Ami Koren added the comment:
Thanks David. using spawn - multiprocessing.get_context('spawn').Pool(... -
does the job . It does has it's flows - fork allows me to share data between
workers (especially large readonly memory database, which I don't want to
duplicate for each worker), which spawn (which uses fork-exec python script)
doesn't. So I'll have to see about that.
I still don't understand why forking has to be done under the worker thread
context. It doesn't seem like a good design - When forking from a thread you
can never be sure what is being forked. A better approach seems to be to fork
missing workers on-demand, synchronous to the main thread. But I probably lack
the historic context of the multiprocess module.
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<http://bugs.python.org/issue25829>
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