Re-hi, I tried to debug it more precisely, and it seems that the problem is even more basic than I thought. It is very hard to solve in general. The issue is that when we are really out of memory, then *every* single allocation is going to fail. The difference with CPython is that in the same situation, the latter can still (randomly) satisfy a small number of allocations of different sizes. This is an internal detail of how the memory allocators work.
What occurs then in PyPy is that when we are out of memory, we can really not allocate any single object at more. So we cannot even execute anything from the "except MemoryError" block, because when catching the exception, we try to allocate a small internal object --- which re-raises MemoryError. I tried to look if we would go anywhere by making sure that no small internal object is allocated, but I doubt it, because then it would crash in the first line (e.g. "size = size / 2", which of course allocates a new "long" object). In the end you get this "fatal RPython error: MemoryError" because even printing the traceback requires some memory (and thus re-raises MemoryError instead). Generally, we have no hope to pass cleanly the test you gave. Even in CPython it works by chance; I can tweak it in "reasonable" ways and have it fail too (e.g. if you need to do a bit more than "size = size / 2" here, then you're likely to get a MemoryError in the except handler too). So, I don't think this is ever going to be fixed (or fixable)... A bientôt, Armin. _______________________________________________ pypy-dev mailing list pypy-dev@python.org http://mail.python.org/mailman/listinfo/pypy-dev
