Skip to content

C
cpython
Commit 0ba584c0 authored by Ezio Melotti's avatar Ezio Melotti
Browse files
Options

#6696: add documentation for the Profile objects, and improve profile/cProfile... 

#6696: add documentation for the Profile objects, and improve profile/cProfile docs.  Patch by Tom Pinckney.
parent 53dc4f01
Hide whitespace changes
Inline Side-by-side
Showing with 404 additions and 374 deletions
Doc/library/profile.rst
View file @ 0ba584c0
...@@ -4,11 +4,6 @@ ...@@ -4,11 +4,6 @@
The Python Profilers The Python Profilers
******************** ********************
.. sectionauthor:: James Roskind
.. module:: profile
:synopsis: Python source profiler.
**Source code:** :source:`Lib/profile.py` and :source:`Lib/pstats.py` **Source code:** :source:`Lib/profile.py` and :source:`Lib/pstats.py`
-------------- --------------
...@@ -22,34 +17,31 @@ Introduction to the profilers ...@@ -22,34 +17,31 @@ Introduction to the profilers
single: deterministic profiling single: deterministic profiling
single: profiling, deterministic single: profiling, deterministic
A :dfn:`profiler` is a program that describes the run time performance :mod:`cProfile` and :mod:`profile` provide :dfn:`deterministic profiling` of
of a program, providing a variety of statistics. This documentation Python programs. A :dfn:`profile` is a set of statistics that describes how
describes the profiler functionality provided in the modules often and for how long various parts of the program executed. These statistics
:mod:`cProfile`, :mod:`profile` and :mod:`pstats`. This profiler can be formatted into reports via the :mod:`pstats` module.
provides :dfn:`deterministic profiling` of Python programs. It also
provides a series of report generation tools to allow users to rapidly
examine the results of a profile operation.
The Python standard library provides three different profilers: The Python standard library provides three different implementations of the same
profiling interface:
#. :mod:`cProfile` is recommended for most users; it's a C extension 1. :mod:`cProfile` is recommended for most users; it's a C extension with
with reasonable overhead reasonable overhead that makes it suitable for profiling long-running
that makes it suitable for profiling long-running programs. programs. Based on :mod:`lsprof`, contributed by Brett Rosen and Ted
Based on :mod:`lsprof`, Czotter.
contributed by Brett Rosen and Ted Czotter.
.. versionadded:: 2.5 .. versionadded:: 2.5
#. :mod:`profile`, a pure Python module whose interface is imitated by 2. :mod:`profile`, a pure Python module whose interface is imitated by
:mod:`cProfile`. Adds significant overhead to profiled programs. :mod:`cProfile`, but which adds significant overhead to profiled programs.
If you're trying to extend If you're trying to extend the profiler in some way, the task might be easier
the profiler in some way, the task might be easier with this module. with this module.
.. versionchanged:: 2.4 .. versionchanged:: 2.4
Now also reports the time spent in calls to built-in functions Now also reports the time spent in calls to built-in functions
and methods. and methods.
#. :mod:`hotshot` was an experimental C module that focused on minimizing 3. :mod:`hotshot` was an experimental C module that focused on minimizing
the overhead of profiling, at the expense of longer data the overhead of profiling, at the expense of longer data
post-processing times. It is no longer maintained and may be post-processing times. It is no longer maintained and may be
dropped in a future version of Python. dropped in a future version of Python.
...@@ -66,6 +58,15 @@ is newer and might not be available on all systems. ...@@ -66,6 +58,15 @@ is newer and might not be available on all systems.
:mod:`_lsprof` module. The :mod:`hotshot` module is reserved for specialized :mod:`_lsprof` module. The :mod:`hotshot` module is reserved for specialized
usage. usage.
.. note::
The profiler modules are designed to provide an execution profile for a given
program, not for benchmarking purposes (for that, there is :mod:`timeit` for
reasonably accurate results). This particularly applies to benchmarking
Python code against C code: the profilers introduce overhead for Python code,
but not for C-level functions, and so the C code would seem faster than any
Python one.
.. _profile-instant: .. _profile-instant:
...@@ -76,57 +77,94 @@ This section is provided for users that "don't want to read the manual." It ...@@ -76,57 +77,94 @@ This section is provided for users that "don't want to read the manual." It
provides a very brief overview, and allows a user to rapidly perform profiling provides a very brief overview, and allows a user to rapidly perform profiling
on an existing application. on an existing application.
To profile an application with a main entry point of :func:`foo`, you would add To profile a function that takes a single argument, you can do::
the following to your module::
import cProfile import cProfile
cProfile.run('foo()') import re
cProfile.run('re.compile("foo|bar")')
(Use :mod:`profile` instead of :mod:`cProfile` if the latter is not available on (Use :mod:`profile` instead of :mod:`cProfile` if the latter is not available on
your system.) your system.)
The above action would cause :func:`foo` to be run, and a series of informative The above action would run :func:`re.compile` and print profile results like
lines (the profile) to be printed. The above approach is most useful when the following::
working with the interpreter. If you would like to save the results of a
profile into a file for later examination, you can supply a file name as the
second argument to the :func:`run` function::
import cProfile 197 function calls (192 primitive calls) in 0.002 seconds
cProfile.run('foo()', 'fooprof')
The file :file:`cProfile.py` can also be invoked as a script to profile another Ordered by: standard name
script. For example::
python -m cProfile myscript.py ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.001 0.001 <string>:1(<module>)
1 0.000 0.000 0.001 0.001 re.py:212(compile)
1 0.000 0.000 0.001 0.001 re.py:268(_compile)
1 0.000 0.000 0.000 0.000 sre_compile.py:172(_compile_charset)
1 0.000 0.000 0.000 0.000 sre_compile.py:201(_optimize_charset)
4 0.000 0.000 0.000 0.000 sre_compile.py:25(_identityfunction)
3/1 0.000 0.000 0.000 0.000 sre_compile.py:33(_compile)
:file:`cProfile.py` accepts two optional arguments on the command line:: The first line indicates that 197 calls were monitored. Of those calls, 192
were :dfn:`primitive`, meaning that the call was not induced via recursion. The
next line: ``Ordered by: standard name``, indicates that the text string in the
far right column was used to sort the output. The column headings include:
cProfile.py [-o output_file] [-s sort_order] ncalls
for the number of calls,
``-s`` only applies to standard output (``-o`` is not supplied). tottime
Look in the :class:`Stats` documentation for valid sort values. for the total time spent in the given function (and excluding time made in
calls to sub-functions)
When you wish to review the profile, you should use the methods in the percall
:mod:`pstats` module. Typically you would load the statistics data as follows:: is the quotient of ``tottime`` divided by ``ncalls``
import pstats cumtime
p = pstats.Stats('fooprof') is the cumulative time spent in this and all subfunctions (from invocation
till exit). This figure is accurate *even* for recursive functions.
The class :class:`Stats` (the above code just created an instance of this class) percall
has a variety of methods for manipulating and printing the data that was just is the quotient of ``cumtime`` divided by primitive calls
read into ``p``. When you ran :func:`cProfile.run` above, what was printed was
the result of three method calls::
p.strip_dirs().sort_stats(-1).print_stats() filename:lineno(function)
provides the respective data of each function
When there are two numbers in the first column (for example ``3/1``), it means
that the function recursed. The second value is the number of primitive calls
and the former is the total number of calls. Note that when the function does
not recurse, these two values are the same, and only the single figure is
printed.
The first method removed the extraneous path from all the module names. The Instead of printing the output at the end of the profile run, you can save the
second method sorted all the entries according to the standard module/line/name results to a file by specifying a filename to the :func:`run` function::
string that is printed. The third method printed out all the statistics. You
might try the following sort calls:
.. (this is to comply with the semantics of the old profiler). import cProfile
import re
cProfile.run('re.compile("foo|bar")', 'restats')
The :class:`pstats.Stats` class reads profile results from a file and formats
them in various ways.
The file :mod:`cProfile` can also be invoked as a script to profile another
script. For example::
python -m cProfile [-o output_file] [-s sort_order] myscript.py
``-o`` writes the profile results to a file instead of to stdout
:: ``-s`` specifies one of the :func:`~pstats.Stats.sort_stats` sort values to sort
the output by. This only applies when ``-o`` is not supplied.
The :mod:`pstats` module's :class:`~pstats.Stats` class has a variety of methods
for manipulating and printing the data saved into a profile results file::
import pstats
p = pstats.Stats('restats')
p.strip_dirs().sort_stats(-1).print_stats()
The :meth:`~pstats.Stats.strip_dirs` method removed the extraneous path from all
the module names. The :meth:`~pstats.Stats.sort_stats` method sorted all the
entries according to the standard module/line/name string that is printed. The
:meth:`~pstats.Stats.print_stats` method printed out all the statistics. You
might try the following sort calls::
p.sort_stats('name') p.sort_stats('name')
p.print_stats() p.print_stats()
...@@ -175,343 +213,338 @@ If you want more functionality, you're going to have to read the manual, or ...@@ -175,343 +213,338 @@ If you want more functionality, you're going to have to read the manual, or
guess what the following functions do:: guess what the following functions do::
p.print_callees() p.print_callees()
p.add('fooprof') p.add('restats')
Invoked as a script, the :mod:`pstats` module is a statistics browser for Invoked as a script, the :mod:`pstats` module is a statistics browser for
reading and examining profile dumps. It has a simple line-oriented interface reading and examining profile dumps. It has a simple line-oriented interface
(implemented using :mod:`cmd`) and interactive help. (implemented using :mod:`cmd`) and interactive help.
:mod:`profile` and :mod:`cProfile` Module Reference
=======================================================
.. _deterministic-profiling: .. module:: cProfile
.. module:: profile
:synopsis: Python source profiler.
What Is Deterministic Profiling? Both the :mod:`profile` and :mod:`cProfile` modules provide the following
================================ functions:
:dfn:`Deterministic profiling` is meant to reflect the fact that all *function .. function:: run(command, filename=None, sort=-1)
call*, *function return*, and *exception* events are monitored, and precise
timings are made for the intervals between these events (during which time the
user's code is executing). In contrast, :dfn:`statistical profiling` (which is
not done by this module) randomly samples the effective instruction pointer, and
deduces where time is being spent. The latter technique traditionally involves
less overhead (as the code does not need to be instrumented), but provides only
relative indications of where time is being spent.
In Python, since there is an interpreter active during execution, the presence This function takes a single argument that can be passed to the :func:`exec`
of instrumented code is not required to do deterministic profiling. Python function, and an optional file name. In all cases this routine executes::
automatically provides a :dfn:`hook` (optional callback) for each event. In
addition, the interpreted nature of Python tends to add so much overhead to
execution, that deterministic profiling tends to only add small processing
overhead in typical applications. The result is that deterministic profiling is
not that expensive, yet provides extensive run time statistics about the
execution of a Python program.
Call count statistics can be used to identify bugs in code (surprising counts), exec(command, __main__.__dict__, __main__.__dict__)
and to identify possible inline-expansion points (high call counts). Internal
time statistics can be used to identify "hot loops" that should be carefully
optimized. Cumulative time statistics should be used to identify high level
errors in the selection of algorithms. Note that the unusual handling of
cumulative times in this profiler allows statistics for recursive
implementations of algorithms to be directly compared to iterative
implementations.
and gathers profiling statistics from the execution. If no file name is
present, then this function automatically creates a :class:`~pstats.Stats`
instance and prints a simple profiling report. If the sort value is specified
it is passed to this :class:`~pstats.Stats` instance to control how the
results are sorted.
Reference Manual -- :mod:`profile` and :mod:`cProfile` .. function:: runctx(command, globals, locals, filename=None)
======================================================
.. module:: cProfile This function is similar to :func:`run`, with added arguments to supply the
:synopsis: Python profiler globals and locals dictionaries for the *command* string. This routine
executes::
exec(command, globals, locals)
The primary entry point for the profiler is the global function and gathers profiling statistics as in the :func:`run` function above.
:func:`profile.run` (resp. :func:`cProfile.run`). It is typically used to create
any profile information. The reports are formatted and printed using methods of
the class :class:`pstats.Stats`. The following is a description of all of these
standard entry points and functions. For a more in-depth view of some of the
code, consider reading the later section on Profiler Extensions, which includes
discussion of how to derive "better" profilers from the classes presented, or
reading the source code for these modules.
.. class:: Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)
.. function:: run(command[, filename][, sort]) This class is normally only used if more precise control over profiling is
needed than what the :func:`cProfile.run` function provides.
This function takes a single argument that can be passed to the A custom timer can be supplied for measuring how long code takes to run via
:keyword:`exec` statement, and optionally a file name and a sorting the *timer* argument. This must be a function that returns a single number
directive. In all cases this routine attempts to :keyword:`exec` representing the current time. If the number is an integer, the *timeunit*
its first argument, and gather profiling statistics from the specifies a multiplier that specifies the duration of each unit of time. For
execution. If no file name is present, then this function example, if the timer returns times measured in thousands of seconds, the
automatically prints a simple profiling report, sorted by the time unit would be ``.001``.
standard name string (file/line/function-name) that is presented in
each line. The following is a typical output from such a call::
2706 function calls (2004 primitive calls) in 4.504 CPU seconds Directly using the :class:`Profile` class allows formatting profile results
without writing the profile data to a file::
Ordered by: standard name import cProfile, pstats, io
pr = cProfile.Profile()
pr.enable()
... do something ...
pr.disable()
s = io.StringIO()
ps = pstats.Stats(pr, stream=s)
ps.print_results()
ncalls tottime percall cumtime percall filename:lineno(function) .. method:: enable()
2 0.006 0.003 0.953 0.477 pobject.py:75(save_objects)
43/3 0.533 0.012 0.749 0.250 pobject.py:99(evaluate)
...
The first line indicates that 2706 calls were monitored. Of those Start collecting profiling data.
calls, 2004 were :dfn:`primitive`. We define :dfn:`primitive` to
mean that the call was not induced via recursion. The next line:
``Ordered by: standard name``, indicates that the text string in
the far right column was used to sort the output. The column
headings include:
ncalls .. method:: disable()
for the number of calls,
tottime Stop collecting profiling data.
for the total time spent in the given function (and
excluding time made in calls to sub-functions),
percall .. method:: create_stats()
is the quotient of ``tottime`` divided by ``ncalls``
cumtime Stop collecting profiling data and record the results internally
is the total time spent in this and all subfunctions (from invocation till as the current profile.
exit). This figure is accurate *even* for recursive functions.
percall .. method:: print_stats(sort=-1)
is the quotient of ``cumtime`` divided by primitive calls
filename:lineno(function) Create a :class:`~pstats.Stats` object based on the current
provides the respective data of each function profile and print the results to stdout.
When there are two numbers in the first column (for example, .. method:: dump_stats(filename)
``43/3``), then the latter is the number of primitive calls, and
the former is the actual number of calls. Note that when the
function does not recurse, these two values are the same, and only
the single figure is printed. For information on the sort
argument, refer to :meth:`pstats.Stats.sort_stats`.
Write the results of the current profile to *filename*.
.. function:: runctx(command, globals, locals[, filename]) .. method:: run(cmd)
This function is similar to :func:`run`, with added arguments to supply the Profile the cmd via :func:`exec`.
globals and locals dictionaries for the *command* string.
Analysis of the profiler data is done using the :class:`Stats` class. .. method:: runctx(cmd, globals, locals)
.. note:: Profile the cmd via :func:`exec` with the specified global and
local environment.
.. method:: runcall(func, *args, **kwargs)
Profile ``func(*args, **kwargs)``
.. _profile-stats:
The :class:`Stats` class is defined in the :mod:`pstats` module. The :class:`Stats` Class
========================
Analysis of the profiler data is done using the :class:`~pstats.Stats` class.
.. module:: pstats .. module:: pstats
:synopsis: Statistics object for use with the profiler. :synopsis: Statistics object for use with the profiler.
.. class:: Stats(*filenames or profile, stream=sys.stdout)
.. class:: Stats(filename, stream=sys.stdout[, ...]) This class constructor creates an instance of a "statistics object" from a
*filename* (or list of filenames) or from a :class:`Profile` instance. Output
will be printed to the stream specified by *stream*.
This class constructor creates an instance of a "statistics object" The file selected by the above constructor must have been created by the
from a *filename* (or set of filenames). :class:`Stats` objects corresponding version of :mod:`profile` or :mod:`cProfile`. To be specific,
are manipulated by methods, in order to print useful reports. You there is *no* file compatibility guaranteed with future versions of this
may specify an alternate output stream by giving the keyword profiler, and there is no compatibility with files produced by other
argument, ``stream``. profilers. If several files are provided, all the statistics for identical
functions will be coalesced, so that an overall view of several processes can
be considered in a single report. If additional files need to be combined
with data in an existing :class:`~pstats.Stats` object, the
:meth:`~pstats.Stats.add` method can be used.
The file selected by the above constructor must have been created Instead of reading the profile data from a file, a :class:`cProfile.Profile`
by the corresponding version of :mod:`profile` or :mod:`cProfile`. or :class:`profile.Profile` object can be used as the profile data source.
To be specific, there is *no* file compatibility guaranteed with
future versions of this profiler, and there is no compatibility
with files produced by other profilers. If several files are
provided, all the statistics for identical functions will be
coalesced, so that an overall view of several processes can be
considered in a single report. If additional files need to be
combined with data in an existing :class:`Stats` object, the
:meth:`add` method can be used.
.. (such as the old system profiler). :class:`Stats` objects have the following methods:
.. versionchanged:: 2.5 .. method:: strip_dirs()
The *stream* parameter was added.
This method for the :class:`Stats` class removes all leading path
information from file names. It is very useful in reducing the size of
the printout to fit within (close to) 80 columns. This method modifies
the object, and the stripped information is lost. After performing a
strip operation, the object is considered to have its entries in a
"random" order, as it was just after object initialization and loading.
If :meth:`~pstats.Stats.strip_dirs` causes two function names to be
indistinguishable (they are on the same line of the same filename, and
have the same function name), then the statistics for these two entries
are accumulated into a single entry.
.. _profile-stats:
The :class:`Stats` Class .. method:: add(*filenames)
------------------------
:class:`Stats` objects have the following methods: This method of the :class:`Stats` class accumulates additional profiling
information into the current profiling object. Its arguments should refer
to filenames created by the corresponding version of :func:`profile.run`
or :func:`cProfile.run`. Statistics for identically named (re: file, line,
name) functions are automatically accumulated into single function
statistics.
.. method:: Stats.strip_dirs() .. method:: dump_stats(filename)
This method for the :class:`Stats` class removes all leading path Save the data loaded into the :class:`Stats` object to a file named
information from file names. It is very useful in reducing the *filename*. The file is created if it does not exist, and is overwritten
size of the printout to fit within (close to) 80 columns. This if it already exists. This is equivalent to the method of the same name
method modifies the object, and the stripped information is lost. on the :class:`profile.Profile` and :class:`cProfile.Profile` classes.
After performing a strip operation, the object is considered to
have its entries in a "random" order, as it was just after object
initialization and loading. If :meth:`strip_dirs` causes two
function names to be indistinguishable (they are on the same line
of the same filename, and have the same function name), then the
statistics for these two entries are accumulated into a single
entry.
.. versionadded:: 2.3
.. method:: Stats.add(filename[, ...])
This method of the :class:`Stats` class accumulates additional profiling .. method:: sort_stats(*keys)
information into the current profiling object. Its arguments should refer to
filenames created by the corresponding version of :func:`profile.run` or This method modifies the :class:`Stats` object by sorting it according to
:func:`cProfile.run`. Statistics for identically named (re: file, line, name) the supplied criteria. The argument is typically a string identifying the
functions are automatically accumulated into single function statistics. basis of a sort (example: ``'time'`` or ``'name'``).
When more than one key is provided, then additional keys are used as
secondary criteria when there is equality in all keys selected before
them. For example, ``sort_stats('name', 'file')`` will sort all the
entries according to their function name, and resolve all ties (identical
function names) by sorting by file name.
Abbreviations can be used for any key names, as long as the abbreviation
is unambiguous. The following are the keys currently defined:
+------------------+----------------------+
| Valid Arg | Meaning |
+==================+======================+
| ``'calls'`` | call count |
+------------------+----------------------+
| ``'cumulative'`` | cumulative time |
+------------------+----------------------+
| ``'cumtime'`` | cumulative time |
+------------------+----------------------+
| ``'file'`` | file name |
+------------------+----------------------+
| ``'filename'`` | file name |
+------------------+----------------------+
| ``'module'`` | file name |
+------------------+----------------------+
| ``'ncalls'`` | call count |
+------------------+----------------------+
| ``'pcalls'`` | primitive call count |
+------------------+----------------------+
| ``'line'`` | line number |
+------------------+----------------------+
| ``'name'`` | function name |
+------------------+----------------------+
| ``'nfl'`` | name/file/line |
+------------------+----------------------+
| ``'stdname'`` | standard name |
+------------------+----------------------+
| ``'time'`` | internal time |
+------------------+----------------------+
| ``'tottime'`` | internal time |
+------------------+----------------------+
Note that all sorts on statistics are in descending order (placing most
time consuming items first), where as name, file, and line number searches
are in ascending order (alphabetical). The subtle distinction between
``'nfl'`` and ``'stdname'`` is that the standard name is a sort of the
name as printed, which means that the embedded line numbers get compared
in an odd way. For example, lines 3, 20, and 40 would (if the file names
were the same) appear in the string order 20, 3 and 40. In contrast,
``'nfl'`` does a numeric compare of the line numbers. In fact,
``sort_stats('nfl')`` is the same as ``sort_stats('name', 'file',
'line')``.
For backward-compatibility reasons, the numeric arguments ``-1``, ``0``,
``1``, and ``2`` are permitted. They are interpreted as ``'stdname'``,
``'calls'``, ``'time'``, and ``'cumulative'`` respectively. If this old
style format (numeric) is used, only one sort key (the numeric key) will
be used, and additional arguments will be silently ignored.
.. For compatibility with the old profiler.
.. method:: reverse_order()
This method for the :class:`Stats` class reverses the ordering of the
basic list within the object. Note that by default ascending vs
descending order is properly selected based on the sort key of choice.
.. This method is provided primarily for compatibility with the old
profiler.
.. method:: print_stats(*restrictions)
This method for the :class:`Stats` class prints out a report as described
in the :func:`profile.run` definition.
The order of the printing is based on the last
:meth:`~pstats.Stats.sort_stats` operation done on the object (subject to
caveats in :meth:`~pstats.Stats.add` and
:meth:`~pstats.Stats.strip_dirs`).
The arguments provided (if any) can be used to limit the list down to the
significant entries. Initially, the list is taken to be the complete set
of profiled functions. Each restriction is either an integer (to select a
count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to
select a percentage of lines), or a regular expression (to pattern match
the standard name that is printed. If several restrictions are provided,
then they are applied sequentially. For example::
print_stats(.1, 'foo:')
would first limit the printing to first 10% of list, and then only print
functions that were part of filename :file:`.\*foo:`. In contrast, the
command::
print_stats('foo:', .1)
would limit the list to all functions having file names :file:`.\*foo:`,
and then proceed to only print the first 10% of them.
.. method:: print_callers(*restrictions)
This method for the :class:`Stats` class prints a list of all functions
that called each function in the profiled database. The ordering is
identical to that provided by :meth:`~pstats.Stats.print_stats`, and the
definition of the restricting argument is also identical. Each caller is
reported on its own line. The format differs slightly depending on the
profiler that produced the stats:
* With :mod:`profile`, a number is shown in parentheses after each caller
to show how many times this specific call was made. For convenience, a
second non-parenthesized number repeats the cumulative time spent in the
function at the right.
* With :mod:`cProfile`, each caller is preceded by three numbers: the
number of times this specific call was made, and the total and
cumulative times spent in the current function while it was invoked by
this specific caller.
.. method:: print_callees(*restrictions)
This method for the :class:`Stats` class prints a list of all function
that were called by the indicated function. Aside from this reversal of
direction of calls (re: called vs was called by), the arguments and
ordering are identical to the :meth:`~pstats.Stats.print_callers` method.
.. method:: Stats.dump_stats(filename)
Save the data loaded into the :class:`Stats` object to a file named .. _deterministic-profiling:
*filename*. The file is created if it does not exist, and is
overwritten if it already exists. This is equivalent to the method
of the same name on the :class:`profile.Profile` and
:class:`cProfile.Profile` classes.
.. versionadded:: 2.3 What Is Deterministic Profiling?
================================
:dfn:`Deterministic profiling` is meant to reflect the fact that all *function
call*, *function return*, and *exception* events are monitored, and precise
timings are made for the intervals between these events (during which time the
user's code is executing). In contrast, :dfn:`statistical profiling` (which is
not done by this module) randomly samples the effective instruction pointer, and
deduces where time is being spent. The latter technique traditionally involves
less overhead (as the code does not need to be instrumented), but provides only
relative indications of where time is being spent.
.. method:: Stats.sort_stats(key[, ...]) In Python, since there is an interpreter active during execution, the presence
of instrumented code is not required to do deterministic profiling. Python
This method modifies the :class:`Stats` object by sorting it automatically provides a :dfn:`hook` (optional callback) for each event. In
according to the supplied criteria. The argument is typically a addition, the interpreted nature of Python tends to add so much overhead to
string identifying the basis of a sort (example: ``'time'`` or execution, that deterministic profiling tends to only add small processing
``'name'``). overhead in typical applications. The result is that deterministic profiling is
not that expensive, yet provides extensive run time statistics about the
When more than one key is provided, then additional keys are used execution of a Python program.
as secondary criteria when there is equality in all keys selected
before them. For example, ``sort_stats('name', 'file')`` will sort
all the entries according to their function name, and resolve all
ties (identical function names) by sorting by file name.
Abbreviations can be used for any key names, as long as the abbreviation is
unambiguous. The following are the keys currently defined:
+------------------+----------------------+
| Valid Arg | Meaning |
+==================+======================+
| ``'calls'`` | call count |
+------------------+----------------------+
| ``'cumulative'`` | cumulative time |
+------------------+----------------------+
| ``'cumtime'`` | cumulative time |
+------------------+----------------------+
| ``'file'`` | file name |
+------------------+----------------------+
| ``'filename'`` | file name |
+------------------+----------------------+
| ``'module'`` | file name |
+------------------+----------------------+
| ``'ncalls'`` | call count |
+------------------+----------------------+
| ``'pcalls'`` | primitive call count |
+------------------+----------------------+
| ``'line'`` | line number |
+------------------+----------------------+
| ``'name'`` | function name |
+------------------+----------------------+
| ``'nfl'`` | name/file/line |
+------------------+----------------------+
| ``'stdname'`` | standard name |
+------------------+----------------------+
| ``'time'`` | internal time |
+------------------+----------------------+
| ``'tottime'`` | internal time |
+------------------+----------------------+
Note that all sorts on statistics are in descending order (placing
most time consuming items first), where as name, file, and line
number searches are in ascending order (alphabetical). The subtle
distinction between ``'nfl'`` and ``'stdname'`` is that the
standard name is a sort of the name as printed, which means that
the embedded line numbers get compared in an odd way. For example,
lines 3, 20, and 40 would (if the file names were the same) appear
in the string order 20, 3 and 40. In contrast, ``'nfl'`` does a
numeric compare of the line numbers. In fact,
``sort_stats('nfl')`` is the same as ``sort_stats('name', 'file',
'line')``.
For backward-compatibility reasons, the numeric arguments ``-1``,
``0``, ``1``, and ``2`` are permitted. They are interpreted as
``'stdname'``, ``'calls'``, ``'time'``, and ``'cumulative'``
respectively. If this old style format (numeric) is used, only one
sort key (the numeric key) will be used, and additional arguments
will be silently ignored.
.. For compatibility with the old profiler,
.. method:: Stats.reverse_order()
This method for the :class:`Stats` class reverses the ordering of
the basic list within the object. Note that by default ascending
vs descending order is properly selected based on the sort key of
choice.
.. This method is provided primarily for compatibility with the old profiler.
.. method:: Stats.print_stats([restriction, ...])
This method for the :class:`Stats` class prints out a report as
described in the :func:`profile.run` definition.
The order of the printing is based on the last :meth:`sort_stats`
operation done on the object (subject to caveats in :meth:`add` and
:meth:`strip_dirs`).
The arguments provided (if any) can be used to limit the list down
to the significant entries. Initially, the list is taken to be the
complete set of profiled functions. Each restriction is either an
integer (to select a count of lines), or a decimal fraction between
0.0 and 1.0 inclusive (to select a percentage of lines), or a
regular expression (to pattern match the standard name that is
printed; as of Python 1.5b1, this uses the Perl-style regular
expression syntax defined by the :mod:`re` module). If several
restrictions are provided, then they are applied sequentially. For
example::
print_stats(.1, 'foo:')
would first limit the printing to first 10% of list, and then only print
functions that were part of filename :file:`.\*foo:`. In contrast, the
command::
print_stats('foo:', .1)
would limit the list to all functions having file names :file:`.\*foo:`, and
then proceed to only print the first 10% of them.
.. method:: Stats.print_callers([restriction, ...])
This method for the :class:`Stats` class prints a list of all functions that
called each function in the profiled database. The ordering is identical to
that provided by :meth:`print_stats`, and the definition of the restricting
argument is also identical. Each caller is reported on its own line. The
format differs slightly depending on the profiler that produced the stats:
* With :mod:`profile`, a number is shown in parentheses after each caller to
show how many times this specific call was made. For convenience, a second
non-parenthesized number repeats the cumulative time spent in the function
at the right.
* With :mod:`cProfile`, each caller is preceded by three numbers:
the number of times this specific call was made, and the total
and cumulative times spent in the current function while it was
invoked by this specific caller.
.. method:: Stats.print_callees([restriction, ...])
This method for the :class:`Stats` class prints a list of all Call count statistics can be used to identify bugs in code (surprising counts),
function that were called by the indicated function. Aside from and to identify possible inline-expansion points (high call counts). Internal
this reversal of direction of calls (re: called vs was called by), time statistics can be used to identify "hot loops" that should be carefully
the arguments and ordering are identical to the optimized. Cumulative time statistics should be used to identify high level
:meth:`print_callers` method. errors in the selection of algorithms. Note that the unusual handling of
cumulative times in this profiler allows statistics for recursive
implementations of algorithms to be directly compared to iterative
implementations.
.. _profile-limits: .. _profile-limitations:
Limitations Limitations
=========== ===========
...@@ -554,7 +587,7 @@ The profiler of the :mod:`profile` module subtracts a constant from each event ...@@ -554,7 +587,7 @@ The profiler of the :mod:`profile` module subtracts a constant from each event
handling time to compensate for the overhead of calling the time function, and handling time to compensate for the overhead of calling the time function, and
socking away the results. By default, the constant is 0. The following socking away the results. By default, the constant is 0. The following
procedure can be used to obtain a better constant for a given platform (see procedure can be used to obtain a better constant for a given platform (see
discussion in section Limitations above). :: :ref:`profile-limitations`). ::
import profile import profile
pr = profile.Profile() pr = profile.Profile()
...@@ -564,8 +597,8 @@ discussion in section Limitations above). :: ...@@ -564,8 +597,8 @@ discussion in section Limitations above). ::
The method executes the number of Python calls given by the argument, directly The method executes the number of Python calls given by the argument, directly
and again under the profiler, measuring the time for both. It then computes the and again under the profiler, measuring the time for both. It then computes the
hidden overhead per profiler event, and returns that as a float. For example, hidden overhead per profiler event, and returns that as a float. For example,
on an 800 MHz Pentium running Windows 2000, and using Python's time.clock() as on a 1.8Ghz Intel Core i5 running Mac OS X, and using Python's time.clock() as
the timer, the magical number is about 12.5e-6. the timer, the magical number is about 4.04e-6.
The object of this exercise is to get a fairly consistent result. If your The object of this exercise is to get a fairly consistent result. If your
computer is *very* fast, or your timer function has poor resolution, you might computer is *very* fast, or your timer function has poor resolution, you might
...@@ -588,57 +621,50 @@ When you have a consistent answer, there are three ways you can use it: [#]_ :: ...@@ -588,57 +621,50 @@ When you have a consistent answer, there are three ways you can use it: [#]_ ::
If you have a choice, you are better off choosing a smaller constant, and then If you have a choice, you are better off choosing a smaller constant, and then
your results will "less often" show up as negative in profile statistics. your results will "less often" show up as negative in profile statistics.
.. _profile-timers:
.. _profiler-extensions: Using a customer timer
======================
Extensions --- Deriving Better Profilers
========================================
The :class:`Profile` class of both modules, :mod:`profile` and :mod:`cProfile`,
were written so that derived classes could be developed to extend the profiler.
The details are not described here, as doing this successfully requires an
expert understanding of how the :class:`Profile` class works internally. Study
the source code of the module carefully if you want to pursue this.
If all you want to do is change how current time is determined (for example, to If you want to change how current time is determined (for example, to force use
force use of wall-clock time or elapsed process time), pass the timing function of wall-clock time or elapsed process time), pass the timing function you want
you want to the :class:`Profile` class constructor:: to the :class:`Profile` class constructor::
pr = profile.Profile(your_time_func) pr = profile.Profile(your_time_func)
The resulting profiler will then call :func:`your_time_func`. The resulting profiler will then call ``your_time_func``. Depending on whether
you are using :class:`profile.Profile` or :class:`cProfile.Profile`,
``your_time_func``'s return value will be interpreted differently:
:class:`profile.Profile` :class:`profile.Profile`
:func:`your_time_func` should return a single number, or a list of ``your_time_func`` should return a single number, or a list of numbers whose
numbers whose sum is the current time (like what :func:`os.times` sum is the current time (like what :func:`os.times` returns). If the
returns). If the function returns a single time number, or the function returns a single time number, or the list of returned numbers has
list of returned numbers has length 2, then you will get an length 2, then you will get an especially fast version of the dispatch
especially fast version of the dispatch routine. routine.
Be warned that you should calibrate the profiler class for the Be warned that you should calibrate the profiler class for the timer function
timer function that you choose. For most machines, a timer that that you choose (see :ref:`profile-calibration`). For most machines, a timer
returns a lone integer value will provide the best results in terms that returns a lone integer value will provide the best results in terms of
of low overhead during profiling. (:func:`os.times` is *pretty* low overhead during profiling. (:func:`os.times` is *pretty* bad, as it
bad, as it returns a tuple of floating point values). If you want returns a tuple of floating point values). If you want to substitute a
to substitute a better timer in the cleanest fashion, derive a better timer in the cleanest fashion, derive a class and hardwire a
class and hardwire a replacement dispatch method that best handles replacement dispatch method that best handles your timer call, along with the
your timer call, along with the appropriate calibration constant. appropriate calibration constant.
:class:`cProfile.Profile` :class:`cProfile.Profile`
:func:`your_time_func` should return a single number. If it ``your_time_func`` should return a single number. If it returns integers,
returns plain integers, you can also invoke the class constructor you can also invoke the class constructor with a second argument specifying
with a second argument specifying the real duration of one unit of the real duration of one unit of time. For example, if
time. For example, if :func:`your_integer_time_func` returns times ``your_integer_time_func`` returns times measured in thousands of seconds,
measured in thousands of seconds, you would construct the you would construct the :class:`Profile` instance as follows::
:class:`Profile` instance as follows::
pr = cProfile.Profile(your_integer_time_func, 0.001)
pr = profile.Profile(your_integer_time_func, 0.001)
As the :mod:`cProfile.Profile` class cannot be calibrated, custom timer
As the :mod:`cProfile.Profile` class cannot be calibrated, custom functions should be used with care and should be as fast as possible. For
timer functions should be used with care and should be as fast as the best results with a custom timer, it might be necessary to hard-code it
possible. For the best results with a custom timer, it might be in the C source of the internal :mod:`_lsprof` module.
necessary to hard-code it in the C source of the internal
:mod:`_lsprof` module.
.. rubric:: Footnotes .. rubric:: Footnotes
......
Misc/ACKS
View file @ 0ba584c0
...@@ -791,6 +791,7 @@ Jim St. Pierre ...@@ -791,6 +791,7 @@ Jim St. Pierre
Dan Pierson Dan Pierson
Martijn Pieters Martijn Pieters
François Pinard François Pinard
Tom Pinckney
Zach Pincus Zach Pincus
Michael Piotrowski Michael Piotrowski
Antoine Pitrou Antoine Pitrou
......
Misc/NEWS
View file @ 0ba584c0
...@@ -65,6 +65,9 @@ Documentation ...@@ -65,6 +65,9 @@ Documentation
- Issue #15940: Specify effect of locale on time functions. - Issue #15940: Specify effect of locale on time functions.
- Issue #6696: add documentation for the Profile objects, and improve
profile/cProfile docs. Patch by Tom Pinckney.
What's New in Python 2.7.4? What's New in Python 2.7.4?
=========================== ===========================
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment
GitLab Nexedi Edition | About GitLab | About Nexedi | 沪ICP备2021021310号-2 | 沪ICP备2021021310号-7