# Incremental Features Implementation Status
## ✅ Completed Features
### 1. GROUP BY with Aggregates
- **Status**: COMPLETE
- **Implementation**: DataView-based grouping with aggregate function support
- **Supported Aggregates**: COUNT(*), COUNT(col), SUM, AVG, MIN, MAX
- **Test Coverage**: 12 Python tests in test_group_by.py
### 2. HAVING Clause
- **Status**: COMPLETE
- **Implementation**: Post-aggregation filtering on grouped results
- **Features**:
- Works with all aggregate functions
- Supports arithmetic expressions (e.g., `HAVING total_fee > total * 0.04`)
- Can be combined with WHERE for pre-aggregation filtering
- Requires GROUP BY (SQL standard compliance)
- **Test Coverage**: 13 Python tests in test_having_clause.py
- **Example**:
```sql
SELECT trader, COUNT(*) as count, SUM(quantity) as total
FROM trades
GROUP BY trader
HAVING count > 5 AND total > 1000
```
## 🚧 Next Steps (In Priority Order)
### 1. Simple Window Functions (LAG/LEAD)
**Why Next**: Builds on DataView architecture, only needs adjacent row access
**Design Approach**:
- Add window function expressions to SelectItem enum
- Create sorted DataView based on ORDER BY
- Access adjacent rows using offset calculations
- Return as computed columns
**Example**:
```sql
SELECT
price,
LAG(price, 1) OVER (ORDER BY trade_time) as prev_price,
price - LAG(price, 1) OVER (ORDER BY trade_time) as price_change
FROM trades
```
### 2. ROW_NUMBER()
**Why Manageable**: Natural extension of LAG/LEAD, just position tracking
**Example**:
```sql
SELECT
trader,
price,
ROW_NUMBER() OVER (ORDER BY price DESC) as price_rank
FROM trades
```
### 3. PARTITION BY with ROW_NUMBER
**Why More Complex**: Requires combining grouping + ordering
**Design Approach**:
- Reuse GROUP BY's group_by() method for partitioning
- Apply ROW_NUMBER within each partition
- Merge results back
**Example**:
```sql
SELECT
trader,
book,
price,
ROW_NUMBER() OVER (PARTITION BY trader ORDER BY price DESC) as rank_within_trader
FROM trades
```
## 📋 Backlog Features
### Advanced Window Functions
- RANK(), DENSE_RANK(), PERCENT_RANK()
- Moving aggregates (SUM/AVG OVER with frame specs)
- FIRST_VALUE(), LAST_VALUE()
- NTILE()
### SQL Enhancements
- Alias support in WHERE clause (requires pre-computation)
- DISTINCT keyword
- Subqueries (complex architecture change)
- CTEs (WITH clause)
## 💡 Key Design Principles
1. **Incremental Approach**: Each feature builds on previous work
2. **DataView-Centric**: Leverage DataView for lightweight data manipulation
3. **No Major Refactoring**: Work within current architecture
4. **Test-Driven**: Comprehensive test coverage for each feature
5. **User Value First**: Focus on commonly needed features
## 🎯 Success Metrics
- ✅ GROUP BY: Enables data aggregation and summarization
- ✅ HAVING: Allows filtering on aggregate results
- 🔜 LAG/LEAD: Enables time-series analysis and comparisons
- 🔜 ROW_NUMBER: Provides ranking and top-N queries
- 🔜 PARTITION BY: Allows windowed calculations within groups
## Notes
- HAVING implementation currently requires using aliases for aggregate functions (e.g., `COUNT(*) as count` then `HAVING count > 5`)
- Complex boolean expressions in HAVING (AND/OR) work but may have limitations
- BETWEEN operator in HAVING not yet supported (use `>= AND <=` instead)