"""``PickleDataset`` loads/saves data from/to a Pickle file using an underlying
filesystem (e.g.: local, S3, GCS). The underlying functionality is supported by
the specified backend library passed in (defaults to the ``pickle`` library), so it
supports all allowed options for loading and saving pickle files.
"""
from __future__ import annotations
import importlib
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any
import fsspec
from kedro.io.core import (
AbstractVersionedDataset,
DatasetError,
Version,
get_filepath_str,
get_protocol_and_path,
)
[docs]
class PickleDataset(AbstractVersionedDataset[Any, Any]):
"""``PickleDataset`` loads/saves data from/to a Pickle file using an underlying
filesystem (e.g.: local, S3, GCS). The underlying functionality is supported by
the specified backend library passed in (defaults to the ``pickle`` library), so it
supports all allowed options for loading and saving pickle files.
Example usage for the
`YAML API <https://docs.kedro.org/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
test_model: # simple example without compression
type: pickle.PickleDataset
filepath: data/07_model_output/test_model.pkl
backend: pickle
final_model: # example with load and save args
type: pickle.PickleDataset
filepath: s3://your_bucket/final_model.pkl.lz4
backend: joblib
credentials: s3_credentials
save_args:
compress: lz4
Example usage for the
`Python API <https://docs.kedro.org/en/stable/data/\
advanced_data_catalog_usage.html>`_:
.. code-block:: pycon
>>> from kedro_datasets.pickle import PickleDataset
>>> import pandas as pd
>>>
>>> data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
>>>
>>> dataset = PickleDataset(filepath="test.pkl", backend="pickle")
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert data.equals(reloaded)
>>>
>>> dataset = PickleDataset(
... filepath=tmp_path / "test.pickle.lz4",
... backend="compress_pickle",
... load_args={"compression": "lz4"},
... save_args={"compression": "lz4"},
... )
>>> dataset.save(data)
>>> reloaded = dataset.load()
>>> assert data.equals(reloaded)
"""
DEFAULT_LOAD_ARGS: dict[str, Any] = {}
DEFAULT_SAVE_ARGS: dict[str, Any] = {}
DEFAULT_FS_ARGS: dict[str, Any] = {"open_args_save": {"mode": "wb"}}
[docs]
def __init__( # noqa: PLR0913
self,
*,
filepath: str,
backend: str = "pickle",
load_args: dict[str, Any] | None = None,
save_args: dict[str, Any] | None = None,
version: Version | None = None,
credentials: dict[str, Any] | None = None,
fs_args: dict[str, Any] | None = None,
metadata: dict[str, Any] | None = None,
) -> None:
"""Creates a new instance of ``PickleDataset`` pointing to a concrete Pickle
file on a specific filesystem. ``PickleDataset`` supports custom backends to
serialise/deserialise objects.
Example backends that are compatible (non-exhaustive):
* `pickle`
* `joblib`
* `dill`
* `compress_pickle`
Example backends that are incompatible:
* `torch`
Args:
filepath: Filepath in POSIX format to a Pickle file prefixed with a protocol like
`s3://`. If prefix is not provided, `file` protocol (local filesystem) will be used.
The prefix should be any protocol supported by ``fsspec``.
Note: `http(s)` doesn't support versioning.
backend: Backend to use, must be an import path to a module which satisfies the
``pickle`` interface. That is, contains a `load` and `dump` function.
Defaults to 'pickle'.
load_args: Pickle options for loading pickle files.
You can pass in arguments that the backend load function specified accepts, e.g:
pickle.load: https://docs.python.org/3/library/pickle.html#pickle.load
joblib.load: https://joblib.readthedocs.io/en/latest/generated/joblib.load.html
dill.load: https://dill.readthedocs.io/en/latest/index.html#dill.load
compress_pickle.load:
https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.load
cloudpickle.load:
https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py
All defaults are preserved.
save_args: Pickle options for saving pickle files.
You can pass in arguments that the backend dump function specified accepts, e.g:
pickle.dump: https://docs.python.org/3/library/pickle.html#pickle.dump
joblib.dump: https://joblib.readthedocs.io/en/latest/generated/joblib.dump.html
dill.dump: https://dill.readthedocs.io/en/latest/index.html#dill.dump
compress_pickle.dump:
https://lucianopaz.github.io/compress_pickle/html/api/compress_pickle.html#compress_pickle.compress_pickle.dump
cloudpickle.dump:
https://github.com/cloudpipe/cloudpickle/blob/master/tests/cloudpickle_test.py
All defaults are preserved.
version: If specified, should be an instance of
``kedro.io.core.Version``. If its ``load`` attribute is
None, the latest version will be loaded. If its ``save``
attribute is None, save version will be autogenerated.
credentials: Credentials required to get access to the underlying filesystem.
E.g. for ``GCSFileSystem`` it should look like `{"token": None}`.
fs_args: Extra arguments to pass into underlying filesystem class constructor
(e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
to pass to the filesystem's `open` method through nested keys
`open_args_load` and `open_args_save`.
Here you can find all available arguments for `open`:
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
All defaults are preserved, except `mode`, which is set to `wb` when saving.
metadata: Any arbitrary metadata.
This is ignored by Kedro, but may be consumed by users or external plugins.
Raises:
ValueError: If ``backend`` does not satisfy the `pickle` interface.
ImportError: If the ``backend`` module could not be imported.
"""
# We do not store `imported_backend` as an attribute to be used in `load`/`save`
# as this would mean the dataset cannot be deepcopied (module objects cannot be
# pickled). The import here is purely to raise any errors as early as possible.
# Repeated imports in the `load` and `save` methods should not be a significant
# performance hit as Python caches imports.
try:
imported_backend = importlib.import_module(backend)
except ImportError as exc:
raise ImportError(
f"Selected backend '{backend}' could not be imported. "
"Make sure it is installed and importable."
) from exc
if not (
hasattr(imported_backend, "load") and hasattr(imported_backend, "dump")
):
raise ValueError(
f"Selected backend '{backend}' should satisfy the pickle interface. "
"Missing one of 'load' and 'dump' on the backend."
)
_fs_args = deepcopy(fs_args) or {}
_fs_open_args_load = _fs_args.pop("open_args_load", {})
_fs_open_args_save = _fs_args.pop("open_args_save", {})
_credentials = deepcopy(credentials) or {}
protocol, path = get_protocol_and_path(filepath, version)
if protocol == "file":
_fs_args.setdefault("auto_mkdir", True)
self._protocol = protocol
self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args)
self.metadata = metadata
super().__init__(
filepath=PurePosixPath(path),
version=version,
exists_function=self._fs.exists,
glob_function=self._fs.glob,
)
self._backend = backend
# Handle default load and save arguments
self._load_args = {**self.DEFAULT_LOAD_ARGS, **(load_args or {})}
self._save_args = {**self.DEFAULT_SAVE_ARGS, **(save_args or {})}
self._fs_open_args_load = {
**self.DEFAULT_FS_ARGS.get("open_args_load", {}),
**(_fs_open_args_load or {}),
}
self._fs_open_args_save = {
**self.DEFAULT_FS_ARGS.get("open_args_save", {}),
**(_fs_open_args_save or {}),
}
def _describe(self) -> dict[str, Any]:
return {
"filepath": self._filepath,
"backend": self._backend,
"protocol": self._protocol,
"load_args": self._load_args,
"save_args": self._save_args,
"version": self._version,
}
[docs]
def load(self) -> Any:
load_path = get_filepath_str(self._get_load_path(), self._protocol)
with self._fs.open(load_path, **self._fs_open_args_load) as fs_file:
imported_backend = importlib.import_module(self._backend)
return imported_backend.load(fs_file, **self._load_args) # type: ignore
[docs]
def save(self, data: Any) -> None:
save_path = get_filepath_str(self._get_save_path(), self._protocol)
with self._fs.open(save_path, **self._fs_open_args_save) as fs_file:
try:
imported_backend = importlib.import_module(self._backend)
imported_backend.dump(data, fs_file, **self._save_args) # type: ignore
except Exception as exc:
raise DatasetError(
f"{data.__class__} was not serialised due to: {exc}"
) from exc
self._invalidate_cache()
def _exists(self) -> bool:
try:
load_path = get_filepath_str(self._get_load_path(), self._protocol)
except DatasetError:
return False
return self._fs.exists(load_path)
def _release(self) -> None:
super()._release()
self._invalidate_cache()
def _invalidate_cache(self) -> None:
"""Invalidate underlying filesystem caches."""
filepath = get_filepath_str(self._filepath, self._protocol)
self._fs.invalidate_cache(filepath)
