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.. _profile:
********************
The Python Profilers
********************
**Source code:** :source:`Lib/profile.py` and :source:`Lib/pstats.py`
--------------
.. _profiler-introduction:
Introduction to the profilers
=============================
.. index::
single: deterministic profiling
single: profiling, deterministic
: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 two different implementations of the same
profiling interface:
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.
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. Originally designed and written by Jim Roskind.
.. 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:
Instant User's Manual
=====================
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 a function that takes a single argument, you can do::
import cProfile
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 run :func:`re.compile` and print profile results like
the following::
197 function calls (192 primitive calls) in 0.002 seconds
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)
percall
is the quotient of ``tottime`` divided by ``ncalls``
cumtime
is the cumulative time spent in this and all subfunctions (from invocation
till exit). This figure is accurate *even* for recursive functions.
percall
is the quotient of ``cumtime`` divided by primitive calls
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.
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::
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] (-m module | 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.
``-m`` specifies that a module is being profiled instead of a script.
.. versionadded:: 3.7
Added the ``-m`` option.
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
from pstats import SortKey
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(SortKey.NAME)
p.print_stats()
The first call will actually sort the list by function name, and the second call
will print out the statistics. The following are some interesting calls to
experiment with::
p.sort_stats(SortKey.CUMULATIVE).print_stats(10)
This sorts the profile by cumulative time in a function, and then only prints
the ten most significant lines. If you want to understand what algorithms are
taking time, the above line is what you would use.
If you were looking to see what functions were looping a lot, and taking a lot
of time, you would do::
p.sort_stats(SortKey.TIME).print_stats(10)
to sort according to time spent within each function, and then print the
statistics for the top ten functions.
You might also try::
p.sort_stats(SortKey.FILENAME).print_stats('__init__')
This will sort all the statistics by file name, and then print out statistics
for only the class init methods (since they are spelled with ``__init__`` in
them). As one final example, you could try::
p.sort_stats(SortKey.TIME, SortKey.CUMULATIVE).print_stats(.5, 'init')
This line sorts statistics with a primary key of time, and a secondary key of
cumulative time, and then prints out some of the statistics. To be specific, the
list is first culled down to 50% (re: ``.5``) of its original size, then only
lines containing ``init`` are maintained, and that sub-sub-list is printed.
If you wondered what functions called the above functions, you could now (``p``
is still sorted according to the last criteria) do::
p.print_callers(.5, 'init')
and you would get a list of callers for each of the listed functions.
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('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
=======================================================
.. module:: cProfile
.. module:: profile
:synopsis: Python source profiler.
Both the :mod:`profile` and :mod:`cProfile` modules provide the following
functions:
.. function:: run(command, filename=None, sort=-1)
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__)
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.
.. function:: runctx(command, globals, locals, filename=None, sort=-1)
This function is similar to :func:`run`, with added arguments to supply the
globals and locals dictionaries for the *command* string. This routine
executes::
exec(command, globals, locals)
and gathers profiling statistics as in the :func:`run` function above.
.. class:: Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)
This class is normally only used if more precise control over profiling is
needed than what the :func:`cProfile.run` function provides.
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``.
Directly using the :class:`Profile` class allows formatting profile results
without writing the profile data to a file::
import cProfile, pstats, io
from pstats import SortKey
pr = cProfile.Profile()
pr.enable()
# ... do something ...
pr.disable()
s = io.StringIO()
sortby = SortKey.CUMULATIVE
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
print(s.getvalue())
.. method:: enable()
Start collecting profiling data.
.. method:: disable()
Stop collecting profiling data.
.. method:: create_stats()
Stop collecting profiling data and record the results internally
as the current profile.
.. method:: print_stats(sort=-1)
Create a :class:`~pstats.Stats` object based on the current
profile and print the results to stdout.
.. method:: dump_stats(filename)
Write the results of the current profile to *filename*.
.. method:: run(cmd)
Profile the cmd via :func:`exec`.
.. method:: runctx(cmd, globals, locals)
Profile the cmd via :func:`exec` with the specified global and
local environment.
.. method:: runcall(func, *args, **kwargs)
Profile ``func(*args, **kwargs)``
Note that profiling will only work if the called command/function actually
returns. If the interpreter is terminated (e.g. via a :func:`sys.exit` call
during the called command/function execution) no profiling results will be
printed.
.. _profile-stats:
The :class:`Stats` Class
========================
Analysis of the profiler data is done using the :class:`~pstats.Stats` class.
.. module:: pstats
:synopsis: Statistics object for use with the profiler.
.. class:: Stats(*filenames or profile, 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*.
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, or the same profiler run on a different operating system. 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.
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:: 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:`~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:: 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.
.. 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.
.. method:: sort_stats(*keys)
This method modifies the :class:`Stats` object by sorting it according to
the supplied criteria. The argument can be either a string or a SortKey
enum identifying the basis of a sort (example: ``'time'``, ``'name'``,
``SortKey.TIME`` or ``SortKey.NAME``). The SortKey enums argument have
advantage over the string argument in that it is more robust and less
error prone.
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(SortKey.NAME, SortKey.FILE)`` will sort
all the entries according to their function name, and resolve all ties
(identical function names) by sorting by file name.
For the string argument, abbreviations can be used for any key names, as
long as the abbreviation is unambiguous.
The following are the valid string and SortKey:
+------------------+---------------------+----------------------+
| Valid String Arg | Valid enum Arg | Meaning |
+==================+=====================+======================+
| ``'calls'`` | SortKey.CALLS | call count |
+------------------+---------------------+----------------------+
| ``'cumulative'`` | SortKey.CUMULATIVE | cumulative time |
+------------------+---------------------+----------------------+
| ``'cumtime'`` | N/A | cumulative time |
+------------------+---------------------+----------------------+
| ``'file'`` | N/A | file name |
+------------------+---------------------+----------------------+
| ``'filename'`` | SortKey.FILENAME | file name |
+------------------+---------------------+----------------------+
| ``'module'`` | N/A | file name |
+------------------+---------------------+----------------------+
| ``'ncalls'`` | N/A | call count |
+------------------+---------------------+----------------------+
| ``'pcalls'`` | SortKey.PCALLS | primitive call count |
+------------------+---------------------+----------------------+
| ``'line'`` | SortKey.LINE | line number |
+------------------+---------------------+----------------------+
| ``'name'`` | SortKey.NAME | function name |
+------------------+---------------------+----------------------+
| ``'nfl'`` | SortKey.NFL | name/file/line |
+------------------+---------------------+----------------------+
| ``'stdname'`` | SortKey.STDNAME | standard name |
+------------------+---------------------+----------------------+
| ``'time'`` | SortKey.TIME | internal time |
+------------------+---------------------+----------------------+
| ``'tottime'`` | N/A | 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
``SortKey.NFL`` and ``SortKey.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, ``SortKey.NFL`` does a numeric compare of the line numbers.
In fact, ``sort_stats(SortKey.NFL)`` is the same as
``sort_stats(SortKey.NAME, SortKey.FILENAME, SortKey.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.
.. versionadded:: 3.7
Added the SortKey enum.
.. 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 string that will interpreted as 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.
.. _deterministic-profiling:
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.
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.
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-limitations:
Limitations
===========
One limitation has to do with accuracy of timing information. There is a
fundamental problem with deterministic profilers involving accuracy. The most
obvious restriction is that the underlying "clock" is only ticking at a rate
(typically) of about .001 seconds. Hence no measurements will be more accurate
than the underlying clock. If enough measurements are taken, then the "error"
will tend to average out. Unfortunately, removing this first error induces a
second source of error.
The second problem is that it "takes a while" from when an event is dispatched
until the profiler's call to get the time actually *gets* the state of the
clock. Similarly, there is a certain lag when exiting the profiler event
handler from the time that the clock's value was obtained (and then squirreled
away), until the user's code is once again executing. As a result, functions
that are called many times, or call many functions, will typically accumulate
this error. The error that accumulates in this fashion is typically less than
the accuracy of the clock (less than one clock tick), but it *can* accumulate
and become very significant.
The problem is more important with :mod:`profile` than with the lower-overhead
:mod:`cProfile`. For this reason, :mod:`profile` provides a means of
calibrating itself for a given platform so that this error can be
probabilistically (on the average) removed. After the profiler is calibrated, it
will be more accurate (in a least square sense), but it will sometimes produce
negative numbers (when call counts are exceptionally low, and the gods of
probability work against you :-). ) Do *not* be alarmed by negative numbers in
the profile. They should *only* appear if you have calibrated your profiler,
and the results are actually better than without calibration.
.. _profile-calibration:
Calibration
===========
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
:ref:`profile-limitations`). ::
import profile
pr = profile.Profile()
for i in range(5):
print(pr.calibrate(10000))
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 a 1.8Ghz Intel Core i5 running Mac OS X, and using Python's time.process_time() 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
have to pass 100000, or even 1000000, to get consistent results.
When you have a consistent answer, there are three ways you can use it::
import profile
# 1. Apply computed bias to all Profile instances created hereafter.
profile.Profile.bias = your_computed_bias
# 2. Apply computed bias to a specific Profile instance.
pr = profile.Profile()
pr.bias = your_computed_bias
# 3. Specify computed bias in instance constructor.
pr = profile.Profile(bias=your_computed_bias)
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:
Using a custom timer
====================
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 ``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`
``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`
``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 :class:`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.
Python 3.3 adds several new functions in :mod:`time` that can be used to make
precise measurements of process or wall-clock time. For example, see
:func:`time.perf_counter`.