If you don't have some organized way of managing concurrency, you're going to have problems. Without OOP, what? "Critical sections" lock relative to the code, not the data. "Which lock covers what data?" is a big issue, and the cause of many race conditions.
(The dislike of OOP seems to stem from the problems of getting objects into and out of databases in web services. One anti-OOP article suggests stored procedures as an alternative. Many database-oriented programs effectively use the database as their concurrency management tool. Nothing wrong with that, but it doesn't help if your problem isn't database driven.)
Python has the threading model of C - no language constructs for threads. It's all done in libraries. There's no protection against race conditions in user code. The underlying memory model is protected, by making operations that could break the memory model atomic, but that's all. CPython also has some major thread performance problems due to the Global Interpreter Lock. Having more CPUs doesn't speed things up; it makes programs slower, due to lock contention inefficiencies. So the use of real threads is discouraged in Python.
There's a suggested workaround with the "multiprocessing" module. This creates ultra-heavyweight threads, with a process for each thread, and talks to them with inefficient message passing. It's used mostly to run other programs from Python programs, and doesn't scale well.
So Python needed something to be competitive. There are armies of Javascript programmers with no experience in locking, but familiarity with a callback model. This seems to be the source of the push to put it in Python. Like many language retrofits, it's painful.
Does this imply that the major libraries will all have to be overhauled to make them async-compatible?
