Source code for kedro.contrib.io.parquet.parquet_s3

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"""``ParquetS3DataSet`` is a data set used to load and save
data to parquet files on S3
"""
from copy import deepcopy
from pathlib import PurePosixPath
from typing import Any, Dict, Optional

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from s3fs.core import S3FileSystem

from kedro.io.core import AbstractVersionedDataSet, DataSetError, Version


[docs]class ParquetS3DataSet(AbstractVersionedDataSet): """``ParquetS3DataSet`` loads and saves data to a file in S3. It uses s3fs to read and write from S3 and pandas to handle the parquet file. Example: :: >>> from kedro.contrib.io.parquet.parquet_s3 import ParquetS3DataSet >>> import pandas as pd >>> >>> data = pd.DataFrame({'col1': [1, 2], 'col2': [4, 5], >>> 'col3': [5, 6]}) >>> >>> data_set = ParquetS3DataSet( >>> filepath="temp3.parquet", >>> bucket_name="test_bucket", >>> credentials={ >>> 'aws_access_key_id': 'YOUR_KEY', >>> 'aws_access_secredt_key': 'YOUR SECRET'}, >>> save_args={"compression": "GZIP"}) >>> data_set.save(data) >>> reloaded = data_set.load() >>> >>> assert data.equals(reloaded) """ # pylint: disable=too-many-arguments
[docs] def __init__( self, filepath: str, bucket_name: str, credentials: Optional[Dict[str, Any]] = None, load_args: Optional[Dict[str, Any]] = None, save_args: Optional[Dict[str, Any]] = None, version: Version = None, ) -> None: """Creates a new instance of ``ParquetS3DataSet`` pointing to a concrete parquet file on S3. Args: filepath: Path to a parquet file parquet collection or the directory of a multipart parquet. bucket_name: S3 bucket name. credentials: Credentials to access the S3 bucket, such as ``aws_access_key_id``, ``aws_secret_access_key``. load_args: Additional loading options `pyarrow`: https://arrow.apache.org/docs/python/generated/pyarrow.parquet.read_table.html or `fastparquet`: https://fastparquet.readthedocs.io/en/latest/api.html#fastparquet.ParquetFile.to_pandas save_args: Additional saving options for `pyarrow`: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_pandas or `fastparquet`: https://fastparquet.readthedocs.io/en/latest/api.html#fastparquet.write 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. """ _credentials = deepcopy(credentials) or {} _s3 = S3FileSystem(client_kwargs=_credentials) super().__init__( PurePosixPath("{}/{}".format(bucket_name, filepath)), version, exists_function=_s3.exists, glob_function=_s3.glob, ) default_load_args = {} # type: Dict[str, Any] default_save_args = {} # type: Dict[str, Any] self._load_args = ( {**default_load_args, **load_args} if load_args is not None else default_load_args ) self._save_args = ( {**default_save_args, **save_args} if save_args is not None else default_save_args ) self._bucket_name = bucket_name self._credentials = _credentials self._s3 = _s3
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, bucket_name=self._bucket_name, load_args=self._load_args, save_args=self._save_args, version=self._version, ) def _load(self) -> pd.DataFrame: load_path = PurePosixPath(self._get_load_path()) with self._s3.open(str(load_path), mode="rb") as s3_file: return pd.read_parquet(s3_file, **self._load_args) def _save(self, data: pd.DataFrame) -> None: save_path = PurePosixPath(self._get_save_path()) pq.write_table( table=pa.Table.from_pandas(data), where=str(save_path), filesystem=self._s3, **self._save_args, ) load_path = PurePosixPath(self._get_load_path()) self._check_paths_consistency(load_path, save_path) def _exists(self) -> bool: try: load_path = self._get_load_path() except DataSetError: return False return self._s3.isfile(str(PurePosixPath(load_path)))