We are building web applications since the mid-90s with Python. Historically, we have used a
As Python developer you often have the need for profiling and benchmarking applications. The standard tools are the Python profiler hotshot or standard debugging code with timestamp. Within one of our current project I had to track down performance issues related to LDAP connectivity. The goal was to measure the time spend within the connect() and search() method of the LDAPUserFolder implementation. As result I wrote a small @timeit decorator that allows you to measure the execution times of dedicated methods (module-level methods or class methods) by just adding the @timeit decorator in in front of the method call.
import time def timeit(method): def timed(*args, **kw): ts = time.time() result = method(*args, **kw) te = time.time() print '%r (%r, %r) %2.2f sec' % \ (method.__name__, args, kw, te-ts) return result return timed class Foo(object): @timeit def foo(self, a=2, b=3): time.sleep(0.2) @timeit def f1(): time.sleep(1) print 'f1' @timeit def f2(a): time.sleep(2) print 'f2',a @timeit def f3(a, *args, **kw): time.sleep(0.3) print 'f3', args, kw f1() f2(42) f3(42, 43, foo=2) Foo().foo()
There is also the profilehooks module by Marius Gedminas basically doing something similar (in detail: it profiles the hooked method). It is perhaps the better alternative.