---
title: "Test Implementation Plan: Comprehensive Error Handling and Edge Cases"
by: "pydapter-implementer"
created: "2025-05-04"
updated: "2025-05-04"
version: "1.0"
doc_type: TI
output_subdir: tis
description: "Detailed test implementation plan for comprehensive error handling and edge case testing in pydapter"
---

# Test Implementation Plan: Comprehensive Error Handling and Edge Cases

## 1. Overview

### 1.1 Component Under Test

This test implementation plan covers comprehensive error handling and edge case
testing for all pydapter adapters, including:

- Core adapters (JSON, CSV, TOML)
- Database adapters (SQL, PostgreSQL, MongoDB, Neo4j, Qdrant)
- Asynchronous adapters

### 1.2 Test Approach

We will use a combination of:

- Unit tests for isolated error handling
- Integration tests for database-related errors
- Property-based tests for edge cases
- Mocking for simulating error conditions

### 1.3 Key Testing Goals

- Verify that all adapters handle invalid inputs gracefully
- Ensure proper error messages are provided for debugging
- Test boundary conditions and edge cases
- Verify resource cleanup in error scenarios
- Test handling of special characters and encoding issues
- Ensure database errors are properly propagated and contextualized
- Test async error handling patterns

## 2. Test Environment

### 2.1 Test Framework

```
pytest
pytest-asyncio
pytest-mock
pytest-cov
hypothesis (for property-based testing)
```

### 2.2 Mock Framework

```
pytest-mock (for mocking)
unittest.mock
```

### 2.3 Test Database

- PostgreSQL container for SQL adapter tests
- MongoDB container for MongoDB adapter tests
- Neo4j container for Neo4j adapter tests
- Qdrant container for vector database tests

## 3. Unit Tests

### 3.1 Test Suite: Core Adapter Error Handling

#### 3.1.1 Test Case: JSON Adapter - Malformed JSON

**Purpose:** Verify that the JSON adapter properly handles malformed JSON input.

**Setup:**

```python
@pytest.fixture
def test_model():
    class TestModel(Adaptable, BaseModel):
        id: int
        name: str
        value: float
    
    TestModel.register_adapter(JsonAdapter)
    return TestModel
```

**Test Implementation:**

```python
def test_json_adapter_malformed_json(test_model):
    """Test that the JSON adapter properly handles malformed JSON."""
    # Test with malformed JSON
    with pytest.raises(json.JSONDecodeError) as exc_info:
        test_model.adapt_from("{invalid json}", obj_key="json")
    
    # Verify the error message is helpful
    assert "Expecting property name" in str(exc_info.value)
```

#### 3.1.2 Test Case: JSON Adapter - Empty Input

**Purpose:** Verify that the JSON adapter properly handles empty input.

**Test Implementation:**

```python
def test_json_adapter_empty_input(test_model):
    """Test that the JSON adapter properly handles empty input."""
    # Test with empty string
    with pytest.raises(json.JSONDecodeError) as exc_info:
        test_model.adapt_from("", obj_key="json")
    
    # Verify the error message is helpful
    assert "Expecting value" in str(exc_info.value)
```

#### 3.1.3 Test Case: CSV Adapter - Missing Headers

**Purpose:** Verify that the CSV adapter properly handles CSV data with missing
headers.

**Test Implementation:**

```python
def test_csv_adapter_missing_headers(test_model):
    """Test that the CSV adapter properly handles CSV data with missing headers."""
    # CSV data without headers
    csv_data = "1,test,42.5"
    
    # This should raise an appropriate error or return empty results
    result = test_model.adapt_from(csv_data, obj_key="csv")
    
    # Verify the result is empty or an error was raised
    if isinstance(result, list):
        assert len(result) == 0
    else:
        assert not isinstance(result, test_model)
```

#### 3.1.4 Test Case: CSV Adapter - Invalid Data Types

**Purpose:** Verify that the CSV adapter properly handles CSV data with invalid
data types.

**Test Implementation:**

```python
def test_csv_adapter_invalid_data_types(test_model):
    """Test that the CSV adapter properly handles CSV data with invalid data types."""
    # CSV data with invalid data types
    csv_data = "id,name,value\nnot_an_int,test,42.5"
    
    # This should raise a validation error
    with pytest.raises(ValidationError):
        test_model.adapt_from(csv_data, obj_key="csv")
```

#### 3.1.5 Test Case: CSV Adapter - Special Characters

**Purpose:** Verify that the CSV adapter properly handles CSV data with special
characters.

**Test Implementation:**

```python
def test_csv_adapter_special_characters(test_model):
    """Test that the CSV adapter properly handles CSV data with special characters."""
    # CSV data with special characters
    csv_data = 'id,name,value\n1,"name with, comma",42.5'
    
    # This should parse correctly
    result = test_model.adapt_from(csv_data, obj_key="csv")
    
    # Verify the result
    assert isinstance(result, test_model)
    assert result.name == "name with, comma"
```

#### 3.1.6 Test Case: CSV Adapter - Different Dialects

**Purpose:** Verify that the CSV adapter properly handles different CSV
dialects.

**Test Implementation:**

```python
@pytest.mark.parametrize("dialect_params", [
    {"delimiter": ","},
    {"delimiter": ";"},
    {"delimiter": "\t"},
    {"quotechar": "'"},
    {"quotechar": '"'},
])
def test_csv_adapter_dialects(test_model, dialect_params):
    """Test that the CSV adapter properly handles different CSV dialects."""
    # Create CSV data with the specified dialect
    delimiter = dialect_params.get("delimiter", ",")
    quotechar = dialect_params.get("quotechar", '"')
    
    csv_data = f'id{delimiter}name{delimiter}value\n1{delimiter}{quotechar}test{quotechar}{delimiter}42.5'
    
    # This should parse correctly with the specified dialect parameters
    result = test_model.adapt_from(csv_data, obj_key="csv", **dialect_params)
    
    # Verify the result
    assert isinstance(result, test_model)
    assert result.name == "test"
    assert result.value == 42.5
```

#### 3.1.7 Test Case: TOML Adapter - Syntax Errors

**Purpose:** Verify that the TOML adapter properly handles TOML data with syntax
errors.

**Test Implementation:**

```python
def test_toml_adapter_syntax_errors(test_model):
    """Test that the TOML adapter properly handles TOML data with syntax errors."""
    # TOML data with syntax errors
    toml_data = "invalid toml = data"
    
    # This should raise an appropriate error
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from(toml_data, obj_key="toml")
    
    # Verify the error message is helpful
    assert "TOML" in str(exc_info.value).upper() or "parse" in str(exc_info.value).lower()
```

### 3.2 Test Suite: Database Adapter Error Handling

#### 3.2.1 Test Case: SQL Adapter - Connection Errors

**Purpose:** Verify that the SQL adapter properly handles connection errors.

**Setup:**

```python
@pytest.fixture
def mock_sqlalchemy(mocker):
    """Mock SQLAlchemy to simulate connection errors."""
    mock_engine = mocker.patch("sqlalchemy.create_engine")
    mock_engine.side_effect = sqlalchemy.exc.SQLAlchemyError("Connection failed")
    return mock_engine
```

**Test Implementation:**

```python
def test_sql_adapter_connection_error(test_model, mock_sqlalchemy):
    """Test that the SQL adapter properly handles connection errors."""
    # Register the SQL adapter
    test_model.register_adapter(SQLAdapter)
    
    # Test with connection error
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from({"engine_url": "invalid://url", "table": "test"}, obj_key="sql")
    
    # Verify the error message is helpful
    assert "Connection failed" in str(exc_info.value)
```

#### 3.2.2 Test Case: SQL Adapter - Invalid Table

**Purpose:** Verify that the SQL adapter properly handles invalid table names.

**Setup:**

```python
@pytest.fixture
def mock_sqlalchemy_table_error(mocker):
    """Mock SQLAlchemy to simulate table not found errors."""
    mock_engine = mocker.MagicMock()
    mock_metadata = mocker.MagicMock()
    mock_metadata.bind = mock_engine
    
    # Mock the Table constructor to raise an error
    mock_table = mocker.patch("sqlalchemy.Table")
    mock_table.side_effect = sqlalchemy.exc.NoSuchTableError("Table 'nonexistent' not found")
    
    # Mock create_engine to return our mock engine
    mock_create_engine = mocker.patch("sqlalchemy.create_engine")
    mock_create_engine.return_value = mock_engine
    
    # Mock MetaData to return our mock metadata
    mock_metadata_cls = mocker.patch("sqlalchemy.MetaData")
    mock_metadata_cls.return_value = mock_metadata
    
    return mock_table
```

**Test Implementation:**

```python
def test_sql_adapter_invalid_table(test_model, mock_sqlalchemy_table_error):
    """Test that the SQL adapter properly handles invalid table names."""
    # Register the SQL adapter
    test_model.register_adapter(SQLAdapter)
    
    # Test with invalid table name
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from({"engine_url": "sqlite://", "table": "nonexistent"}, obj_key="sql")
    
    # Verify the error message is helpful
    assert "Table 'nonexistent' not found" in str(exc_info.value)
```

#### 3.2.3 Test Case: MongoDB Adapter - Authentication Failure

**Purpose:** Verify that the MongoDB adapter properly handles authentication
failures.

**Setup:**

```python
@pytest.fixture
def mock_mongodb_auth_error(mocker):
    """Mock MongoDB to simulate authentication errors."""
    mock_client = mocker.MagicMock()
    mock_client.side_effect = pymongo.errors.OperationFailure("Authentication failed")
    
    # Mock MongoClient to return our mock client
    mock_mongo_client = mocker.patch("pymongo.MongoClient")
    mock_mongo_client.side_effect = mock_client.side_effect
    
    return mock_mongo_client
```

**Test Implementation:**

```python
def test_mongodb_adapter_auth_error(test_model, mock_mongodb_auth_error):
    """Test that the MongoDB adapter properly handles authentication failures."""
    # Register the MongoDB adapter
    test_model.register_adapter(MongoAdapter)
    
    # Test with authentication failure
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from({
            "url": "mongodb://invalid:invalid@localhost:27017",
            "db": "testdb",
            "collection": "test"
        }, obj_key="mongo")
    
    # Verify the error message is helpful
    assert "Authentication failed" in str(exc_info.value)
```

### 3.3 Test Suite: Async Adapter Error Handling

#### 3.3.1 Test Case: Async SQL Adapter - Connection Errors

**Purpose:** Verify that the async SQL adapter properly handles connection
errors.

**Setup:**

```python
@pytest.fixture
def mock_async_sqlalchemy(mocker):
    """Mock async SQLAlchemy to simulate connection errors."""
    mock_engine = mocker.AsyncMock()
    mock_engine.side_effect = sqlalchemy.exc.SQLAlchemyError("Connection failed")
    
    # Mock create_async_engine to return our mock engine
    mock_create_engine = mocker.patch("sqlalchemy.ext.asyncio.create_async_engine")
    mock_create_engine.side_effect = mock_engine.side_effect
    
    return mock_create_engine
```

**Test Implementation:**

```python
@pytest.mark.asyncio
async def test_async_sql_adapter_connection_error(async_model_factory, mock_async_sqlalchemy):
    """Test that the async SQL adapter properly handles connection errors."""
    # Register the async SQL adapter
    async_model = async_model_factory()
    async_model.__class__.register_async_adapter(AsyncSQLAdapter)
    
    # Test with connection error
    with pytest.raises(Exception) as exc_info:
        await async_model.__class__.adapt_from_async({
            "dsn": "postgresql+asyncpg://invalid:invalid@localhost/nonexistent",
            "table": "test"
        }, obj_key="async_sql")
    
    # Verify the error message is helpful
    assert "Connection failed" in str(exc_info.value)
```

#### 3.3.2 Test Case: Async Adapter - Cancellation

**Purpose:** Verify that async adapters properly handle task cancellation.

**Test Implementation:**

```python
@pytest.mark.asyncio
async def test_async_adapter_cancellation(async_model_factory, mocker):
    """Test that async adapters properly handle task cancellation."""
    # Create a mock adapter that sleeps and can be cancelled
    class MockAsyncAdapter:
        obj_key = "mock_async"
        
        @classmethod
        async def from_obj(cls, subj_cls, obj, /, **kw):
            await asyncio.sleep(10)  # Long operation that will be cancelled
            return subj_cls()
        
        @classmethod
        async def to_obj(cls, subj, /, **kw):
            await asyncio.sleep(10)  # Long operation that will be cancelled
            return {}
    
    # Register the mock adapter
    async_model = async_model_factory()
    async_model.__class__.register_async_adapter(MockAsyncAdapter)
    
    # Create a task that will be cancelled
    task = asyncio.create_task(
        async_model.__class__.adapt_from_async({}, obj_key="mock_async")
    )
    
    # Wait a bit and then cancel the task
    await asyncio.sleep(0.1)
    task.cancel()
    
    # Verify the task was cancelled
    with pytest.raises(asyncio.CancelledError):
        await task
```

## 4. Integration Tests

### 4.1 Test Suite: Database Integration Error Handling

#### 4.1.1 Test Case: PostgreSQL Adapter - Real Database Errors

**Purpose:** Verify that the PostgreSQL adapter properly handles real database
errors.

**Setup:**

```python
@pytest.fixture(scope="module")
def pg_container():
    """Start a PostgreSQL container for testing."""
    from testcontainers.postgres import PostgresContainer
    
    with PostgresContainer("postgres:16-alpine") as container:
        yield container
```

**Test Implementation:**

```python
def test_postgres_adapter_real_errors(test_model, pg_container):
    """Test that the PostgreSQL adapter properly handles real database errors."""
    # Register the PostgreSQL adapter
    test_model.register_adapter(PostgresAdapter)
    
    # Get the connection URL
    url = pg_container.get_connection_url()
    
    # Test with non-existent table
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from({
            "engine_url": url,
            "table": "nonexistent_table"
        }, obj_key="postgres")
    
    # Verify the error message is helpful
    assert "table" in str(exc_info.value).lower() and "not" in str(exc_info.value).lower()
```

#### 4.1.2 Test Case: MongoDB Adapter - Real Database Errors

**Purpose:** Verify that the MongoDB adapter properly handles real database
errors.

**Setup:**

```python
@pytest.fixture(scope="module")
def mongo_container():
    """Start a MongoDB container for testing."""
    from testcontainers.mongodb import MongoDbContainer
    
    with MongoDbContainer("mongo:6.0") as container:
        yield container
```

**Test Implementation:**

```python
def test_mongodb_adapter_real_errors(test_model, mongo_container):
    """Test that the MongoDB adapter properly handles real database errors."""
    # Register the MongoDB adapter
    test_model.register_adapter(MongoAdapter)
    
    # Get the connection URL
    url = f"mongodb://{mongo_container.get_container_host_ip()}:{mongo_container.get_exposed_port(27017)}"
    
    # Test with invalid query
    with pytest.raises(Exception) as exc_info:
        test_model.adapt_from({
            "url": url,
            "db": "testdb",
            "collection": "test_collection",
            "filter": {"$invalidOperator": 1}  # Invalid MongoDB operator
        }, obj_key="mongo")
    
    # Verify the error message is helpful
    assert "operator" in str(exc_info.value).lower() or "invalid" in str(exc_info.value).lower()
```

## 5. API Tests

### 5.1 Endpoint: Custom Exception Hierarchy

**Purpose:** Verify that custom exceptions provide appropriate context and can
be caught properly.

**Test Implementation:**

```python
def test_custom_exception_hierarchy():
    """Test that custom exceptions provide appropriate context and can be caught properly."""
    from pydapter.exceptions import AdapterError, ValidationError, ConnectionError
    
    # Test that exceptions can be caught by their base class
    try:
        raise ValidationError("Invalid data")
    except AdapterError as e:
        assert isinstance(e, ValidationError)
        assert "Invalid data" in str(e)
    
    # Test that exceptions provide appropriate context
    try:
        raise ConnectionError("Failed to connect", adapter="postgres", url="postgresql://localhost")
    except ConnectionError as e:
        assert "Failed to connect" in str(e)
        assert e.adapter == "postgres"
        assert e.url == "postgresql://localhost"
```

## 6. Error Handling Tests

### 6.1 Test Suite: Resource Cleanup

**Purpose:** Verify that resources are properly cleaned up in error scenarios.

**Test Implementation:**

```python
def test_sql_adapter_resource_cleanup(mocker):
    """Test that the SQL adapter properly cleans up resources in error scenarios."""
    # Mock SQLAlchemy engine and connection
    mock_engine = mocker.MagicMock()
    mock_conn = mocker.MagicMock()
    mock_engine.begin.return_value.__enter__.return_value = mock_conn
    mock_conn.execute.side_effect = Exception("Query failed")
    
    # Mock create_engine to return our mock engine
    mock_create_engine = mocker.patch("sqlalchemy.create_engine")
    mock_create_engine.return_value = mock_engine
    
    class TestModel(Adaptable, BaseModel):
        id: int
        name: str
        value: float
    
    TestModel.register_adapter(SQLAdapter)
    
    # Test with query error
    with pytest.raises(Exception):
        TestModel.adapt_from({
            "engine_url": "sqlite://",
            "table": "test"
        }, obj_key="sql")
    
    # Verify that the connection was closed
    mock_engine.begin.return_value.__exit__.assert_called()
```

## 7. Performance Tests

### 7.1 Benchmark / Load Testing

**Purpose:** Verify that error handling doesn't significantly impact
performance.

**Test Implementation:**

```python
def test_json_adapter_performance_with_errors(benchmark):
    """Test that error handling doesn't significantly impact performance."""
    class TestModel(Adaptable, BaseModel):
        id: int
        name: str
        value: float
    
    TestModel.register_adapter(JsonAdapter)
    
    # Valid JSON for comparison
    valid_json = '{"id": 1, "name": "test", "value": 42.5}'
    
    # Function to benchmark with valid input
    def parse_valid():
        for _ in range(100):
            TestModel.adapt_from(valid_json, obj_key="json")
    
    # Benchmark valid parsing
    valid_result = benchmark(parse_valid)
    
    # Invalid JSON
    invalid_json = '{"id": 1, "name": "test", "value": "not_a_float"}'
    
    # Function to benchmark with invalid input
    def parse_invalid():
        for _ in range(100):
            try:
                TestModel.adapt_from(invalid_json, obj_key="json")
            except Exception:
                pass
    
    # Benchmark invalid parsing
    invalid_result = benchmark(parse_invalid)
    
    # Verify that error handling doesn't add excessive overhead
    # The invalid case should not be more than 2x slower than the valid case
    assert invalid_result.stats.mean < valid_result.stats.mean * 2
```

## 8. Mock Implementation Details

```python
class MockSQLAlchemyEngine:
    """Mock SQLAlchemy engine for testing."""
    
    def __init__(self, error=None):
        self.error = error
        self.closed = False
    
    def begin(self):
        """Mock context manager for transactions."""
        return self
    
    def __enter__(self):
        """Enter the context manager."""
        if self.error:
            raise self.error
        return self
    
    def __exit__(self, exc_type, exc_val, exc_tb):
        """Exit the context manager."""
        self.closed = True
        return False
    
    def execute(self, statement):
        """Mock execute method."""
        if self.error:
            raise self.error
        return MockResult([{"id": 1, "name": "test", "value": 42.5}])


class MockResult:
    """Mock SQLAlchemy result for testing."""
    
    def __init__(self, rows):
        self.rows = rows
    
    def fetchall(self):
        """Mock fetchall method."""
        return [MockRow(r) for r in self.rows]


class MockRow:
    """Mock SQLAlchemy row for testing."""
    
    def __init__(self, mapping):
        self._mapping = mapping
```

## 9. Test Data

```python
# Valid test data
valid_json = '{"id": 1, "name": "test", "value": 42.5}'
valid_csv = 'id,name,value\n1,test,42.5'
valid_toml = 'id = 1\nname = "test"\nvalue = 42.5'

# Invalid test data
invalid_json = '{"id": "not_an_int", "name": "test", "value": 42.5}'
invalid_csv = 'id,name,value\nnot_an_int,test,42.5'
invalid_toml = 'id = "not_an_int"\nname = "test"\nvalue = 42.5'

# Edge case test data
empty_json = '{}'
empty_csv = ''
empty_toml = ''

# Special character test data
special_json = '{"id": 1, "name": "test\\nwith\\nnewlines", "value": 42.5}'
special_csv = 'id,name,value\n1,"test with, comma",42.5'
special_toml = 'id = 1\nname = "test with \\" quotes"\nvalue = 42.5'
```

## 10. Helper Functions

```python
def create_test_model():
    """Create a test model for testing."""
    from pydantic import BaseModel
    from pydapter import Adaptable
    
    class TestModel(Adaptable, BaseModel):
        id: int
        name: str
        value: float
    
    return TestModel


def register_all_adapters(model_cls):
    """Register all adapters for a model class."""
    from pydapter.adapters import CsvAdapter, JsonAdapter, TomlAdapter
    from pydapter.extras import SQLAdapter, PostgresAdapter, MongoAdapter
    
    model_cls.register_adapter(JsonAdapter)
    model_cls.register_adapter(CsvAdapter)
    model_cls.register_adapter(TomlAdapter)
    model_cls.register_adapter(SQLAdapter)
    model_cls.register_adapter(PostgresAdapter)
    model_cls.register_adapter(MongoAdapter)
    
    return model_cls
```

## 11. Test Coverage Targets

- **Line Coverage Target:** 90%
- **Branch Coverage Target:** 85%
- **Critical Modules:** 95% coverage for core error handling code

## 12. Continuous Integration

```yaml
name: Test Error Handling
on: [push, pull_request]
jobs:
  tests:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16-alpine
        env:
          POSTGRES_PASSWORD: postgres
          POSTGRES_USER: postgres
          POSTGRES_DB: testdb
        ports:
          - 5432:5432
      mongodb:
        image: mongo:6.0
        ports:
          - 27017:27017
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.10"
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -e .
          pip install pytest pytest-asyncio pytest-cov pytest-mock hypothesis
      - name: Run tests
        run: |
          pytest tests/test_error_handling.py --cov=src/pydapter --cov-report=xml
      - name: Upload coverage
        uses: codecov/codecov-action@v3
```

## 13. Notes and Caveats

### 13.1 Known Limitations

- Some database errors may be difficult to simulate in tests
- Async error handling tests may be flaky due to timing issues
- Error messages may vary between different versions of dependencies

### 13.2 Future Improvements

- Add more property-based tests for edge cases
- Implement a more comprehensive custom exception hierarchy
- Add support for error recovery strategies
- Improve error reporting with more context information
