I am noticing the 16 execute calls.  There should only be 10 with a batch 
size of 1000 and a record size of 9,000 and some change.

Are you just wanting to see the sql via a print(q)?

On Wednesday, August 30, 2017 at 2:01:31 PM UTC-4, Mike Bayer wrote:
>
> it looks like you have 16 SQL statements that take a very different 
> amount of time depending on format.  Can you please paste the output 
> with echo=True on create_engine() so that the difference in SQL 
> statements can be seen? 
>
> On Wed, Aug 30, 2017 at 12:30 PM, Ken MacKenzie <devil...@gmail.com 
> <javascript:>> wrote: 
> > Not sure how I would iterate through a non predetermined number of 
> primary 
> > keys. 
> > 
> > I guess part of me is wondering that although textual sql is not 
> inherently 
> > db neutral how different between the db targets is the where field = 
> 'value' 
> > syntax? 
> > 
> > On Wednesday, August 30, 2017 at 12:07:52 PM UTC-4, Simon King wrote: 
> >> 
> >> You could also try using executemany: 
> >> 
> >> 
> >> 
> http://docs.sqlalchemy.org/en/latest/core/tutorial.html#executing-multiple-statements
>  
> >> 
> >> I think it would look something like this: 
> >> 
> >> table = cls.__table__ 
> >> condition = sa.and_( 
> >>     table.c.pk1 == sa.bindparam('pk1'), 
> >>     table.c.pk2 == sa.bindparam('pk2'), 
> >> ) 
> >> statement = sa.delete(table, whereclause=condition) 
> >> batchparams = [{'pk1': v[0], 'pk2': v[1]} for v in id_batch] 
> >> session.execute(statement, batchparams) 
> >> 
> >> Simon 
> >> 
> >> On Wed, Aug 30, 2017 at 4:28 PM, Ken MacKenzie <devil...@gmail.com> 
> wrote: 
> >> > After the current sorted profile finishes I will revert to the 
> textual 
> >> > version and run a profile on that.  I expect another 10-15 minutes 
> for 
> >> > this 
> >> > to finish right now. 
> >> > 
> >> > At present the batch size is set to 1000, total record count is just 
> >> > over 
> >> > 9000 in these tests. 
> >> > 
> >> > The reason for 1000 was at first I was looking at doing this as a 
> >> > tuple_(fld, fld).in_((val, val),(val,val)) format.  The 1000 should 
> keep 
> >> > me 
> >> > under most DB restrictions on the in statement. 
> >> > 
> >> > However since SQL Server does not seem to support the tuple_ usage I 
> >> > reverted to this method. 
> >> > 
> >> > I technically have one more method and that is a concat_ in_ where I 
> >> > concat 
> >> > the fields. 
> >> > 
> >> > Other specifics, the table in question has 2 fields for the PK, both 
> are 
> >> > varchar, one length 3, the other length 10.  There are 5 non key 
> fields, 
> >> > 3 
> >> > short varchars, one decimal at 14,2 precision and one varchar(800) 
> which 
> >> > contains description text. 
> >> > 
> >> > Total record count of the table before any deletion is about 1.05 
> >> > million. 
> >> > 
> >> > Python version is 3.4.5, running on a modest CentOS desktop and to be 
> >> > fair 
> >> > the SQL Server instance is sub optimal for development. 
> >> > 
> >> > On Wednesday, August 30, 2017 at 11:18:13 AM UTC-4, Simon King wrote: 
> >> >> 
> >> >> It would be interesting to see the profile of the textual SQL 
> version. 
> >> >> It looks like most of the time is being spent inside pyodbc, rather 
> >> >> than SQLAlchemy, so I guess it must be something to do with the 
> >> >> processing of bind parameters. How many parameters are being sent in 
> >> >> per query? ie. what is len(id_batch) * len(cls.SQL_PK)? 
> >> >> 
> >> >> You could try playing with your batch sizes to see what sort of 
> effect 
> >> >> that has. 
> >> >> 
> >> >> Simon 
> >> > 
> >> > -- 
> >> > SQLAlchemy - 
> >> > The Python SQL Toolkit and Object Relational Mapper 
> >> > 
> >> > http://www.sqlalchemy.org/ 
> >> > 
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> >> > description. 
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> > 
> > http://www.sqlalchemy.org/ 
> > 
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