for "dm_exec_sessions" ? that's an old SQLAlchemy bug that was fixed
long ago. see https://github.com/sqlalchemy/sqlalchemy/issues/3994
please upgrade.
On Wed, Jan 30, 2019 at 10:52 PM <mjpe...@lixar.com> wrote:
>
> Any solution for this?
>
> On Monday, September 11, 2017 at 6:34:47 PM UTC-4, dirk.biesinger wrote:
>>
>> I am encountering errors when trying to use the pd.to_sql function to write
>> a dataframe to MS SQL Data Warehouse.
>> The connection works when NOT using sqlalchemy engines.
>> I can read dataframes as well as row-by-row via select statements when I use
>> pyodbc connections
>> I can write data via insert statements (as well as delete data) when using
>> pyodbc.
>> However, when I try to connect using a sqlalchemy engine I run into a string
>> of error messages starting with:
>>
>> ProgrammingError: (pyodbc.ProgrammingError) ('42000', "[42000]
>> [Microsoft][ODBC Driver 13 for SQL Server][SQL Server]Catalog view
>> 'dm_exec_sessions' is not supported in this version. (104385)
>> (SQLExecDirectW)")
>>
>>
>> I have searched online, and this exact error seems to have been reported /
>> evaluated in May of this year as issue #3994:
>>
>>
>> https://bitbucket.org/zzzeek/sqlalchemy/issues/3994/azure-sql-datawarehouse-basic
>>
>>
>> I could not find a solution to this, and I'd really dislike to do a
>> line-wise or blob insert statement (I'm working with multiple datasets that
>> each has a few million rows, so execution time is a consideration, although
>> the result sets I'm getting are more like in the 100k lines area each.)
>>
>>
>> I get the same error messages even when I replace the pd.to_sql command with
>> a simple engine.connect()
>>
>>
>> Enclosed my installed packages (packages.list)
>>
>> Enclosed the full traceback (traceback.txt)
>>
>>
>> This is the code I'm using:
>>
>> connection_string =
>> "mssql+pyodbc://<username>:<password>@<sqlhost>.database.windows.net:<port>/<database>?driver=ODBC+Driver+13+for+SQL+Server"
>> engn = sqlalchemy.engine.create_engine(connection_string, echo=True)
>> engn.connect()
>>
>>
>> I'm very well aware that MS SQL DataWarehouse behaves a bit different, so
>> I'm open for some experimenting to get this issue narrowed down.
>>
>> In case it matters: I'm running an ubuntu 16.04 VM on azure with jupyter
>> notebook server and python 3.6.1.
>>
>> Best,
>>
>> DB
>
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