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""" ``AbstractDataSet`` implementation to access JSON(L) files directly from
Microsoft's Azure blob storage.
"""
import io
from typing import Any, Dict, Optional
import pandas as pd
from azure.storage.blob import BlockBlobService
from kedro.io import AbstractDataSet
[docs]class JSONBlobDataSet(AbstractDataSet):
# pylint: disable=too-many-instance-attributes
"""``JSONBlobDataSet`` loads and saves json(line-delimited) files in Microsoft's Azure
blob storage. It uses Azure storage SDK to read and write in Azure and
pandas to handle the json(l) file locally.
Example:
::
>>> import pandas as pd
>>>
>>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5],
>>> 'col3': [5, 6]})
>>>
>>> data_set = JSONBlobDataSet(filepath="test.jsonl", credentials={"sas_token":"1234"},
>>> load_args={"lines":True}, container_name="test",
>>> save_args={"orient":"records", "lines":True})
>>> data_set.save(data)
>>> reloaded = data_set.load()
>>>
>>> assert data.equals(reloaded)
"""
def _describe(self) -> Dict[str, Any]:
return dict(
filepath=self._filepath,
container_name=self._container_name,
blob_to_bytes_args=self._blob_to_bytes_args,
blob_from_bytes_args=self._blob_from_bytes_args,
load_args=self._load_args,
save_args=self._save_args,
)
# pylint: disable=too-many-arguments
[docs] def __init__(
self,
filepath: str,
container_name: str,
credentials: Dict[str, Any],
encoding: str = "utf-8",
blob_from_bytes_args: Optional[Dict[str, Any]] = None,
blob_to_bytes_args: Optional[Dict[str, Any]] = None,
load_args: Optional[Dict[str, Any]] = None,
save_args: Optional[Dict[str, Any]] = None,
) -> None:
"""Creates a new instance of ``JSONBlobDataSet`` pointing to a
concrete json(l) file on Azure blob storage.
Args:
filepath: path to a azure blob of a json(l) file.
container_name: Azure container name.
credentials: Credentials (``account_name`` and
``account_key`` or ``sas_token``) to access the Azure blob storage
encoding: Default utf-8. Defines encoding of json files downloaded as binary streams.
blob_to_bytes_args: Any additional arguments to pass to Azure's
``get_blob_to_bytes`` method:
https://docs.microsoft.com/en-us/python/api/azure.storage.blob.baseblobservice.baseblobservice?view=azure-python#get-blob-to-bytes
blob_from_bytes_args: Any additional arguments to pass to Azure's
``create_blob_from_bytes`` method:
https://docs.microsoft.com/en-us/python/api/azure.storage.blob.blockblobservice.blockblobservice?view=azure-python#create-blob-from-bytes
load_args: Pandas options for loading json(l) files.
Here you can find all available arguments:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_json.html
All defaults are preserved.
save_args: Pandas options for saving json(l) files.
Here you can find all available arguments:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_json.html
All defaults are preserved, but "index", which is set to False.
"""
self._save_args = {**save_args} if save_args else {}
self._load_args = load_args if load_args else {}
self._filepath = filepath
self._encoding = encoding
self._container_name = container_name
self._credentials = credentials if credentials else {}
self._blob_to_bytes_args = blob_to_bytes_args if blob_to_bytes_args else {}
self._blob_from_bytes_args = (
blob_from_bytes_args if blob_from_bytes_args else {}
)
def _load(self) -> pd.DataFrame:
blob_service = BlockBlobService(**self._credentials)
blob = blob_service.get_blob_to_bytes(
container_name=self._container_name,
blob_name=self._filepath,
**self._blob_to_bytes_args
)
bytes_stream = io.BytesIO(blob.content)
return pd.read_json(bytes_stream, encoding=self._encoding, **self._load_args)
def _save(self, data: pd.DataFrame) -> None:
blob_service = BlockBlobService(**self._credentials)
blob_service.create_blob_from_bytes(
container_name=self._container_name,
blob_name=self._filepath,
blob=data.to_json(**self._save_args).encode(self._encoding),
**self._blob_from_bytes_args
)