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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)))