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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 import S3Hook

    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] 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)"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:"Writing data to temp file") getattr(data_df, file_options.function)(, **self.pd_kwargs)"Uploading data to S3") s3_conn.load_file(, 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

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