What Are Window Functions?
Unlike standard aggregate functions like SUM() and AVG(), window functions don’t merge rows into a single result. Instead, they compute values across a specific "window" of rows using the OVER() clause.
Example: Ranking employees by salary
SELECT name, salary,
RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;
Key Window Functions
Ranking Functions: RANK(), DENSE_RANK(), ROW_NUMBER()
Aggregation Functions: SUM(), AVG(), COUNT()
Offset Functions: LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE()
Using PARTITION BY for Grouping
To calculate values within specific categories, use PARTITION BY:
SELECT department, name,
AVG(salary) OVER (PARTITION BY department) AS avg_dept_salary
FROM employees;
This computes the average salary within each department while keeping individual row details intact.
Comparing Rows with LAG() & LEAD()
The LAG() and LEAD() functions allow you to access previous or next row values:
SELECT name, salary,
LAG(salary) OVER (ORDER BY salary) AS prev_salary,
LEAD(salary) OVER (ORDER BY salary) AS next_salary
FROM employees;
Perfect for tracking salary changes over time!
Moving Averages & Running Totals
Use window frames (ROWS BETWEEN)to calculate rolling averages or running totals:
SELECT name, salary,
AVG(salary) OVER (ORDER BY salary ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg
FROM employees;
Use windows functions to:
✅ Maintain row-level details while computing aggregates
✅ Efficiently perform ranking, running totals, and comparisons
✅ Eliminate the need for complex self-joins and subqueries
Platform-Specific Considerations
1. General SQL Support
Most modern relational databases support window functions, but syntax and performance optimizations may differ:
✅ Supported: PostgreSQL, MySQL (8.0+), SQL Server (2012+), Oracle (11g+), IBM Db2
⚠️ Limited Support: MySQL (<8.0), SQLite (Partial)
2. Platform-Specific Optimizations & Differences
PostgreSQL
✅ Fully supports window functions with PARTITION BY, ORDER BY, and frame clauses.
✅ Strong optimizer for LAG(), LEAD(), and RANK().
⚠️ No parallelism for window functions (performance can degrade with large datasets).
MySQL (8.0+)
✅ First MySQL version to introduce full window function support.
✅ Supports ranking, offsets, and aggregates within OVER().
⚠️ No RANGE-based window frame (only ROWS).
⚠️ No DISTINCT inside window aggregates (e.g., COUNT(DISTINCT col) OVER (...) fails).
SQL Server (2012+)
✅ Full support for window functions and frame clauses.
✅ Optimized for parallel query execution with indexed partitions.
⚠️ Pre-2012 versions require workarounds (e.g., using CROSS APPLY for ranking).
Oracle (11g+)
✅ Supports advanced window functions like FIRST_VALUE(), LAST_VALUE().
✅ MODEL clause allows complex analytics beyond window functions.
⚠️ Requires explicit indexing for performance tuning.
IBM Db2
✅ Offers robust support for window functions.
✅ Can perform parallel processing of window functions.
⚠️ Some advanced analytics require custom extensions.
3. Performance Considerations
Indexing Matters: Ensure that columns used in PARTITION BY or ORDER BY are indexed.
Query Execution Plans: Use EXPLAIN ANALYZE (PostgreSQL), EXPLAIN FORMAT=JSON (MySQL), or SET SHOWPLAN_ALL ON (SQL Server) to check performance.
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