Window function performance scales with partition size and frame complexity. A 1M row table with 100 partitions performs much better than 10 partitions. Index all PARTITION BY and ORDER BY columns using composite indexes. Use ROWS frames instead of RANGE when possible. Monitor memory usage to prevent disk spills. Consider materialized views for frequently accessed complex window calculations.

ROWS frames are 2-10x faster than RANGE frames for most use cases

Smaller partitions (1K-100K rows) perform better than large partitions

Bounded frames use less memory than unbounded frames

Covering indexes eliminate key lookups for better I/O performance

Parallel processing can be used for independent partition calculations

Monitor work_mem setting to prevent disk spills in PostgreSQL

Use LIMIT with window functions to reduce processing overhead

Consider approximate functions (e.g., approx_percentile) for large datasets

Missing indexes on PARTITION BY and ORDER BY columns causes full table scans

Using RANGE frames with complex expressions creates expensive calculations

Large unbounded windows consume excessive memory and cause disk spills

Not monitoring execution plans leads to undetected performance degradation

Processing entire datasets instead of using time-based filtering

Creating too many small partitions can increase overhead

Not considering data distribution when designing partition strategies

Using window functions in WHERE clauses (not allowed, use subqueries)