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#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.
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Doc/library/profile.rst
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......@@ -4,11 +4,6 @@
The Python Profilers
********************
.. sectionauthor:: James Roskind
.. module:: profile
:synopsis: Python source profiler.
**Source code:** :source:`Lib/profile.py` and :source:`Lib/pstats.py`
--------------
......@@ -22,34 +17,31 @@ Introduction to the profilers
single: deterministic profiling
single: profiling, deterministic
A :dfn:`profiler` is a program that describes the run time performance
of a program, providing a variety of statistics. This documentation
describes the profiler functionality provided in the modules
:mod:`cProfile`, :mod:`profile` and :mod:`pstats`. This profiler
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.
:mod:`cProfile` and :mod:`profile` provide :dfn:`deterministic profiling` of
Python programs. A :dfn:`profile` is a set of statistics that describes how
often and for how long various parts of the program executed. These statistics
can be formatted into reports via the :mod:`pstats` module.
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
with reasonable overhead
that makes it suitable for profiling long-running programs.
Based on :mod:`lsprof`,
contributed by Brett Rosen and Ted Czotter.
1. :mod:`cProfile` is recommended for most users; it's a C extension with
reasonable overhead that makes it suitable for profiling long-running
programs. Based on :mod:`lsprof`, contributed by Brett Rosen and Ted
Czotter.
.. versionadded:: 2.5
#. :mod:`profile`, a pure Python module whose interface is imitated by
:mod:`cProfile`. Adds significant overhead to profiled programs.
If you're trying to extend
the profiler in some way, the task might be easier with this module.
2. :mod:`profile`, a pure Python module whose interface is imitated by
:mod:`cProfile`, but which adds significant overhead to profiled programs.
If you're trying to extend the profiler in some way, the task might be easier
with this module.
.. versionchanged:: 2.4
Now also reports the time spent in calls to built-in functions
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
post-processing times. It is no longer maintained and may be
dropped in a future version of Python.
......@@ -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
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:
......@@ -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
on an existing application.
To profile an application with a main entry point of :func:`foo`, you would add
the following to your module::
To profile a function that takes a single argument, you can do::
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
your system.)
The above action would cause :func:`foo` to be run, and a series of informative
lines (the profile) to be printed. The above approach is most useful when
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::
The above action would run :func:`re.compile` and print profile results like
the following::
import cProfile
cProfile.run('foo()', 'fooprof')
197 function calls (192 primitive calls) in 0.002 seconds
The file :file:`cProfile.py` can also be invoked as a script to profile another
script. For example::
Ordered by: standard name
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)
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:
ncalls
for the number of calls,
tottime
for the total time spent in the given function (and excluding time made in
calls to sub-functions)
python -m cProfile myscript.py
percall
is the quotient of ``tottime`` divided by ``ncalls``
:file:`cProfile.py` accepts two optional arguments on the command line::
cumtime
is the cumulative time spent in this and all subfunctions (from invocation
till exit). This figure is accurate *even* for recursive functions.
cProfile.py [-o output_file] [-s sort_order]
percall
is the quotient of ``cumtime`` divided by primitive calls
``-s`` only applies to standard output (``-o`` is not supplied).
Look in the :class:`Stats` documentation for valid sort values.
filename:lineno(function)
provides the respective data of each function
When you wish to review the profile, you should use the methods in the
:mod:`pstats` module. Typically you would load the statistics data as follows::
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.
import pstats
p = pstats.Stats('fooprof')
Instead of printing the output at the end of the profile run, you can save the
results to a file by specifying a filename to the :func:`run` function::
The class :class:`Stats` (the above code just created an instance of this class)
has a variety of methods for manipulating and printing the data that was just
read into ``p``. When you ran :func:`cProfile.run` above, what was printed was
the result of three method calls::
import cProfile
import re
cProfile.run('re.compile("foo|bar")', 'restats')
p.strip_dirs().sort_stats(-1).print_stats()
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
The first method removed the extraneous path from all the module names. The
second method sorted all the entries according to the standard module/line/name
string that is printed. The third method printed out all the statistics. You
might try the following sort calls:
``-o`` writes the profile results to a file instead of to stdout
.. (this is to comply with the semantics of the old profiler).
``-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.print_stats()
......@@ -175,217 +213,183 @@ If you want more functionality, you're going to have to read the manual, or
guess what the following functions do::
p.print_callees()
p.add('fooprof')
p.add('restats')
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
(implemented using :mod:`cmd`) and interactive help.
:mod:`profile` and :mod:`cProfile` Module Reference
=======================================================
.. _deterministic-profiling:
What Is Deterministic Profiling?
================================
.. module:: cProfile
.. module:: profile
:synopsis: Python source profiler.
: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.
Both the :mod:`profile` and :mod:`cProfile` modules provide the following
functions:
In Python, since there is an interpreter active during execution, the presence
of instrumented code is not required to do deterministic profiling. Python
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.
.. function:: run(command, filename=None, sort=-1)
Call count statistics can be used to identify bugs in code (surprising counts),
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.
This function takes a single argument that can be passed to the :func:`exec`
function, and an optional file name. In all cases this routine executes::
exec(command, __main__.__dict__, __main__.__dict__)
Reference Manual -- :mod:`profile` and :mod:`cProfile`
======================================================
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.
.. module:: cProfile
:synopsis: Python profiler
.. function:: runctx(command, globals, locals, filename=None)
This function is similar to :func:`run`, with added arguments to supply the
globals and locals dictionaries for the *command* string. This routine
executes::
The primary entry point for the profiler is the global function
: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.
exec(command, globals, locals)
and gathers profiling statistics as in the :func:`run` function above.
.. function:: run(command[, filename][, sort])
.. class:: Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)
This function takes a single argument that can be passed to the
:keyword:`exec` statement, and optionally a file name and a sorting
directive. In all cases this routine attempts to :keyword:`exec`
its first argument, and gather profiling statistics from the
execution. If no file name is present, then this function
automatically prints a simple profiling report, sorted by the
standard name string (file/line/function-name) that is presented in
each line. The following is a typical output from such a call::
This class is normally only used if more precise control over profiling is
needed than what the :func:`cProfile.run` function provides.
2706 function calls (2004 primitive calls) in 4.504 CPU seconds
A custom timer can be supplied for measuring how long code takes to run via
the *timer* argument. This must be a function that returns a single number
representing the current time. If the number is an integer, the *timeunit*
specifies a multiplier that specifies the duration of each unit of time. For
example, if the timer returns times measured in thousands of seconds, the
time unit would be ``.001``.
Ordered by: standard name
Directly using the :class:`Profile` class allows formatting profile results
without writing the profile data to a file::
ncalls tottime percall cumtime percall filename:lineno(function)
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
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
for the number of calls,
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()
tottime
for the total time spent in the given function (and
excluding time made in calls to sub-functions),
.. method:: enable()
percall
is the quotient of ``tottime`` divided by ``ncalls``
Start collecting profiling data.
cumtime
is the total time spent in this and all subfunctions (from invocation till
exit). This figure is accurate *even* for recursive functions.
.. method:: disable()
percall
is the quotient of ``cumtime`` divided by primitive calls
Stop collecting profiling data.
filename:lineno(function)
provides the respective data of each function
.. method:: create_stats()
When there are two numbers in the first column (for example,
``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`.
Stop collecting profiling data and record the results internally
as the current profile.
.. method:: print_stats(sort=-1)
.. function:: runctx(command, globals, locals[, filename])
Create a :class:`~pstats.Stats` object based on the current
profile and print the results to stdout.
This function is similar to :func:`run`, with added arguments to supply the
globals and locals dictionaries for the *command* string.
.. method:: dump_stats(filename)
Analysis of the profiler data is done using the :class:`Stats` class.
Write the results of the current profile to *filename*.
.. note::
.. method:: run(cmd)
The :class:`Stats` class is defined in the :mod:`pstats` module.
Profile the cmd via :func:`exec`.
.. method:: runctx(cmd, globals, locals)
.. module:: pstats
:synopsis: Statistics object for use with the profiler.
Profile the cmd via :func:`exec` with the specified global and
local environment.
.. method:: runcall(func, *args, **kwargs)
.. class:: Stats(filename, stream=sys.stdout[, ...])
Profile ``func(*args, **kwargs)``
This class constructor creates an instance of a "statistics object"
from a *filename* (or set of filenames). :class:`Stats` objects
are manipulated by methods, in order to print useful reports. You
may specify an alternate output stream by giving the keyword
argument, ``stream``.
.. _profile-stats:
The file selected by the above constructor must have been created
by the corresponding version of :mod:`profile` or :mod:`cProfile`.
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.
The :class:`Stats` Class
========================
.. (such as the old system profiler).
Analysis of the profiler data is done using the :class:`~pstats.Stats` class.
.. versionchanged:: 2.5
The *stream* parameter was added.
.. module:: pstats
:synopsis: Statistics object for use with the profiler.
.. class:: Stats(*filenames or profile, stream=sys.stdout)
.. _profile-stats:
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*.
The :class:`Stats` Class
------------------------
The file selected by the above constructor must have been created by the
corresponding version of :mod:`profile` or :mod:`cProfile`. 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:`~pstats.Stats` object, the
:meth:`~pstats.Stats.add` method can be used.
:class:`Stats` objects have the following methods:
Instead of reading the profile data from a file, a :class:`cProfile.Profile`
or :class:`profile.Profile` object can be used as the profile data source.
:class:`Stats` objects have the following methods:
.. method:: Stats.strip_dirs()
.. method:: strip_dirs()
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:`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.
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.
.. method:: Stats.add(filename[, ...])
.. method:: add(*filenames)
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.
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.dump_stats(filename)
.. method:: dump_stats(filename)
Save the data loaded into the :class:`Stats` object to a file named
*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.
*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
.. method:: Stats.sort_stats(key[, ...])
.. method:: sort_stats(*keys)
This method modifies the :class:`Stats` object by sorting it
according to the supplied criteria. The argument is typically a
string identifying the basis of a sort (example: ``'time'`` or
``'name'``).
This method modifies the :class:`Stats` object by sorting it according to
the supplied criteria. The argument is typically a string identifying the
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.
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:
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 |
......@@ -419,57 +423,53 @@ The :class:`Stats` Class
| ``'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,
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 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,
.. For compatibility with the old profiler.
.. method:: Stats.reverse_order()
.. 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 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.
.. This method is provided primarily for compatibility with the old
profiler.
.. method:: Stats.print_stats([restriction, ...])
.. method:: print_stats(*restrictions)
This method for the :class:`Stats` class prints out a report as
described in the :func:`profile.run` definition.
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 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; 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::
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:')
......@@ -479,39 +479,72 @@ The :class:`Stats` Class
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.
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, ...])
.. 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:`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:
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:`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.
.. _deterministic-profiling:
* 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.
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.print_callees([restriction, ...])
In Python, since there is an interpreter active during execution, the presence
of instrumented code is not required to do deterministic profiling. Python
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.
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:`print_callers` method.
Call count statistics can be used to identify bugs in code (surprising counts),
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.
.. _profile-limits:
.. _profile-limitations:
Limitations
===========
......@@ -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
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
discussion in section Limitations above). ::
:ref:`profile-limitations`). ::
import profile
pr = profile.Profile()
......@@ -564,8 +597,8 @@ discussion in section Limitations above). ::
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
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
the timer, the magical number is about 12.5e-6.
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 4.04e-6.
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
......@@ -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
your results will "less often" show up as negative in profile statistics.
.. _profile-timers:
.. _profiler-extensions:
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.
Using a customer timer
======================
If all you want to do is change how current time is determined (for example, to
force use of wall-clock time or elapsed process time), pass the timing function
you want to the :class:`Profile` class constructor::
If you want to change how current time is determined (for example, to force use
of wall-clock time or elapsed process time), pass the timing function you want
to the :class:`Profile` class constructor::
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`
:func:`your_time_func` should return a single number, or a list of
numbers whose sum is the current time (like what :func:`os.times`
returns). If the function returns a single time number, or the
list of returned numbers has length 2, then you will get an
especially fast version of the dispatch routine.
Be warned that you should calibrate the profiler class for the
timer function that you choose. For most machines, a timer that
returns a lone integer value will provide the best results in terms
of low overhead during profiling. (:func:`os.times` is *pretty*
bad, as it returns a tuple of floating point values). If you want
to substitute a better timer in the cleanest fashion, derive a
class and hardwire a replacement dispatch method that best handles
your timer call, along with the appropriate calibration constant.
``your_time_func`` should return a single number, or a list of numbers whose
sum is the current time (like what :func:`os.times` returns). If the
function returns a single time number, or the list of returned numbers has
length 2, then you will get an especially fast version of the dispatch
routine.
Be warned that you should calibrate the profiler class for the timer function
that you choose (see :ref:`profile-calibration`). For most machines, a timer
that returns a lone integer value will provide the best results in terms of
low overhead during profiling. (:func:`os.times` is *pretty* bad, as it
returns a tuple of floating point values). If you want to substitute a
better timer in the cleanest fashion, derive a class and hardwire a
replacement dispatch method that best handles your timer call, along with the
appropriate calibration constant.
:class:`cProfile.Profile`
:func:`your_time_func` should return a single number. If it
returns plain integers, you can also invoke the class constructor
with a second argument specifying the real duration of one unit of
time. For example, if :func:`your_integer_time_func` returns times
measured in thousands of seconds, you would construct the
:class:`Profile` instance as follows::
pr = profile.Profile(your_integer_time_func, 0.001)
As the :mod:`cProfile.Profile` class cannot be calibrated, custom
timer functions should be used with care and should be as fast as
possible. For the best results with a custom timer, it might be
necessary to hard-code it in the C source of the internal
:mod:`_lsprof` module.
``your_time_func`` should return a single number. If it returns integers,
you can also invoke the class constructor with a second argument specifying
the real duration of one unit of time. For example, if
``your_integer_time_func`` returns times measured in thousands of seconds,
you would construct the :class:`Profile` instance as follows::
pr = cProfile.Profile(your_integer_time_func, 0.001)
As the :mod:`cProfile.Profile` class cannot be calibrated, custom timer
functions should be used with care and should be as fast as possible. For
the best results with a custom timer, it might be necessary to hard-code it
in the C source of the internal :mod:`_lsprof` module.
.. rubric:: Footnotes
......
Misc/ACKS
View file @ 0ba584c0
......@@ -791,6 +791,7 @@ Jim St. Pierre
Dan Pierson
Martijn Pieters
François Pinard
Tom Pinckney
Zach Pincus
Michael Piotrowski
Antoine Pitrou
......
Misc/NEWS
View file @ 0ba584c0
......@@ -65,6 +65,9 @@ Documentation
- 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?
===========================
......
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