Principle:Eventual Inc Daft Window Functions
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Data_Analysis |
| Last Updated | 2026-02-08 00:00 GMT |
Overview
Window functions compute values across a set of rows related to the current row without collapsing the result, preserving the original row count.
Description
Window functions perform calculations across a window of rows defined by partitioning and ordering, without reducing the number of rows in the DataFrame. They support ranking (row_number, rank, dense_rank), running totals, moving averages, lead/lag operations, and cumulative aggregations. A window specification defines three components: the partition (which rows to group together), the ordering (how rows are sorted within each partition), and the frame (which rows relative to the current row are included in the calculation). The result of a window function is added as a new column alongside existing data.
Usage
Use window functions when you need to compute values relative to other rows without reducing the row count. Common scenarios include computing rankings within groups, calculating running totals or moving averages over time series, computing percent-of-total within categories, and accessing previous or next row values with lead/lag operations.
Theoretical Basis
Window functions follow SQL window function semantics as defined in the SQL:2003 standard. The computation model is:
For each row in the result:
1. PARTITION BY: Divide rows into groups (partitions) based on column values
2. ORDER BY: Sort rows within each partition
3. Frame specification: Define the window frame relative to the current row
- ROWS BETWEEN: Frame based on row offsets (e.g., 3 preceding to current row)
- RANGE BETWEEN: Frame based on value ranges (e.g., values within 10 of current)
4. Apply the window function over the frame to produce the result for the current row
Key properties:
- Window functions do not reduce the number of rows (unlike GROUP BY aggregation).
- Each row's result is computed independently based on its window frame.
- The partition and ordering can be different from the overall DataFrame ordering.
- Multiple window functions can use different window specifications in the same query.