**GROUP BY Performance:**

- Excellent for large datasets when only summaries needed

- Memory efficient - processes and discards detail rows

- Can leverage hash aggregation and sort-based grouping

- Parallel processing scales well

- Index on GROUP BY columns crucial for performance

**PARTITION BY Performance:**

- Memory intensive - must keep all rows in result set

- Better for complex analytics requiring row-level detail

- Eliminates expensive self-joins

- Single-pass processing for multiple window functions

- Index on PARTITION BY columns essential

**Performance Benchmarks:**

- Simple aggregation: GROUP BY 3-5x faster

- Complex analytics: PARTITION BY 2-10x faster than self-joins

- Memory usage: GROUP BY uses 60-90% less memory

- Query complexity: PARTITION BY reduces query complexity by 40-70%

**Optimization Guidelines:**

- Use GROUP BY for executive dashboards and reports

- Use PARTITION BY for analytical applications and comparisons

- Combine both for comprehensive analysis

- Always index grouping/partitioning columns

- Consider materialized views for frequently accessed patterns

GROUP BY is 3-5x faster for simple aggregations and uses 60-90% less memory

PARTITION BY eliminates expensive self-joins and processes multiple calculations in one pass

Create covering indexes: (group_columns, aggregate_columns) for GROUP BY optimization

Create composite indexes: (partition_columns, order_columns) for PARTITION BY optimization

Use materialized views for frequently accessed GROUP BY summaries

Consider partitioned tables for very large datasets with time-based analysis

Using GROUP BY when individual row details are needed - results in lost information

Using PARTITION BY when only summaries are needed - wastes memory and processing time

Forgetting to index GROUP BY and PARTITION BY columns - causes poor performance

Mixing aggregate and non-aggregate columns in SELECT without proper GROUP BY

Using HAVING instead of WHERE for non-aggregated filtering - reduces performance

Not considering memory requirements when using PARTITION BY on large datasets