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
"""
import copy
from pathlib import PurePosixPath
from typing import Any, Dict

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

from kedro.contrib.io import DefaultArgumentsMixIn
from kedro.io.core import AbstractVersionedDataSet, Version, deprecation_warning


[docs]class ParquetS3DataSet(DefaultArgumentsMixIn, 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 = None, credentials: Dict[str, Any] = None, load_args: Dict[str, Any] = None, save_args: Dict[str, Any] = None, version: Version = None, s3fs_args: Dict[str, Any] = 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. May contain the full path in S3 including bucket and protocol, e.g. `s3://bucket-name/path/to/file.parquet`. bucket_name: S3 bucket name. Must be specified **only** if not present in ``filepath``. 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. s3fs_args: S3FileSystem options. You can see all available arguments at: https://s3fs.readthedocs.io/en/latest/api.html#s3fs.core.S3FileSystem """ deprecation_warning(self.__class__.__name__) _credentials = copy.deepcopy(credentials) or {} _s3fs_args = copy.deepcopy(s3fs_args) or {} _s3 = S3FileSystem(client_kwargs=_credentials, **_s3fs_args) path = _s3._strip_protocol(filepath) path = PurePosixPath("{}/{}".format(bucket_name, path) if bucket_name else path) super().__init__( load_args=load_args, save_args=save_args, filepath=path, version=version, exists_function=_s3.exists, glob_function=_s3.glob, ) self._s3 = _s3
def _describe(self) -> Dict[str, Any]: return dict( filepath=self._filepath, 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 = str(self._get_save_path()) pq.write_table( table=pa.Table.from_pandas(data), where=save_path, filesystem=self._s3, **self._save_args, ) def _exists(self) -> bool: load_path = self._get_load_path() return self._s3.isfile(str(PurePosixPath(load_path)))