Could you post your code with aiohttp. 10x difference seems too big. Sent from Outlook
On Thu, Mar 26, 2015 at 4:36 PM -0700, "Ludovic Gasc" <[email protected]> wrote: Since two weeks, I'm trying to use AsyncIO on top of PyPy3.3 >From my experience, two main elements aren't present on PyPy3.3:1. pip doesn't >work on PyPy3 => For pure Python libraries, you can install Python packages in >CPython pyvenv and change PYTHONPATH2. Monotonic clock and >time.get_clock_info() aren't implemented => The workaround I've found is to >use a standard clock (I know it's important to use monotonic, it's only for >tests) and hardcode the answer of time.get_clock_info(). At least for me, it >isn't very easy to implement monotonic clock in PyPy. Nevertheless, I've tested:1. aiotests: All tests passed2. Several random scripts from AsyncIO doc: everything is ok3. aiohttp examples: no issues4. API-Hour+aiohttp.web: It runs like on CPython 3.4 It's a good surprise for me, I didn't think it should be possible to use PyPy directly right now: I found only one bug with "yield from" quickly fixed by PyPy developers. As usual, I've did some benchmarks. You must note four points:1. PyPy 3.3 is not yet released: certainly some improvements should be available when it will be released2. My PyPy benchmark doesn't use ujson, because ujson works only with CPython. It should change some values if ujson is ported for PyPy.3. More I launch benchmarks on PyPy daemon, more I've performances. I've launched several 5 minutes benchmarks before to launch theses 1 minutes.4. this use case it's a micro-benchmark, you don't have any connections to a backend like PostgreSQL or Redis, more realistic use cases where AsyncIO has better results. I've redid the same benchmark I've told you few days ago with simple JSON payload. PyPy + API-Hour + aiohttp.web: $ wrk -t8 -c256 -d1m http://192.168.2.100:8008/jsonRunning 1m test @ http://192.168.2.100:8008/json 8 threads and 256 connections Thread Stats Avg Stdev Max +/- Stdev Latency 32.89ms 18.58ms 260.08ms 78.10% Req/Sec 0.99k 117.11 1.42k 71.97% 472966 requests in 1.00m, 87.96MB readRequests/sec: 7883.06Transfer/sec: 1.47MB PyPy + API-Hour + AsyncIO: $ wrk -t8 -c256 -d1m http://192.168.2.100:8009/jsonRunning 1m test @ http://192.168.2.100:8009/json 8 threads and 256 connections Thread Stats Avg Stdev Max +/- Stdev Latency 3.32ms 11.99ms 224.86ms 95.26% Req/Sec 12.63k 4.46k 43.67k 70.53% 5744657 requests in 1.00m, 0.96GB readRequests/sec: 95760.07Transfer/sec: 16.35MB To remember, the results I give you for WSGI and API-Hour on CPython: WSGI: $ wrk -t8 -c256 -d1m http://192.168.2.100:8080/jsonRunning 1m test @ http://192.168.2.100:8080/json 8 threads and 256 connections Thread Stats Avg Stdev Max +/- Stdev Latency 1.81ms 2.24ms 32.56ms 99.04% Req/Sec 20.49k 3.09k 52.56k 81.39% 9300719 requests in 1.00m, 1.59GB readRequests/sec: 155019.04Transfer/sec: 27.05MB API-Hour + aiohttp.web: $ wrk -t8 -c256 -d1m http://192.168.2.100:8008/jsonRunning 1m test @ http://192.168.2.100:8008/json 8 threads and 256 connections Thread Stats Avg Stdev Max +/- Stdev Latency 18.36ms 11.36ms 117.66ms 67.44% Req/Sec 1.79k 238.97 2.65k 74.02% 854843 requests in 1.00m, 158.16MB readRequests/sec: 14248.79Transfer/sec: 2.64MB API-Hour + AsyncIO: $ wrk -t8 -c256 -d1m http://192.168.2.100:8009/jsonRunning 1m test @ http://192.168.2.100:8009/json 8 threads and 256 connections Thread Stats Avg Stdev Max +/- Stdev Latency 1.96ms 3.55ms 60.51ms 99.05% Req/Sec 19.77k 3.06k 55.78k 85.49% 8972814 requests in 1.00m, 1.49GB readRequests/sec: 149565.74Transfer/sec: 25.39MB As you can see, in this specific benchmark with theses hit values, PyPy3.3 is slower than CPython with AsyncIO for now.But, I imagine that the PyPy dev team goal is more to be fully compliant with CPython 3.3 than have the best performances. Finally, if you are interested in to help PyPy project but you have no time, you can donate money: http://pypy.org/py3donate.htmlI've did a recurring donation for the PyPy project.-- Ludovic Gasc (GMLudo)http://www.gmludo.eu/
