Architecture Flask vs FastAPI
This seemed a little interesting, so i ran a little tests with ApacheBench:
Flask
from flask import Flask
from flask_restful import Resource, Api
app = Flask(__name__)
api = Api(app)
class Root(Resource):
def get(self):
return {"message": "hello"}
api.add_resource(Root, "/")
FastAPI
from fastapi import FastAPI
app = FastAPI(debug=False)
@app.get("/")
async def root():
return {"message": "hello"}
I ran 2 tests for FastAPI, there was a huge difference:
gunicorn -w 4 -k uvicorn.workers.UvicornWorker fast_api:appuvicorn fast_api:app --reload
So here is the benchmarking results for 5000 requests with a concurrency of 500:
FastAPI with Uvicorn Workers
Concurrency Level: 500
Time taken for tests: 0.577 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 720000 bytes
HTML transferred: 95000 bytes
Requests per second: 8665.48 [#/sec] (mean)
Time per request: 57.700 [ms] (mean)
Time per request: 0.115 [ms] (mean, across all concurrent requests)
Transfer rate: 1218.58 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 6 4.5 6 30
Processing: 6 49 21.7 45 126
Waiting: 1 42 19.0 39 124
Total: 12 56 21.8 53 127
Percentage of the requests served within a certain time (ms)
50% 53
66% 64
75% 69
80% 73
90% 81
95% 98
98% 112
99% 116
100% 127 (longest request)
FastAPI - Pure Uvicorn
Concurrency Level: 500
Time taken for tests: 1.562 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 720000 bytes
HTML transferred: 95000 bytes
Requests per second: 3200.62 [#/sec] (mean)
Time per request: 156.220 [ms] (mean)
Time per request: 0.312 [ms] (mean, across all concurrent requests)
Transfer rate: 450.09 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 8 4.8 7 24
Processing: 26 144 13.1 143 195
Waiting: 2 132 13.1 130 181
Total: 26 152 12.6 150 203
Percentage of the requests served within a certain time (ms)
50% 150
66% 155
75% 158
80% 160
90% 166
95% 171
98% 195
99% 199
100% 203 (longest request)
For Flask:
Concurrency Level: 500
Time taken for tests: 27.827 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 830000 bytes
HTML transferred: 105000 bytes
Requests per second: 179.68 [#/sec] (mean)
Time per request: 2782.653 [ms] (mean)
Time per request: 5.565 [ms] (mean, across all concurrent requests)
Transfer rate: 29.13 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 87 293.2 0 3047
Processing: 14 1140 4131.5 136 26794
Waiting: 1 1140 4131.5 135 26794
Total: 14 1227 4359.9 136 27819
Percentage of the requests served within a certain time (ms)
50% 136
66% 148
75% 179
80% 198
90% 295
95% 7839
98% 14518
99% 27765
100% 27819 (longest request)
Total results
Flask: Time taken for tests: 27.827 seconds
FastAPI - Uvicorn: Time taken for tests: 1.562 seconds
FastAPI - Uvicorn Workers: Time taken for tests: 0.577 seconds
With Uvicorn Workers FastAPI is nearly 48x faster than Flask, which is very understandable. ASGI vs WSGI, so i ran with 1 concurreny:
FastAPI - UvicornWorkers: Time taken for tests: 1.615 seconds
FastAPI - Pure Uvicorn: Time taken for tests: 2.681 seconds
Flask: Time taken for tests: 5.541 seconds
I ran more tests to test out Flask with a production server.
5000 Request 1000 Concurrency
Flask with Waitress
Server Software: waitress
Server Hostname: 127.0.0.1
Server Port: 8000
Document Path: /
Document Length: 21 bytes
Concurrency Level: 1000
Time taken for tests: 3.403 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 830000 bytes
HTML transferred: 105000 bytes
Requests per second: 1469.47 [#/sec] (mean)
Time per request: 680.516 [ms] (mean)
Time per request: 0.681 [ms] (mean, across all concurrent requests)
Transfer rate: 238.22 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 4 8.6 0 30
Processing: 31 607 156.3 659 754
Waiting: 1 607 156.3 658 753
Total: 31 611 148.4 660 754
Percentage of the requests served within a certain time (ms)
50% 660
66% 678
75% 685
80% 691
90% 702
95% 728
98% 743
99% 750
100% 754 (longest request)
Gunicorn with Uvicorn Workers
Server Software: uvicorn
Server Hostname: 127.0.0.1
Server Port: 8000
Document Path: /
Document Length: 19 bytes
Concurrency Level: 1000
Time taken for tests: 0.634 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 720000 bytes
HTML transferred: 95000 bytes
Requests per second: 7891.28 [#/sec] (mean)
Time per request: 126.722 [ms] (mean)
Time per request: 0.127 [ms] (mean, across all concurrent requests)
Transfer rate: 1109.71 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 28 13.8 30 62
Processing: 18 89 35.6 86 203
Waiting: 1 75 33.3 70 171
Total: 20 118 34.4 116 243
Percentage of the requests served within a certain time (ms)
50% 116
66% 126
75% 133
80% 137
90% 161
95% 189
98% 217
99% 230
100% 243 (longest request)
Pure Uvicorn, but this time 4 workers uvicorn fastapi:app --workers 4
Server Software: uvicorn
Server Hostname: 127.0.0.1
Server Port: 8000
Document Path: /
Document Length: 19 bytes
Concurrency Level: 1000
Time taken for tests: 1.147 seconds
Complete requests: 5000
Failed requests: 0
Total transferred: 720000 bytes
HTML transferred: 95000 bytes
Requests per second: 4359.68 [#/sec] (mean)
Time per request: 229.375 [ms] (mean)
Time per request: 0.229 [ms] (mean, across all concurrent requests)
Transfer rate: 613.08 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 20 16.3 17 70
Processing: 17 190 96.8 171 501
Waiting: 3 173 93.0 151 448
Total: 51 210 96.4 184 533
Percentage of the requests served within a certain time (ms)
50% 184
66% 209
75% 241
80% 260
90% 324
95% 476
98% 504
99% 514
100% 533 (longest request)
You are using the time.sleep() function, in a async endpoint. time.sleep() is blocking and should never be used in asynchronous code. What you should be using is probably the asyncio.sleep() function:
import asyncio
import uvicorn
from fastapi import FastAPI
app = FastAPI()
@app.get('/')
async def root():
print('Sleeping for 10')
await asyncio.sleep(10)
print('Awake')
return {'message': 'hello'}
if __name__ == "__main__":
uvicorn.run(app, host="127.0.0.1", port=8000)
That way, each request will take ~10 sec to complete, but you will be able to server multiple requests concurrently.
In general, async frameworks offer replacements for all blocking functions inside the standard library (sleep functions, IO functions, etc.). You are meant to use those replacements when writing async code and (optionally) await them.
Some non-blocking frameworks and libraries such as gevent, do not offer replacements. They instead monkey-patch functions in the standard library to make them non-blocking. This is not the case, as far as I know, for the newer async frameworks and libraries though, because they are meant to allow the developer to use the async-await syntax.
I think you are blocking an event queue in FastAPI which is asynchronous framework whereas in Flask requests are probably run each in new thread. Move all CPU bound tasks to separate processes or in your FastAPI example just sleep on event loop (do not use time.sleep here). In FastAPI run IO bound tasks asynchronously