praveenc7 opened a new pull request, #16678:
URL: https://github.com/apache/pinot/pull/16678

   # ANY_VALUE Aggregation Function
   
   ## Overview
   
   The `ANY_VALUE` aggregation function returns any arbitrary value from the 
specified column for each group in a `GROUP BY` query. This function is 
particularly useful for optimizing queries where you need to include a column 
in the `SELECT` clause that has a 1:1 mapping with the `GROUP BY` columns, 
without forcing that column into the `GROUP BY` clause.
   
   ## Syntax
   
   ```sql
   ANY_VALUE(column_name)
   ```
   
   ## Use Cases
   
   ### Primary Use Case: Avoiding Unnecessary GROUP BY Columns
   
   Standard SQL requires that all non-aggregation expressions in the `SELECT` 
clause must also be part of the `GROUP BY` clause. However, there are scenarios 
where:
   
   1. **Functional Dependency**: A column has a 1:1 mapping with the grouped 
columns
   2. **Performance Optimization**: Adding the column to `GROUP BY` would 
increase computation without changing the logical result
   3. **Query Simplification**: You want to display related data without 
affecting grouping behavior
   
   ### Example Scenario
   
   Consider an `ORDERS` table with `CustomerID`, `CustomerName`, and 
`OrderValue`:
   
   **Without ANY_VALUE (Standard SQL):**
   ```sql
   -- Option 1: Add CustomerName to GROUP BY (more computation)
   SELECT CustomerID, CustomerName, SUM(OrderValue)
   FROM Orders 
   GROUP BY CustomerID, CustomerName;
   
   -- Option 2: Remove CustomerName from SELECT (lose information)
   SELECT CustomerID, SUM(OrderValue)
   FROM Orders 
   GROUP BY CustomerID;
   ```
   
   **With ANY_VALUE (Optimized):**
   ```sql
   -- Get CustomerName without adding to GROUP BY
   SELECT CustomerID, ANY_VALUE(CustomerName), SUM(OrderValue)
   FROM Orders 
   GROUP BY CustomerID;
   ```
   
   ## Behavior
   
   - **Returns**: Any arbitrary value from the column for each group
   - **Null Handling**: Supports both null-handling enabled and disabled modes
   - **Data Types**: Works with all Pinot data types (INT, LONG, FLOAT, DOUBLE, 
STRING, BYTES, BIG_DECIMAL)
   - **Performance**: More efficient than adding the column to `GROUP BY` when 
there's a 1:1 mapping
   
   
   


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