Implementing New Formats

Lodum is designed to be format-agnostic. You can add support for a new data format (e.g., XML, Protobuf, or a custom text format) by implementing two core protocols: Dumper and Loader.

The core engine uses these protocols to bridge the gap between Python objects and the specific data format, handling all the complex logic like recursion, type validation, and circular reference detection.

The Dumper Protocol

A Dumper is responsible for taking primitive Python values and converting them into an intermediate representation (IR) or directly into the target format.

Protocol Interface

class Dumper(Protocol):
    def dump_int(self, value: int) -> Any: ...
    def dump_str(self, value: str) -> Any: ...
    def dump_float(self, value: float) -> Any: ...
    def dump_bool(self, value: bool) -> Any: ...
    def dump_bytes(self, value: bytes) -> Any: ...
    def dump_list(self, value: List[Any]) -> Any: ...
    def dump_dict(self, value: Dict[str, Any]) -> Any: ...
    def begin_struct(self, cls: Type) -> Any: ...
    def end_struct(self) -> Any: ...

Implementing a Dumper

Most formats can inherit from BaseDumper, which provides default implementations that return the values as-is. This is useful for formats that work with standard Python collections (like JSON or YAML libraries).

from lodum.core import BaseDumper

class MyFormatDumper(BaseDumper):
    def dump_bytes(self, value: bytes) -> Any:
        # Example: encode bytes to hex for a text format
        return value.hex()

The Loader Protocol

A Loader is responsible for reading primitive values and collections from the source data.

Protocol Interface

class Loader(Protocol):
    def load_int(self) -> int: ...
    def load_str(self) -> str: ...
    def load_float(self) -> float: ...
    def load_bool(self) -> bool: ...
    def load_bytes(self) -> bytes: ...
    def load_list(self) -> Iterator["Loader"]: ...
    def load_dict(self) -> Iterator[tuple[str, "Loader"]]: ...
    def load_any(self) -> Any: ...
    def mark(self) -> Any: ...
    def rewind(self, marker: Any) -> None: ...
    def get_dict(self) -> Optional[Union[Dict[str, Any], List[Any]]]: ...

Key Methods

• load_list / load_dict: These should return an iterator of new Loader instances, each wrapping a nested element.
• mark / rewind: Required for supporting Union types. mark() should return the current state of the loader, and rewind(marker) should restore it. This allows lodum to try decoding data into multiple different types.
• get_dict: An optimization. If the current data is already a raw Python dict or list, returning it here allows the compiler to bypass creating multiple Loader wrappers.

Implementing a Loader

Inheriting from BaseLoader is highly recommended. It provides standardized type checking and error messages (e.g., "Expected int, got str").

from lodum.core import BaseLoader

class MyFormatLoader(BaseLoader):
    # BaseLoader handles load_int, load_str, etc. using load_any()
    # You only need to override specific behavior.
    pass

Creating the Public API

Once you have your Dumper and Loader, you typically expose dumps and loads functions that wrap the lodum.internal calls.

from typing import Any, Type, TypeVar
from lodum.internal import dump, load

T = TypeVar("T")

def dumps(obj: Any) -> str:
    dumper = MyFormatDumper()
    data = dump(obj, dumper)
    return str(data) # Or your format's encoding logic

def loads(cls: Type[T], data_string: str) -> T:
    # Your format's parsing logic to get a Python dict/list
    raw_data = parse_my_format(data_string) 
    loader = MyFormatLoader(raw_data)
    return load(cls, loader)

Best Practices

1. Use BaseLoader: It ensures your format provides the same high-quality error messages as the built-in formats.
2. Handle bytes: If your format doesn't support raw binary data, implement dump_bytes and load_bytes_value to handle Base64 or Hex encoding.
3. Recursive Safety: You don't need to worry about recursion limits or circular references; the lodum.internal.dump and load functions handle this automatically.
4. Performance: If your format returns standard Python dicts/lists, ensure get_dict() returns them to enable the compiler's fast-path optimizations.
5. Thoroughly Test Your Implementation: After implementing a new format, it's crucial to ensure its correctness and robustness. Run the project's comprehensive test suite and add new tests specifically for your format. Refer to the Contributing Guide for detailed instructions on running tests and maintaining code quality.