<p align="center">
<img src="https://raw.githubusercontent.com/sumerc/yappi/master/Misc/logo.png" alt="yappi">
</p>
<h1 align="center">Yappi</h1>
<p align="center">
A tracing profiler that is <b>multithreading, asyncio and gevent</b> aware.
</p>
[](https://freepalestine.dev)
<p align="center">
<img src="https://github.com/sumerc/yappi/workflows/CI/badge.svg?branch=master">
<img src="https://img.shields.io/pypi/v/yappi.svg">
<img src="https://img.shields.io/pypi/dw/yappi.svg">
<img src="https://img.shields.io/pypi/pyversions/yappi.svg">
<img src="https://img.shields.io/github/last-commit/sumerc/yappi.svg">
<img src="https://img.shields.io/github/license/sumerc/yappi.svg">
<a href="https://freepalestine.dev"><img src="https://freepalestine.dev/badge?t=d&u=0&r=1" alt="From the river to the sea, Palestine will be free" /></a>
</p>
## Highlights
- **Fast**: Yappi is fast. It is completely written in C and lots of love and care went into making it fast.
- **Unique**: Yappi supports multithreaded, [asyncio](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md) and [gevent](https://github.com/sumerc/yappi/blob/master/doc/greenlet-profiling.md) profiling. Tagging/filtering multiple profiler results has interesting [use cases](https://github.com/sumerc/yappi/blob/master/doc/api.md#set_tag_callback).
- **Intuitive**: Profiler can be started/stopped and results can be obtained from any time and any thread.
- **Standards Compliant**: Profiler results can be saved in [callgrind](http://valgrind.org/docs/manual/cl-format.html) or [pstat](http://docs.python.org/3.4/library/profile.html#pstats.Stats) formats.
- **Rich in Feature set**: Profiler results can show either [Wall Time](https://en.wikipedia.org/wiki/Elapsed_real_time) or actual [CPU Time](http://en.wikipedia.org/wiki/CPU_time) and can be aggregated from different sessions. Various flags are defined for filtering and sorting profiler results.
- **Robust**: Yappi has been around for years.
## Motivation
CPython standard distribution comes with three deterministic profilers. `cProfile`, `Profile` and `hotshot`. `cProfile` is implemented as a C module based on `lsprof`, `Profile` is in pure Python and `hotshot` can be seen as a small subset of a cProfile. The major issue is that all of these profilers lack support for multi-threaded programs and CPU time.
If you want to profile a multi-threaded application, you must give an entry point to these profilers and then maybe merge the outputs. None of these profilers are designed to work on long-running multi-threaded applications. It is also not possible to profile an application that start/stop/retrieve traces on the fly with these profilers.
Now fast forwarding to 2019: With the latest improvements on `asyncio` library and asynchronous frameworks, most of the current profilers lacks the ability to show correct wall/cpu time or even call count information per-coroutine. Thus we need a different kind of approach to profile asynchronous code. Yappi, with v1.2 introduces the concept of `coroutine profiling`. With `coroutine-profiling`, you should be able to profile correct wall/cpu time and call count of your coroutine. (including the time spent in context switches, too). You can see details [here](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md).
## Installation
Can be installed via PyPI
```
$ pip install yappi
```
OR from the source directly.
```
$ pip install git+https://github.com/sumerc/yappi#egg=yappi
```
## Examples
### A simple example:
```python
import yappi
def a():
for _ in range(10000000): # do something CPU heavy
pass
yappi.set_clock_type("cpu") # Use set_clock_type("wall") for wall time
yappi.start()
a()
yappi.get_func_stats().print_all()
yappi.get_thread_stats().print_all()
'''
Clock type: CPU
Ordered by: totaltime, desc
name ncall tsub ttot tavg
doc.py:5 a 1 0.117907 0.117907 0.117907
name id tid ttot scnt
_MainThread 0 139867147315008 0.118297 1
'''
```
### Profile a multithreaded application:
You can profile a multithreaded application via Yappi and can easily retrieve
per-thread profile information by filtering on `ctx_id` with `get_func_stats` API.
```python
import yappi
import time
import threading
_NTHREAD = 3
def _work(n):
time.sleep(n * 0.1)
yappi.start()
threads = []
# generate _NTHREAD threads
for i in range(_NTHREAD):
t = threading.Thread(target=_work, args=(i + 1, ))
t.start()
threads.append(t)
# wait all threads to finish
for t in threads:
t.join()
yappi.stop()
# retrieve thread stats by their thread id (given by yappi)
threads = yappi.get_thread_stats()
for thread in threads:
print(
"Function stats for (%s) (%d)" % (thread.name, thread.id)
) # it is the Thread.__class__.__name__
yappi.get_func_stats(ctx_id=thread.id).print_all()
'''
Function stats for (Thread) (3)
name ncall tsub ttot tavg
..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000062 0.000062
doc3.py:8 _work 1 0.000012 0.000045 0.000045
Function stats for (Thread) (2)
name ncall tsub ttot tavg
..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000065 0.000065
doc3.py:8 _work 1 0.000010 0.000048 0.000048
Function stats for (Thread) (1)
name ncall tsub ttot tavg
..hon3.7/threading.py:859 Thread.run 1 0.000010 0.000043 0.000043
doc3.py:8 _work 1 0.000006 0.000033 0.000033
'''
```
### Different ways to filter/sort stats:
You can use `filter_callback` on `get_func_stats` API to filter on functions, modules
or whatever available in `YFuncStat` object.
```python
import package_a
import yappi
import sys
def a():
pass
def b():
pass
yappi.start()
a()
b()
package_a.a()
yappi.stop()
# filter by module object
current_module = sys.modules[__name__]
stats = yappi.get_func_stats(
filter_callback=lambda x: yappi.module_matches(x, [current_module])
) # x is a yappi.YFuncStat object
stats.sort("name", "desc").print_all()
'''
Clock type: CPU
Ordered by: name, desc
name ncall tsub ttot tavg
doc2.py:10 b 1 0.000001 0.000001 0.000001
doc2.py:6 a 1 0.000001 0.000001 0.000001
'''
# filter by function object
stats = yappi.get_func_stats(
filter_callback=lambda x: yappi.func_matches(x, [a, b])
).print_all()
'''
name ncall tsub ttot tavg
doc2.py:6 a 1 0.000001 0.000001 0.000001
doc2.py:10 b 1 0.000001 0.000001 0.000001
'''
# filter by module name
stats = yappi.get_func_stats(filter_callback=lambda x: 'package_a' in x.module
).print_all()
'''
name ncall tsub ttot tavg
package_a/__init__.py:1 a 1 0.000001 0.000001 0.000001
'''
# filter by function name
stats = yappi.get_func_stats(filter_callback=lambda x: 'a' in x.name
).print_all()
'''
name ncall tsub ttot tavg
doc2.py:6 a 1 0.000001 0.000001 0.000001
package_a/__init__.py:1 a 1 0.000001 0.000001 0.000001
'''
```
### Profile an asyncio application:
You can see that coroutine wall-time's are correctly profiled.
```python
import asyncio
import yappi
async def foo():
await asyncio.sleep(1.0)
await baz()
await asyncio.sleep(0.5)
async def bar():
await asyncio.sleep(2.0)
async def baz():
await asyncio.sleep(1.0)
yappi.set_clock_type("WALL")
with yappi.run():
asyncio.run(foo())
asyncio.run(bar())
yappi.get_func_stats().print_all()
'''
Clock type: WALL
Ordered by: totaltime, desc
name ncall tsub ttot tavg
doc4.py:5 foo 1 0.000030 2.503808 2.503808
doc4.py:11 bar 1 0.000012 2.002492 2.002492
doc4.py:15 baz 1 0.000013 1.001397 1.001397
'''
```
### Profile a gevent application:
You can use yappi to profile greenlet applications now!
```python
import yappi
from greenlet import greenlet
import time
class GreenletA(greenlet):
def run(self):
time.sleep(1)
yappi.set_context_backend("greenlet")
yappi.set_clock_type("wall")
yappi.start(builtins=True)
a = GreenletA()
a.switch()
yappi.stop()
yappi.get_func_stats().print_all()
'''
name ncall tsub ttot tavg
tests/test_random.py:6 GreenletA.run 1 0.000007 1.000494 1.000494
time.sleep 1 1.000487 1.000487 1.000487
'''
```
## Documentation
- [Introduction](https://github.com/sumerc/yappi/blob/master/doc/introduction.md)
- [Clock Types](https://github.com/sumerc/yappi/blob/master/doc/clock_types.md)
- [API](https://github.com/sumerc/yappi/blob/master/doc/api.md)
- [Coroutine Profiling](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md) _(new in 1.2)_
- [Greenlet Profiling](https://github.com/sumerc/yappi/blob/master/doc/greenlet-profiling.md) _(new in 1.3)_
Note: Yes. I know I should be moving docs to readthedocs.io. Stay tuned!
## Related Talks
Special thanks to A.Jesse Jiryu Davis:
- [Python Performance Profiling: The Guts And The Glory (PyCon 2015)](https://www.youtube.com/watch?v=4uJWWXYHxaM)
## PyCharm Integration
Yappi is the default profiler in `PyCharm`. If you have Yappi installed, `PyCharm` will use it. See [the official](https://www.jetbrains.com/help/pycharm/profiler.html) documentation for more details.
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"description": "<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/sumerc/yappi/master/Misc/logo.png\" alt=\"yappi\">\n</p>\n\n<h1 align=\"center\">Yappi</h1>\n<p align=\"center\">\n A tracing profiler that is <b>multithreading, asyncio and gevent</b> aware.\n</p>\n\n[](https://freepalestine.dev)\n\n<p align=\"center\">\n <img src=\"https://github.com/sumerc/yappi/workflows/CI/badge.svg?branch=master\">\n <img src=\"https://img.shields.io/pypi/v/yappi.svg\">\n <img src=\"https://img.shields.io/pypi/dw/yappi.svg\">\n <img src=\"https://img.shields.io/pypi/pyversions/yappi.svg\">\n <img src=\"https://img.shields.io/github/last-commit/sumerc/yappi.svg\">\n <img src=\"https://img.shields.io/github/license/sumerc/yappi.svg\">\n <a href=\"https://freepalestine.dev\"><img src=\"https://freepalestine.dev/badge?t=d&u=0&r=1\" alt=\"From the river to the sea, Palestine will be free\" /></a>\n</p>\n\n## Highlights\n\n- **Fast**: Yappi is fast. It is completely written in C and lots of love and care went into making it fast.\n- **Unique**: Yappi supports multithreaded, [asyncio](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md) and [gevent](https://github.com/sumerc/yappi/blob/master/doc/greenlet-profiling.md) profiling. Tagging/filtering multiple profiler results has interesting [use cases](https://github.com/sumerc/yappi/blob/master/doc/api.md#set_tag_callback).\n- **Intuitive**: Profiler can\u00a0be\u00a0started/stopped and results can be obtained from any time and any thread.\n- **Standards Compliant**: Profiler\u00a0results\u00a0can\u00a0be\u00a0saved\u00a0in\u00a0[callgrind](http://valgrind.org/docs/manual/cl-format.html)\u00a0or\u00a0[pstat](http://docs.python.org/3.4/library/profile.html#pstats.Stats)\u00a0formats.\n- **Rich in Feature set**: Profiler results can show either [Wall Time](https://en.wikipedia.org/wiki/Elapsed_real_time) or actual [CPU Time](http://en.wikipedia.org/wiki/CPU_time) and can be aggregated\u00a0from\u00a0different\u00a0sessions. Various flags are defined for filtering and sorting profiler results.\n- **Robust**: Yappi has been around for years.\n\n## Motivation\n\nCPython standard distribution comes with three deterministic profilers. `cProfile`, `Profile` and `hotshot`. `cProfile` is implemented as a C module based on `lsprof`, `Profile` is in pure Python and `hotshot` can be seen as a small subset of a cProfile. The major issue is that all of these profilers lack support for multi-threaded programs and CPU time.\n\nIf you want to profile a multi-threaded application, you must give an entry point to these profilers and then maybe merge the outputs. None of these profilers are designed to work on long-running multi-threaded applications. It is also not possible to profile an application that start/stop/retrieve traces on the fly with these profilers. \n\nNow fast forwarding to 2019: With the latest improvements on `asyncio` library and asynchronous frameworks, most of the current profilers lacks the ability to show correct wall/cpu time or even call count information per-coroutine. Thus we need a different kind of approach to profile asynchronous code. Yappi, with v1.2 introduces the concept of `coroutine profiling`. With `coroutine-profiling`, you should be able to profile correct wall/cpu time and call count of your coroutine. (including the time spent in context switches, too). You can see details [here](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md).\n\n\n## Installation\n\nCan be installed via PyPI\n\n```\n$ pip install yappi\n```\n\nOR from the source directly.\n\n```\n$ pip install git+https://github.com/sumerc/yappi#egg=yappi\n```\n\n## Examples\n\n### A simple example:\n\n```python\nimport yappi\n\ndef a():\n for _ in range(10000000): # do something CPU heavy\n pass\n\nyappi.set_clock_type(\"cpu\") # Use set_clock_type(\"wall\") for wall time\nyappi.start()\na()\n\nyappi.get_func_stats().print_all()\nyappi.get_thread_stats().print_all()\n'''\n\nClock type: CPU\nOrdered by: totaltime, desc\n\nname ncall tsub ttot tavg \ndoc.py:5 a 1 0.117907 0.117907 0.117907\n\nname id tid ttot scnt \n_MainThread 0 139867147315008 0.118297 1\n'''\n```\n\n### Profile a multithreaded application:\n\nYou can profile a multithreaded application via Yappi and can easily retrieve\nper-thread profile information by filtering on `ctx_id` with `get_func_stats` API.\n\n```python\nimport yappi\nimport time\nimport threading\n\n_NTHREAD = 3\n\n\ndef _work(n):\n time.sleep(n * 0.1)\n\n\nyappi.start()\n\nthreads = []\n# generate _NTHREAD threads\nfor i in range(_NTHREAD):\n t = threading.Thread(target=_work, args=(i + 1, ))\n t.start()\n threads.append(t)\n# wait all threads to finish\nfor t in threads:\n t.join()\n\nyappi.stop()\n\n# retrieve thread stats by their thread id (given by yappi)\nthreads = yappi.get_thread_stats()\nfor thread in threads:\n print(\n \"Function stats for (%s) (%d)\" % (thread.name, thread.id)\n ) # it is the Thread.__class__.__name__\n yappi.get_func_stats(ctx_id=thread.id).print_all()\n'''\nFunction stats for (Thread) (3)\n\nname ncall tsub ttot tavg\n..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000062 0.000062\ndoc3.py:8 _work 1 0.000012 0.000045 0.000045\n\nFunction stats for (Thread) (2)\n\nname ncall tsub ttot tavg\n..hon3.7/threading.py:859 Thread.run 1 0.000017 0.000065 0.000065\ndoc3.py:8 _work 1 0.000010 0.000048 0.000048\n\n\nFunction stats for (Thread) (1)\n\nname ncall tsub ttot tavg\n..hon3.7/threading.py:859 Thread.run 1 0.000010 0.000043 0.000043\ndoc3.py:8 _work 1 0.000006 0.000033 0.000033\n'''\n```\n\n### Different ways to filter/sort stats:\n\nYou can use `filter_callback` on `get_func_stats` API to filter on functions, modules\nor whatever available in `YFuncStat` object.\n\n```python\nimport package_a\nimport yappi\nimport sys\n\ndef a():\n pass\n\ndef b():\n pass\n\nyappi.start()\na()\nb()\npackage_a.a()\nyappi.stop()\n\n# filter by module object\ncurrent_module = sys.modules[__name__]\nstats = yappi.get_func_stats(\n filter_callback=lambda x: yappi.module_matches(x, [current_module])\n) # x is a yappi.YFuncStat object\nstats.sort(\"name\", \"desc\").print_all()\n'''\nClock type: CPU\nOrdered by: name, desc\n\nname ncall tsub ttot tavg\ndoc2.py:10 b 1 0.000001 0.000001 0.000001\ndoc2.py:6 a 1 0.000001 0.000001 0.000001\n'''\n\n# filter by function object\nstats = yappi.get_func_stats(\n filter_callback=lambda x: yappi.func_matches(x, [a, b])\n).print_all()\n'''\nname ncall tsub ttot tavg\ndoc2.py:6 a 1 0.000001 0.000001 0.000001\ndoc2.py:10 b 1 0.000001 0.000001 0.000001\n'''\n\n# filter by module name\nstats = yappi.get_func_stats(filter_callback=lambda x: 'package_a' in x.module\n ).print_all()\n'''\nname ncall tsub ttot tavg\npackage_a/__init__.py:1 a 1 0.000001 0.000001 0.000001\n'''\n\n# filter by function name\nstats = yappi.get_func_stats(filter_callback=lambda x: 'a' in x.name\n ).print_all()\n'''\nname ncall tsub ttot tavg\ndoc2.py:6 a 1 0.000001 0.000001 0.000001\npackage_a/__init__.py:1 a 1 0.000001 0.000001 0.000001\n'''\n```\n\n### Profile an asyncio application:\n\nYou can see that coroutine wall-time's are correctly profiled.\n\n```python\nimport asyncio\nimport yappi\n\nasync def foo():\n await asyncio.sleep(1.0)\n await baz()\n await asyncio.sleep(0.5)\n\nasync def bar():\n await asyncio.sleep(2.0)\n\nasync def baz():\n await asyncio.sleep(1.0)\n\nyappi.set_clock_type(\"WALL\")\nwith yappi.run():\n asyncio.run(foo())\n asyncio.run(bar())\nyappi.get_func_stats().print_all()\n'''\nClock type: WALL\nOrdered by: totaltime, desc\n\nname ncall tsub ttot tavg \ndoc4.py:5 foo 1 0.000030 2.503808 2.503808\ndoc4.py:11 bar 1 0.000012 2.002492 2.002492\ndoc4.py:15 baz 1 0.000013 1.001397 1.001397\n'''\n```\n\n### Profile a gevent application:\n\nYou can use yappi to profile greenlet applications now!\n\n```python\nimport yappi\nfrom greenlet import greenlet\nimport time\n\nclass GreenletA(greenlet):\n def run(self):\n time.sleep(1)\n\nyappi.set_context_backend(\"greenlet\")\nyappi.set_clock_type(\"wall\")\n\nyappi.start(builtins=True)\na = GreenletA()\na.switch()\nyappi.stop()\n\nyappi.get_func_stats().print_all()\n'''\nname ncall tsub ttot tavg\ntests/test_random.py:6 GreenletA.run 1 0.000007 1.000494 1.000494\ntime.sleep 1 1.000487 1.000487 1.000487\n'''\n```\n\n## Documentation\n\n- [Introduction](https://github.com/sumerc/yappi/blob/master/doc/introduction.md)\n- [Clock Types](https://github.com/sumerc/yappi/blob/master/doc/clock_types.md)\n- [API](https://github.com/sumerc/yappi/blob/master/doc/api.md)\n- [Coroutine Profiling](https://github.com/sumerc/yappi/blob/master/doc/coroutine-profiling.md) _(new in 1.2)_\n- [Greenlet Profiling](https://github.com/sumerc/yappi/blob/master/doc/greenlet-profiling.md) _(new in 1.3)_\n\n Note: Yes. I know I should be moving docs to readthedocs.io. Stay tuned!\n\n\n## Related Talks\n\n Special thanks to A.Jesse Jiryu Davis:\n- [Python Performance Profiling: The Guts And The Glory (PyCon 2015)](https://www.youtube.com/watch?v=4uJWWXYHxaM)\n\n## PyCharm Integration\n\nYappi is the default profiler in `PyCharm`. If you have Yappi installed, `PyCharm` will use it. See [the official](https://www.jetbrains.com/help/pycharm/profiler.html) documentation for more details.\n\n",
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