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I am using py.test (version 2.4, on Windows 7) with xdist to run a number of numerical regression and interface tests for a C++ library that provides a Python interface through a C module.
The number of tests has grown to ~2,000 over time, but we are running into some memory issues now. Whether using xdist or not, the memory usage of the python process running the tests seems to be ever increasing.
In single-process mode we have even seen a few issues of bad allocation errors, whereas with xdist total memory usage may bring down the OS (8 processes, each using >1GB towards the end).
Is this expected behaviour? Or did somebody else experience the same issue when using py.test for a large number of tests? Is there something I can do in tearDown(Class) to reduce the memory usage over time?
At the moment I cannot exclude the possibility of the problem lying somewhere inside the C/C++ code, but when running some long-running program using that code through the Python interface outside of py.test, I do see relatively constant memory usage over time. I also do not see any excessive memory usage when using nose instead of py.test (we are using py.test as we need junit-xml reporting to work with multiple processes)
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Those numbers can easily fit in a 64-bit integer, so one would hope Python would store those million integers in no more than ~8MB: a million 8-byte objects. In fact, Python uses more like 35MB of RAM to store these numbers. Why? Because Python integers are objects, and objects have a lot of memory overhead.
You can use it by putting the @profile decorator around any function or method and running python -m memory_profiler myscript. You'll see line-by-line memory usage once your script exits.
Python doesn't limit memory usage on your program. It will allocate as much memory as your program needs until your computer is out of memory. The most you can do is reduce the limit to a fixed upper cap. That can be done with the resource module, but it isn't what you're looking for.
py.test's memory usage will grow with the number of tests. Each test is collected before they are executed and for each test run a test report is stored in memory, which will be much larger for failures, so that all the information can be reported at the end. So to some extend this is expected and normal.
However I have no hard numbers and have never closely investigated this. We did run out of memory on some CI hosts ourselves before but just gave them more memory to solve it instead of investigating. Currently our CI hosts have 2G of mem and run about 3500 tests in one test run, it would probably work on half of that but might involve more swapping. Pypy is also a project that manages to run a huge test suite with py.test so this should certainly be possible.
If you suspect the C code to leak memory I recommend building a (small) test script which just tests the extension module API (with or without py.test) and invoke that in an infinite loop while gathering memory stats after every loop. After a few loops the memory should never increase anymore.
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We also experience similar problems. In our case we run about ~4600 test cases. We use extensively pytest fixtures and we managed to save the few MB by scoping the fixtures slightly differently (scoping several from "session" to "class" of "function"). However we dropped in test performances.
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