[GitHub] [airflow] jj-ookla commented on a change in pull request #6670: [AIRFLOW-4816]MySqlToS3Operator

[GitBox <gi...@apache.org>]

jj-ookla commented on a change in pull request #6670: [AIRFLOW-4816]MySqlToS3Operator
URL: https://github.com/apache/airflow/pull/6670#discussion_r351426086
 
 

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 File path: airflow/operators/mysql_to_s3_operator.py
 ##########
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+# -*- coding: utf-8 -*-
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+from typing import Optional, Union
+
+from io import StringIO
+
+from airflow.hooks.mysql_hook import MySqlHook
+from airflow.models import BaseOperator
+from airflow.providers.amazon.aws.hooks.s3 import S3Hook
+from airflow.utils.decorators import apply_defaults
+
+class MySQLToS3Operator(BaseOperator):
+    """
+    Saves data from an specific MySQL query into a file in S3.
+    
+    :param query: the sql query to be executed.
+    :type query: str
+    :param s3_bucket: bucket where the data will be stored
+    :type s3_bucket: str
+    :param s3_key: desired key for the file. It includes the name of the file.
+        If a csv file is wanted, the param must end with ".csv".
+    :type s3_key: str
+    :param mysql_conn_id: reference to a specific mysql database
+    :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
+    """
+    
+    @apply_defaults
+    def __init__(
+            self,
+            query: str,
+            s3_bucket: str,
+            s3_key: str,
+            mysql_conn_id: str = 'mysql_default',
+            aws_conn_id: str = 'aws_default',
+            verify: Optional[Union[bool, str]] = None,
+            header: Optional[bool] = False,
+            index: Optional[bool] = False,
+            *args, **kwargs) -> None:
+        super().__init__(*args, **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.header = header
+        self.index = index
+    
+    def execute(self, context, **kwargs):
+        self.hook = MySqlHook(mysql_conn_id=self.mysql_conn_id)
+        self.s3 = S3Hook(aws_conn_id=self.aws_conn_id, verify=self.verify)
+        
+        data_df = self.hook.get_pandas_df(self.query)
 
 Review comment:
   Pandas DataFrame will alter the data type of an integer column which contains any `NULL` values. If the generated S3 files are used to copy into Redshift, this will generate errors due to a mismatch in data types. We have a similar work flow and the funcs below will conform int columns to a dtype which supports `None`.
   
   ```Python
   import numpy as np
   import pandas as pd
   
   
   def fill_na_with_none(series):
       return np.where(series.isnull(), None, series)
   
   
   def fix_int_dtypes(df):
       """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] = fill_na_with_none(df[col]).astype(pd.Int64Dtype)
   
   ```
   
   Basic test DataFrame below to display issue:
   
   ```Python
   data = {
       "a": [1, 2, 3, 4],
       "b": [1, None, 3, 4],
       "c": [0.1, 0.2, 0.3, 0.4],
       "d": [0.1, None, 0.3, 0.4],
       "e": [0, 0, 0, 0],
       "f": [None, None, None, None],
   }
   
   df = pd.DataFrame(data)
   print(df.dtypes)
   print(df) 
   
   fix_int_dtypes(df)
   print(df.dtypes)
   print(df) 
   ```

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