800|
|David|        IT|  1700|       4800|
+-----+----------+------+-----------+

        

Here:

• No Order Clause: The function operates over the entire partition, treating all rows equally.

Key Notes on Unbounded Windows

1. Boundaries: Window.unboundedPreceding: All rows before the current row (including the first row). Window.unboundedFollowing: All rows after the current row (including the last row). Window.currentRow: The current row only.
2. Performance: Unbounded windows can be computationally expensive on large datasets. Consider optimizing partitions for better performance.
3. Order: Including an orderBy clause in the window specification is crucial for cumulative operations.

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