I've been trying threads recently in my webapp, and I've come across an issue that I cannot seem to solve.
The issue is that I have an index page, and every time the user enters that page, a new thread is being started which checks for changes in my database in a while loop, although I only want one thread to be on at that moment. Is there a way to "kill" a thread when the index page is accessed the second time and then start a new one?
I did try to use is_alive() to check if a thread is already running, but had no success since all the time they're different.
Code example below:
#app.route("/")
def index():
#copy_current_request_context
def check_for_updates():
while True:
...... # Query for information
if something_changed:
socketio.emit('new_notifications', {'data': new_data})
if index_opened_again:
break
sleep(5)
index_thread = threading.Thread(target=check_for_updates, daemon=True)
index_thread.start()
return render_template("index.html")
I user the below code to kill threads when I am existing a server ,
My suggestion is to kill all active threadss before opening a new one
for id, thread in threading._active.items():
ctypes.pythonapi.PyThreadState_SetAsyncExc(thread_id, ctypes.py_object(SystemExit))
In my example I use a global variable in combination with a lock. It's certainly not optimal, but you can check if a thread is already running.
If you're using flask-socketio anyway, I recommend you take a look at start_background_task. The functionality is compatible with that of a standard thread.
from threading import Lock
from flask import Flask, render_template
from flask_socketio import SocketIO
app = Flask(__name__)
app.secret_key = 'your secret here'
sio = SocketIO(app)
thread = None
thread_lock = Lock()
def background_task():
while True:
# ...
sleep(5)
#app.route('/')
def index():
global thread
with thread_lock:
if thread is None:
thread = sio.start_background_task(background_task)
return render_template('index.html')
if __name__ == '__main__':
sio.run(app)
I am new to tornado and have an API that makes a blocking database call. Because of this blocking call, tornado isn't able to serve all the requests if multiple requests come at the same time.
I looked about it and found out that it could be solved using two approaches: Making the code asynchronous and/or using Process Pool Executors. My assumption here is that having multiple process pool executors is like having multiple processes on tornado to serve multiple requests. Every single example I looked at about implementing Process Pool Executor also makes the code asynchronous.
I cannot make the code asynchronous for the time being because it would require more code changes and so I was looking at simple fix using Process Pool Executors.
What I have currently
import tornado.ioloop
import tornado.web
def blocking_call():
import time
time.sleep(60)
return "Done"
class MainHandler(tornado.web.RequestHandler):
def get(self):
val = blocking_call()
self.write(val)
if __name__ == "__main__":
app = tornado.web.Application([(r"/", MainHandler)])
app.listen(8888)
tornado.ioloop.IOLoop.current().start()
What I tried
import tornado.ioloop
import tornado.web
from concurrent.futures import ProcessPoolExecutor
def blocking_call():
import time
time.sleep(60)
return "Done"
class MainHandler(tornado.web.RequestHandler):
def initialize(self, executor):
self.executor = executor
def get(self):
val = self.executor.submit(blocking_call)
self.write(val)
if __name__ == "__main__":
executor = ProcessPoolExecutor(5)
app = tornado.web.Application(
[(r"/", MainHandler, dict(executor=executor))])
app.listen(8888)
tornado.ioloop.IOLoop.current().start()
My problem with this approach is that now I am getting a future object instead of actual response. How do I make the Get request wait for self.executor to complete before sending back the response?
The executor.submit() returns a concurrent.futures.Future which is not awaitable.
I suggest you use Tornado's run_in_executor method to execute the blocking task.
async def get(self):
loop = tornado.ioloop.IOLoop.current()
val = await loop.run_in_executor(self.executor, blocking_call)
self.write(val)
I have a flask app with waitress that gets some data in post-request, then it runs some long computations in long_function and returns result. These computations are parallel and I'm using pebble because I need a timeout option. Also I want the user to be able to send a request to restart the server (i.e. he want to change the number of threads for waitress)
I've found this solution https://gist.github.com/naushadzaman/b65534d912f1551c7d8366b326b7a151
It mostly works, but it doesn't interact well with my pebble pool. I'm having trouble reloading the server while it is in the pool. If I use long_function_without_pool, that doesn't use any multiprocessing, I can reload server even if it is currently does some job (results are lost, of course, but this is what I want). But with long_function I have to wait for the pool to be closed and only then can I restart the server. If I try to send restart request while the pool is still open, I get an error:
OSError: [Errno 98] Address already in use
So I suppose that p.terminate() doesn't work if there is a Pool running.
How can I fix this code, or maybe should I use a different solution?
Brief instructions on how to replicate this error:
start the app
send POST-request with empty body to http://localhost:5221/
before you get a response (you'll have 5 seconds) send GET-request without variables to http://localhost:5221/restart/
enjoy. Server is stuck now and is not responding to anything
import subprocess
from flask import Flask
from flask_restful import Api, Resource
from flask_cors import CORS
from webargs.flaskparser import parser, abort
import json
import time
import sys
from waitress import serve
from multiprocessing import Process, Queue
from concurrent.futures import TimeoutError
from pebble import ProcessPool, ProcessExpired
import functools
some_queue = None
APP = Flask(__name__)
API = Api(APP)
CORS(APP)
#APP.route('/restart/', methods=['GET'], endpoint='start_flaskapp')
def restart():
try:
some_queue.put("something")
print("Restarted successfully")
return("Quit")
except:
print("Failed in restart")
return "Failed"
def start_flaskapp(queue):
global some_queue
some_queue = queue
API.add_resource(FractionsResource, "/")
serve(APP, host='0.0.0.0', port=5221, threads=2)
def long_function():
with ProcessPool(5) as pool:
data = [0, 1, 2, 3, 4]
future = pool.map(functools.partial(add_const, const=1), data, timeout=5)
iterator = future.result()
result=[]
while True:
try:
result.append(next(iterator))
except StopIteration:
break
except TimeoutError as error:
print("function took longer than %d seconds" % error.args[1])
return(result)
def long_function_without_pool():
data = [0, 1, 2, 3, 4]
result = list(map(functools.partial(add_const, const=1), data))
return(result)
def add_const(number, const=0):
time.sleep(5)
return number+const
class FractionsResource(Resource):
#APP.route('/', methods=['POST'])
def post():
response = long_function()
return(json.dumps(response))
if __name__ == "__main__":
q = Queue()
p = Process(target=start_flaskapp, args=(q,))
p.start()
while True: #wathing queue, if there is no call than sleep, otherwise break
if q.empty():
time.sleep(1)
else:
break
p.terminate() #terminate flaskapp and then restart the app on subprocess
args = [sys.executable] + [sys.argv[0]]
subprocess.call(args)
I want to execute an async function every time the Flask route is executed. Why is the abar function never executed?
import asyncio
from flask import Flask
async def abar(a):
print(a)
loop = asyncio.get_event_loop()
app = Flask(__name__)
#app.route("/")
def notify():
asyncio.ensure_future(abar("abar"), loop=loop)
return "OK"
if __name__ == "__main__":
app.run(debug=False, use_reloader=False)
loop.run_forever()
I also tried putting the blocking call in a separate thread. But it is still not calling the abar function.
import asyncio
from threading import Thread
from flask import Flask
async def abar(a):
print(a)
app = Flask(__name__)
def start_worker(loop):
asyncio.set_event_loop(loop)
try:
loop.run_forever()
finally:
loop.close()
worker_loop = asyncio.new_event_loop()
worker = Thread(target=start_worker, args=(worker_loop,))
#app.route("/")
def notify():
asyncio.ensure_future(abar("abar"), loop=worker_loop)
return "OK"
if __name__ == "__main__":
worker.start()
app.run(debug=False, use_reloader=False)
You can incorporate some async functionality into Flask apps without having to completely convert them to asyncio.
import asyncio
from flask import Flask
async def abar(a):
print(a)
loop = asyncio.get_event_loop()
app = Flask(__name__)
#app.route("/")
def notify():
loop.run_until_complete(abar("abar"))
return "OK"
if __name__ == "__main__":
app.run(debug=False, use_reloader=False)
This will block the Flask response until the async function returns, but it still allows you to do some clever things. I've used this pattern to perform many external requests in parallel using aiohttp, and then when they are complete, I'm back into traditional flask for data processing and template rendering.
import aiohttp
import asyncio
import async_timeout
from flask import Flask
loop = asyncio.get_event_loop()
app = Flask(__name__)
async def fetch(url):
async with aiohttp.ClientSession() as session, async_timeout.timeout(10):
async with session.get(url) as response:
return await response.text()
def fight(responses):
return "Why can't we all just get along?"
#app.route("/")
def index():
# perform multiple async requests concurrently
responses = loop.run_until_complete(asyncio.gather(
fetch("https://google.com/"),
fetch("https://bing.com/"),
fetch("https://duckduckgo.com"),
fetch("http://www.dogpile.com"),
))
# do something with the results
return fight(responses)
if __name__ == "__main__":
app.run(debug=False, use_reloader=False)
A simpler solution to your problem (in my biased view) is to switch to Quart from Flask. If so your snippet simplifies to,
import asyncio
from quart import Quart
async def abar(a):
print(a)
app = Quart(__name__)
#app.route("/")
async def notify():
await abar("abar")
return "OK"
if __name__ == "__main__":
app.run(debug=False)
As noted in the other answers the Flask app run is blocking, and does not interact with an asyncio loop. Quart on the other hand is the Flask API built on asyncio, so it should work how you expect.
Also as an update, Flask-Aiohttp is no longer maintained.
Your mistake is to try to run the asyncio event loop after calling app.run(). The latter doesn't return, it instead runs the Flask development server.
In fact, that's how most WSGI setups will work; either the main thread is going to busy dispatching requests, or the Flask server is imported as a module in a WSGI server, and you can't start an event loop here either.
You'll instead have to run your asyncio event loop in a separate thread, then run your coroutines in that separate thread via asyncio.run_coroutine_threadsafe(). See the Coroutines and Multithreading section in the documentation for what this entails.
Here is an implementation of a module that will run such an event loop thread, and gives you the utilities to schedule coroutines to be run in that loop:
import asyncio
import itertools
import threading
__all__ = ["EventLoopThread", "get_event_loop", "stop_event_loop", "run_coroutine"]
class EventLoopThread(threading.Thread):
loop = None
_count = itertools.count(0)
def __init__(self):
self.started = threading.Event()
name = f"{type(self).__name__}-{next(self._count)}"
super().__init__(name=name, daemon=True)
def __repr__(self):
loop, r, c, d = self.loop, False, True, False
if loop is not None:
r, c, d = loop.is_running(), loop.is_closed(), loop.get_debug()
return (
f"<{type(self).__name__} {self.name} id={self.ident} "
f"running={r} closed={c} debug={d}>"
)
def run(self):
self.loop = loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.call_later(0, self.started.set)
try:
loop.run_forever()
finally:
try:
shutdown_asyncgens = loop.shutdown_asyncgens()
except AttributeError:
pass
else:
loop.run_until_complete(shutdown_asyncgens)
try:
shutdown_executor = loop.shutdown_default_executor()
except AttributeError:
pass
else:
loop.run_until_complete(shutdown_executor)
asyncio.set_event_loop(None)
loop.close()
def stop(self):
loop, self.loop = self.loop, None
if loop is None:
return
loop.call_soon_threadsafe(loop.stop)
self.join()
_lock = threading.Lock()
_loop_thread = None
def get_event_loop():
global _loop_thread
if _loop_thread is None:
with _lock:
if _loop_thread is None:
_loop_thread = EventLoopThread()
_loop_thread.start()
# give the thread up to a second to produce a loop
_loop_thread.started.wait(1)
return _loop_thread.loop
def stop_event_loop():
global _loop_thread
with _lock:
if _loop_thread is not None:
_loop_thread.stop()
_loop_thread = None
def run_coroutine(coro):
"""Run the coroutine in the event loop running in a separate thread
Returns a Future, call Future.result() to get the output
"""
return asyncio.run_coroutine_threadsafe(coro, get_event_loop())
You can use the run_coroutine() function defined here to schedule asyncio routines. Use the returned Future instance to control the coroutine:
Get the result with Future.result(). You can give this a timeout; if no result is produced within the timeout, the coroutine is automatically cancelled.
You can query the state of the coroutine with the .cancelled(), .running() and .done() methods.
You can add callbacks to the future, which will be called when the coroutine has completed, or is cancelled or raised an exception (take into account that this is probably going to be called from the event loop thread, not the thread that you called run_coroutine() in).
For your specific example, where abar() doesn't return any result, you can just ignore the returned future, like this:
#app.route("/")
def notify():
run_coroutine(abar("abar"))
return "OK"
Note that before Python 3.8 that you can't use an event loop running on a separate thread to create subprocesses with! See my answer to Python3 Flask asyncio subprocess in route hangs for backport of the Python 3.8 ThreadedChildWatcher class for a work-around for this.
For same reason you won't see this print:
if __name__ == "__main__":
app.run(debug=False, use_reloader=False)
print('Hey!')
loop.run_forever()
loop.run_forever() is never called since as #dirn already noted app.run is also blocking.
Running global blocking event loop - is only way you can run asyncio coroutines and tasks, but it's not compatible with running blocking Flask app (or with any other such thing in general).
If you want to use asynchronous web framework you should choose one created to be asynchronous. For example, probably most popular now is aiohttp:
from aiohttp import web
async def hello(request):
return web.Response(text="Hello, world")
if __name__ == "__main__":
app = web.Application()
app.router.add_get('/', hello)
web.run_app(app) # this runs asyncio event loop inside
Upd:
About your try to run event loop in background thread. I didn't investigate much, but it seems problem somehow related with tread-safety: many asyncio objects are not thread-safe. If you change your code this way, it'll work:
def _create_task():
asyncio.ensure_future(abar("abar"), loop=worker_loop)
#app.route("/")
def notify():
worker_loop.call_soon_threadsafe(_create_task)
return "OK"
But again, this is very bad idea. It's not only very inconvenient, but I guess wouldn't make much sense: if you're going to use thread to start asyncio, why don't just use threads in Flask instead of asyncio? You will have Flask you want and parallelization.
If I still didn't convince you, at least take a look at Flask-aiohttp project. It has close to Flask api and I think still better that what you're trying to do.
The main issue, as already explained in the other answers by #Martijn Pieters and #Mikhail Gerasimov is that app.run is blocking, so the line loop.run_forever() is never called. You will need to manually set up and maintain a run loop on a separate thread.
Fortunately, with Flask 2.0, you don't need to create, run, and manage your own event loop anymore. You can define your route as async def and directly await on coroutines from your route functions.
https://flask.palletsprojects.com/en/2.0.x/async-await/
Using async and await
New in version 2.0.
Routes, error handlers, before request, after request, and teardown
functions can all be coroutine functions if Flask is installed with
the async extra (pip install flask[async]). It requires Python 3.7+
where contextvars.ContextVar is available. This allows views to be
defined with async def and use await.
Flask will take care of creating the event loop on each request. All you have to do is define your coroutines and await on them to finish:
https://flask.palletsprojects.com/en/2.0.x/async-await/#performance
Performance
Async functions require an event loop to run. Flask, as a WSGI
application, uses one worker to handle one request/response cycle.
When a request comes into an async view, Flask will start an event
loop in a thread, run the view function there, then return the result.
Each request still ties up one worker, even for async views. The
upside is that you can run async code within a view, for example to
make multiple concurrent database queries, HTTP requests to an
external API, etc. However, the number of requests your application
can handle at one time will remain the same.
Tweaking the original example from the question:
import asyncio
from flask import Flask, jsonify
async def send_notif(x: int):
print(f"Called coro with {x}")
await asyncio.sleep(1)
return {"x": x}
app = Flask(__name__)
#app.route("/")
async def notify():
futures = [send_notif(x) for x in range(5)]
results = await asyncio.gather(*futures)
response = list(results)
return jsonify(response)
# The recommended way now is to use `flask run`.
# See: https://flask.palletsprojects.com/en/2.0.x/cli/
# if __name__ == "__main__":
# app.run(debug=False, use_reloader=False)
$ time curl -s -XGET 'http://localhost:5000'
[{"x":0},{"x":1},{"x":2},{"x":3},{"x":4}]
real 0m1.016s
user 0m0.005s
sys 0m0.006s
Most common recipes using asyncio can be applied the same way. The one thing to take note of is, as of Flask 2.0.1, we cannot use asyncio.create_task to spawn background tasks:
https://flask.palletsprojects.com/en/2.0.x/async-await/#background-tasks
Async functions will run in an event loop until they complete, at which
stage the event loop will stop. This means any additional spawned
tasks that haven’t completed when the async function completes will be
cancelled. Therefore you cannot spawn background tasks, for example
via asyncio.create_task.
If you wish to use background tasks it is best to use a task queue to
trigger background work, rather than spawn tasks in a view function.
Other than the limitation with create_task, it should work for use-cases where you want to make async database queries or multiple calls to external APIs.
I'm trying to use threading in my flask app, like:
#app.route('/index')
def index():
t = threading.Thread(do_sth_else())
t.start()
print('ready to response')
return render_template('index.html')
def do_sth_else():
time.sleep(5)
print('sth else done')
When calling 127.0.0.1:5000/index in the browser, the result in the server console is not what I expected:
sth else done
ready to response
I want the do_sth_else() function to run in some other thread, while the index() function go on returning the response right away, which means I should see the above result in defferent order.
So I want to know:
Why the index() function kept waiting until do_sth_else() is finished
How do I get the app working as I wanted
Thanks!
t = threading.Thread(do_sth_else()) calls do_sth_else() and pass it's result to Thread.
You should use it like t = threading.Thread(do_sth_else).
This example working as you want (tested on Python 3.4.3)
from time import sleep
from concurrent.futures import ThreadPoolExecutor
# DOCS https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ThreadPoolExecutor
executor = ThreadPoolExecutor(2)
#app.route('/index')
def index():
executor.submit(do_sth_else)
print('ready to response')
return render_template('index.html')
def do_sth_else():
print("Task started!")
sleep(10)
print("Task is done!")
For actual parallelization in Python, you should use the multiprocessing module to fork multiple processes that execute in parallel.
Python threads provide interleaving, but are in fact executed serially, not in parallel.
This applies to CPython due to the existence of global interpreter lock, otherwise true concurrency is bound to number of cpu's you have