I would select out the total set of rows and then hand off groups of those rows, converted into serializable objects first, using the "imap" function of a multiprocessing.Pool object. It would be best if the rows are returned via a ResultProxy so that work can begin on results before all results are fetched.
See http://docs.python.org/library/multiprocessing.html#module-multiprocessing.pool . Its critical that you call create_engine() within each subprocess if additional queries are to be issued in workers, and don't pass any other database resources like connections or cursors across. Pool allows initialization activities via the "initializer" argument. On Feb 24, 7:05 am, dagooglaa <danielso...@gmx.de> wrote: > Hi, > > I am using SQLAlchemy 0.4.8 with Python 2.4. > > I have a process which is selecting some rows, doing something with > the data of these rows and then writes a result into the database. > > How can I achieve a solution where multiple of these processes are > running without having them selecting the same rows and doing the same > stuff? > > Normally I would suggest s.th. like Select for update or an trigger > which sets a timestamp after a select... > > Do you have any suggestions for having multiple processes working on > the same table but not on the same rows? > > Thanks --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "sqlalchemy" group. To post to this group, send email to sqlalchemy@googlegroups.com To unsubscribe from this group, send email to sqlalchemy+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sqlalchemy?hl=en -~----------~----~----~----~------~----~------~--~---
