"""``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 __future__ import annotations
from copy import deepcopy
from pathlib import PurePosixPath
from threading import Lock
from typing import Any
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[pd.DataFrame, pd.DataFrame]):
"""``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 usage for the
`YAML API <https://docs.kedro.org/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
hdf_dataset:
type: pandas.HDFDataset
filepath: s3://my_bucket/raw/sensor_reading.h5
credentials: aws_s3_creds
key: data
Example usage for the
`Python API <https://docs.kedro.org/en/stable/data/\
advanced_data_catalog_usage.html>`_:
.. code-block:: pycon
>>> from kedro_datasets.pandas import HDFDataset
>>> import pandas as pd
>>>
>>> data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [5, 6]})
>>>
>>> dataset = HDFDataset(filepath=tmp_path / "test.h5", key="data")
>>> dataset.save(data)
>>> reloaded = dataset.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: 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,
key: str,
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 ``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 `open_args_save` `mode`, which is set `wb` when saving.
metadata: Any arbitrary metadata.
This is ignored by Kedro, but may be consumed by users or external plugins.
"""
_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._key = key
# Handle default load and save and fs 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,
"key": self._key,
"protocol": self._protocol,
"load_args": self._load_args,
"save_args": self._save_args,
"version": self._version,
}
[docs]
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]
[docs]
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")
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)
