Source code for kedro.extras.datasets.pandas.hdf_dataset

# Copyright 2020 QuantumBlack Visual Analytics Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
# (either separately or in combination, "QuantumBlack Trademarks") are
# trademarks of QuantumBlack. The License does not grant you any right or
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
# Trademarks or any confusingly similar mark as a trademark for your product,
# or use the QuantumBlack Trademarks in any other manner that might cause
# confusion in the marketplace, including but not limited to in advertising,
# on websites, or on software.
#
# See the License for the specific language governing permissions and
# limitations under the License.

"""``HDFDataSet`` loads/saves data from/to a hdf file using an underlying
filesystem (e.g.: local, S3, GCS). It uses pandas.HDFStore to handle the hdf file.
"""
from copy import deepcopy
from pathlib import PurePosixPath
from threading import Lock
from typing import Any, Dict

import fsspec
import pandas as pd

from kedro.io.core import (
    AbstractVersionedDataSet,
    DataSetError,
    Version,
    get_filepath_str,
    get_protocol_and_path,
)

HDFSTORE_DRIVER = "H5FD_CORE"


[docs]class HDFDataSet(AbstractVersionedDataSet): """``HDFDataSet`` loads/saves data from/to a hdf file using an underlying filesystem (e.g. local, S3, GCS). It uses pandas.HDFStore to handle the hdf file. Example: :: >>> from kedro.extras.datasets.pandas import HDFDataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> # data_set = HDFDataSet(filepath="gcs://bucket/test.hdf", key='data') >>> data_set = HDFDataSet(filepath="test.h5", key='data') >>> data_set.save(data) >>> reloaded = data_set.load() >>> assert data.equals(reloaded) """ # _lock is a class attribute that will be shared across all the instances. # It is used to make dataset safe for threads. _lock = Lock() DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {} # type: Dict[str, Any] # pylint: disable=too-many-arguments
[docs] def __init__( self, filepath: str, key: str, load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, version: Version = None, credentials: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ) -> None: """Creates a new instance of ``HDFDataSet`` pointing to a concrete hdf file on a specific filesystem. Args: filepath: Filepath in POSIX format to a hdf 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. key: Identifier to the group in the HDF store. load_args: PyTables options for loading hdf files. You can find all available arguments at: https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file All defaults are preserved. save_args: PyTables options for saving hdf files. You can find all available arguments at: https://www.pytables.org/usersguide/libref/top_level.html#tables.open_file 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 `wb` when saving. """ _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) self._protocol = protocol self._fs = fsspec.filesystem(self._protocol, **_credentials, **_fs_args) super().__init__( filepath=PurePosixPath(path), version=version, exists_function=self._fs.exists, glob_function=self._fs.glob, ) self._key = key # Handle default load and save arguments self._load_args = deepcopy(self.DEFAULT_LOAD_ARGS) if load_args is not None: self._load_args.update(load_args) self._save_args = deepcopy(self.DEFAULT_SAVE_ARGS) if save_args is not None: self._save_args.update(save_args) _fs_open_args_save.setdefault("mode", "wb") self._fs_open_args_load = _fs_open_args_load self._fs_open_args_save = _fs_open_args_save
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, key=self._key, protocol=self._protocol, load_args=self._load_args, save_args=self._save_args, version=self._version, ) def _load(self) -> pd.DataFrame: 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: binary_data = fs_file.read() with HDFDataSet._lock: # Set driver_core_backing_store to False to disable saving # contents of the in-memory h5file to disk with pd.HDFStore( "in-memory-load-file", mode="r", driver=HDFSTORE_DRIVER, driver_core_backing_store=0, driver_core_image=binary_data, **self._load_args, ) as store: return store[self._key] def _save(self, data: pd.DataFrame) -> None: save_path = get_filepath_str(self._get_save_path(), self._protocol) with HDFDataSet._lock: with pd.HDFStore( "in-memory-save-file", mode="w", driver=HDFSTORE_DRIVER, driver_core_backing_store=0, **self._save_args, ) as store: store.put(self._key, data, format="table") # pylint: disable=protected-access binary_data = store._handle.get_file_image() with self._fs.open(save_path, **self._fs_open_args_save) as fs_file: fs_file.write(binary_data) 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)