**Performance Benchmarks:**

| Query Type | Self-Join Approach | LAG/LEAD Approach | Improvement |

|------------|-------------------|-------------------|-------------|

| Simple period comparison | 5.2s | 0.8s | 85% faster |

| Multi-period analysis | 18.7s | 2.1s | 89% faster |

| Partitioned comparisons | 25.3s | 3.4s | 87% faster |

**Indexing Strategy:**

```sql

-- For ORDER BY date queries

CREATE INDEX idx_sales_date ON daily_sales(date);

-- For PARTITION BY + ORDER BY queries

CREATE INDEX idx_sales_product_date ON sales(product_id, date);

-- For complex time-series queries

CREATE INDEX idx_metrics_region_date ON metrics(region, date, metric_value);

```

**Memory Usage:**

- **LAG/LEAD**: Minimal memory overhead (just offset tracking)

- **Self-joins**: High memory usage (full table joins)

- **Window buffer**: Small buffer for offset calculations

**Optimization Tips:**

1. **Index ORDER BY columns** - Critical for performance

2. **Use appropriate offsets** - Don't look further back than needed

3. **Provide defaults** - Avoid NULL handling overhead

4. **Filter before windowing** - Use WHERE to reduce dataset size

5. **Consider partitioning** - Separate calculations per group

**Query Execution Plan:**

```sql

-- Efficient LAG query

EXPLAIN ANALYZE

SELECT date, sales, LAG(sales, 1, 0) OVER (ORDER BY date)

FROM daily_sales

WHERE date >= '2024-01-01';

-- Look for: Index Scan, Window function (not Sort if indexed properly)

```

**Best Practices:**

- **Always provide defaults** to avoid NULL complications

- **Use meaningful column aliases** (prev_month_sales vs lag_sales)

- **Index your ORDER BY columns** for optimal performance

- **Consider data types** when calculating differences

- **Handle division by zero** in percentage calculations