XML parser performance in CPython 3.3 and PyPy 1.7


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In a recent article, I compared the performance of MiniDOM and the three ElementTree implementations ElementTree, cElementTree and lxml.etree for parsing XML in CPython 3.3. Given the utterly poor performance of the pure Python library MiniDOM in this competition, I decided to give it another chance and tried the same in PyPy 1.7. Because lxml.etree and cElementTree are not available on this platform, I only ran the tests with plain ElementTree and MiniDOM. I also report the original benchmark results for CPython below for comparison.

Parser performance of XML libraries in CPython 3.3 and PyPy 1.7

While I also provide numbers regarding the memory usage of each library in this comparison, they are not directly comparable between PyPy and CPython because of the different memory management of both platforms and because the overall memory that PyPy uses right from the start is much larger than for CPython. So the relative increase in memory may or may not be an accurate way to tell what each runtime does with the memory. However, it appears that PyPy manages to kill at least the severe memory problems of MiniDOM, as the total amount of memory used for the larger files is several times smaller than that used by CPython.

Memory usage of XML trees in CPython 3.3 and PyPy 1.7

So, what do I take from this benchmark? If you have legacy MiniDOM code lying around, you want PyPy to run it. It exhibits several times better performance in terms of memory and runtime. It also performs substantially better for ElementTree than the plain Python ElementTree in CPython.

However, for fast XML processing in general, the better performance of PyPy even for plain Python ElementTree is not really all that interesting, because it is still several times slower than cElementTree or lxml.etree in CPython. That means that you will often be able to process multiple files in CPython in the time that you need for just one in PyPy, even if your actual application code that does the processing manages to get a substantial JIT speed-up in PyPy. Even worse, the GIL in PyPy will keep your code from getting a parallel speedup that you usually get with multi-threaded processing in lxml and CPython, e.g. in a web server setting.

So, as always, the decision depends on what your actual application does and which library it uses. Do your own benchmarks.