High SQLAlchemy initialization overhead

Question

We're currently using Flask RQ along with Flask SQLAlchemy and running into some performance problems. Here's our high level architecture:

1. API endpoint is hit
2. Time consuming tasks get queued into RQ
3. RQ worker forks a new process to perform the job
4. The job typically consists of a database query via Flask SQLAlchemy + additional processing

When looking at the performance of (4) using cProfile, I see

1       5.7e-05     5.7e-05     4.064   4.064   __init__.py:496(__get__)
535/1   0.002901    0.002901    3.914   3.914 base.py:389(_inspect_mapped_class)
1       0.001281    0.001281    3.914   3.914   mapper.py:2782(configure_mappers)
462/1   0.000916    0.000916    3.914   3.914   base.py:404(class_mapper)
1       1.4e-05     1.4e-05     3.914   3.914   mapper.py:1218(_configure_all)
59      0.01247     0.0002113   3.895   0.06601 mapper.py:1750(_post_configure_properties)
985/907 0.01748     1.927e-05   3.29    0.003627    interfaces.py:176(init)
235/157 0.00914     5.822e-05   3.162   0.02014 relationships.py:1650(do_init)
...

And

I see a lot of time being spent in SQLAlchemy; and I assume that is some overhead that maps the SQL data to the ORM object. So, I have a two questions:

1. Is the amount of time spent to initialize the SQL-ORM mapping expected? I'm running on AWS xlarge instances at 70% CPU. All my relationships are loaded dynamically using lazy='dynamic' and the corresponding query takes < 10 milliseconds, according to pg_stat_statements.
2. Assuming there's no way to work around (1), another way to avoid the constant overhead is to have a queue like this. So, instead of forking a new process for every job, the job runs in the thread directly. Is this advisable for distributed systems? I wasn't able to find a framework that did this so maybe it isn't a good idea?

Last note, if I'm being stupid and not seeing an obvious solution, please let me know!

Answer 1

configure_mappers is usually only called once during the lifetime of the application. It sets up some internal bookkeeping to make your models usable. You should avoid running it for every forked process. To do that, call it manually once in the parent process before forking:

from sqlalchemy.orm import configure_mappers

configure_mappers()