code: # Copyright 2018-2019 QuantumBlack Visual Analytics Limited
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  # 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 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"
  
  
  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.io.hdf_dataset 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)
  
      """
  
      DEFAULT_LOAD_ARGS = {}  # type: Dict[str, Any]
      DEFAULT_SAVE_ARGS = {}  # type: Dict[str, Any]
  
      # pylint: disable=too-many-arguments
      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 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.
                  E.g. for ``GCSFileSystem`` class: `{"project": "my-project", ...}`
          """
          _fs_args = deepcopy(fs_args) or {}
          _credentials = deepcopy(credentials) or {}
          self._key = key
  
          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,
          )
  
          # 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)
  
      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, mode="rb") as fs_file:
              binary_data = fs_file.read()
  
          # 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 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, mode="wb") 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:
          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)
  
apis: pandas.HDFStore