On a side note the literal_column change worked fine performance wise
allowing me to remove the literal_bind/compile step.
I guess I am 50/50 on which is cleaner. Is either one more advantageous
from a SQL injection perspective?
SQL Injection is not a primary concern at the moment for this process as
what we are dealing with is a data replication service pulling from a u2
data store to sql server. The delete is part of batch cleanup testing.
On Thursday, August 31, 2017 at 5:13:58 PM UTC-4, Ken MacKenzie wrote:
>
> Tried doing various conversions on the pk values as they enter the
> statement:
> 1. to bytes
> 2. to ascii
> 3. to latin1 (technically the same encoding as the extract source before
> entering the db)
>
> None of which yielded a performance improvement for the non-compiled
> version.
>
> I have read that this can be an issue with pyodbc and that there are
> engine settings related to it. Also perhaps I should try using the pymssql
> driver to see if that changes anything.
>
> On Thursday, August 31, 2017 at 5:00:47 PM UTC-4, Ken MacKenzie wrote:
>>
>> So inspecting the elements of the tuple, they are both str, so hence
>> unicode.
>>
>> Are you saying that if I convert those values to bytes it could improve
>> performance?
>>
>>
>>
>>> I'd not bother with the literal_binds and just use a literal value:
>>>
>>> pkf = [(col == literal_column("'%s'" % v)) for (col, v) in zip(cols, x)]
>>>
>>> but also I'd look to see what the nature of "v" is, if it's like a
>>> Unicode object or something, you might be getting bogged down on the
>>> decode/encode or something like that. Sending as bytes() perhaps
>>> might change that.
>>>
>>
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
To post example code, please provide an MCVE: Minimal, Complete, and Verifiable
Example. See http://stackoverflow.com/help/mcve for a full description.
---
You received this message because you are subscribed to the Google Groups
"sqlalchemy" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to [email protected].
To post to this group, send email to [email protected].
Visit this group at https://groups.google.com/group/sqlalchemy.
For more options, visit https://groups.google.com/d/optout.
