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

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

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


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

"cProfile" and "profile" provide *deterministic profiling* of Python
programs. A *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 "pstats" module.

The Python standard library provides two different implementations of
the same profiling interface:

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.

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


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 "profile" instead of "cProfile" if the latter is not available on
your system.)

The above action would run "re.compile()" and print profile results
like the following:

         214 function calls (207 primitive calls) in 0.002 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
        1    0.000    0.000    0.001    0.001 <string>:1(<module>)
        1    0.000    0.000    0.001    0.001 __init__.py:250(compile)
        1    0.000    0.000    0.001    0.001 __init__.py:289(_compile)
        1    0.000    0.000    0.000    0.000 _compiler.py:759(compile)
        1    0.000    0.000    0.000    0.000 _parser.py:937(parse)
        1    0.000    0.000    0.000    0.000 _compiler.py:598(_code)
        1    0.000    0.000    0.000    0.000 _parser.py:435(_parse_sub)

The first line indicates that 214 calls were monitored.  Of those
calls, 207 were *primitive*, meaning that the call was not induced via
recursion. The next line: "Ordered by: cumulative time" indicates the
output is sorted by the "cumtime" values. 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 "run()"
function:

   import cProfile
   import re
   cProfile.run('re.compile("foo|bar")', 'restats')

The "pstats.Stats" class reads profile results from a file and formats
them in various ways.

The files "cProfile" and "profile" 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 <output_file>

   Writes the profile results to a file instead of to stdout.

-s <sort_order>

   Specifies one of the "sort_stats()" sort values to sort the output
   by. This only applies when "-o" is not supplied.

-m <module>

   Specifies that a module is being profiled instead of a script.

   Added in version 3.7: Added the "-m" option to "cProfile".

   Added in version 3.8: Added the "-m" option to "profile".

The "pstats" module’s "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 "strip_dirs()" method removed the extraneous path from all the
module names. The "sort_stats()" method sorted all the entries
according to the standard module/line/name string that is printed. The
"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 "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.


"profile" and "cProfile" Module Reference
=========================================

Both the "profile" and "cProfile" modules provide the following
functions:

profile.run(command, filename=None, sort=-1)

   This function takes a single argument that can be passed to the
   "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 "Stats"
   instance and prints a simple profiling report. If the sort value is
   specified, it is passed to this "Stats" instance to control how the
   results are sorted.

profile.runctx(command, globals, locals, filename=None, sort=-1)

   This function is similar to "run()", with added arguments to supply
   the globals and locals mappings for the *command* string. This
   routine executes:

      exec(command, globals, locals)

   and gathers profiling statistics as in the "run()" function above.

class profile.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 "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 "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())

   The "Profile" class can also be used as a context manager
   (supported only in "cProfile" module. see Context Manager Types):

      import cProfile

      with cProfile.Profile() as pr:
          # ... do something ...

          pr.print_stats()

   Changed in version 3.8: Added context manager support.

   enable()

      Start collecting profiling data. Only in "cProfile".

   disable()

      Stop collecting profiling data. Only in "cProfile".

   create_stats()

      Stop collecting profiling data and record the results internally
      as the current profile.

   print_stats(sort=-1)

      Create a "Stats" object based on the current profile and print
      the results to stdout.

      The *sort* parameter specifies the sorting order of the
      displayed statistics. It accepts a single key or a tuple of keys
      to enable multi-level sorting, as in "Stats.sort_stats".

      Added in version 3.13: "print_stats()" now accepts a tuple of
      keys.

   dump_stats(filename)

      Write the results of the current profile to *filename*.

   run(cmd)

      Profile the cmd via "exec()".

   runctx(cmd, globals, locals)

      Profile the cmd via "exec()" with the specified global and local
      environment.

   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
"sys.exit()" call during the called command/function execution) no
profiling results will be printed.


The "Stats" Class
=================

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

class pstats.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 "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 "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, 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 "Stats" object, the "add()" method can be used.

   Instead of reading the profile data from a file, a
   "cProfile.Profile" or "profile.Profile" object can be used as the
   profile data source.

   "Stats" objects have the following methods:

   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.

   add(*filenames)

      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.

   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.

   sort_stats(*keys)

      This method modifies the "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.

      Added in version 3.7: Added the SortKey enum.

   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.

   print_stats(*restrictions)

      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 string that will be 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 ".*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.

   print_callers(*restrictions)

      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.

   print_callees(*restrictions)

      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.

   get_stats_profile()

      This method returns an instance of StatsProfile, which contains
      a mapping of function names to instances of FunctionProfile.
      Each FunctionProfile instance holds information related to the
      function’s profile such as how long the function took to run,
      how many times it was called, etc…

      Added in version 3.9: Added the following dataclasses:
      StatsProfile, FunctionProfile. Added the following function:
      get_stats_profile.


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 in order 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.


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 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 macOS, 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.


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 "Profile" class constructor:

   pr = profile.Profile(your_time_func)

The resulting profiler will then call "your_time_func". Depending on
whether you are using "profile.Profile" or "cProfile.Profile",
"your_time_func"’s return value will be interpreted differently:

"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 (see Calibration).  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
   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 = cProfile.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.

Python 3.3 adds several new functions in "time" that can be used to
make precise measurements of process or wall-clock time. For example,
see "time.perf_counter()".
