👷 Support Profiling for checking service performance

[yezz123]

Idea

Profiling is a technique to figure out how time is spent in a program. With these statistics, we can find the “hot spot” of a program and think about ways of improvement. Sometimes, a hot spot in an unexpected location may also hint at a bug in the program.

Pyinstrument is a Python profiler. A profiler is a tool to help you optimize your code - make it faster.

Profile a web request in FastAPI

To profile call stacks in FastAPI, you can write a middleware extension for pyinstrument.

Create an async function and decorate it with app.middleware('http') where the app is the name of your FastAPI application instance.

Make sure you configure a setting to only make this available when required.

from pyinstrument import Profiler


PROFILING = True  # Set this from a settings model

if PROFILING:
    @app.middleware("http")
    async def profile_request(request: Request, call_next):
        profiling = request.query_params.get("profile", False)
        if profiling:
            profiler = Profiler(interval=settings.profiling_interval, async_mode="enabled")
            profiler.start()
            await call_next(request)
            profiler.stop()
            return HTMLResponse(profiler.output_html())
        else:
            return await call_next(request)

To invoke, make any request to your application with the GET parameter profile=1 and it will print the HTML result from pyinstrument.

AuthX's Support

With AuthX the abstract of profiling is easy, it's just about calling the ProfilerMiddleware 's class and calling it in add_middleware(ProfilerMiddleware) func that FastAPI provides.

References:

• Profiling Python Code
• profile-a-web-request-in-fastapi

TODO

• Add Function of Exporting results locally.
• Add Tests for testing the ProfilerMiddleware's class.
• Add Documentation for this Part of Middleware's support
• Add a Little example of how we can use it to Profile our FastAPI application

[sourcery-ai]

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 0.16%.

Quality metrics	Before	After	Change
Complexity	0.00 ⭐	0.00 ⭐	0.00
Method Length	31.00 ⭐	33.50 ⭐	2.50 👎
Working memory	1.00 ⭐	1.00 ⭐	0.00
Quality	96.31% ⭐	96.15% ⭐	-0.16% 👎

Other metrics	Before	After	Change
Lines	32	34	2

Changed files	Quality Before	Quality After	Quality Change
authx/init.py	92.62% ⭐	92.31% ⭐	-0.31% 👎
authx/middleware/init.py	100.00% ⭐	100.00% ⭐	0.00%

Here are some functions in these files that still need a tune-up:

File	Function	Complexity	Length	Working Memory	Quality	Recommendation

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[codecov]

Codecov Report

Merging #240 (65f08f8) into main (203cd10) will increase coverage by 0.20%.
The diff coverage is 94.73%.

@@            Coverage Diff             @@
##             main     #240      +/-   ##
==========================================
+ Coverage   91.20%   91.41%   +0.20%     
==========================================
  Files          54       56       +2     
  Lines        1785     1898     +113     
==========================================
+ Hits         1628     1735     +107     
- Misses        157      163       +6     

Impacted Files	Coverage Δ
tests/middleware/test_middleware.py	94.33% <94.33%> (ø)
authx/middleware/profiler.py	94.82% <94.82%> (ø)
authx/__init__.py	100.00% <100.00%> (ø)
authx/middleware/__init__.py	100.00% <100.00%> (ø)

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[WP-LKL]

Have you considered OpenTelemetry + Digma?

[yezz123]

Have you considered OpenTelemetry + Digma?

Hmmm, looks like really good tools to support, I'm gonna be involved in checking how they gonna fit with a FastAPI application, and give you feedback!

thank you @WP-LKL