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Update

Methods for updating documents.

Update

Methods for updating documents.

Source code in src/scruby/mixins/update.py 
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class Update:
    """Methods for updating documents."""

    @final
    @staticmethod
    async def _task_update(
        branch_number: int,
        filter_fn: Callable,
        hash_reduce_left: str,
        db_root: str,
        class_model: Any,
        new_data: dict[str, Any],
    ) -> int:
        """Asynchronous task for find documents.

        This method is for internal use.

        Returns:
            The number of updated documents.
        """
        branch_number_as_hash: str = f"{branch_number:08x}"[hash_reduce_left:]
        separated_hash: str = "/".join(list(branch_number_as_hash))
        leaf_path = Path(
            *(
                db_root,
                class_model.__name__,
                separated_hash,
                "leaf.json",
            ),
        )
        counter: int = 0
        if await leaf_path.exists():
            data_json: bytes = await leaf_path.read_bytes()
            data: dict[str, str] = orjson.loads(data_json) or {}
            new_state: dict[str, str] = {}
            for _, val in data.items():
                doc = class_model.model_validate_json(val)
                if filter_fn(doc):
                    for key, value in new_data.items():
                        doc.__dict__[key] = value
                        new_state[key] = doc.model_dump_json()
                    counter += 1
            await leaf_path.write_bytes(orjson.dumps(new_state))
        return counter

    @final
    async def update_many(
        self,
        new_data: dict[str, Any],
        filter_fn: Callable = lambda _: True,
    ) -> int:
        """Asynchronous method for updates many documents matching the filter.

        Attention:
            - For a complex case, a custom task may be needed.
            - See documentation on creating custom tasks.
            - The search is based on the effect of a quantum loop.
            - The search effectiveness depends on the number of processor threads.

        Args:
            filter_fn (Callable): A function that execute the conditions of filtering.
            new_data (dict[str, Any]): New data for the fields that need to be updated.

        Returns:
            The number of updated documents.
        """
        # Variable initialization
        update_task_fn: Callable = self._task_update
        branch_numbers: range = range(self._max_number_branch)
        hash_reduce_left: int = self._hash_reduce_left
        db_root: str = self._db_root
        class_model: Any = self._class_model
        counter: int = 0
        # Run quantum loop
        with concurrent.futures.ThreadPoolExecutor(self._max_workers) as executor:
            for branch_number in branch_numbers:
                future = executor.submit(
                    update_task_fn,
                    branch_number,
                    filter_fn,
                    hash_reduce_left,
                    db_root,
                    class_model,
                    copy.deepcopy(new_data),
                )
                counter += await future.result()
        return counter

update_many(new_data, filter_fn=lambda _: True) async

Asynchronous method for updates many documents matching the filter.

Attention
  • For a complex case, a custom task may be needed.
  • See documentation on creating custom tasks.
  • The search is based on the effect of a quantum loop.
  • The search effectiveness depends on the number of processor threads.

Parameters:

Name Type Description Default
filter_fn Callable

A function that execute the conditions of filtering.

 lambda _: True
new_data dict[str, Any]

New data for the fields that need to be updated.

 required

Returns:

Type Description
int

The number of updated documents.
Source code in src/scruby/mixins/update.py 
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@final
async def update_many(
    self,
    new_data: dict[str, Any],
    filter_fn: Callable = lambda _: True,
) -> int:
    """Asynchronous method for updates many documents matching the filter.

    Attention:
        - For a complex case, a custom task may be needed.
        - See documentation on creating custom tasks.
        - The search is based on the effect of a quantum loop.
        - The search effectiveness depends on the number of processor threads.

    Args:
        filter_fn (Callable): A function that execute the conditions of filtering.
        new_data (dict[str, Any]): New data for the fields that need to be updated.

    Returns:
        The number of updated documents.
    """
    # Variable initialization
    update_task_fn: Callable = self._task_update
    branch_numbers: range = range(self._max_number_branch)
    hash_reduce_left: int = self._hash_reduce_left
    db_root: str = self._db_root
    class_model: Any = self._class_model
    counter: int = 0
    # Run quantum loop
    with concurrent.futures.ThreadPoolExecutor(self._max_workers) as executor:
        for branch_number in branch_numbers:
            future = executor.submit(
                update_task_fn,
                branch_number,
                filter_fn,
                hash_reduce_left,
                db_root,
                class_model,
                copy.deepcopy(new_data),
            )
            counter += await future.result()
    return counter