kedro_datasets.dask.ParquetDataset

class kedro_datasets.dask.ParquetDataset(*, filepath, load_args=None, save_args=None, credentials=None, fs_args=None, metadata=None)

ParquetDataset loads and saves data to parquet file(s). It uses Dask remote data services to handle the corresponding load and save operations: https://docs.dask.org/en/latest/how-to/connect-to-remote-data.html

Example usage for the YAML API:

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:

 import dask.dataframe as dd
 import pandas as pd
 from kedro_datasets.dask import ParquetDataset
 import numpy as np

 data = pd.DataFrame({"col1": [1, 2], "col2": [4, 5], "col3": [6, 7]})
 ddf = dd.from_pandas(data, npartitions=2)

 dataset = ParquetDataset(
...     filepath=tmp_path / "path/to/folder", save_args={"compression": "GZIP"}
... )
 dataset.save(ddf)
 reloaded = dataset.load()

 assert np.array_equal(ddf.compute(), reloaded.compute())

The output schema can also be explicitly specified using Triad. This is processed to map specific columns to PyArrow field types or schema. For instance:

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]]

Attributes

DEFAULT_LOAD_ARGS
DEFAULT_SAVE_ARGS
fs_args	Property of optional file system parameters.

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
save(data)	Saves data by delegation to the provided save method.

DEFAULT_LOAD_ARGS: dict[str, Any] = {}

DEFAULT_SAVE_ARGS: dict[str, Any] = {'write_index': False}

__init__(*, filepath, load_args=None, save_args=None, credentials=None, fs_args=None, metadata=None)

Creates a new instance of ParquetDataset pointing to concrete parquet files.

Parameters:

• filepath (str) – Filepath in POSIX format to a parquet file parquet collection or the directory of a multipart parquet.

• load_args (Optional[dict[str, Any]]) – Additional loading options dask.dataframe.read_parquet: https://docs.dask.org/en/latest/generated/dask.dataframe.read_parquet.html

• save_args (Optional[dict[str, Any]]) – Additional saving options for dask.dataframe.to_parquet: https://docs.dask.org/en/latest/generated/dask.dataframe.to_parquet.html

• credentials (Optional[dict[str, Any]]) – Credentials required to get access to the underlying filesystem. E.g. for GCSFileSystem it should look like {“token”: None}.

• fs_args (Optional[dict[str, Any]]) – Optional parameters to the backend file system driver: https://docs.dask.org/en/latest/how-to/connect-to-remote-data.html#optional-parameters

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

property fs_args: dict[str, Any]

Property of optional file system parameters.

Return type:

dict[str, Any]

Returns:

A dictionary of backend file system parameters, including credentials.

load()

Loads data by delegation to the provided load method.

Return type:

DataFrame

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (DataFrame) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

None