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【机器学习 Azure Machine Learning】Azure Machine Learning 访问SQL Server 无法写入问题 (使用微软Python AML Core SDK)

时间:2021-07-01 10:21:17 帮助过:15人阅读

pyodbc import itertools import sys from sqlalchemy import create_engine import urllib import scipy.stats as stats conn = pyodbc.connect(rDRIVER={SQL Server Native Client 11.0};SERVER=database.database.chinacloudapi.cn;DATABASE=db;UID=user;PWD=pwd) rmdf[[‘]].to_sql(xxxx_base,con = conn,index=False, if_exists=append, schema=ai)

错误截图:

 技术图片

详细日志

ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Failure, Duration=672.71 [ms], Info = 
{activity_id: e850f767-0c12-4864-8d01-d11dc5817ec9, activity_name: to_pandas_dataframe, activity_type: PublicApi, app_name: TabularDataset,
source: azureml.dataset, version: 1.0.76, completionStatus: Success, durationMs: 6.05},
Exception=DatasetExecutionError; Could not connect to specified database.|session_id=f648402f-f619-469d-a6f4-aee7031bd438
---------------------------------------------------------------------------
ExecutionError Traceback (most recent call last) /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/data/dataset_error_handling.py in _try_execute(action, **kwargs) 82 else:
---> 83 return action() 84 except Exception as e: /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/_loggerfactory.py in wrapper(*args, **kwargs) 130 try:
--> 131 return func(*args, **kwargs) 132 except Exception as e: /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/dataflow.py
in to_pandas_dataframe(self, extended_types, nulls_as_nan) 676 self._engine_api.execute_anonymous_activity(
--> 677 ExecuteAnonymousActivityMessageArguments(anonymous_activity=Dataflow._dataflow_to_anonymous_activity_data(dataflow_to_execute)))
678 /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/_aml_helper.py in wrapper(op_code, message, cancellation_token)
37 engine_api_func().update_environment_variable(changed)
---> 38 return send_message_func(op_code, message, cancellation_token) 39 /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/engineapi/api.py
in execute_anonymous_activity(self, message_args, cancellation_token) 93
def execute_anonymous_activity(self, message_args: typedefinitions.ExecuteAnonymousActivityMessageArguments, cancellation_token: CancellationToken = None) -> None:
---> 94 response = self._message_channel.send_message(Engine.ExecuteActivity, message_args, cancellation_token)
95 return response /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/engineapi/engine.py
in send_message(self, op_code, message, cancellation_token) 118 if error in response:
--> 119 raise_engine_error(response[error]) 120 elif response.get(id) == message_id: /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/dataprep/api/errorhandlers.py
in raise_engine_error(error_response) 21 if ActivityExecutionFailed in error_code:
---> 22 raise ExecutionError(error_response) 23 elif UnableToPreviewDataSource in error_code: ExecutionError: Could not connect to specified database.
|session_id=f648402f-f619-469d-a6f4-aee7031bd438 During handling of the above exception, another exception occurred:
DatasetExecutionError Traceback (most recent call last) <ipython-input-7-7f54b930998f> in <module>
----> 1 dataset.to_pandas_dataframe() /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/data/_loggerfactory.py in wrapper(*args, **kwargs) 76
with _LoggerFactory.track_activity(logger, func.__name__, activity_type, custom_dimensions) as al: 77 try:
---> 78 return func(*args, **kwargs) 79 except Exception as e: 80 if hasattr(al, activity_info)
and hasattr(e, error_code): /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/data/tabular_dataset.py
in to_pandas_dataframe(self) 138 """ 139 dataflow = get_dataflow_for_execution(self._dataflow, ‘to_pandas_dataframe‘, ‘TabularDataset‘)
--> 140 df = _try_execute(dataflow.to_pandas_dataframe) 141 return df
142 /anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/data/dataset_error_handling.py in _try_execute(action, **kwargs)
83 return action() 84 except Exception as e:
---> 85 raise DatasetExecutionError(str(e)) DatasetExecutionError: Could not connect to specified database.|session_id=f648402f-f619-469d-a6f4-aee7031bd438

 

问题原因

根据代码判断,问题是在to_sql方法中使用的con对象的问题,此处需要使用的是由 sqlalchemy所创建的 create_engine对象,而不能使用 pyodbc的conn对象。 同时也必须根据环境选择正确的DB驱动。如Windows环境中,则可以使用‘Driver={SQL Server};‘,而在Linux中,则可以使用DRIVER={SQL Server Native Client 11.0};

错误的连接对象:

import pyodbc

conn = pyodbc.connect(rDRIVER={SQL Server Native Client 11.0};SERVER=xxxx.database.chinacloudapi.cn;DATABASE=xx;UID=xx;PWD=)

 正确的SQL连接对象:

from sqlalchemy import create_engine
 
engine = create_engine(mssql+pyodbc://%s:%s@%s/%s?driver=SQL Server % (
user name,                      
pwd,                             
<service name>.database.chinacloudapi.cn,                             
#cf.ju_db_post,                             
DB Name                                                         
),connect_args={charset:utf8})

 

解决方案

使用Create_engine创建engine并且使用在to_sql方法中,具体代码如下图:

技术图片

 

注意:如出现类似错误消息是“Error: (‘01000‘, "[01000] [unixODBC][Driver Manager]Can‘t open lib ‘SQL Server‘ : file not found (0) (SQLDriverConnect)")”,则需要检查当前VM中的ODBC Driver。

参考资料:

 pandas.DataFrame.to_sql:https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.to_sql.html

【机器学习 Azure Machine Learning】Azure Machine Learning 访问SQL Server 无法写入问题 (使用微软Python AML Core SDK)

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