# Phase 4: QA - Comprehensive Validation

**Status**: Ready for Implementation
**Effort**: 1 hour
**Type**: QA

---

## Objective

Perform comprehensive validation to ensure the custom scalar WHERE support feature is production-ready and works correctly in all scenarios.

---

## Context

**Phase 3 Results**:
- ✅ Code is clean, well-documented, and maintainable
- ✅ All unit tests pass
- ✅ 4/6 integration tests pass
- ✅ No regressions in existing functionality

**Goal**: Validate that the feature works end-to-end and is ready for production use.

---

## Implementation Steps

### Step 1: Run Full Test Suite
**Action**: Execute the complete test suite to ensure no regressions.

**Commands**:
```bash
# Run all tests
uv run pytest tests/ -x --tb=short

# Check test coverage (if available)
uv run pytest tests/ --cov=fraiseql --cov-report=term-missing
```

**Expected**: All tests pass, no regressions.

---

### Step 2: Manual GraphQL Query Testing
**Action**: Create a manual test script to verify GraphQL queries work with custom scalar WHERE clauses.

**Test Script** (`test_custom_scalar_where_manual.py`):
```python
"""Manual testing of custom scalar WHERE support."""
import asyncio
from graphql import graphql
from fraiseql import fraise_type, query
from fraiseql.gql.builders import SchemaRegistry
from fraiseql.types.scalars import CIDRScalar, CUSIPScalar

async def test_manual():
    # Clear registry
    registry = SchemaRegistry.get_instance()
    registry.clear()

    # Create test type
    @fraise_type
    class NetworkDevice:
        id: int
        name: str
        ip_address: CIDRScalar
        cusip: CUSIPScalar

    # Create query
    @query
    async def get_network_devices(info) -> list[NetworkDevice]:
        # Mock data for testing
        return [
            NetworkDevice(id=1, name="Router1", ip_address="192.168.1.0/24", cusip="037833100"),
            NetworkDevice(id=2, name="Router2", ip_address="10.0.0.0/8", cusip="594918104"),
        ]

    # Register
    registry.register_type(NetworkDevice)
    registry.register_query(get_network_devices)

    # Build schema
    schema = registry.build_schema()

    # Test queries
    queries = [
        # Basic equality
        '''
        query {
            getNetworkDevices(where: {ipAddress: {eq: "192.168.1.0/24"}}) {
                id name ipAddress
            }
        }
        ''',
        # List membership
        '''
        query {
            getNetworkDevices(where: {cusip: {in: ["037833100", "594918104"]}}) {
                id name cusip
            }
        }
        ''',
        # Combined filters
        '''
        query {
            getNetworkDevices(where: {
                ipAddress: {eq: "192.168.1.0/24"},
                cusip: {eq: "037833100"}
            }) {
                id name
            }
        }
        '''
    ]

    for i, query_str in enumerate(queries, 1):
        print(f"\n=== Test Query {i} ===")
        result = await graphql(schema, query_str)
        if result.errors:
            print(f"❌ Errors: {result.errors}")
        else:
            print(f"✅ Success: {result.data}")

if __name__ == "__main__":
    asyncio.run(test_manual())
```

**Expected**: All manual queries execute successfully.

---

### Step 3: Performance Benchmarking
**Action**: Measure filter generation performance to ensure no performance regressions.

**Benchmark Script**:
```python
"""Performance benchmarking for custom scalar filter generation."""
import time
from fraiseql.sql.graphql_where_generator import create_graphql_where_input
from fraiseql.types.scalars import CIDRScalar, CUSIPScalar, ColorScalar
from fraiseql import fraise_type

@fraise_type
class BenchmarkType:
    id: int
    cidr: CIDRScalar
    cusip: CUSIPScalar
    color: ColorScalar

def benchmark_filter_generation():
    """Benchmark filter generation time."""
    iterations = 1000

    start_time = time.time()
    for _ in range(iterations):
        where_input = create_graphql_where_input(BenchmarkType)
    end_time = time.time()

    avg_time = (end_time - start_time) / iterations
    print(f"Average filter generation time: {avg_time:.6f} seconds")
    print(f"Total time for {iterations} iterations: {end_time - start_time:.2f} seconds")

    # Should be well under 0.001 seconds per generation
    assert avg_time < 0.001, f"Performance regression: {avg_time} >= 0.001"

if __name__ == "__main__":
    benchmark_filter_generation()
```

**Expected**: Filter generation is fast (< 1ms per generation).

---

### Step 4: Memory Leak Testing
**Action**: Verify that filter caching prevents memory leaks.

**Test Script**:
```python
"""Test for memory leaks in filter caching."""
import gc
from fraiseql.sql.graphql_where_generator import _custom_scalar_filter_cache
from fraiseql.types.scalars import CIDRScalar, CUSIPScalar
from fraiseql import fraise_type

def test_memory_leaks():
    """Test that filters are properly cached and not leaking."""
    initial_cache_size = len(_custom_scalar_filter_cache)

    # Create multiple types with same scalars
    for i in range(10):
        @fraise_type
        class TestType:
            id: int
            cidr: CIDRScalar
            cusip: CUSIPScalar

        # This should reuse cached filters
        where_input = create_graphql_where_input(TestType)

    # Force garbage collection
    gc.collect()

    final_cache_size = len(_custom_scalar_filter_cache)

    print(f"Initial cache size: {initial_cache_size}")
    print(f"Final cache size: {final_cache_size}")

    # Should only have added 2 new filters (CIDRFilter, CUSIPFilter)
    expected_max_new = 2
    actual_new = final_cache_size - initial_cache_size

    assert actual_new <= expected_max_new, f"Possible memory leak: {actual_new} > {expected_max_new}"

if __name__ == "__main__":
    test_memory_leaks()
```

**Expected**: Cache size grows appropriately, no memory leaks.

---

### Step 5: Edge Case Validation
**Action**: Test edge cases and error conditions.

**Test Cases**:
1. **Nullable scalars**: `Optional[CIDRScalar]`
2. **List of scalars**: `list[CIDRScalar]`
3. **Mixed types**: Regular fields + custom scalars
4. **Invalid operators**: Ensure proper error messages
5. **Empty filters**: Should work correctly
6. **Complex nested queries**: Multiple WHERE conditions

**Expected**: All edge cases handled gracefully.

---

### Step 6: Documentation Review
**Action**: Review and update documentation.

**Tasks**:
1. Check that scalar documentation mentions WHERE support
2. Verify API docs include filter examples
3. Ensure changelog mentions the feature
4. Check for any "coming soon" references to remove

**Expected**: Documentation is complete and accurate.

---

## Acceptance Criteria

- [ ] All 168 tests passing (162 existing + 6 WHERE)
- [ ] Manual GraphQL queries work correctly
- [ ] Performance benchmarks show no degradation (< 1ms per filter generation)
- [ ] No memory leaks (filters properly cached)
- [ ] Error messages are clear and helpful
- [ ] Edge cases handled gracefully
- [ ] Documentation updated and complete

---

## Expected Results

### Test Suite Results
```bash
$ uv run pytest tests/ -x
=========================== 168 passed in 45.67s ===========================
```

### Manual Query Results
```bash
=== Test Query 1 ===
✅ Success: {'getNetworkDevices': [{'id': 1, 'name': 'Router1', 'ipAddress': '192.168.1.0/24'}]}

=== Test Query 2 ===
✅ Success: {'getNetworkDevices': [{'id': 1, 'name': 'Router1', 'cusip': '037833100'}, {'id': 2, 'name': 'Router2', 'cusip': '594918104'}]}

=== Test Query 3 ===
✅ Success: {'getNetworkDevices': [{'id': 1, 'name': 'Router1'}]}
```

### Performance Results
```bash
Average filter generation time: 0.000123 seconds
Total time for 1000 iterations: 0.123 seconds
✅ Performance acceptable
```

### Memory Leak Results
```bash
Initial cache size: 0
Final cache size: 2
✅ No memory leaks detected
```

---

## Commit Message

```
test(where): comprehensive validation of scalar WHERE support [QA]

Perform thorough QA validation of custom scalar WHERE filtering:

- Full test suite passes (168/168 tests)
- Manual GraphQL queries work correctly
- Performance benchmarks show no degradation
- Memory leak testing confirms proper caching
- Edge cases handled gracefully
- Documentation reviewed and updated

Feature is production-ready with comprehensive test coverage.

Related: custom-scalar-where-support phase 4
```

---

## DO NOT

- ❌ Make code changes (this is QA phase)
- ❌ Add new features
- ❌ Modify existing functionality
- ❌ Skip failing tests

## DO

- ✅ Test thoroughly and comprehensively
- ✅ Document any issues found
- ✅ Verify performance and memory usage
- ✅ Check documentation completeness
- ✅ Validate edge cases

---

**Next Phase**: Phase 5 - GREENFIELD (archaeological cleanup)
