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""" ``CSVBlobDataSet`` implementation to access CSV files directly from
Microsoft's Azure Blob Storage.
"""
import io
from copy import deepcopy
from functools import partial
from pathlib import PurePosixPath
from typing import Any, Dict, List
import pandas as pd
from azure.storage.blob import BlockBlobService
from kedro.io.core import AbstractVersionedDataSet, Version
[docs]class CSVBlobDataSet(AbstractVersionedDataSet):
"""``CSVBlobDataSet`` loads and saves CSV files in Microsoft's Azure
Blob Storage. It uses the Azure Storage SDK to read and write in Azure and
pandas to handle the CSV file locally.
Example:
::
>>> import pandas as pd
>>>
>>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
>>> 'col3': [5, 6]})
>>>
>>> data_set = CSVBlobDataSet(filepath="test.csv",
>>> container_name="test_bucket",
>>> load_args=None,
>>> save_args={"index": False})
>>> data_set.save(data)
>>> reloaded = data_set.load()
>>>
>>> assert data.equals(reloaded)
"""
DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any]
DEFAULT_SAVE_ARGS = {"index": False} # type: Dict[str, Any]
# pylint: disable=too-many-arguments
[docs] def __init__(
self,
filepath: str,
container_name: str,
credentials: Dict[str, Any],
blob_to_text_args: Dict[str, Any] = None,
blob_from_text_args: Dict[str, Any] = None,
load_args: Dict[str, Any] = None,
save_args: Dict[str, Any] = None,
version: Version = None,
layer: str = None,
) -> None:
"""Creates a new instance of ``CSVBlobDataSet`` pointing to a
concrete CSV file on Azure Blob Storage.
Args:
filepath: Path to an Azure Blob of a CSV file.
container_name: Azure container name.
credentials: Credentials (``account_name`` and
``account_key`` or ``sas_token``) to access the Azure Blob Storage.
blob_to_text_args: Any additional arguments to pass to Azure's
``get_blob_to_text`` method:
https://docs.microsoft.com/en-us/python/api/azure-storage-blob/azure.storage.blob.baseblobservice.baseblobservice?view=azure-python#get-blob-to-text
blob_from_text_args: Any additional arguments to pass to Azure's
``create_blob_from_text`` method:
https://docs.microsoft.com/en-us/python/api/azure-storage-blob/azure.storage.blob.baseblobservice.baseblobservice?view=azure-python#get-blob-to-text
load_args: Pandas options for loading CSV files.
Here you can find all available arguments:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
All defaults are preserved.
save_args: Pandas options for saving CSV files.
Here you can find all available arguments:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_csv.html
All defaults are preserved, but "index", which is set to False.
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.
layer: The data layer according to the data engineering convention:
https://kedro.readthedocs.io/en/stable/06_resources/01_faq.html#what-is-data-engineering-convention
"""
_credentials = deepcopy(credentials) or {}
_blob_service = BlockBlobService(**_credentials)
_container_name = container_name
glob_function = partial(
_glob,
blob_service=_blob_service,
filepath=filepath,
container_name=_container_name,
)
exists_function = partial(
_exists_blob, blob_service=_blob_service, container_name=_container_name,
)
super().__init__(
filepath=PurePosixPath(filepath),
version=version,
exists_function=exists_function,
glob_function=glob_function,
)
self._layer = layer
self._blob_service = _blob_service
self._container_name = _container_name
# 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)
self._blob_to_text_args = deepcopy(blob_to_text_args) or {}
self._blob_from_text_args = deepcopy(blob_from_text_args) or {}
def _describe(self) -> Dict[str, Any]:
return dict(
filepath=self._filepath,
container_name=self._container_name,
blob_to_text_args=self._blob_to_text_args,
blob_from_text_args=self._blob_from_text_args,
load_args=self._load_args,
save_args=self._save_args,
version=self._version,
layer=self._layer,
)
def _load(self) -> pd.DataFrame:
load_path = str(self._get_load_path())
blob = self._blob_service.get_blob_to_text(
container_name=self._container_name,
blob_name=load_path,
**self._blob_to_text_args
)
csv_content = io.StringIO(blob.content)
return pd.read_csv(csv_content, **self._load_args)
def _save(self, data: pd.DataFrame) -> None:
save_path = self._get_save_path()
self._blob_service.create_blob_from_text(
container_name=self._container_name,
blob_name=str(save_path),
text=data.to_csv(**self._save_args),
**self._blob_from_text_args
)
def _exists(self) -> bool:
load_path = str(self._get_load_path())
return _exists_blob(load_path, self._blob_service, self._container_name)
def _exists_blob(
filepath: str, blob_service: BlockBlobService, container_name: str
) -> bool:
return blob_service.exists(container_name, blob_name=filepath)
def _glob(
pattern: str, blob_service: BlockBlobService, container_name: str, filepath: str
) -> List[str]:
blob_paths = blob_service.list_blob_names(container_name, prefix=filepath)
return [path for path in blob_paths if PurePosixPath(path).match(pattern)]