I am trying to learn how to scheduled a task in Django using schedule package. Here is the code I have added to my view. I should mention that I only have one view so I need to run scheduler in my index view.. I know there is a problem in code logic and it only render scheduler and would trap in the loop.. Can you tell me how can I use it?
def job():
print "this is scheduled job", str(datetime.now())
def index(request):
schedule.every(10).second.do(job())
while True:
schedule.run_pending()
time.sleep(1)
objs= objsdb.objects.all()
template = loader.get_template('objtest/index.html')
context= { 'objs': objs}
return HttpResponse(template.render(context, request))
You picked the wrong approach. If you want to schedule something that should run periodically you should not do this within a web request. The request never ends, because of the wile loop - and browsers and webservers very much dislike this behavior.
Instead you might want to write a management command that runs on its own and is responsible to call your tasks.
Additionally you might want to read Django - Set Up A Scheduled Job? - they also tell something about other approaches like AMPQ and cron. But those would replace your choice of the schedule module.
I need to run celery task only when django request finished.
Is it possible?
I've found that the best way to make sure your task happens after the request is finished is to write a custom middleware. In the process_response method, you can handle any quick actions that don't impact page load time or performance too much. Anything else, you can hand off to Celery. Any saving or database transactions are completed by the time process_response is called (AFAICT).
Try something like this:
Django sends request_finished at the end of every request.
You can access request object through sender argument,
from django.dispatch import receiver
from django.core.signals import request_finished
from app.tasks import my_task
#receiver(request_finished)
def add_celery_task(sender):
if sender.__name__ != 'StaticFilesHandler':
my_task.delay()
If you are running server in development environment it's good to check sender's name to avoid adding too many celery task for every static file you are serving.
You can run the task in the background, using delay method of celery. I mean just before returning the response you can call the delay method to put the task in the background.
Some thing like this:
task_name.delay(arg1, arg2, ...)
By doing this your task will be put into background and run asynchronously, this is not going to block the request response cycle .
I read http://bottlepy.org/docs/dev/tutorial_app.html#server-setup
and running Apache + Bottle + Python
and Bottle + Apache + WSGI + Sessions
and I would like to know if one can run asynchronous rest api calls to bottle on mod_wsgi server to a py function that does not return anything(its a backend logic) and is non blocking - so I looked up gevent but i am haven't found a solution where you can run mod_wsgi with gevents.
Is there any solution to async calls to run on apache server using mod_wsgi or any other alternative?
UPDATE
as per andreans' answer below;
I ran a simple myip address return with bottle + celery. so one has to run a celery as #celery.task and then run(host='localhost', port=8080, debug=True)? does it require to start celery worker on terminal as well? never used celery before [runnin locally] also running bottle with decorator #route(/something) works but app.route doesnt where app = Bottle() possibly due to some .wsgi file error?
Sorry, can't fit into the comment box. Every request must get a response eventually (or fail/time out). If you really don't need to return any data to the client, send back just an empty response with a status code. If the processing of the request takes time, it should run asynchronously, and that's where celery comes in. So a blocking implementation of your request handler:
def request_processor_long_running_blocking_func(request_data):
# process request data, which takes a lot of time
# result is probably written into db
pass
def request_handler_func(request):
request_processor_long_running_blocking_func(request.data)
return HttpResponse(status=200)
If I understood correctly this is what you're trying to avoid, by making the request_processor_long_running_blocking_func run asynchronously, so the request_handler_func won't block. This would be solved with celery like this:
from celery.task import task
#task
def request_processor_long_running_blocking_func(request_data):
# the task decorator wraps your blocking function, with celery's Task class
# which has a delay method available for you to call, which will run your function
# asynchronously on one of your celery background worker processes
pass
def request_handler_func(request):
request_processor_long_running_blocking_func.delay(request.data)
# calling the function with delay won't block, it returns immediately
# and your response is sent back instantly
return HttpResponse(status=200)
One more thing, send these task requests with ajax, so your web interface won't be reloaded or anything, so the user can continue using your app after sending the request
In my django application I am using django-notification to send notifications. However I noticed that in some cases (when sending multiple notifications) my web application is giving delayed responses. Although I am sending notifications through Ajax requests, I still think it would be best if I could implement mailtools library which provide threaded emails.
Has anyone implemented such a thing? Is it easy? How can I use ThreadedMailer from mailtools in django-notification?
or, is there another alternative?
Use Celery for this purpose. It's easy to setup with django and you can use the code you're using right now.
The ajax request puts the email into task queue and returns. You could return your task id if you want to check later if the task succeeded.
Update:
Celery only enables you to call your functions in backgound. Say in ajax view you called:
send_email(…)
Now in tasks.py you should define function:
#task
def send_email(…)
And in the view you will call it by:
send_email.delay(…)
And that's it. The email will be sent by background worker deamon using your existing python code.
This doesn't make django-notification obsolete. Celery does completly different thing and can be used with any lib you can imagine.
The only change is task arguments have to be pickable. It means you have to pass db ids, not whole objects, etc.
Background:
I'm working a project which uses Django with a Postgres database. We're also using mod_wsgi in case that matters, since some of my web searches have made mention of it. On web form submit, the Django view kicks off a job that will take a substantial amount of time (more than the user would want to wait), so we kick off the job via a system call in the background. The job that is now running needs to be able to read and write to the database. Because this job takes so long, we use multiprocessing to run parts of it in parallel.
Problem:
The top level script has a database connection, and when it spawns off child processes, it seems that the parent's connection is available to the children. Then there's an exception about how SET TRANSACTION ISOLATION LEVEL must be called before a query. Research has indicated that this is due to trying to use the same database connection in multiple processes. One thread I found suggested calling connection.close() at the start of the child processes so that Django will automatically create a new connection when it needs one, and therefore each child process will have a unique connection - i.e. not shared. This didn't work for me, as calling connection.close() in the child process caused the parent process to complain that the connection was lost.
Other Findings:
Some stuff I read seemed to indicate you can't really do this, and that multiprocessing, mod_wsgi, and Django don't play well together. That just seems hard to believe I guess.
Some suggested using celery, which might be a long term solution, but I am unable to get celery installed at this time, pending some approval processes, so not an option right now.
Found several references on SO and elsewhere about persistent database connections, which I believe to be a different problem.
Also found references to psycopg2.pool and pgpool and something about bouncer. Admittedly, I didn't understand most of what I was reading on those, but it certainly didn't jump out at me as being what I was looking for.
Current "Work-Around":
For now, I've reverted to just running things serially, and it works, but is slower than I'd like.
Any suggestions as to how I can use multiprocessing to run in parallel? Seems like if I could have the parent and two children all have independent connections to the database, things would be ok, but I can't seem to get that behavior.
Thanks, and sorry for the length!
Multiprocessing copies connection objects between processes because it forks processes, and therefore copies all the file descriptors of the parent process. That being said, a connection to the SQL server is just a file, you can see it in linux under /proc//fd/.... any open file will be shared between forked processes. You can find more about forking here.
My solution was just simply close db connection just before launching processes, each process recreate connection itself when it will need one (tested in django 1.4):
from django import db
db.connections.close_all()
def db_worker():
some_paralell_code()
Process(target = db_worker,args = ())
Pgbouncer/pgpool is not connected with threads in a meaning of multiprocessing. It's rather solution for not closing connection on each request = speeding up connecting to postgres while under high load.
Update:
To completely remove problems with database connection simply move all logic connected with database to db_worker - I wanted to pass QueryDict as an argument... Better idea is simply pass list of ids... See QueryDict and values_list('id', flat=True), and do not forget to turn it to list! list(QueryDict) before passing to db_worker. Thanks to that we do not copy models database connection.
def db_worker(models_ids):
obj = PartModelWorkerClass(model_ids) # here You do Model.objects.filter(id__in = model_ids)
obj.run()
model_ids = Model.objects.all().values_list('id', flat=True)
model_ids = list(model_ids) # cast to list
process_count = 5
delta = (len(model_ids) / process_count) + 1
# do all the db stuff here ...
# here you can close db connection
from django import db
db.connections.close_all()
for it in range(0:process_count):
Process(target = db_worker,args = (model_ids[it*delta:(it+1)*delta]))
When using multiple databases, you should close all connections.
from django import db
for connection_name in db.connections.databases:
db.connections[connection_name].close()
EDIT
Please use the same as #lechup mentionned to close all connections(not sure since which django version this method was added):
from django import db
db.connections.close_all()
For Python 3 and Django 1.9 this is what worked for me:
import multiprocessing
import django
django.setup() # Must call setup
def db_worker():
for name, info in django.db.connections.databases.items(): # Close the DB connections
django.db.connection.close()
# Execute parallel code here
if __name__ == '__main__':
multiprocessing.Process(target=db_worker)
Note that without the django.setup() I could not get this to work. I am guessing something needs to be initialized again for multiprocessing.
I had "closed connection" issues when running Django test cases sequentially. In addition to the tests, there is also another process intentionally modifying the database during test execution. This process is started in each test case setUp().
A simple fix was to inherit my test classes from TransactionTestCase instead of TestCase. This makes sure that the database was actually written, and the other process has an up-to-date view on the data.
Another way around your issue is to initialise a new connection to the database inside the forked process using:
from django.db import connection
connection.connect()
(not a great solution, but a possible workaround)
if you can't use celery, maybe you could implement your own queueing system, basically adding tasks to some task table and having a regular cron that picks them off and processes? (via a management command)
Hey I ran into this issue and was able to resolve it by performing the following (we are implementing a limited task system)
task.py
from django.db import connection
def as_task(fn):
""" this is a decorator that handles task duties, like setting up loggers, reporting on status...etc """
connection.close() # this is where i kill the database connection VERY IMPORTANT
# This will force django to open a new unique connection, since on linux at least
# Connections do not fare well when forked
#...etc
ScheduledJob.py
from django.db import connection
def run_task(request, job_id):
""" Just a simple view that when hit with a specific job id kicks of said job """
# your logic goes here
# ...
processor = multiprocessing.Queue()
multiprocessing.Process(
target=call_command, # all of our tasks are setup as management commands in django
args=[
job_info.management_command,
],
kwargs= {
'web_processor': processor,
}.items() + vars(options).items()).start()
result = processor.get(timeout=10) # wait to get a response on a successful init
# Result is a tuple of [TRUE|FALSE,<ErrorMessage>]
if not result[0]:
raise Exception(result[1])
else:
# THE VERY VERY IMPORTANT PART HERE, notice that up to this point we haven't touched the db again, but now we absolutely have to call connection.close()
connection.close()
# we do some database accessing here to get the most recently updated job id in the database
Honestly, to prevent race conditions (with multiple simultaneous users) it would be best to call database.close() as quickly as possible after you fork the process. There may still be a chance that another user somewhere down the line totally makes a request to the db before you have a chance to flush the database though.
In all honesty it would likely be safer and smarter to have your fork not call the command directly, but instead call a script on the operating system so that the spawned task runs in its own django shell!
If all you need is I/O parallelism and not processing parallelism, you can avoid this problem by switch your processes to threads. Replace
from multiprocessing import Process
with
from threading import Thread
The Thread object has the same interface as Procsess
If you're also using connection pooling, the following worked for us, forcibly closing the connections after being forked. Before did not seem to help.
from django.db import connections
from django.db.utils import DEFAULT_DB_ALIAS
connections[DEFAULT_DB_ALIAS].dispose()
One possibility is to use multiprocessing spawn child process creation method, which will not copy django's DB connection details to the child processes. The child processes need to bootstrap from scratch, but are free to create/close their own django DB connections.
In calling code:
import multiprocessing
from myworker import work_one_item # <-- Your worker method
...
# Uses connection A
list_of_items = djago_db_call_one()
# 'spawn' starts new python processes
with multiprocessing.get_context('spawn').Pool() as pool:
# work_one_item will create own DB connection
parallel_results = pool.map(work_one_item, list_of_items)
# Continues to use connection A
another_db_call(parallel_results)
In myworker.py:
import django. # <-\
django.setup() # <-- needed if you'll make DB calls in worker
def work_one_item(item):
try:
# This will create a new DB connection
return len(MyDjangoModel.objects.all())
except Exception as ex:
return ex
Note that if you're running the calling code inside a TestCase, mocks will not be propagated to the child processes (will need to re-apply them).
You could give more resources to Postgre, in Debian/Ubuntu you can edit :
nano /etc/postgresql/9.4/main/postgresql.conf
by replacing 9.4 by your postgre version .
Here are some useful lines that should be updated with example values to do so, names speak for themselves :
max_connections=100
shared_buffers = 3000MB
temp_buffers = 800MB
effective_io_concurrency = 300
max_worker_processes = 80
Be careful not to boost too much these parameters as it might lead to errors with Postgre trying to take more ressources than available. Examples above are running fine on a Debian 8GB Ram machine equiped with 4 cores.
Overwrite the thread class and close all DB connections at the end of the thread. Bellow code works for me:
class MyThread(Thread):
def run(self):
super().run()
connections.close_all()
def myasync(function):
def decorator(*args, **kwargs):
t = MyThread(target=function, args=args, kwargs=kwargs)
t.daemon = True
t.start()
return decorator
When you need to call a function asynchronized:
#myasync
def async_function():
...