Source code for kedro.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
import triad
from import AbstractDataSet, get_protocol_and_path

[docs]class ParquetDataSet(AbstractDataSet[dd.DataFrame, dd.DataFrame]): """``ParquetDataSet`` loads and saves data to parquet file(s). It uses Dask remote data services to handle the corresponding load and save operations: Example usage for the `YAML API <\ data_catalog.html#use-the-data-catalog-with-the-yaml-api>`_: .. code-block:: yaml cars: type: dask.ParquetDataSet filepath: s3://bucket_name/path/to/folder save_args: compression: GZIP credentials: client_kwargs: aws_access_key_id: YOUR_KEY aws_secret_access_key: YOUR_SECRET Example usage for the `Python API <\ data_catalog.html#use-the-data-catalog-with-the-code-api>`_: :: >>> 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], [7, 8]]}) >>> 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()) The output schema can also be explicitly specified using `Triad <\ triad.collections.html#module-triad.collections.schema>`_. This is processed to map specific columns to `PyArrow field types <\ datatypes.html>`_ or schema. For instance: .. code-block:: yaml parquet_dataset: type: dask.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 schema: col1: [int32] col2: [int32] col3: [[int32]] """ 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 { "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: self._process_schema() data.to_parquet(self._filepath, storage_options=self.fs_args, **self._save_args) def _process_schema(self) -> None: """This method processes the schema in the catalog.yml or the API, if provided. This assumes that the schema is specified using Triad's grammar for schema definition. When the value of the `schema` variable is a string, it is assumed that it corresponds to the full schema specification for the data. Alternatively, if the `schema` is specified as a dictionary, then only the columns that are specified will be strictly mapped to a field type. The other unspecified columns, if present, will be inferred from the data. This method converts the Triad-parsed schema into a pyarrow schema. The output directly supports Dask's specifications for providing a schema when saving to a parquet file. Note that if a `pa.Schema` object is passed directly in the `schema` argument, no processing will be done. Additionally, the behavior when passing a `pa.Schema` object is assumed to be consistent with how Dask sees it. That is, it should fully define the schema for all fields. """ schema = self._save_args.get("schema") if isinstance(schema, dict): # The schema may contain values of different types, e.g., pa.DataType, Python types, # strings, etc. The latter requires a transformation, then we use triad handle all # other value types. # Create a schema from values that triad can handle directly triad_schema = triad.Schema( {k: v for k, v in schema.items() if not isinstance(v, str)} ) # Handle the schema keys that are represented as string and add them to the triad schema triad_schema.update( triad.Schema( ",".join( [f"{k}:{v}" for k, v in schema.items() if isinstance(v, str)] ) ) ) # Update the schema argument with the normalized schema self._save_args["schema"].update( {col: field.type for col, field in triad_schema.items()} ) elif isinstance(schema, str): self._save_args["schema"] = triad.Schema(schema).pyarrow_schema 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)