.. _profile:

********************

The Python Profilers

********************

.. sectionauthor:: James Roskind

.. index:: single: InfoSeek Corporation

Copyright © 1994, by InfoSeek Corporation, all rights reserved.

Written by James Roskind. [#]_

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.. _profiler-introduction:

Introduction to the profilers

=============================

.. index::

   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.

The Python standard library provides three different profilers:

#. :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.

   Copyright © 1994, by InfoSeek Corporation.

   .. 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

   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  It is no longer maintained and may be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.

   .. versionchanged:: 2.5

      The results should be more meaningful than in the past: the timing core

      contained a critical bug.

The :mod:`profile` and :mod:`cProfile` modules export the same interface, so

they are mostly interchangeable; :mod:`cProfile` has a much lower overhead but

is newer and might not be available on all systems.

:mod:`cProfile` is really a compatibility layer on top of the internal

:mod:`_lsprof` module.  The :mod:`hotshot` module is reserved for specialized

usage.

.. _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 an application with a main entry point of :func:`foo`, you would add

the following to your module::

   import cProfile

   cProfile.run('foo()')

(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::

   import cProfile

   cProfile.run('foo()', 'fooprof')

The file :file:`cProfile.py` can also be invoked as a script to profile another

script.  For example::

   python -m cProfile myscript.py

:file:`cProfile.py` accepts two optional arguments on the command line::

   cProfile.py [-o output_file] [-s sort_order]

:option:`-s` only applies to standard output (:option:`-o` is not supplied).

Look in the :class:`Stats` documentation for valid sort values.

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::

   import pstats

   p = pstats.Stats('fooprof')

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::

   p.strip_dirs().sort_stats(-1).print_stats()

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:

.. (this is to comply with the semantics of the old profiler).

::

   p.sort_stats('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('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('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('file').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('time', 'cum').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('fooprof')

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.

.. _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.

Reference Manual -- :mod:`profile` and :mod:`cProfile`

======================================================

.. module:: cProfile

   :synopsis: Python profiler

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.

.. function:: run(command[, filename])

   This function takes a single argument that can be passed to the

   :keyword:`exec` statement, and an optional file name.  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::

            2706 function calls (2004 primitive calls) in 4.504 CPU seconds

      Ordered by: standard name

      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,

   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 total 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, ``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.

.. function:: runctx(command, globals, locals[, filename])

   This function is similar to :func:`run`, with added arguments to supply the

   globals and locals dictionaries for the *command* string.

Analysis of the profiler data is done using the :class:`Stats` class.

.. note::

   The :class:`Stats` class is defined in the :mod:`pstats` module.

.. module:: pstats

   :synopsis: Statistics object for use with the profiler.

.. class:: Stats(filename[, stream=sys.stdout[, ...]])

   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``.

   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.

   .. (such as the old system profiler).

   .. versionchanged:: 2.5

      The *stream* parameter was added.

.. _profile-stats:

The :class:`Stats` Class

------------------------

:class:`Stats` objects have the following methods:

.. method:: Stats.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.

.. method:: Stats.add(filename[, ...])

   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.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.

   .. versionadded:: 2.3

.. method:: Stats.sort_stats(key[, ...])

   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.

   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      |

   +------------------+----------------------+

   | ``'file'``       | file name            |

   +------------------+----------------------+

   | ``'module'``     | file name            |

   +------------------+----------------------+

   | ``'pcalls'``     | primitive call count |

   +------------------+----------------------+

   | ``'line'``       | line number          |

   +------------------+----------------------+

   | ``'name'``       | function name        |

   +------------------+----------------------+

   | ``'nfl'``        | name/file/line       |

   +------------------+----------------------+

   | ``'stdname'``    | standard name        |

   +------------------+----------------------+

   | ``'time'``       | 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 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.

.. _profile-limits:

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