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Profil-o-matic

A hybrid profiler / mini APM tool for Python, that links slow high-level actions to low-level code issues, even under load. It has hooks for WSGI and Eliot to enable it to identify high-level actions.

It actively monitors profiling overhead, and can reduce granularity or shed load to ensure the application is not adversely affected. Hot loops are implemented in Cython, with pure Python fallbacks.

How it works

Profilomatic supports integration with Eliot, the context based logging framework:

import eliot
import profilomatic
import profilomatic.eliot

profilomatic.eliot.patch()

# Configure output the same way as eliot
log_destination = eliot.FileDestination(open('app_log.log', 'w'))
eliot.add_destination(log_destination)

prof_destination = eliot.FileDestination(open('app_prof.log', 'w'))
profilomatic.add_destination(prof_destination)


# Optionally set your options
profilomatic.configure(
    max_overhead=0.02,  # Autotune granularity to target a desired profiling overhead - 2% in this case
    time_granularity=0.01,  # Sampling frequency - 10ms in this case
    code_granularity='method',  # Get file, method, or line-level performance data
    store_all_logs=False,  # Incorporate all log messages into call graph
    max_actions_per_run=10,  # When heavily loaded, limit how many new actions you profile per cycle
)

# Now run a program
import urllib2
import wsgiref.simple_server
def app(environ, start_response):
    with eliot.startAction(event='app:hello-world'):
        data = urllib2.urlopen('http://www.python.org').read()
        start_response('200 OK', [('Content-Type', 'text/html')])
        return [data.replace('Python 3', 'Python 3000')]

wsgiref.simple_server.make_server('', 8000, app).serve_forever()

It also supports WSGI, so can easily integrate into WSGI-based frameworks, such as Flask or Django:

import profilomatic

# Configure output destinations
prof_destination = profilomatic.file_destination(open('app_prof.log', 'w'))
# Or maybe hook it up directly to Profil-o-matic analysis
prof_destination = profilomatic.RestDestination(
  'monitoring_server', 443, ssl_context=ssl.create_default_context())

profilomatic.add_destination(prof_destination)

# Optionally set your options
profilomatic.configure(
    max_overhead=0.02,  # Autotune granularity to target a desired profiling overhead - 2% in this case
    time_granularity=0.01,  # Sampling frequency - 10ms in this case
    code_granularity='method',  # Get file, method, or line-level performance data
    store_all_logs=False,  # Incorporate all log messages into call graph
    max_actions_per_run=10,  # When heavily loaded, limit how many new actions you profile per cycle
)

# Now run a program
import urllib2
import wsgiref.simple_server
import profilomatic.wsgi

@profilomatic.wsgi.wrap
def app(environ, start_response):
    data = urllib2.urlopen('http://www.python.org').read()
    start_response('200 OK', [('Content-Type', 'text/html')])
    return [data.replace('Python 3', 'Python 3000')]

wsgiref.simple_server.make_server('', 8000, app).serve_forever()