profiling.tracing --- 确定型性能分析器

Added in version 3.15.

源代码: Lib/profiling/tracing/

profiling.tracing 模块提供 Python 程序的确定型性能分析。 它会监视每个函数调用、函数返回以及异常事件，为它们分别记录精确的耗时。 这种方式提供了程序执行的准确调用计数和完整可视化，使它成为适合开发和测试场景的理想工具。

备注

本模块也可作为 cProfile 访问以保持向下兼容性。 cProfile 名称将继续在未来所有 Python 版本中可用。 请使用适合你的代码的任一导入风格:

# 首选（新风格）
import profiling.tracing
profiling.tracing.run('my_function()')

# 仍然可用（向下兼容）
import cProfile
cProfile.run('my_function()')

什么是确定型性能分析？

确定型性能分析 会捕获程序运行期间的每个函数调用、函数返回以及异常事件。 性能分析器将计量这些事件所用时间的精确间隔，提供有关程序行为的准确统计信息。

与定期对调用栈进行采样来计算耗时的 统计型性能分析 不同，确定型性能分析会记录每个事件。 这意味着你将获得准确的调用计数而不是统计近似值。 这种方式的代价是记录每个事件会引入可能减慢程序执行速度的额外开销。

Python 的解释型性质使得确定型性能分析是可行的。 解释器已经针对函数调用与返回发送事件，因此性能分析器可以利用这一机制设置钩子而无需修改代码。 增加的开销相对于固有的解释消耗是有限的，使得确定型性能分析适用于大多数开发流程。

确定型性能分析有助于解答这样的问题：Deterministic profiling helps answer questions like:

  • 这个函数调用花费了多少时间？

  • 我的程序的完整调用图景是怎样的？

  • Which functions are called by a particular function?

  • Are there unexpected function calls happening?

Call count statistics can identify bugs (surprising counts) and inline expansion opportunities (high call counts). Internal time statistics reveal "hot loops" that warrant optimization. Cumulative time statistics help identify algorithmic inefficiencies. The handling of cumulative times in this profiler allows direct comparison of recursive and iterative implementations.

命令行接口

profiling.tracing 模块可以作为脚本被唤起以分析另一个脚本或模块：

python -m profiling.tracing [-o output_file] [-s sort_order] (-m module | script.py)

这将在性能分析器中运行指定的脚本或模块并将结果打印到标准输出（或将其保存到文件）。

-o <output_file>

将性能分析结果写入文件而不是标准输出。 输出文件可由 pstats 模块读取供后续分析。

-s <sort_order>

Sort the output by the specified key. This accepts any of the sort keys recognized by pstats.Stats.sort_stats(), such as cumulative, time, calls, or name. This option only applies when -o is not specified.

-m <module>

Profile a module instead of a script. The module is located using the standard import mechanism.

Added in version 3.7: 用于 cProfile 的 -m 选项。

Added in version 3.8: 用于 profile 的 -m 选项。

编程使用示例

要对性能分析进行更多控制，请直接使用此模块的函数和类。

基本性能分析

The simplest approach uses the run() function:

import profiling.tracing
profiling.tracing.run('my_function()')

This profiles the given code string and prints a summary to standard output. To save results for later analysis:

profiling.tracing.run('my_function()', 'output.prof')

使用 Profile 类

Profile 类提供了细粒度的控制:

import profiling.tracing
import pstats
from io import StringIO

pr = profiling.tracing.Profile()
pr.enable()
# ... 要分析的代码 ...
pr.disable()

# 打印结果
s = StringIO()
ps = pstats.Stats(pr, stream=s).sort_stats(pstats.SortKey.CUMULATIVE)
ps.print_stats()
print(s.getvalue())

Profile 类还可作为上下文管理器:

import profiling.tracing

with profiling.tracing.Profile() as pr:
    # ... 要分析的代码 ...

pr.print_stats()

模块参考

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

执行一条命令分析性能并打印或保存结果。

此函数会在 __main__ 模块的命名空间中使用 exec() 执行 command 字符串:

exec(command, __main__.__dict__, __main__.__dict__)

If filename is not provided, the function creates a pstats.Stats instance and prints a summary to standard output. If filename is provided, the raw profile data is saved to that file for later analysis with pstats.

The sort argument specifies the sort order for printed output, accepting any value recognized by pstats.Stats.sort_stats().

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

Profile execution of a command with explicit namespaces.

Like run(), but executes the command with the specified globals and locals mappings instead of using the __main__ module's namespace:

exec(command, globals, locals)
class profiling.tracing.Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)

负责收集执行统计信息的性能分析对象。

The optional timer argument specifies a custom timing function. If not provided, the profiler uses a platform-appropriate default timer. When supplying a custom timer, it must return a single number representing the current time. If the timer returns integers, use timeunit to specify the duration of one time unit (for example, 0.001 for milliseconds).

The subcalls argument controls whether the profiler tracks call relationships between functions. The builtins argument controls whether built-in functions are profiled.

在 3.8 版本发生变更: 添加了上下文管理器支持。

enable()

开始收集性能分析数据。

disable()

Stop collecting profiling data.

create_stats()

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

print_stats(sort=-1)

Create a pstats.Stats object from the current profile and print the results to standard output.

The sort argument specifies the sorting order. It accepts a single key or a tuple of keys for multi-level sorting, using the same values as pstats.Stats.sort_stats().

Added in version 3.13: Support for a tuple of sort keys.

dump_stats(filename)

Write the current profile data to filename. The file can be read by pstats.Stats for later analysis.

run(cmd)

Profile the command string via exec().

runctx(cmd, globals, locals)

Profile the command string via exec() with the specified namespaces.

runcall(func, /, *args, **kwargs)

Profile a function call. Returns whatever func returns:

result = pr.runcall(my_function, arg1, arg2, keyword=value)

备注

Profiling requires that the profiled code returns normally. If the interpreter terminates (for example, via sys.exit()) during profiling, no results will be available.

使用自定义计时器

The Profile class accepts a custom timing function, allowing you to measure different aspects of execution such as wall-clock time or CPU time. Pass the timing function to the constructor:

pr = profiling.tracing.Profile(my_timer_function)

The timer function must return a single number representing the current time. If it returns integers, also specify timeunit to indicate the duration of one unit:

# Timer returns time in milliseconds
pr = profiling.tracing.Profile(my_ms_timer, 0.001)

For best performance, the timer function should be as fast as possible. The profiler calls it frequently, so timer overhead directly affects profiling overhead.

The time module provides several functions suitable for use as custom timers:

局限性

Deterministic profiling has inherent limitations related to timing accuracy.

The underlying timer typically has a resolution of about one millisecond. Measurements cannot be more accurate than this resolution. With enough measurements, timing errors tend to average out, but individual measurements may be imprecise.

There is also latency between when an event occurs and when the profiler captures the timestamp. Similarly, there is latency after reading the timestamp before user code resumes. Functions called frequently accumulate this latency, which can make them appear slower than they actually are. This error is typically less than one clock tick per call but can become significant for functions called many times.

The profiling.tracing module (and its cProfile alias) is implemented as a C extension with low overhead, so these timing issues are less pronounced than with the deprecated pure Python profile module.

参见

profiling

Python 性能分析工具概览和性能分析器选择指南。

profiling.sampling

Statistical sampling profiler for production use.

pstats

性能分析数据的统计分析和格式化。

profile

Deprecated pure Python profiler (includes calibration documentation).