"""``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 adding a catalog entry with
`YAML API <https://kedro.readthedocs.io/en/stable/05_data/\
01_data_catalog.html#using-the-data-catalog-with-the-yaml-api>`_:
.. code-block:: yaml
>>> hdf_dataset:
>>> type: pandas.HDFDataSet
>>> filepath: s3://my_bucket/raw/sensor_reading.h5
>>> credentials: aws_s3_creds
>>> key: data
Example using Python API:
::
>>> 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)
if protocol == "file":
_fs_args.setdefault("auto_mkdir", True)
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)