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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
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#
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# software distributed under the License is distributed on an
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from collections import namedtuple
from enum import Enum
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Iterable, Mapping, Optional, Sequence, Union
import numpy as np
import pandas as pd
from typing_extensions import Literal
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.hooks.dbapi import DbApiHook
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
if TYPE_CHECKING:
from airflow.utils.context import Context
)
[docs]FileOptions = namedtuple('FileOptions', ['mode', 'suffix', 'function'])
[docs]FILE_OPTIONS_MAP = {
FILE_FORMAT.CSV: FileOptions('r+', '.csv', 'to_csv'),
FILE_FORMAT.JSON: FileOptions('r+', '.json', 'to_json'),
FILE_FORMAT.PARQUET: FileOptions('rb+', '.parquet', 'to_parquet'),
}
[docs]class SqlToS3Operator(BaseOperator):
"""
Saves data from a specific SQL query into a file in S3.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:SqlToS3Operator`
:param query: the sql query to be executed. If you want to execute a file, place the absolute path of it,
ending with .sql extension. (templated)
:param s3_bucket: bucket where the data will be stored. (templated)
:param s3_key: desired key for the file. It includes the name of the file. (templated)
:param replace: whether or not to replace the file in S3 if it previously existed
:param sql_conn_id: reference to a specific database.
:param parameters: (optional) the parameters to render the SQL query with.
:param aws_conn_id: reference to a specific S3 connection
:param verify: Whether or not to verify SSL certificates for S3 connection.
By default SSL certificates are verified.
You can provide the following values:
- ``False``: do not validate SSL certificates. SSL will still be used
(unless use_ssl is False), but SSL certificates will not be verified.
- ``path/to/cert/bundle.pem``: A filename of the CA cert bundle to uses.
You can specify this argument if you want to use a different
CA cert bundle than the one used by botocore.
:param file_format: the destination file format, only string 'csv', 'json' or 'parquet' is accepted.
:param pd_kwargs: arguments to include in DataFrame ``.to_parquet()``, ``.to_json()`` or ``.to_csv()``.
"""
[docs] template_fields: Sequence[str] = (
's3_bucket',
's3_key',
'query',
)
[docs] template_ext: Sequence[str] = ('.sql',)
[docs] template_fields_renderers = {
"query": "sql",
"pd_kwargs": "json",
}
def __init__(
self,
*,
query: str,
s3_bucket: str,
s3_key: str,
sql_conn_id: str,
parameters: Union[None, Mapping, Iterable] = None,
replace: bool = False,
aws_conn_id: str = 'aws_default',
verify: Optional[Union[bool, str]] = None,
file_format: Literal['csv', 'json', 'parquet'] = 'csv',
pd_kwargs: Optional[dict] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.query = query
self.s3_bucket = s3_bucket
self.s3_key = s3_key
self.sql_conn_id = sql_conn_id
self.aws_conn_id = aws_conn_id
self.verify = verify
self.replace = replace
self.pd_kwargs = pd_kwargs or {}
self.parameters = parameters
if "path_or_buf" in self.pd_kwargs:
raise AirflowException('The argument path_or_buf is not allowed, please remove it')
self.file_format = getattr(FILE_FORMAT, file_format.upper(), None)
if self.file_format is None:
raise AirflowException(f"The argument file_format doesn't support {file_format} value.")
@staticmethod
def _fix_int_dtypes(df: pd.DataFrame) -> None:
"""Mutate DataFrame to set dtypes for int columns containing NaN values."""
for col in df:
if "float" in df[col].dtype.name and df[col].hasnans:
# inspect values to determine if dtype of non-null values is int or float
notna_series = df[col].dropna().values
if np.equal(notna_series, notna_series.astype(int)).all():
# set to dtype that retains integers and supports NaNs
df[col] = np.where(df[col].isnull(), None, df[col])
df[col] = df[col].astype(pd.Int64Dtype())
elif np.isclose(notna_series, notna_series.astype(int)).all():
# set to float dtype that retains floats and supports NaNs
df[col] = np.where(df[col].isnull(), None, df[col])
df[col] = df[col].astype(pd.Float64Dtype())
[docs] def execute(self, context: 'Context') -> None:
sql_hook = self._get_hook()
s3_conn = S3Hook(aws_conn_id=self.aws_conn_id, verify=self.verify)
data_df = sql_hook.get_pandas_df(sql=self.query, parameters=self.parameters)
self.log.info("Data from SQL obtained")
self._fix_int_dtypes(data_df)
file_options = FILE_OPTIONS_MAP[self.file_format]
with NamedTemporaryFile(mode=file_options.mode, suffix=file_options.suffix) as tmp_file:
self.log.info("Writing data to temp file")
getattr(data_df, file_options.function)(tmp_file.name, **self.pd_kwargs)
self.log.info("Uploading data to S3")
s3_conn.load_file(
filename=tmp_file.name, key=self.s3_key, bucket_name=self.s3_bucket, replace=self.replace
)
def _get_hook(self) -> DbApiHook:
self.log.debug("Get connection for %s", self.sql_conn_id)
conn = BaseHook.get_connection(self.sql_conn_id)
hook = conn.get_hook()
if not callable(getattr(hook, 'get_pandas_df', None)):
raise AirflowException(
"This hook is not supported. The hook class must have get_pandas_df method."
)
return hook