airbyte_cdk/sources/declarative/models/declarative_component_schema.py (1)

718-721: LGTM! Would you consider adding docstring documentation?

The FlattenFields class implementation looks good and follows the established patterns. Consider adding a docstring to describe its purpose and behavior, wdyt?

 class FlattenFields(BaseModel):
+    """A transformation that flattens nested records into a single-level format.
+    
+    This transformation is useful for sources that require simplified record structures
+    on their path to manifest.
+    """
     type: Literal["FlattenFields"]
     parameters: Optional[Dict[str, Any]] = Field(None, alias="$parameters")

unit_tests/sources/declarative/transformations/test_flatten_fields.py (2)

12-50: Consider adding more test cases for edge scenarios

The current test cases cover the basic functionality well, but would you consider adding tests for:

1. Empty records {}
2. Records with null values
3. Deep nesting (>2 levels)
4. Records with special characters in keys
5. Performance test with a large nested record

This would help ensure the transformation handles edge cases gracefully. wdyt?

@pytest.mark.parametrize(
    "input_record, expected_output",
    [
        # Empty record
        ({}, {}),
        
        # Null values
        ({"nested": {"key": None}}, {"key": None}),
        
        # Deep nesting
        (
            {"l1": {"l2": {"l3": {"value": "deep"}}}},
            {"value": "deep"}
        ),
        
        # Special characters
        ({"@special": {"$key": "value"}}, {"$key": "value"}),
        
        # Add to existing cases...
    ]
)

---

51-54: Consider enhancing test implementation robustness

The current implementation is clean, but would you consider:

1. Verifying that only the expected fields are modified by making a deep copy of the input and comparing non-flattened fields
2. Adding error handling tests for invalid inputs
3. Adding a test helper to verify the flattening depth

Here's a possible implementation, wdyt?

from copy import deepcopy

def test_flatten_fields(input_record, expected_output):
    original_record = deepcopy(input_record)
    flattener = FlattenFields()
    flattener.transform(input_record)
    assert input_record == expected_output
    
    # Verify only expected fields were modified
    for k, v in original_record.items():
        if not isinstance(v, (dict, list)):
            assert input_record[k] == v

@pytest.mark.parametrize("invalid_input", [None, 42, "not_a_dict"])
def test_flatten_fields_error_handling(invalid_input):
    flattener = FlattenFields()
    with pytest.raises(ValueError):
        flattener.transform(invalid_input)

airbyte_cdk/sources/declarative/transformations/flatten_fields.py (1)

25-50: Consider optimizing the flattening algorithm

The current implementation is clean but could be optimized. Would you consider:

1. Adding early returns for non-nested records
2. Using a more memory-efficient approach for large records
3. Adding depth limit to prevent stack overflow
4. Handling circular references

Here's a possible optimization, wdyt?

def flatten_record(self, record: Dict[str, Any], max_depth: int = 100) -> Dict[str, Any]:
    # Early return if no nested structures
    if not any(isinstance(v, (dict, list)) for v in record.values()):
        return record.copy()
        
    transformed_record = {}
    stack = [(record, "_", 0)]  # (value, parent_key, depth)
    seen = set()  # For circular reference detection
    
    while stack:
        current_record, parent_key, depth = stack.pop()
        
        if depth > max_depth:
            raise ValueError(f"Maximum nesting depth of {max_depth} exceeded")
            
        if isinstance(current_record, dict):
            record_id = id(current_record)
            if record_id in seen:
                raise ValueError("Circular reference detected")
            seen.add(record_id)
            
            for current_key, value in current_record.items():
                new_key = (
                    f"{parent_key}.{current_key}"
                    if parent_key != "_"
                    else current_key
                )
                if isinstance(value, (dict, list)):
                    stack.append((value, new_key, depth + 1))
                else:
                    transformed_record[new_key] = value
                    
        elif isinstance(current_record, list):
            for i, item in enumerate(current_record):
                stack.append((item, f"{parent_key}.{i}", depth + 1))
                
    return transformed_record

airbyte_cdk/sources/declarative/parsers/model_to_component_factory.py (1)

597-601: Consider aligning with other transformation implementations

The implementation is simple but could be more consistent with other transformations in the factory. Would you consider:

1. Adding parameter validation similar to other transformations
2. Adding type hints for the return value
3. Following the pattern used by similar transformations like create_add_fields

Here's a suggestion, wdyt?

def create_flatten_fields(
    self,
    model: FlattenFieldsModel,
    config: Config,
    **kwargs: Any
) -> FlattenFields:
    """Creates a FlattenFields transformation component.
    
    Args:
        model: The model containing the component configuration.
        config: The connector configuration.
        **kwargs: Additional keyword arguments.
        
    Returns:
        FlattenFields: The created component.
    """
    return FlattenFields(
        parameters=model.parameters or {},
    )

airbyte_cdk/sources/declarative/declarative_component_schema.yaml (1)

1855-1867: Consider adding configuration options for customizing the flattening behavior?

The schema definition for FlattenFields is quite minimal. Would it be helpful to add configuration options to customize the flattening behavior? For example:

• separator - to specify the character used to join nested keys
• max_depth - to limit how deep the flattening goes
• array_handling - to specify how to handle arrays (e.g., 'expand', 'first_element', 'ignore')

This would give users more control over the transformation, wdyt?

Also, would you consider adding some examples to help users understand the transformation? Something like:

examples:
  - input: {"user": {"name": {"first": "John", "last": "Doe"}}}
    output: {"user_name_first": "John", "user_name_last": "Doe"}
  - input: {"items": [{"id": 1}, {"id": 2}]}
    output: {"items_0_id": 1, "items_1_id": 2}

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