This document introduces django-celery, an open source asynchronous task queue/job queue for Django projects. It discusses how celery allows distributing tasks across multiple workers for parallel processing. Tasks can run independently, in chains, groups or with callbacks. Celery integrates with Django and allows offloading long-running tasks from the main request-response cycle for improved performance. Examples show how celery can be used to parallelize operations like generating PDFs or processing uploaded images.

1. django-celery
a distributed task queue
alex@eftimie.ro
2013/04/11

2. problem
fast sites > slow sites
caching, load balancing, CDN, nosql, ajax, ...

3. special problems
send an email to 10.000 users
add watermark to an uploaded video
generate a big PDF

4. oldschool solution: crontab
the good:
- simple
- easy to setup
the bad:
- too simple
- hard to scale

5. alternatives
scheduled (crontab-ish):
- APScheduler
parallel work (celery-ish):
- gearman
- Huey
- django-ztask
not a fan :-)

6. introducing Celery

7. introducing Celery
asynchronous job queue/task queue
based on distributed message passing
focused on real time operation
works for scheduled tasks too!
open source and integrated with Django

8. how it works?
worker 1
website .
(view) celery MB
.
.
worker n
results

9. task
@celery.task
def add(x, y):
return x + y
...
add.delay(2, 2) #somewhere in a view
atomic
ideally idempotent
same environment as the website
real-time or scheduled

10. task states
PENDING
STARTED
SUCCESS
FAILURE
RETRY
REVOKED

11. few tips about tasks
granularity
data locality
state
“asserting the world is the responsibility of the task”

12. subtasks
tasks spawned from within another task
calling:
add.subtask(1, 1).delay()
add.s(1, 1).delay() # this is a shortcut
the primitives:
chains, groups, chords, maps, chunks

13. subtasks - partials
add.s(1, 1).delay()
1 + 1
partial:
partial = add.s(1) # incomplete definition
partial.delay(2)
1 + 2 # same as: add.s(1, 2).delay()
partial.delay(0)
1 + 0

14. subtasks: chains
subtasks running one after another
chain(add.s(4, 4), mul.s(8), mul.s(10))
or
add.s(4, 4) | mul.s(8) | mul.s(10)
((4 + 4) * 8) * 10)

15. subtasks: groups
independent tasks running in parallel
g = group(add.s(2, 2), add.s(4, 4))
res = g()
res.get()
[4, 8]

16. subtasks: chords
same as groups, but apply callback on results
@task
def ts(numbers):
return sum(numbers)
chord(add.s(i,i) for i in range(3))(ts.s())
6 # sum([0, 2, 4])
chord(headers)(callback)

17. subtasks: chunks
split a long list of arguments into parts
res = add.chunks(zip(range(100), range(100)), 10)()
>>> res.get()
[[0, 2, 4, 6, 8, 10, 12, 14, 16, 18],
[20, 22, 24, 26, 28, 30, 32, 34, 36, 38],
[40, 42, 44, 46, 48, 50, 52, 54, 56, 58],
[60, 62, 64, 66, 68, 70, 72, 74, 76, 78],
[80, 82, 84, 86, 88, 90, 92, 94, 96, 98],
[100, 102, 104, 106, 108, 110, 112, 114, 116, 118],
[120, 122, 124, 126, 128, 130, 132, 134, 136, 138],
[140, 142, 144, 146, 148, 150, 152, 154, 156, 158],
[160, 162, 164, 166, 168, 170, 172, 174, 176, 178],
[180, 182, 184, 186, 188, 190, 192, 194, 196, 198]]
(10 tasks of 10 adds each)

18. integrate with django project
tasks module inside django app
configuration setup
- message broker
- result backend
start a worker
$ python manage.py celery worker
(start another, tune concurrency, monitor, ...)

19. hidden ad
supervisord.org

20. case study: korect
quiz exam management and automatic paper
processing using OMR
existing solution: desktop app, ~2 tests/minute
using ReportLab, PyPDF, OpenCV

21. django + celery version
~ 20 tests/minute
same machine, 4 worker processes
parallelized parts:
- print file generation
- paper scanning
- correcting and grading
- question usage report

22. example - generate print file
add a Download object to db
delay the task (not the real task):
chord([test_pdf for t in tests])(merge_pdf)
update page with dw status
...
at the end of merge_pdf:
update status, flash user

23. last slide
high availability, high performance solution
easy to set up
fun to use
celeryproject.org

24. ?