Ok, here's the reality. Benchmarking on 10k records (either method), you get a through put of approximately 100 records a second (should complete in 1.5 hours).
The row.update completes in 3.5 hours. The update_or_insert takes > 7 hours. (with available memory maxed out, no caching involved. Ditto with the former with/ without caching) Performance of either method is significantly slower than expected. Was an attempt to explore alternative ways of doing simple batch updates within the DAL. With the insert_or_update, my assumption was that it not possible to do a row.update, where the row was the subject of a join. (correct me if I'm wrong). Also didn't like additional query to get source data in the row.update method. (that was the only data that was cached) Point taken with the post example (close to reality but trashed for security reasons). Sorry. Will do an example on hash files at the first opportunity. Point understood on SQLite vs production databases, but focus was on simple updates/ data prep. Be gentle, haven't had a lot of sleep in the last 3 weeks :) -- --- You received this message because you are subscribed to the Google Groups "web2py-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to web2py+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/groups/opt_out.
