# GraphQL Cascade Implementation - Planning Documents

**Date**: 2025-11-11
**Status**: Approved - Simplified Approach

---

## Decision Summary

After analyzing SpecQL's implementation, we chose the **simplified PostgreSQL-first approach** over the original complex Python tracking system.

**Result**: 90% less code (50 lines vs 2000+), 75% faster to production (5 days vs 4 weeks).

---

## Documents

### 1. Implementation Plan (Simplified)
**File**: `graphql-cascade-simplified-approach.md`

The approved implementation plan using PostgreSQL native JSONB for cascade data.

**Key Features**:
- PostgreSQL functions build `_cascade` directly in return JSONB
- Python just passes through the data (~50 lines)
- Zero tracking overhead
- 3-5 days implementation time

### 2. Decision Rationale
**File**: `cascade-implementation-recommendation.md`

Detailed comparison between original complex approach and simplified approach, including:
- Technical comparison
- Code examples side-by-side
- Decision matrix
- Time and complexity analysis

---

## Implementation Timeline

### Week 1 (Days 1-2): Core Implementation
- [x] Day 1: Clean up Phase 1 files
- [ ] Day 1-2: Add `_cascade` passthrough in mutation resolver
- [ ] Day 2: Response formatting

### Week 1 (Days 3-5): Documentation & Examples
- [ ] Day 3: Document PostgreSQL pattern
- [ ] Day 4: Create complete example application
- [ ] Day 5: Integration tests and polish

**Target Completion**: 2025-11-18

---

## Quick Reference

### PostgreSQL Pattern

```sql
CREATE OR REPLACE FUNCTION fn_your_mutation(input jsonb)
RETURNS jsonb AS $$
BEGIN
    -- Your mutation logic...

    RETURN jsonb_build_object(
        'success', true,
        'data', jsonb_build_object('id', v_id),
        '_cascade', jsonb_build_object(
            'updated', jsonb_build_array(
                jsonb_build_object(
                    '__typename', 'YourType',
                    'id', v_id,
                    'operation', 'CREATED',
                    'entity', (SELECT data FROM v_your_type WHERE id = v_id)
                )
            ),
            'deleted', '[]'::jsonb,
            'invalidations', jsonb_build_array(...),
            'metadata', jsonb_build_object(
                'timestamp', now(),
                'affectedCount', 1
            )
        )
    );
END;
$$ LANGUAGE plpgsql;
```

### Python Decorator

```python
@mutation(enable_cascade=True)
class YourMutation:
    input: YourInput
    success: YourSuccess
    failure: YourError
```

### Response Structure

```json
{
  "data": {
    "yourMutation": {
      "result": { ... },
      "cascade": {
        "updated": [ ... ],
        "deleted": [ ... ],
        "invalidations": [ ... ],
        "metadata": { ... }
      }
    }
  }
}
```

---

## Key Benefits

1. **90% Less Code**: 50 lines vs 2000+ lines
2. **PostgreSQL-Native**: Zero Python tracking overhead
3. **Flexible**: PostgreSQL has full control over what to cascade
4. **Fast**: 3-5 days vs 4 weeks implementation
5. **Simple**: Easy to understand and maintain
6. **Performant**: Native JSONB operations

---

## Related Documentation

- Implementation plan: `graphql-cascade-simplified-approach.md`
- Decision rationale: `cascade-implementation-recommendation.md`
- SpecQL reference: `../../../specql/` (sibling project)

---

**Status**: ✅ Phase 1 cleanup complete
**Next**: Implement simplified passthrough
