confluent/bin/confluent-server.py

import sys
import os
path = os.path.dirname(os.path.realpath(__file__))
path = os.path.realpath(os.path.join(path, '..'))
sys.path.append(path)
from confluent import main

#import cProfile
#import time
#p = cProfile.Profile(time.clock)
#p.enable()
#try:
main.run()
#except:
#   pass
#p.disable()
#p.print_stats(sort='cumulative')
#p.print_stats(sort='time')
Arrange things so that the startup py is in bin 2013-07-19 10:31:02 -04:00			`import sys`
			`import os`
			`path = os.path.dirname(os.path.realpath(__file__))`
			`path = os.path.realpath(os.path.join(path, '..'))`
			`sys.path.append(path)`
			`from confluent import main`

Put comments in to hint a decent strategy to profile runtime performance To do performance optimization in this sort of application, this is about as well as I have been able to manage in python. I will say perl with NYTProf seems to be significantly better for data, but this is servicable. I tried yappi, but it goes wildly inaccurate with this codebase. Because of the eventlet plumbing, cProfile is still pretty misleading. Best strategy seems to be review cumulative time with a healthy grain of salt around the top items until you get down to info that makes sense. For example, trampoline unfairly gets a great deal of the 'blame' by taking on nearly all the activity. internal time seems to miss a great deal of important information. 2014-04-03 09:54:46 -04:00			`#import cProfile`
			`#import time`
			`#p = cProfile.Profile(time.clock)`
			`#p.enable()`
			`#try:`
Arrange things so that the startup py is in bin 2013-07-19 10:31:02 -04:00			`main.run()`
Put comments in to hint a decent strategy to profile runtime performance To do performance optimization in this sort of application, this is about as well as I have been able to manage in python. I will say perl with NYTProf seems to be significantly better for data, but this is servicable. I tried yappi, but it goes wildly inaccurate with this codebase. Because of the eventlet plumbing, cProfile is still pretty misleading. Best strategy seems to be review cumulative time with a healthy grain of salt around the top items until you get down to info that makes sense. For example, trampoline unfairly gets a great deal of the 'blame' by taking on nearly all the activity. internal time seems to miss a great deal of important information. 2014-04-03 09:54:46 -04:00			`#except:`
			`# pass`
			`#p.disable()`
			`#p.print_stats(sort='cumulative')`
			`#p.print_stats(sort='time')`