comphead commented on code in PR #66:
URL: https://github.com/apache/datafusion-site/pull/66#discussion_r2044952546
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content/blog/2025-04-17-user-defined-window-functions.md:
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+---
+layout: post
+title: User defined Window Functions in DataFusion
+date: 2025-04-17
+author: Aditya Singh Rathore
+categories: [tutorial]
+---
+
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+
+
+Window functions are a powerful feature in SQL, allowing for complex
analytical computations over a subset of data. However, efficiently
implementing them, especially sliding windows, can be quite challenging. With
[Apache DataFusion]'s user-defined window functions, developers can easily take
advantage of all the effort put into DataFusion's implementation.
+
+In this post, we'll explore:
+
+- What window functions are and why they matter
+
+- Understanding sliding windows
+
+- The challenges of computing window aggregates efficiently
+
+- How to implement user-defined window functions in DataFusion
+
+
+[Apache DataFusion]: https://datafusion.apache.org/
+
+## Understanding Window Functions in SQL
+
+
+Imagine you're analyzing sales data and want insights without losing the finer
details. This is where **[window functions]** come into play. Unlike **GROUP
BY**, which condenses data, window functions let you retain each row while
performing calculations over a defined **range** —like having a moving lens
over your dataset.
+
+[window functions]: https://en.wikipedia.org/wiki/Window_function_(SQL)
+
+
+Picture a business tracking daily sales. They need a running total to
understand cumulative revenue trends without collapsing individual
transactions. SQL makes this easy:
+```sql
+SELECT id, value, SUM(value) OVER (ORDER BY id) AS running_total
+FROM sales;
+```
+
+```text
+example:
++------------+--------+-------------------------------+
+| Date | Sales | Rows Considered |
++------------+--------+-------------------------------+
+| Jan 01 | 100 | [100] |
+| Jan 02 | 120 | [100, 120] |
+| Jan 03 | 130 | [100, 120, 130] |
+| Jan 04 | 150 | [100, 120, 130, 150] |
+| Jan 05 | 160 | [100, 120, 130, 150, 160] |
+| Jan 06 | 180 | [100, 120, 130, 150, 160, 180]|
+| Jan 07 | 170 | [100, ..., 170] (7 days) |
+| Jan 08 | 175 | [120, ..., 175] |
++------------+--------+-------------------------------+
+```
+**Figure 1**: A row-by-row representation of how a 7-day moving average
includes the previous 6 days and the current one.
+
+
+This helps in analytical queries where we need cumulative sums, moving
averages, or ranking without losing individual records.
+
+
+## User Defined Window Functions
+DataFusion's [Built-in window functions] such as `first_value`, `rank` and
`row_number` serve many common use cases, but sometimes custom logic is
needed—for example:
+
+- Calculating moving averages with complex conditions (e.g. exponential
averages, integrals, etc)
+
+- Implementing a custom ranking strategy
+
+- Tracking non-standard cumulative logic
+
+Thus, **User-Defined Window Functions (UDWFs)** allow developers to define
their own behavior while allowing DataFusion to handle the calculations of the
windows and grouping specified in the `OVER` clause
+
+Writing a user defined window function is slightly more complex than an
aggregate function due
+to the variety of ways that window functions are called. I recommend reviewing
the
+[online
documentation](https://datafusion.apache.org/library-user-guide/adding-udfs.html#registering-a-window-udf)
+for a description of which functions need to be implemented.
+
+[Built-in window functions]:
https://datafusion.apache.org/user-guide/sql/window_functions.html
+
+## Understanding Sliding Window
+
+Sliding windows define a **moving range** of data over which aggregations are
computed. Unlike simple cumulative functions, these windows are dynamically
updated as new data arrives.
+
+For instance, if we want a 7-day moving average of sales:
+
+```sql
+SELECT date, sales,
+ AVG(sales) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT
ROW) AS moving_avg
+FROM sales;
+```
+Here, each row’s result is computed based on the last 7 days, making it
computationally intensive as data grows.
+
+## Why Computing Sliding Windows Is Hard
+
+Imagine you’re at a café, and the barista is preparing coffee orders. If they
made each cup from scratch without using pre-prepared ingredients, the process
would be painfully slow. This is exactly the problem with naïve sliding window
computations.
Review Comment:
reg to this paragraph computing sliding window is hard because it is slow?
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