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pprofile 1.7.1

Line-granularity, thread-aware deterministic and statistic pure-python profiler

Line-granularity, thread-aware deterministic and statistic pure-python profiler

Inspired from Robert Kern's line_profiler .

Overview

Python's standard profiling tools have a callable-level granularity, which means it is only possible to tell which function is a hot-spot, not which lines in that function.

Robert Kern's line_profiler is a very nice alternative providing line-level profiling granularity, but in my opinion it has a few drawbacks which (in addition to the attractive technical challenge) made me start pprofile:

  • It is not pure-python. This choice makes sense for performance but makes usage with pypy difficult and requires installation (I value execution straight from checkout).
  • It requires source code modification to select what should be profiled. I prefer to have the option to do an in-depth, non-intrusive profiling.
  • As an effect of previous point, it does not have a notion above individual callable, annotating functions but not whole files - preventing module import profiling.
  • Profiling recursive code provides unexpected results (recursion cost is accumulated on callable's first line) because it doesn't track call stack. This may be unintended, and may be fixed at some point in line_profiler.

Usage

As a command:

$ pprofile some_python_executable

Once some_python_executable returns, prints annotated code of each file involved in the execution (output can be directed to a file using -o/--out arguments).

As a command with conflicting argument names: use "--" before profiled executable name:

$ pprofile -- foo --out bar

As a module:

import pprofile

profiler = pprofile.Profile()
def someHotSpotCallable():
    with profiler:
        # Some hot-spot code

Alternative to with, allowing to end profiling in a different place:

def someHotSpotCallable():
    profiler.enable()
    # Some hot-spot code
    someOtherFunction()

def someOtherFunction():
    # Some more hot-spot code
    profiler.disable()

Then, to display annotated source on stdout:

profiler.print_stats()

(several similar methods are available).

Sample output (standard threading.py removed from output for readability):

$ pprofile --threads 0 demo/threads.py
Command line: ['demo/threads.py']
Total duration: 1.00573s
File: demo/threads.py
File duration: 1.00168s (99.60%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
     1|         2|  3.21865e-05|  1.60933e-05|  0.00%|import threading
     2|         1|  5.96046e-06|  5.96046e-06|  0.00%|import time
     3|         0|            0|            0|  0.00%|
     4|         2|   1.5974e-05|  7.98702e-06|  0.00%|def func():
     5|         1|      1.00111|      1.00111| 99.54%|  time.sleep(1)
     6|         0|            0|            0|  0.00%|
     7|         2|  2.00272e-05|  1.00136e-05|  0.00%|def func2():
     8|         1|  1.69277e-05|  1.69277e-05|  0.00%|  pass
     9|         0|            0|            0|  0.00%|
    10|         1|  1.81198e-05|  1.81198e-05|  0.00%|t1 = threading.Thread(target=func)
(call)|         1|  0.000610828|  0.000610828|  0.06%|# /usr/lib/python2.7/threading.py:436 __init__
    11|         1|  1.52588e-05|  1.52588e-05|  0.00%|t2 = threading.Thread(target=func)
(call)|         1|  0.000438929|  0.000438929|  0.04%|# /usr/lib/python2.7/threading.py:436 __init__
    12|         1|  4.79221e-05|  4.79221e-05|  0.00%|t1.start()
(call)|         1|  0.000843048|  0.000843048|  0.08%|# /usr/lib/python2.7/threading.py:485 start
    13|         1|  6.48499e-05|  6.48499e-05|  0.01%|t2.start()
(call)|         1|   0.00115609|   0.00115609|  0.11%|# /usr/lib/python2.7/threading.py:485 start
    14|         1|  0.000205994|  0.000205994|  0.02%|(func(), func2())
(call)|         1|      1.00112|      1.00112| 99.54%|# demo/threads.py:4 func
(call)|         1|  3.09944e-05|  3.09944e-05|  0.00%|# demo/threads.py:7 func2
    15|         1|  7.62939e-05|  7.62939e-05|  0.01%|t1.join()
(call)|         1|  0.000423908|  0.000423908|  0.04%|# /usr/lib/python2.7/threading.py:653 join
    16|         1|  5.26905e-05|  5.26905e-05|  0.01%|t2.join()
(call)|         1|  0.000320196|  0.000320196|  0.03%|# /usr/lib/python2.7/threading.py:653 join

Note that time.sleep call is not counted as such. For some reason, python is not generating c_call/c_return/c_exception events (which are ignored by current code, as a result).

Generating callgrind-format output in a file instead of stdout:

$ pprofile --format callgrind --out cachegrind.out.threads demo/threads.py

Callgrind format is implicitly enabled if --out basename starts with cachegrind.out., so above command can be simplified as:

$ pprofile --out cachegrind.out.threads demo/threads.py

Callgrind format can be opened, for example, with kcachegrind.

If you are analyzing callgrind traces on a different machine, you may want to use the --zipfile option to generate a zip file containing all files:

$ pprofile --out cachegrind.out.threads --zipfile threads_source.zip demo/threads.py

Generated files will use relative paths, so you can extract generated archive in the same path as profiling result, and kcachegrind will load them - and not your system-wide files, which may differ.

Statistic profiling

Deterministic profiling collects samples on each trigger (per line as in this module, or per call in traditional python profiling). Statistic profiling, on the other hand, triggers periodically. Samples accumulate where execution spends most time. As a result:

  • output lacks timing information
  • profiler overhead can be be balanced at will with measure duration by changing trigger period
  • profiling can be turned on an off without having to reach specific points in the call stack

Sample output (standard threading.py trimmed from output for readability):

$ pprofile --statistic .01 demo/threads.py
Command line: ['demo/threads.py']
Total duration: 1.0026s
File: demo/threads.py
File duration: 0s (0.00%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
     1|         0|            0|            0|  0.00%|import threading
     2|         0|            0|            0|  0.00%|import time
     3|         0|            0|            0|  0.00%|
     4|         0|            0|            0|  0.00%|def func():
     5|       288|            0|            0|  0.00%|  time.sleep(1)
     6|         0|            0|            0|  0.00%|
     7|         0|            0|            0|  0.00%|def func2():
     8|         0|            0|            0|  0.00%|  pass
     9|         0|            0|            0|  0.00%|
    10|         0|            0|            0|  0.00%|t1 = threading.Thread(target=func)
    11|         0|            0|            0|  0.00%|t2 = threading.Thread(target=func)
    12|         0|            0|            0|  0.00%|t1.start()
    13|         0|            0|            0|  0.00%|t2.start()
    14|         0|            0|            0|  0.00%|(func(), func2())
(call)|        96|            0|            0|  0.00%|# demo/threads.py:4 func
    15|         0|            0|            0|  0.00%|t1.join()
    16|         0|            0|            0|  0.00%|t2.join()
File: /usr/lib/python2.7/threading.py
File duration: 0s (0.00%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
[...]
   308|         0|            0|            0|  0.00%|    def wait(self, timeout=None):
[...]
   338|         0|            0|            0|  0.00%|            if timeout is None:
   339|         1|            0|            0|  0.00%|                waiter.acquire()
   340|         0|            0|            0|  0.00%|                if __debug__:
[...]
   600|         0|            0|            0|  0.00%|    def wait(self, timeout=None):
[...]
   617|         0|            0|            0|  0.00%|            if not self.__flag:
   618|         0|            0|            0|  0.00%|                self.__cond.wait(timeout)
(call)|         1|            0|            0|  0.00%|# /usr/lib/python2.7/threading.py:308 wait
[...]
   724|         0|            0|            0|  0.00%|    def start(self):
[...]
   748|         0|            0|            0|  0.00%|        self.__started.wait()
(call)|         1|            0|            0|  0.00%|# /usr/lib/python2.7/threading.py:600 wait
   749|         0|            0|            0|  0.00%|
   750|         0|            0|            0|  0.00%|    def run(self):
[...]
   760|         0|            0|            0|  0.00%|            if self.__target:
   761|         0|            0|            0|  0.00%|                self.__target(*self.__args, **self.__kwargs)
(call)|       192|            0|            0|  0.00%|# demo/threads.py:4 func
   762|         0|            0|            0|  0.00%|        finally:
[...]
   767|         0|            0|            0|  0.00%|    def __bootstrap(self):
[...]
   780|         0|            0|            0|  0.00%|        try:
   781|         0|            0|            0|  0.00%|            self.__bootstrap_inner()
(call)|       192|            0|            0|  0.00%|# /usr/lib/python2.7/threading.py:790 __bootstrap_inner
[...]
   790|         0|            0|            0|  0.00%|    def __bootstrap_inner(self):
[...]
   807|         0|            0|            0|  0.00%|            try:
   808|         0|            0|            0|  0.00%|                self.run()
(call)|       192|            0|            0|  0.00%|# /usr/lib/python2.7/threading.py:750 run

Some details are lost (not all executed lines have a non-null hit-count), but the hot spot is still easily identifiable in this trivial example, and its call stack is still visible.

Advanced

Warning: API described here may change as I get a better understanding of what is really needed (are file name + globals enough ? maybe the whole frame is needed ?).

Both classes can be sub-classed to customize file name generation. This is for example useful when profiling Zope's Python Scripts. The following can be used to allow profiling from restricted environment:

import pprofile
class ZopeProfiler(pprofile.Profile):
    __allow_access_to_unprotected_subobjects__ = 1
    def _getFilename(self, filename, f_globals):
        if 'Script (Python)' in filename and 'script' in f_globals:
            filename = f_globals['script'].id
        return filename

You will also want to monkey-patch linecache so that it becomes able to fetch source code from Python Scripts:

import linecache
linecache_getlines = linecache.getlines
def getlines(filename, module_globals=None):
    if module_globals is not None and \
            'Script (Python)' in filename and \
            'script' in module_globals:
        return module_globals['script'].body().splitlines()
    return linecache_getlines(filename, module_globals)
linecache.getlines = getlines

Of course, allowing such access from Restricted Python has security implications, depending on who has access to it. You decide and take responsibility.

Profiling such level of complex code as Zope (bonus points when profiling template rendering) is not an easy task. Tweak proposed ZopeProfiler class as you see fit for your profiling case - this is one of the reasons why no such implementation is proposed ready-to-use (I don't see a one-size-fits-all for this yet).

Thread-aware profiling

ThreadProfile class provides the same features are Profile, but uses threading.settrace to propagate tracing to threading.Thread threads started after profiling is enabled.

Limitations

The time spent in another thread is not discounted from interrupted line. On the long run, it should not be a problem if switches are evenly distributed among lines, but threads executing fewer lines will appear as eating more CPU time than they really do.

This is not specific to simultaneous multi-thread profiling: profiling a single thread of a multi-threaded application will also be polluted by time spent in other threads.

Example (lines are reported as taking longer to execute when profiled along with another thread - although the other thread is not profiled):

$ demo/embedded.py
Total duration: 1.00013s
File: demo/embedded.py
File duration: 1.00003s (99.99%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
     1|         0|            0|            0|  0.00%|#!/usr/bin/env python
     2|         0|            0|            0|  0.00%|import threading
     3|         0|            0|            0|  0.00%|import pprofile
     4|         0|            0|            0|  0.00%|import time
     5|         0|            0|            0|  0.00%|import sys
     6|         0|            0|            0|  0.00%|
     7|         1|   1.5974e-05|   1.5974e-05|  0.00%|def func():
     8|         0|            0|            0|  0.00%|  # Busy loop, so context switches happen
     9|         1|  1.40667e-05|  1.40667e-05|  0.00%|  end = time.time() + 1
    10|    146604|     0.511392|  3.48826e-06| 51.13%|  while time.time() < end:
    11|    146603|      0.48861|  3.33288e-06| 48.85%|    pass
    12|         0|            0|            0|  0.00%|
    13|         0|            0|            0|  0.00%|# Single-treaded run
    14|         0|            0|            0|  0.00%|prof = pprofile.Profile()
    15|         0|            0|            0|  0.00%|with prof:
    16|         0|            0|            0|  0.00%|  func()
(call)|         1|      1.00003|      1.00003| 99.99%|# ./demo/embedded.py:7 func
    17|         0|            0|            0|  0.00%|prof.annotate(sys.stdout, __file__)
    18|         0|            0|            0|  0.00%|
    19|         0|            0|            0|  0.00%|# Dual-threaded run
    20|         0|            0|            0|  0.00%|t1 = threading.Thread(target=func)
    21|         0|            0|            0|  0.00%|prof = pprofile.Profile()
    22|         0|            0|            0|  0.00%|with prof:
    23|         0|            0|            0|  0.00%|  t1.start()
    24|         0|            0|            0|  0.00%|  func()
    25|         0|            0|            0|  0.00%|  t1.join()
    26|         0|            0|            0|  0.00%|prof.annotate(sys.stdout, __file__)
Total duration: 1.00129s
File: demo/embedded.py
File duration: 1.00004s (99.88%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
[...]
     7|         1|  1.50204e-05|  1.50204e-05|  0.00%|def func():
     8|         0|            0|            0|  0.00%|  # Busy loop, so context switches happen
     9|         1|  2.38419e-05|  2.38419e-05|  0.00%|  end = time.time() + 1
    10|     64598|     0.538571|  8.33728e-06| 53.79%|  while time.time() < end:
    11|     64597|     0.461432|  7.14324e-06| 46.08%|    pass
[...]

This also means that the sum of the percentage of all lines can exceed 100%. It can reach the number of concurrent threads (200% with 2 threads being busy for the whole profiled execution time, etc).

Example with 3 threads (same as first example, this time with thread profiling enabled):

$ pprofile demo/threads.py
Command line: ['demo/threads.py']
Total duration: 1.00798s
File: demo/threads.py
File duration: 3.00604s (298.22%)
Line #|      Hits|         Time| Time per hit|      %|Source code
------+----------+-------------+-------------+-------+-----------
     1|         2|  3.21865e-05|  1.60933e-05|  0.00%|import threading
     2|         1|  6.91414e-06|  6.91414e-06|  0.00%|import time
     3|         0|            0|            0|  0.00%|
     4|         4|  3.91006e-05|  9.77516e-06|  0.00%|def func():
     5|         3|      3.00539|       1.0018|298.16%|  time.sleep(1)
     6|         0|            0|            0|  0.00%|
     7|         2|  2.31266e-05|  1.15633e-05|  0.00%|def func2():
     8|         1|  2.38419e-05|  2.38419e-05|  0.00%|  pass
     9|         0|            0|            0|  0.00%|
    10|         1|  1.81198e-05|  1.81198e-05|  0.00%|t1 = threading.Thread(target=func)
(call)|         1|  0.000612974|  0.000612974|  0.06%|# /usr/lib/python2.7/threading.py:436 __init__
    11|         1|  1.57356e-05|  1.57356e-05|  0.00%|t2 = threading.Thread(target=func)
(call)|         1|  0.000438213|  0.000438213|  0.04%|# /usr/lib/python2.7/threading.py:436 __init__
    12|         1|  6.60419e-05|  6.60419e-05|  0.01%|t1.start()
(call)|         1|  0.000913858|  0.000913858|  0.09%|# /usr/lib/python2.7/threading.py:485 start
    13|         1|   6.8903e-05|   6.8903e-05|  0.01%|t2.start()
(call)|         1|   0.00167513|   0.00167513|  0.17%|# /usr/lib/python2.7/threading.py:485 start
    14|         1|  0.000200272|  0.000200272|  0.02%|(func(), func2())
(call)|         1|      1.00274|      1.00274| 99.48%|# demo/threads.py:4 func
(call)|         1|  4.19617e-05|  4.19617e-05|  0.00%|# demo/threads.py:7 func2
    15|         1|  9.58443e-05|  9.58443e-05|  0.01%|t1.join()
(call)|         1|  0.000411987|  0.000411987|  0.04%|# /usr/lib/python2.7/threading.py:653 join
    16|         1|  5.29289e-05|  5.29289e-05|  0.01%|t2.join()
(call)|         1|  0.000316143|  0.000316143|  0.03%|# /usr/lib/python2.7/threading.py:653 join

Note that the call time is not added to file total: it's already accounted for inside "func".

 
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