**Frame Size Optimization:**

- Smaller frames process faster and use less memory

- Fixed-size frames (e.g., 7 PRECEDING) are most efficient

- UNBOUNDED frames require full partition processing

- Consider partition pruning for very large datasets

**ROWS vs RANGE Performance:**

- ROWS is typically 20-40% faster than RANGE

- ROWS uses simple row counting, RANGE requires value comparison

- Use ROWS unless you specifically need RANGE behavior for ties

- RANGE with time intervals can be expensive on large datasets

**Memory Management:**

- Larger frames require more memory for intermediate calculations

- UNBOUNDED FOLLOWING frames must buffer entire remaining partition

- Monitor memory usage with very large partitions (>1M rows)

- Consider breaking large partitions into smaller time-based chunks

**Indexing Strategy:**

- Index ORDER BY columns for optimal frame processing

- Composite indexes: (partition_col, order_col) for best performance

- Consider covering indexes to avoid key lookups

- Proper indexing can improve frame performance by 5-10x

**Query Optimization:**

- Specify frames explicitly rather than relying on defaults

- Use named windows to reuse frame specifications

- Combine multiple window functions with same frame for efficiency

- Filter data early with WHERE clauses to reduce frame processing

ROWS is typically 20-40% faster than RANGE due to simpler processing

Fixed-size frames (e.g., 7 PRECEDING) are more efficient than UNBOUNDED frames

Index PARTITION BY and ORDER BY columns for optimal frame performance

Use covering indexes to avoid key lookups during frame calculations

Monitor memory usage with large frames - UNBOUNDED can be memory intensive

Consider pre-aggregation for complex calculations on very large datasets

Using default frames without understanding behavior - especially with LAST_VALUE

Confusing ROWS (physical) with RANGE (logical) - ROWS is usually what you want

Not specifying full frame for FIRST_VALUE/LAST_VALUE causing incorrect results

Using UNBOUNDED frames on very large partitions without considering memory impact

Forgetting that frames are relative to current row - boundaries move with each row

Not indexing frame columns leading to poor performance on large datasets