I'm trying to figure out how the async functionality works in Python. I have watched countless videos but I guess I'm not 'getting it'. My code looks as follows:
def run_watchers():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(watcher_helper())
loop.close()
async def watcher_helper():
watchers = Watcher.objects.all()
for watcher in watchers:
print("Running watcher : " + str(watcher.pk))
await watcher_helper2(watcher)
async def watcher_helper2(watcher):
for i in range(1,1000000):
x = i * 1000 / 2000
What makes sense to me is to have three functions. One to start the loop, second to iterate through the different options to execute and third to do the work.
I am expecting the following output:
Running watcher : 1
Running watcher : 2
...
...
Calculation done
Calculation done
...
...
however I am getting:
Running watcher : 1
Calculation done
Running watcher : 2
Calculation done
...
...
which obviously shows the calculations are not done in parallel. Any idea what I am doing wrong?
asyncio can be used only to speedup multiple network I/O related functions (send/receive data through internet). While you wait some data from network (which may take long time) you usually idle. Using asyncio allows you to use this idle time for another useful job: for example, to start another parallel network request.
asyncio can't somehow speedup CPU-related job (which is what watcher_helper2 do in your example). While you multiply some numbers there's simply no idle time which can be used to do something different and to achieve benefit through that.
Read also this answer for more detailed explanation.
Related
Concretely, I'm using Flask to process a request, pseudocode like this:
from flask import Flask, request
app = Flask(__name__)
#app.route("/foo", methods=["POST"])
def foo():
data = request.get_json() # {"request_id": "abc", "data": "some text"}
result_a = do_task_a(data) # returns {"result_a": "a"}, maybe about 1 second to finish
result_b = do_task_b(data) # returns {"result_b": "b"}, maybe about 1 second to finish
result_c = do_task_c(data) # returns {"result_c": "c"}, maybe about 1 second to finish
result = {
"result_a": result_a["result_a"],
"result_b": result_b["result_b"],
"result_c": result_c["result_c"]}
return result
app.run(host='0.0.0.0', port=4000, threaded=False)
Here, do_task_a, do_task_b, do_task_c are completely independent subtasks, I know I can use multiprocessing.Process to create processes to finish these three subtasks, and use join() to wait for subtask done, But I don't know it's proper way to create Process for every request?
Maybe I can use multiprocessing.Queue to help, But I don't find a good way.
I search for multiprocessing, but can't figure out a good solution.
I'm not a python guy, but indeed creating processes is sn expensive operation
If its possible - create threads they're cheaper than processes.
If you run the request multiple times - you can do even better than that, because creating threads per request is still quite expensive
Even more advanced setup is to create a "pre-loaded" thread pool. Like N threads that you always keep in memory ready for running arriving task.
In terms of technical solution I've found This article that explains how to create thread pools in python 3.2+
I am measuring the metrics of an encryption algorithm that I designed. I have declared 2 functions and a brief sample is as follows:
import sys, random, timeit, psutil, os, time
from multiprocessing import Process
from subprocess import check_output
pid=0
def cpuUsage():
global running
while pid == 0:
time.sleep(1)
running=true
p = psutil.Process(pid)
while running:
print(f'PID: {pid}\t|\tCPU Usage: {p.memory_info().rss/(1024*1024)} MB')
time.sleep(1)
def Encryption()
global pid, running
pid = os.getpid()
myList=[]
for i in range(1000):
myList.append(random.randint(-sys.maxsize,sys.maxsize)+random.random())
print('Now running timeit function for speed metrics.')
p1 = Process(target=metric_collector())
p1.start()
p1.join()
number=1000
unit='msec'
setup = '''
import homomorphic,random,sys,time,os,timeit
myList={myList}
'''
enc_code='''
for x in range(len(myList)):
myList[x] = encryptMethod(a, b, myList[x], d)
'''
dec_code='''
\nfor x in range(len(myList)):
myList[x] = decryptMethod(myList[x])
'''
time=timeit.timeit(setup=setup,
stmt=(enc_code+dec_code),
number=number)
running=False
print(f'''Average Time:\t\t\t {time/number*.0001} seconds
Total time for {number} Iters:\t\t\t {time} {unit}s
Total Encrypted/Decrypted Values:\t {number*len(myList)}''')
sys.exit()
if __name__ == '__main__':
print('Beginning Metric Evaluation\n...\n')
p2 = Process(target=Encryption())
p2.start()
p2.join()
I am sure there's an implementation error in my code, I'm just having trouble grabbing the PID for the encryption method and I am trying to make the overhead from other calls as minimal as possible so I can get an accurate reading of just the functionality of the methods being called by timeit. If you know a simpler implementation, please let me know. Trying to figure out how to measure all of the metrics has been killing me softly.
I've tried acquiring the pid a few different ways, but I only want to measure performance when timeit is run. Good chance I'll have to break this out separately and run it that way (instead of multiprocessing) to evaluate the function properly, I'm guessing.
There are at least three major problems with your code. The net result is that you are not actually doing any multiprocessing.
The first problem is here, and in a couple of other similar places:
p2 = Process(target=Encryption())
What this code passes to Process is not the function Encryption but the returned value from Encryption(). It is exactly the same as if you had written:
x = Encryption()
p2 = Process(target=x)
What you want is this:
p2 = Process(target=Encryption)
This code tells Python to create a new Process and execute the function Encryption() in that Process.
The second problem has to do with the way Python handles memory for Processes. Each Process lives in its own memory space. Each Process has its own local copy of global variables, so you cannot set a global variable in one Process and have another Process be aware of this change. There are mechanisms to handle this important situation, documented in the multiprocessing module. See the section titled "Sharing state between processes." The bottom line here is that you cannot simply set a global variable inside a Process and expect other Processes to see the change, as you are trying to do with pid. You have to use one of the approaches described in the documentation.
The third problem is this code pattern, which occurs for both p1 and p2.
p2 = Process(target=Encryption)
p2.start()
p2.join()
This tells Python to create a Process and to start it. Then you immediately wait for it to finish, which means that your current Process must stop at that point until the new Process is finished. You never allow two Processes to run at once, so there is no performance benefit. The only reason to use multiprocessing is to run two things at the same time, which you never do. You might as well not bother with multiprocessing at all since it is only making your life more difficult.
Finally I am not sure why you have decided to try to use multiprocessing in the first place. The functions that measure memory usage and execution time are almost certainly very fast, and I would expect them to be much faster than any method of synchronizing one Process to another. If you're worried about errors due to the time used by the diagnostic functions themselves, I doubt that you can make things better by multiprocessing. Why not just start with a simple program and see what results you get?
I was trying various approaches with python multi-threading to see which one fits my requirements. To give an overview, I have a bunch of items that I need to send to an API. Then based on the response, some of the items will go to a database and all the items will be logged; e.g., for an item if the API returns success, that item will only be logged but when it returns failure, that item will be sent to database for future retry along with logging.
Now based on the API response I can separate out success items from failure and make a batch query with all failure items, which will improve my database performance. To do that, I am accumulating all requests at one place and trying to perform multithreaded API calls(since this is an IO bound task, I'm not even thinking about multiprocessing) but at the same time I need to keep track of which response belongs to which request.
Coming to the actual question, I tried two different approaches which I thought would give nearly identical performance, but there turned out to be a huge difference.
To simulate the API call, I created an API in my localhost with a 500ms sleep(for avg processing time). Please note that I want to start logging and inserting to database after all API calls are complete.
Approach - 1(With threading.Thread and queue.Queue())
import requests
import datetime
import threading
import queue
def target(data_q):
while not data_q.empty():
data_q.get()
response = requests.get("https://postman-echo.com/get?foo1=bar1&foo2=bar2")
print(response.status_code)
data_q.task_done()
if __name__ == "__main__":
data_q = queue.Queue()
for i in range(0, 20):
data_q.put(i)
start = datetime.datetime.now()
num_thread = 5
for _ in range(num_thread):
worker = threading.Thread(target=target(data_q))
worker.start()
data_q.join()
print('Time taken multi-threading: '+str(datetime.datetime.now() - start))
I tried with 5, 10, 20 and 30 times and the results are below correspondingly,
Time taken multi-threading: 0:00:06.625710
Time taken multi-threading: 0:00:13.326969
Time taken multi-threading: 0:00:26.435534
Time taken multi-threading: 0:00:40.737406
What shocked me here is, I tried the same without multi-threading and got almost same performance.
Then after some googling around, I was introduced to futures module.
Approach - 2(Using concurrent.futures)
def fetch_url(im_url):
try:
response = requests.get(im_url)
return response.status_code
except Exception as e:
traceback.print_exc()
if __name__ == "__main__":
data = []
for i in range(0, 20):
data.append(i)
start = datetime.datetime.now()
urls = ["https://postman-echo.com/get?foo1=bar1&foo2=bar2" + str(item) for item in data]
with futures.ThreadPoolExecutor(max_workers=5) as executor:
responses = executor.map(fetch_url, urls)
for ret in responses:
print(ret)
print('Time taken future concurrent: ' + str(datetime.datetime.now() - start))
Again with 5, 10, 20 and 30 times and the results are below correspondingly,
Time taken future concurrent: 0:00:01.276891
Time taken future concurrent: 0:00:02.635949
Time taken future concurrent: 0:00:05.073299
Time taken future concurrent: 0:00:07.296873
Now I've heard about asyncio, but I've not used it yet. I've also read that it gives even better performance than futures.ThreadPoolExecutor().
Final question, If both approaches are using threads(or so I think) then why there is a huge performance gap? Am I doing something terribly wrong? I looked around. Was not able to find a satisfying answer. Any thoughts on this would be highly appreciated. Thanks for going through the question.
[Edit 1]The whole thing is running on python 3.8.
[Edit 2] Updated code examples and execution times. Now they should run on anyone's system.
The documentation of ThreadPoolExecutor explains in detail how many threads are started when the max_workers parameter is not given, as in your example. The behaviour is different depending on the exact Python version, but the number of tasks started is most probably more than 3, the number of threads in the first version using a queue. You should use futures.ThreadPoolExecutor(max_workers= 3) to compare the two approaches.
To the updated Approach - 1 I suggest to modify the for loop a bit:
for _ in range(num_thread):
target_to_run= target(data_q)
print('target to run: {}'.format(target_to_run))
worker = threading.Thread(target= target_to_run)
worker.start()
The output will be like this:
200
...
200
200
target to run: None
target to run: None
target to run: None
target to run: None
target to run: None
Time taken multi-threading: 0:00:10.846368
The problem is that the Thread constructor expects a callable object or None as its target. You do not give it a callable, rather queue processing happens on the first invocation of target(data_q) by the main thread, and 5 threads are started that do nothing because their target is None.
In the example code here all asyncio tasks are started first. After that the tasks are resumed if the IO operation is finished.
The output looks like this where you can see the 6 result messages after the first 6 start messages.
-- Starting https://jamanetwork.com/rss/site_3/67.xml...
-- Starting https://www.b-i-t-online.de/bitrss.xml...
-- Starting http://twitrss.me/twitter_user_to_rss/?user=cochranecollab...
-- Starting http://twitrss.me/twitter_user_to_rss/?user=cochranecollab...
-- Starting https://jamanetwork.com/rss/site_3/67.xml...
-- Starting https://www.b-i-t-online.de/bitrss.xml...
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
1938204 size for https://www.b-i-t-online.de/bitrss.xml
1938204 size for https://www.b-i-t-online.de/bitrss.xml
38697 size for https://jamanetwork.com/rss/site_3/67.xml
38697 size for https://jamanetwork.com/rss/site_3/67.xml
FINISHED with 6 results from 6 tasks.
But what I would expect and what whould speed up the thing in my cases is something like this
-- Starting https://jamanetwork.com/rss/site_3/67.xml...
-- Starting https://www.b-i-t-online.de/bitrss.xml...
-- Starting http://twitrss.me/twitter_user_to_rss/?user=cochranecollab...
1938204 size for https://www.b-i-t-online.de/bitrss.xml
-- Starting http://twitrss.me/twitter_user_to_rss/?user=cochranecollab...
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
-- Starting https://jamanetwork.com/rss/site_3/67.xml...
38697 size for https://jamanetwork.com/rss/site_3/67.xml
-- Starting https://www.b-i-t-online.de/bitrss.xml...
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
28337 size for http://twitrss.me/twitter_user_to_rss/?user=cochranecollab
1938204 size for https://www.b-i-t-online.de/bitrss.xml
38697 size for https://jamanetwork.com/rss/site_3/67.xml
FINISHED with 6 results from 6 tasks.
In my real world code I have hundreds of download tasks like this. It is usual that some of the downloads are finished before all of them are started.
Is there a way to handle this with asyncio?
Here is a minimal working example:
#!/usr/bin/env python3
import random
import urllib.request
import asyncio
from concurrent.futures import ThreadPoolExecutor
executor = ThreadPoolExecutor()
loop = asyncio.get_event_loop()
urls = ['https://www.b-i-t-online.de/bitrss.xml',
'https://jamanetwork.com/rss/site_3/67.xml',
'http://twitrss.me/twitter_user_to_rss/?user=cochranecollab']
async def parse_one_url(u):
print('-- Starting {}...'.format(u))
r = await loop.run_in_executor(executor,
urllib.request.urlopen, u)
r = '{} size for {}'.format(len(r.read()), u)
print(r)
async def do_async_parsing():
tasks = [
parse_one_url(u)
for u in urls
]
completed, pending = await asyncio.wait(tasks)
results = [task.result() for task in completed]
print('FINISHED with {} results from {} tasks.'
.format(len(results), len(tasks)))
if __name__ == '__main__':
# blow up the urls
urls = urls * 2
random.shuffle(urls)
try:
#loop.set_debug(True)
loop.run_until_complete(do_async_parsing())
finally:
loop.close()
Side-Question: Isn't asyncio useless in my case? Isn't it easier to use mutliple threads only?
In my real world code I have hundreds of download tasks like this. It is usual that some of the downloads are finished before all of them are started.
Well, you did create all the downloads upfront and instructed asyncio to launch them all using asyncio.wait. Just starting to execute a coroutine is almost free, so there is no reason for this part to be limited in any way. However, the tasks actually submitted to ThreadPoolExecutor are capped to the number of workers in the pool, the default being 5 times the number of CPUs, but configurable. If the number of URLs exceeds the number of workers, you should get the desired behavior. (But to actually observe it, you need to move the logging prints into the function managed by the executor.)
Note that the synchronous call to r.read() must also reside inside the function run by the executor, otherwise it will block the entire event loop. The corrected portion of the code would look like this:
def urlopen(u):
print('-- Starting {}...'.format(u))
r = urllib.request.urlopen(u) # blocking call
content = r.read() # another blocking call
print('{} size for {}'.format(len(content), u))
async def parse_one_url(u):
await loop.run_in_executor(executor, urlopen, u)
The above is, however, not idiomatic use of asyncio. Normally the idea is that you don't use threads at all, but call natively async code, for example using aiohttp. Then you get the benefits of asyncio, such as working cancellation and scalability to a large number of tasks. In that setup you would limit the number of concurrent tasks by trivially wrapping the retrieval in an asyncio.Semaphore.
If your whole actual logic consists of synchronous calls, you don't need asyncio at all; you can directly submit futures to the executor and use concurrent.futures synchronization functions like wait() and as_completed to wait for them to finish.
I write Python 3 code, in which I have 2 functions. The first function insertBlock() inserts data in MongoDB collection 1, the second function insertTransactionData() takes data from collection 1 and inserts it into collection 2. Data is in very large amount so I use threading to increase performance. But when I use threading it is taking more time to insert data than without threading. I am so confused that exactly how threading will work in my code and how to increase performance? Here is the main function :
if __name__ == '__main__':
t1 = threading.Thread(target=insertBlock())
t1.start()
t2 = threading.Thread(target=insertTransactionData())
t2.start()
From the python documentation for threading:
target is the callable object to be invoked by the run() method. Defaults to None, meaning nothing is called.
So the correct usage is
threading.Thread(target=insertBlock)
(without the () after insertBlock), because otherwise insertBlock is called, executed normally (blocking the main thread) and target is set to it's return value None. This causes t1.start() not to do anything and you don't get any performance improvement.
Warning:
Be aware that multithreading gives you no guarantee on what the order of execution in different threads will be. You can not rely on the data that insertBlock has inserted into the database inside the insertTransactionData function, because at the time insertTransactionData uses this data, you can not be sure that it was already inserted. So, maybe multithreading does not work at all for this code or you need to restructure your code and only parallelize those parts that do not depend on each other.
I solved this problem by merging these two functionalities into one new function
insertBlockAndTransaction(startrange,endrange). As these two functionalities depend on each other so what I did is I insert transaction information immediately below where block information is inserted (block number was common and needed for both functionalities).Then did multithreading by creating 10 threads for single function:
for i in range(10):
print('thread:',i)
t1 = threading.Thread(target=insertBlockAndTransaction,args(5000000+i*10000,5000000+(i+1)*10000))
t1.start()
It helps me to deal with increasing execution time for more than 1lakh data.