Profiling Greenlets
February 26, 2026 · View on GitHub
Yappi now supports profiling greenlets!
How do you use it?
Profile simple greenlets
Basic Usage
Here's an example of profiling a simple greenlet based application which runs two greenlets
import yappi
from greenlet import greenlet
import time
## Application logic
def burn_cpu(secs):
t0 = time.process_time()
elapsed = 0
while (elapsed <= secs):
for _ in range(1000):
pass
elapsed = time.process_time() - t0
class GreenletA(greenlet):
def run(self):
burn_cpu(0.1)
class GreenletB(greenlet):
def run(self):
burn_cpu(0.2)
# Running the profiler:
# Step 1: Configure the profiler to work with greenlets
yappi.set_context_backend("greenlet")
yappi.set_clock_type("cpu")
# Step 2: Run the profiler and stop it
yappi.start()
a = GreenletA()
b = GreenletB()
a.switch()
b.switch()
yappi.stop()
# Step 3: View results
print("## Function stats:")
yappi.get_func_stats().print_all()
print("\n## Greenlet stats:")
yappi.get_greenlet_stats().print_all()
Sample Output:
## Function stats:
Clock type: CPU
Ordered by: totaltime, desc
name ncall tsub ttot tavg
test.py:7 burn_cpu 2 0.255467 0.300078 0.150039
test.py:20 GreenletB.run 1 0.000009 0.200057 0.200057
test.py:16 GreenletA.run 1 0.000008 0.100038 0.100038
## Greenlet stats:
name id ttot scnt
GreenletB 3 0.200074 1
GreenletA 2 0.100051 1
greenlet 1 0.000076 3
The meaning of each column and table is explained here - Introduction
Profile gevent applications
With support for greenlets, you can now profile popular async frameworks built on top of greenlets like Gevents.
Basic Usage
import yappi
from gevent import Greenlet
import time
## Application logic
def burn_cpu(secs):
t0 = time.process_time()
elapsed = 0
while (elapsed <= secs):
for _ in range(1000):
pass
elapsed = time.process_time() - t0
class GreenletA(Greenlet):
def _run(self):
burn_cpu(0.1)
class GreenletB(Greenlet):
def _run(self):
burn_cpu(0.2)
# Running the profiler:
# Step 1: Configure the profiler to work with greenlets
yappi.set_context_backend("greenlet")
yappi.set_clock_type("cpu")
# Step 2: Run the profiler and stop it
yappi.start()
a = GreenletA()
b = GreenletB()
a.start()
b.start()
a.get()
b.get()
yappi.stop()
# Step 3: View results
print("## Function stats:")
yappi.get_func_stats().print_all()
print("\n## Greenlet stats:")
yappi.get_greenlet_stats().print_all()
Sample output:
## Function stats:
Clock type: CPU
Ordered by: totaltime, desc
name ncall tsub ttot tavg
tests/test_random.py:7 burn_cpu 2 0.257554 0.300067 0.150033
..s/test_random.py:20 GreenletB._run 1 0.000007 0.200025 0.200025
..s/test_random.py:16 GreenletA._run 1 0.000009 0.100058 0.100058
..
.. More function stats
..
## Greenlet stats:
name id ttot scnt
GreenletB 4 0.200048 1
GreenletA 3 0.100075 1
greenlet 1 0.006496 2
Hub 2 0.000212 1
Customizing greenlet names
By default, set_context_backend("greenlet") installs a name callback that returns getcurrent().__class__.__name__. This works well when each greenlet is a distinct subclass (e.g. GreenletA, GreenletB), but will show the same generic name (e.g. Greenlet, greenlet) for all greenlets spawned from the same class.
If you need more descriptive names, use set_context_name_callback to provide your own naming logic:
yappi.set_context_backend("greenlet")
yappi.set_context_name_callback(lambda: f"worker-{id(greenlet.getcurrent())}")
Important: If you set
set_context_id_callbackmanually instead of usingset_context_backend("greenlet"), yappi's name callback still defaults to reading fromthreading._active, which will return names like_DummyThread,Thread, orN/A— especially under gevent. Always pair a customset_context_id_callbackwith a matchingset_context_name_callbackto get meaningful names.
With 'threading' monkey patched
When the threading module is monkey patched, threading.Thread is used to spawn greenlets instead
of gevent.Greenlet. Since yappi reports the name of each greenlet class by default, the user must
inform yappi to retrieve the class name from the threading library instead. Yappi provides
set_context_name_callback to do so. See below for an example:
from gevent import monkey
monkey.patch_all()
import yappi
import threading
import gevent
import time
## Application logic
def burn_cpu(secs):
t0 = time.process_time()
elapsed = 0
while (elapsed <= secs):
for _ in range(1000):
pass
elapsed = time.process_time() - t0
class ThreadA(threading.Thread):
def run(self):
burn_cpu(0.1)
class ThreadB(threading.Thread):
def run(self):
burn_cpu(0.2)
# Running the profiler:
# Step 1: Configure the profiler to work with greenlets
yappi.set_context_backend("greenlet")
yappi.set_clock_type("cpu")
# Step 2: Configure the system to capture thread names correctly
def _ctx_name_callback():
curr_gl = gevent.getcurrent()
if curr_gl is gevent.get_hub():
return curr_gl.__class__.__name__
# yappi._ctx_name_callback returns the name of the thread
# class
return yappi._ctx_name_callback()
yappi.set_context_name_callback(_ctx_name_callback)
# Step 3: Run the profiler and stop it
yappi.start()
a = ThreadA()
b = ThreadB()
a.start()
b.start()
a.join()
b.join()
yappi.stop()
# Step 4: View results
print("## Function stats:")
yappi.get_func_stats().print_all()
print("\n## Greenlet stats:")
yappi.get_greenlet_stats().print_all()
Sample Output:
## Function stats:
Clock type: CPU
Ordered by: totaltime, desc
name ncall tsub ttot tavg
tests/test_random.py:11 burn_cpu 2 0.255339 0.300063 0.150031
..hreading.py:870 ThreadB._bootstrap 1 0.000011 0.200229 0.200229
..ng.py:901 ThreadB._bootstrap_inner 1 0.000059 0.200218 0.200218
tests/test_random.py:24 ThreadB.run 1 0.000010 0.200042 0.200042
..hreading.py:870 ThreadA._bootstrap 1 0.000012 0.100218 0.100218
..
.. More function stats
..
## Greenlet stats:
name id ttot scnt
ThreadB 4 0.200243 1
ThreadA 3 0.100228 1
_MainThread 1 0.000875 4
Hub 2 0.000182 3
Limitations in gevent profiling
Gevent allows users to run functions on a pool of native threads via ThreadPool. All threads spawned as part of this pool cannot be tracked by yappi and so yappi cannot report stats for functions / greenlets running on them.