
The Python Profilers
********************

**Source code:** Lib/profile.py and Lib/pstats.py

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


Introduction to the profilers
=============================

A *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
``cProfile``, ``profile`` and ``pstats``.  This profiler provides
*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:

1. ``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 ``lsprof``, contributed by Brett Rosen
   and Ted Czotter.

   New in version 2.5.

2. ``profile``, a pure Python module whose interface is imitated by
   ``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.

   Changed in version 2.4: Now also reports the time spent in calls to
   built-in functions and methods.

3. ``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  It is no longer maintained and may be dropped in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.

   Changed in version 2.5: The results should be more meaningful than
   in the past: the timing core contained a critical bug.

The ``profile`` and ``cProfile`` modules export the same interface, so
they are mostly interchangeable; ``cProfile`` has a much lower
overhead but is newer and might not be available on all systems.
``cProfile`` is really a compatibility layer on top of the internal
``_lsprof`` module.  The ``hotshot`` module is reserved for
specialized usage.


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 ``foo()``, you
would add the following to your module:

   import cProfile
   cProfile.run('foo()')

(Use ``profile`` instead of ``cProfile`` if the latter is not
available on your system.)

The above action would cause ``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 ``run()``
function:

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

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

   python -m cProfile myscript.py

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

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

``-s`` only applies to standard output (``-o`` is not supplied). Look
in the ``Stats`` documentation for valid sort values.

When you wish to review the profile, you should use the methods in the
``pstats`` module.  Typically you would load the statistics data as
follows:

   import pstats
   p = pstats.Stats('fooprof')

The 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 ``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:

   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 ``pstats`` module is a statistics browser for
reading and examining profile dumps.  It has a simple line-oriented
interface (implemented using ``cmd``) and interactive help.


What Is Deterministic Profiling?
================================

*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, *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 *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 -- ``profile`` and ``cProfile``
================================================

The primary entry point for the profiler is the global function
``profile.run()`` (resp. ``cProfile.run()``). It is typically used to
create any profile information.  The reports are formatted and printed
using methods of the 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.

cProfile.run(command[, filename][, sort])

   This function takes a single argument that can be passed to the
   ``exec`` statement, and optionally a file name and a sorting
   directive.  In all cases this routine attempts to ``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 *primitive*.  We define *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.  For information on the sort
   argument, refer to ``pstats.Stats.sort_stats()``.

cProfile.runctx(command, globals, locals[, filename])

   This function is similar to ``run()``, with added arguments to
   supply the globals and locals dictionaries for the *command*
   string.

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

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

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

   This class constructor creates an instance of a "statistics object"
   from a *filename* (or set of filenames).  ``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 ``profile`` or ``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 ``Stats`` object, the ``add()`` method can be used.

   Changed in version 2.5: The *stream* parameter was added.


The ``Stats`` Class
-------------------

``Stats`` objects have the following methods:

Stats.strip_dirs()

   This method for the ``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 ``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.

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

   This method of the ``Stats`` class accumulates additional profiling
   information into the current profiling object.  Its arguments
   should refer to filenames created by the corresponding version of
   ``profile.run()`` or ``cProfile.run()``. Statistics for identically
   named (re: file, line, name) functions are automatically
   accumulated into single function statistics.

Stats.dump_stats(filename)

   Save the data loaded into the ``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 ``profile.Profile`` and
   ``cProfile.Profile`` classes.

   New in version 2.3.

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

   This method modifies the ``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        |
   +--------------------+------------------------+
   | ``'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.

Stats.reverse_order()

   This method for the ``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.

Stats.print_stats([restriction, ...])

   This method for the ``Stats`` class prints out a report as
   described in the ``profile.run()`` definition.

   The order of the printing is based on the last ``sort_stats()``
   operation done on the object (subject to caveats in ``add()`` and
   ``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 ``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 ``.*foo:``.  In
   contrast, the command:

      print_stats('foo:', .1)

   would limit the list to all functions having file names ``.*foo:``,
   and then proceed to only print the first 10% of them.

Stats.print_callers([restriction, ...])

   This method for the ``Stats`` class prints a list of all functions
   that called each function in the profiled database.  The ordering
   is identical to that provided by ``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 ``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 ``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.

Stats.print_callees([restriction, ...])

   This method for the ``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 ``print_callers()``
   method.


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 ``profile`` than with the lower-
overhead ``cProfile``.  For this reason, ``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.


Calibration
===========

The profiler of the ``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).

   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 an 800 MHz Pentium running Windows 2000,
and using Python's time.clock() as the timer, the magical number is
about 12.5e-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: [1]

   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.


Extensions --- Deriving Better Profilers
========================================

The ``Profile`` class of both modules, ``profile`` and ``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 ``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 force use of wall-clock time or elapsed process time),
pass the timing function you want to the ``Profile`` class
constructor:

   pr = profile.Profile(your_time_func)

The resulting profiler will then call ``your_time_func()``.

``profile.Profile``
   ``your_time_func()`` should return a single number, or a list of
   numbers whose sum is the current time (like what ``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.  (``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.

``cProfile.Profile``
   ``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 ``your_integer_time_func()`` returns times measured
   in thousands of seconds, you would construct the ``Profile``
   instance as follows:

      pr = profile.Profile(your_integer_time_func, 0.001)

   As the ``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
   ``_lsprof`` module.

-[ Footnotes ]-

[1] Prior to Python 2.2, it was necessary to edit the profiler source
    code to embed the bias as a literal number.  You still can, but
    that method is no longer described, because no longer needed.
