Source code for kedro.extras.datasets.dask.parquet_dataset

"""``ParquetDataSet`` is a data set used to load and save data to parquet files using Dask

from copy import deepcopy
from typing import Any, Dict

import dask.dataframe as dd
import fsspec

from import AbstractDataSet, get_protocol_and_path

[docs]class ParquetDataSet(AbstractDataSet): """``ParquetDataSet`` loads and saves data to parquet file(s). It uses Dask remote data services to handle the corresponding load and save operations: Example (AWS S3): :: >>> from kedro.extras.datasets.dask import ParquetDataSet >>> import pandas as pd >>> import dask.dataframe as dd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> ddf = dd.from_pandas(data, npartitions=2) >>> >>> data_set = ParquetDataSet( >>> filepath="s3://bucket_name/path/to/folder", >>> credentials={ >>> 'client_kwargs':{ >>> 'aws_access_key_id': 'YOUR_KEY', >>> 'aws_secret_access_key': 'YOUR SECRET', >>> } >>> }, >>> save_args={"compression": "GZIP"} >>> ) >>> >>> reloaded = data_set.load() >>> >>> assert ddf.compute().equals(reloaded.compute()) """ DEFAULT_LOAD_ARGS = {} # type: Dict[str, Any] DEFAULT_SAVE_ARGS = {"write_index": False} # type: Dict[str, Any] # pylint: disable=too-many-arguments
[docs] def __init__( self, filepath: str, load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, credentials: Dict[str, Any] = None, fs_args: Dict[str, Any] = None, ) -> None: """Creates a new instance of ``ParquetDataSet`` pointing to concrete parquet files. Args: filepath: Filepath in POSIX format to a parquet file parquet collection or the directory of a multipart parquet. load_args: Additional loading options `dask.dataframe.read_parquet`: save_args: Additional saving options for `dask.dataframe.to_parquet`: credentials: Credentials required to get access to the underlying filesystem. E.g. for ``GCSFileSystem`` it should look like `{"token": None}`. fs_args: Optional parameters to the backend file system driver: """ self._filepath = filepath self._fs_args = deepcopy(fs_args) or {} self._credentials = deepcopy(credentials) or {} # 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)
@property def fs_args(self) -> Dict[str, Any]: """Property of optional file system parameters. Returns: A dictionary of backend file system parameters, including credentials. """ fs_args = deepcopy(self._fs_args) fs_args.update(self._credentials) return fs_args def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, load_args=self._load_args, save_args=self._save_args, ) def _load(self) -> dd.DataFrame: return dd.read_parquet( self._filepath, storage_options=self.fs_args, **self._load_args ) def _save(self, data: dd.DataFrame) -> None: data.to_parquet(self._filepath, storage_options=self.fs_args, **self._save_args) def _exists(self) -> bool: protocol = get_protocol_and_path(self._filepath)[0] file_system = fsspec.filesystem(protocol=protocol, **self.fs_args) return file_system.exists(self._filepath)