Source code for airflow.providers.amazon.aws.transfers.mysql_to_s3

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import os
import warnings
from collections import namedtuple
from enum import Enum
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Optional, Sequence, Union

import numpy as np
import pandas as pd
from typing_extensions import Literal

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.mysql.hooks.mysql import MySqlHook

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]FILE_FORMAT = Enum(
    "FILE_FORMAT",
    "CSV, PARQUET",
)

[docs]FileOptions = namedtuple('FileOptions', ['mode', 'suffix'])

[docs]FILE_OPTIONS_MAP = {
    FILE_FORMAT.CSV: FileOptions('r+', '.csv'),
    FILE_FORMAT.PARQUET: FileOptions('rb+', '.parquet'),
}


[docs]class MySQLToS3Operator(BaseOperator):
    """
    Saves data from an specific MySQL query into a file in S3.

    :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)
    :type query: str
    :param s3_bucket: bucket where the data will be stored. (templated)
    :type s3_bucket: str
    :param s3_key: desired key for the file. It includes the name of the file. (templated)
    :type s3_key: str
    :param replace: whether or not to replace the file in S3 if it previously existed
    :type replace: bool
    :param mysql_conn_id: Reference to :ref:`mysql connection id <howto/connection:mysql>`.
    :type mysql_conn_id: str
    :param aws_conn_id: reference to a specific S3 connection
    :type aws_conn_id: str
    :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.
    :type verify: bool or str
    :param pd_csv_kwargs: arguments to include in pd.to_csv (header, index, columns...)
    :type pd_csv_kwargs: dict
    :param index: whether to have the index or not in the dataframe
    :type index: str
    :param header: whether to include header or not into the S3 file
    :type header: bool
    :param file_format: the destination file format, only string 'csv' or 'parquet' is accepted.
    :type file_format: str
    :param pd_kwargs: arguments to include in ``DataFrame.to_parquet()`` or
        ``DataFrame.to_csv()``. This is preferred than ``pd_csv_kwargs``.
    :type pd_kwargs: dict
    """

[docs]    template_fields: Sequence[str] = (
        's3_bucket',
        's3_key',
        'query',
    )
[docs]    template_ext: Sequence[str] = ('.sql',)
[docs]    template_fields_renderers = {
        "query": "sql",
        "pd_csv_kwargs": "json",
        "pd_kwargs": "json",
    }

    def __init__(
        self,
        *,
        query: str,
        s3_bucket: str,
        s3_key: str,
        replace: bool = False,
        mysql_conn_id: str = 'mysql_default',
        aws_conn_id: str = 'aws_default',
        verify: Optional[Union[bool, str]] = None,
        pd_csv_kwargs: Optional[dict] = None,
        index: bool = False,
        header: bool = False,
        file_format: Literal['csv', '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.mysql_conn_id = mysql_conn_id
        self.aws_conn_id = aws_conn_id
        self.verify = verify
        self.replace = replace

        if file_format == "csv":
            self.file_format = FILE_FORMAT.CSV
        else:
            self.file_format = FILE_FORMAT.PARQUET

        if pd_csv_kwargs:
            warnings.warn(
                "pd_csv_kwargs is deprecated. Please use pd_kwargs.",
                DeprecationWarning,
                stacklevel=2,
            )
        if index or header:
            warnings.warn(
                "index and header are deprecated. Please pass them via pd_kwargs.",
                DeprecationWarning,
                stacklevel=2,
            )

        self.pd_kwargs = pd_kwargs or pd_csv_kwargs or {}
        if self.file_format == FILE_FORMAT.CSV:
            if "path_or_buf" in self.pd_kwargs:
                raise AirflowException('The argument path_or_buf is not allowed, please remove it')
            if "index" not in self.pd_kwargs:
                self.pd_kwargs["index"] = index
            if "header" not in self.pd_kwargs:
                self.pd_kwargs["header"] = header
        else:
            if pd_csv_kwargs is not None:
                raise TypeError("pd_csv_kwargs may not be specified when file_format='parquet'")

    @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.isclose(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())

[docs]    def execute(self, context: 'Context') -> None:
        mysql_hook = MySqlHook(mysql_conn_id=self.mysql_conn_id)
        s3_conn = S3Hook(aws_conn_id=self.aws_conn_id, verify=self.verify)
        data_df = mysql_hook.get_pandas_df(self.query)
        self.log.info("Data from MySQL 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:
            if self.file_format == FILE_FORMAT.CSV:
                data_df.to_csv(tmp_file.name, **self.pd_kwargs)
            else:
                data_df.to_parquet(tmp_file.name, **self.pd_kwargs)
            s3_conn.load_file(
                filename=tmp_file.name, key=self.s3_key, bucket_name=self.s3_bucket, replace=self.replace
            )

        if s3_conn.check_for_key(self.s3_key, bucket_name=self.s3_bucket):
            file_location = os.path.join(self.s3_bucket, self.s3_key)
            self.log.info("File saved correctly in %s", file_location)