comphead commented on code in PR #66:
URL: https://github.com/apache/datafusion-site/pull/66#discussion_r2044957460
##########
content/blog/2025-04-17-user-defined-window-functions.md:
##########
@@ -0,0 +1,427 @@
+---
+layout: post
+title: User defined Window Functions in DataFusion
+date: 2025-04-17
+author: Aditya Singh Rathore
+categories: [tutorial]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+
+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.
+
+Computing sliding windows efficiently is tricky because:
+
+- **High Computation Costs:** Just like making coffee from scratch for each
customer, recalculating aggregates for every row is expensive.
+
+- **Data Shuffling:** In large distributed systems, data must often be
shuffled between nodes, causing delays—like passing orders between multiple
baristas who don’t communicate efficiently.
+
+- **State Management:** Keeping track of past computations is like remembering
previous orders without writing them down—error-prone and inefficient.
+
+Many traditional query engines struggle to optimize these computations
effectively, leading to sluggish performance.
+
+## How DataFusion Evaluates Window Functions Quickly
+In the world of big data, every millisecond counts. Imagine you’re analyzing
stock market data, tracking sensor readings from millions of IoT devices, or
crunching through massive customer logs—speed matters. This is where
[DataFusion](https://datafusion.apache.org/) shines, making window function
computations blazing fast. Let’s break down how it achieves this remarkable
performance.
+
+DataFusion implements the battle tested sort-based approach described in [this
+paper] which is also used in systems such as Postgresql and Vertica. The input
+is first sorted by both the `PARTITION BY` and `ORDER BY` expressions and
+then the [WindowAggExec] operator efficiently determines the partition
boundaries and
+creates appropriate [PartitionEvaluator] instances.
+
+The sort-based approach is well understood, scales to large data sets, and
+leverages DataFusion's highly optimized sort implementation. DataFusion
minimizes
+resorting by leveraging the sort order tracking and optimizations described in
+the [Using Ordering for Better Plans blog].
+
+For example, given the query such as the following to compute the starting,
+ending and average price for each stock:
+
+```sql
+SELECT
+ FIRST_VALUE(price) OVER (PARTITION BY date_bin('1 month', time) ORDER BY
time DESC),
+ FIRST_VALUE(price) OVER (PARTITION BY date_bin('1 month', time) ORDER BY
time DESC)
Review Comment:
is it expected to have two same functions? do you mean to calc last_value?
##########
content/blog/2025-04-17-user-defined-window-functions.md:
##########
@@ -0,0 +1,427 @@
+---
+layout: post
+title: User defined Window Functions in DataFusion
+date: 2025-04-17
+author: Aditya Singh Rathore
+categories: [tutorial]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+
+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.
+
+Computing sliding windows efficiently is tricky because:
+
+- **High Computation Costs:** Just like making coffee from scratch for each
customer, recalculating aggregates for every row is expensive.
+
+- **Data Shuffling:** In large distributed systems, data must often be
shuffled between nodes, causing delays—like passing orders between multiple
baristas who don’t communicate efficiently.
+
+- **State Management:** Keeping track of past computations is like remembering
previous orders without writing them down—error-prone and inefficient.
+
+Many traditional query engines struggle to optimize these computations
effectively, leading to sluggish performance.
+
+## How DataFusion Evaluates Window Functions Quickly
+In the world of big data, every millisecond counts. Imagine you’re analyzing
stock market data, tracking sensor readings from millions of IoT devices, or
crunching through massive customer logs—speed matters. This is where
[DataFusion](https://datafusion.apache.org/) shines, making window function
computations blazing fast. Let’s break down how it achieves this remarkable
performance.
+
+DataFusion implements the battle tested sort-based approach described in [this
+paper] which is also used in systems such as Postgresql and Vertica. The input
+is first sorted by both the `PARTITION BY` and `ORDER BY` expressions and
+then the [WindowAggExec] operator efficiently determines the partition
boundaries and
+creates appropriate [PartitionEvaluator] instances.
+
+The sort-based approach is well understood, scales to large data sets, and
+leverages DataFusion's highly optimized sort implementation. DataFusion
minimizes
+resorting by leveraging the sort order tracking and optimizations described in
+the [Using Ordering for Better Plans blog].
+
+For example, given the query such as the following to compute the starting,
+ending and average price for each stock:
+
+```sql
+SELECT
+ FIRST_VALUE(price) OVER (PARTITION BY date_bin('1 month', time) ORDER BY
time DESC),
+ FIRST_VALUE(price) OVER (PARTITION BY date_bin('1 month', time) ORDER BY
time DESC)
+ AVG(price) OVER (PARTITION BY date_bin('1 month', time)),
Review Comment:
```suggestion
AVG(price) OVER (PARTITION BY date_bin('1 month', time))
```
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