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Comment by lobster_johnson | Hacker News Reader

Yeah, a mutex to protect refcounts? That's ridiculous. (If you want low-overhead locks, David Bacon's thin locks (http://www.research.ibm.com/people/d/dfb/thinlocks-publicati...) is a good place to start.)

I also question the logical premise offered here that data structures must be thread-safe. It's not something guaranteed by most languages (eg. Java), so why should Python guarantee it?

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"all datastructures would have to be thread-safe is pretty common argument used against usage of native threads (as opposed to green threads) or removal of global locks in various VMs. While it's true that they don't have to be thread-safe from user's point of view, they should be thread-safe in the sense that they cannot get corrupted by race condition in user code in a way that could crash (or deadlock) the VM.

Every Python object has a backing dictionary, so if any object has to be shared between threads in a threadsafe fashion, then dictionaries need to be threadsafe.

Right. I think that backing dictionary could probably be replaced with a more thread-friendly implementation, even if we have to offer reduced guarantees.

Or every Python object's backing dictionary needs to use the special threadsafe implementation.

This is tricky because dictionaries need to call __hash__ which can run arbitrary Python code, so it's non-trivial to make the dictionary threadsafe.

__eq__ as well

It's perfectly possible and even reasonable to call __hash__ before acquiring lock on the dictionary (and cache keys' hashes for data in the dictionary).

On the other hand, when almost every object is backed by dictionary, the locking has to be very fast, which seems to me like almost unsolvable problem.

Just make __hash__() wrap the underlying dict in a thread-safe one that delegates the calls.

That's an argument for revamping the backing dictionary, really. If __hash__() is a problem, make it return a thread-safe proxy that delegates to the object's dictionary.

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