%global _empty_manifest_terminate_build 0
Name: python-yappi
Version: 1.4.0
Release: 1
Summary: Yet Another Python Profiler
License: MIT
URL: https://github.com/sumerc/yappi
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/88/4a/e16c320be27ea5ed9015ebe4a5fe834e714a0f0fc9cf46a20b2f87bf4fe3/yappi-1.4.0.tar.gz
Requires: python3-gevent
%description
Yappi
A tracing profiler that is multithreading, asyncio and gevent aware.
## 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.
%package -n python3-yappi
Summary: Yet Another Python Profiler
Provides: python-yappi
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-yappi
Yappi
A tracing profiler that is multithreading, asyncio and gevent aware.
## 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.
%package help
Summary: Development documents and examples for yappi
Provides: python3-yappi-doc
%description help
Yappi
A tracing profiler that is multithreading, asyncio and gevent aware.
## 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.
%prep
%autosetup -n yappi-1.4.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-yappi -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot - 1.4.0-1
- Package Spec generated