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SQL Window Functions

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Maxyang2001 edited this page Nov 19, 2024 · 1 revision

*** jy3460 Jiayi Yang**

The Problem Traditional SQL aggregates (e.g., SUM, AVG, COUNT) work only at the group level, often reducing the result set to a single row per group. This limitation is problematic for advanced analytical tasks, such as:

  1. Ranking rows within groups (e.g., ranking products by sales per region).
  2. Cumulative calculations (e.g., running totals over time).
  3. Sliding window operations (e.g., moving averages for trend analysis).
  4. Row-wise comparisons (e.g., comparing each row’s value to the previous row's value). Achieving such tasks using standard SQL requires complex queries involving nested subqueries or joins, which are less readable, harder to maintain, and often less performant.

How SQL Window Functions Solve the Problem

  1. Row-Level Calculations

Window functions allow you to perform calculations across a subset of rows (a "window") while retaining all rows in the result set. This is different from aggregate functions, which typically collapse rows into a single value per group. Example Problem: You want to calculate the cumulative salary of employees within each department but still need to keep all employee records.

  • SELECT
  • department_id, 

  • employee_id, 

  • salary, 

  • SUM(salary) OVER (PARTITION BY department_id ORDER BY employee_id) AS cumulative_salary
  • FROM employees;
  • Here, the SUM(salary) computes the cumulative salary of employees within the same department (PARTITION BY department_id) while ordering the rows by employee_id. Unlike a GROUP BY query, which would collapse rows into aggregated values, the result retains each employee's detailed record and adds a column showing the cumulative salary.
  1. Flexible Partitioning and Ordering

Window functions enable dynamic and flexible grouping of data using the PARTITION BY clause, combined with an ORDER BY clause to control the processing order within each group. This allows detailed row-level insights while keeping full control of how rows are grouped and ordered. Example Problem: You need to rank products based on their sales within each region while still displaying all product sales data. Solution Using Window Functions:

  • SELECT
  • region_id, 

  • product_id, 

  • sales_amount, 

  • RANK() OVER (PARTITION BY region_id ORDER BY sales_amount DESC) AS sales_rank
  • FROM sales;
  • Here, the RANK() function assigns a rank to each product within each region (PARTITION BY region_id) based on their sales (ORDER BY sales_amount DESC). All rows are retained, and the ranks are dynamically calculated for each region.
  1. Advanced Analytical Operations Window functions also enable sophisticated analytical tasks that are difficult to perform with standard SQL, such as:

Ranking and Row Numbering: Functions like RANK(), DENSE_RANK(), and ROW_NUMBER() provide various ways to rank rows within partitions or assign unique row numbers. Row Comparisons: Functions like LAG() and LEAD() allow comparisons between the current row and previous or next rows in the same partition. Cumulative Calculations: Functions like SUM() and AVG() with OVER() enable cumulative totals, running averages, or window-specific aggregations. Example Problem: You want to compare each employee's salary with the previous employee's salary in the same department.

Solution Using Window Functions:

  • SELECT
  • department_id, 

  • employee_id, 

  • salary, 

  • LAG(salary) OVER (PARTITION BY department_id ORDER BY employee_id) AS previous_salary,

  • salary - LAG(salary) OVER (PARTITION BY department_id ORDER BY employee_id) AS salary_difference
  • FROM employees;
  • The LAG(salary) function retrieves the salary of the previous employee in the same department (PARTITION BY department_id), ordered by employee_id. The salary_difference column calculates the difference between the current and previous employee's salaries.

Alternatives and Comparison

  • Nested Queries: A traditional SQL approach we learned in w4111 where subqueries are used to achieve similar row-wise calculations, such as cumulative sums or rankings. Nested Queries often slower due to repeated subqueries

  • What Makes Window Functions Unique?

  1. Calculations like cumulative sums and rankings are optimized directly within the database engine, reducing query execution time compared to nested queries.
  2. Unlike client-side tools, Window Functions leverage the DBMS's optimizations and can handle large datasets efficiently without requiring data extraction.

** Relation to W4111 Concepts**

  1. Relational Model and SQL Features

Extension of Aggregates: Window Functions build on standard SQL aggregates but preserve rows while adding calculated columns. Projection and Extension: Enhance the relational model by providing calculations that don't collapse the result set, unlike GROUP BY. 2. Query Execution and Optimization

Index Utilization: Functions like PARTITION BY and ORDER BY can leverage indexes for faster processing. Efficient Execution: Optimized query planners ensure calculations like cumulative sums or rankings are computed efficiently.

Tutorial

  • Step 1: Dataset Setup

Using my project1 data

  • 1.Rank Users by Their Highest Ratings

  • SELECT

  • User_ID,

  • Book_ID,

  • Rating_Value,

  • RANK() OVER (PARTITION BY User_ID ORDER BY Rating_Value DESC) AS Rating_Rank

  • FROM

  • Rating;

  • result:

  • User_ID Book_ID Rating_Value Rating_Rank_

  • 1 101 5 1

  • 1 107 4 2

  • 2 102 4 1

  • 2 106 2 2

  • 3 103 3 1

  • 3 109 3 2

  • 4 101 3 1

  • 5 105 4 1

  • 6 106 2 1

  • 7 107 1 1

  • Explanation:

  • What It Does: Assigns a ranking to each user's book ratings in descending order of Rating_Value.

  • Key Functions:

  • PARTITION BY User_ID: Ensures rankings are computed separately for each user.

  • ORDER BY Rating_Value DESC: Sorts each user's ratings in descending order.

  • Comparison with W4111 Concepts:

  • GROUP BY e: A GROUP BY query can calculate the maximum rating per user, but it cannot rank individual ratings while preserving row-level details.

  • Example: GROUP BY User_ID would collapse rows, losing individual ratings.

  • Window Function Benefit: Keeps all rows intact while providing rankings.

  1. Calculate Cumulative Ratings Over Time
  • SELECT
  • User_ID,

  • Rating_Date,

  • Rating_Value,

  • SUM(Rating_Value) OVER (PARTITION BY User_ID ORDER BY Rating_Date) AS Cumulative_Rating
  • FROM
  • Rating;
  • User_ID Rating_Date Rating_Value Cumulative_Rating
  • 1 2024-01-15 5 5
  • 1 2024-01-21 4 9
  • 2 2024-01-16 4 4
  • 2 2024-01-20 2 6
  • 3 2024-01-17 3 3
  • 3 2024-01-23 3 6
  • 4 2024-01-18 3 3
  • 5 2024-01-19 4 4
  • 6 2024-01-20 2 2
  • 7 2024-01-21 1 1
  • Explanation:
  • What It Does: Calculates a running total of Rating_Value for each user over time.
  • Key Functions:
  • SUM(): Calculates the cumulative sum.
  • PARTITION BY User_ID: Computes the cumulative sum independently for each user.
  • ORDER BY Rating_Date: Ensures the sum is calculated chronologically.
  • Comparison with W4111 Concepts:
  • GROUP BY : A GROUP BY query can calculate total ratings per user but not a running total over time.
  • Example: GROUP BY User_ID gives the final total, not incremental totals at each date.
  • Nested Queries : Achieving this without Window Functions requires a correlated subquery, which is significantly less efficient.

3.Identify Most Recent Ratings for Each Book

  • SELECT
  • Book_ID,

  • User_ID,

  • Rating_Date,

  • Rating_Value
  • FROM (
  • SELECT 

  •     Book_ID,

  •     User_ID,

  •     Rating_Date,

  •     Rating_Value,

  •     ROW_NUMBER() OVER (PARTITION BY Book_ID ORDER BY Rating_Date DESC) AS Row_Num

  • FROM 

  •     Rating
  • ) AS Subquery
  • WHERE
  • Row_Num = 1;
  • Book_ID User_ID Rating_Date Rating_Value
  • 101 4 2024-01-18 3
  • 102 2 2024-01-16 4
  • 103 3 2024-01-17 3
  • 105 5 2024-01-19 4
  • 106 6 2024-01-20 2
  • 107 7 2024-01-21 1
  • 108 8 2024-01-22 5
  • 109 3 2024-01-23 3
  • 110 10 2024-01-24 4
  • Explanation:
  • What It Does: Finds the latest rating for each book by assigning row numbers to ratings, ordered by date.
  • Key Functions:
  • ROW_NUMBER(): Assigns a unique rank to each row based on Rating_Date descending.
  • PARTITION BY Book_ID: Ensures rankings are calculated independently for each book.
  • Comparison with W4111 Concepts:
  • GROUP BY : A GROUP BY query can find the maximum date for each book but cannot retrieve the corresponding user and rating value.
  • Example: A JOIN would be required to combine the latest date with other columns.
  • Nested Query : Requires a correlated subquery to find the latest date for each book, followed by a join.
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