Class ThreadSafeFory

python/pyfory/_fory.py:594–780

Thread-safe wrapper for Fory using instance pooling. ThreadSafeFory maintains a pool of Fory instances protected by a lock to enable safe concurrent serialization/deserialization across multiple threads. When a thread needs to serialize or deserialize data, it acquires an instance

class ThreadSafeFory:
    """
    Thread-safe wrapper for Fory using instance pooling.

    ThreadSafeFory maintains a pool of Fory instances protected by a lock to enable
    safe concurrent serialization/deserialization across multiple threads. When a thread
    needs to serialize or deserialize data, it acquires an instance from the pool, uses it,
    and returns it for reuse by other threads.

    All type registrations must be performed before any serialization operations to ensure
    consistency across all pooled instances. Attempting to register types after the first
    serialization will raise a RuntimeError.

    Args:
        xlang (bool): Whether to enable xlang mode. Defaults to True.
        ref (bool): Whether to enable reference tracking. Defaults to False.
        strict (bool): Whether to require type registration. Defaults to True.
        compatible (bool): Whether to enable compatible mode. Defaults to compatible mode
            in both xlang and Python native mode. Set False only when every reader and
            writer always uses the same Python class schema and smaller payloads matter.
        max_depth (int): Maximum depth for deserialization. Defaults to 50.
    Example:
        >>> import pyfory
        >>> import threading
        >>> from dataclasses import dataclass
        >>>
        >>> @dataclass
        >>> class Person:
        ...     name: str
        ...     age: int
        >>>
        >>> # Create thread-safe instance
        >>> fory = pyfory.ThreadSafeFory(xlang=False)
        >>> fory.register(Person)
        >>>
        >>> # Use safely from multiple threads
        >>> def worker(thread_id):
        ...     person = Person(f"User{thread_id}", 25)
        ...     data = fory.serialize(person)
        ...     result = fory.deserialize(data)
        ...     print(f"Thread {thread_id}: {result}")
        >>>
        >>> threads = [threading.Thread(target=worker, args=(i,)) for i in range(5)]
        >>> for t in threads: t.start()
        >>> for t in threads: t.join()

    Note:
        - Register all types before calling serialize/deserialize
        - The pool grows dynamically as needed based on thread contention
        - Instances are automatically returned to the pool after use
        - Both Python and Cython modes are supported automatically
    """

    def __init__(self, fory_factory=None, **kwargs):
        import threading

        self._config = kwargs
        self._fory_factory = fory_factory