Skip to content

Latest commit

 

History

History
180 lines (126 loc) · 7.67 KB

README.md

File metadata and controls

180 lines (126 loc) · 7.67 KB
Raw

DjangoCon 2023 Celery Setup

This repo contains the code accompanying the talk How to Schedule Tasks with Celery and Django from DjangoCon 2023. It's based on Django 4.2 and Celery 5.3.

Prerequisites

Getting started

  1. Checkout this repo:

    git clone [email protected]:tobiasmcnulty/djc23_celery.git
cd djc23_celery

  2. Configure your .envrc file:

    # Use direnv to create a Python 3.11 virtual environment:
echo "layout python python3.11" > .envrc
# On Mac, allow multithreading scripts:
echo "export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES" >> .envrc
# On all systems, activate the .envrc file:
direnv allow

  3. Install requirements:

    python -m pip install -r requirements.txt

  4. Run migrations:

    python manage.py migrate

  5. Start broker (RabbitMQ):

    docker run --rm -p 5672:5672 -d --name rabbitmq rabbitmq

  6. Generate 10,000 fake citizens in the database:

    python manage.py create_fake_voter_data 10000

Starting Celery processes

worker

In one terminal, start the Celery worker with a concurrency of 8 (or another number of your choosing):

celery -A djc23_celery worker -l INFO -c 8

Testing tasks with management commands

These commands should be run in another terminal. You can watch the log output in the Celery worker terminal.

Option 1: All in one

python manage.py queue_task all_in_one

In the celery worker terminal, you should see a single task kick off, and iterate through the centers one by one:

[2023-10-04 15:18:46,535: INFO/MainProcess] Task voter_rolls.tasks.task_all_in_one[5b3cdfef-234a-4202-9e7c-e9c5fb19fe89] received
[2023-10-04 15:18:46,547: WARNING/ForkPoolWorker-7] Writing center list for SUWANNEE MIDDLE SCHOOL (88282)
[2023-10-04 15:18:46,564: WARNING/ForkPoolWorker-7] Assigning stations for 88282 (SUWANNEE MIDDLE SCHOOL)
<snip>
[2023-10-04 15:19:35,894: WARNING/ForkPoolWorker-7] Total pages written: 263
[2023-10-04 15:19:35,900: INFO/ForkPoolWorker-7] Task voter_rolls.tasks.task_all_in_one[5b3cdfef-234a-4202-9e7c-e9c5fb19fe89] succeeded in 49.36396820799564s: None

Option 2:

python manage.py queue_task parallelize_tasks_by_center_with_join

This time, you'll see a number of tasks kick off all at once (one for each center), and then the parent task will terminate with a RuntimeError:

[2023-10-04 15:20:36,676: INFO/MainProcess] Task voter_rolls.tasks.task_parallelize_tasks_by_center_with_join[43988c5a-f145-4e13-acf7-b1413eab813b] received
[2023-10-04 15:20:36,698: INFO/MainProcess] Task voter_rolls.tasks.task_generate_lists_for_one_center[6ff5d8fe-f80b-4511-9394-92537cb4ae67] received
[2023-10-04 15:20:36,699: INFO/MainProcess] Task voter_rolls.tasks.task_generate_lists_for_one_center[5dcfe255-67ef-4e08-a296-cc7b84481a25] received
<snip>
RuntimeError: Never call result.get() within a task!

The other tasks have already been queued, however, so you'll see them continue to execute.

Option 3: Queue tasks by center with chord

python manage.py queue_task queue_tasks_by_center_with_chord

This time, the output should look similar to Option 2, but without the exception. Once all the center-level tasks have been executed, you'll see the task_sum_pages task get queued and immediately return the sum of the results from the previous tasks:

[2023-10-04 15:23:59,002: INFO/MainProcess] Task voter_rolls.tasks.task_sum_pages[a132f8cc-e3ba-49a9-aa8c-595842df9912] received
[2023-10-04 15:23:59,002: WARNING/ForkPoolWorker-7] sum_pages: Total pages written: 263
[2023-10-04 15:23:59,005: INFO/ForkPoolWorker-7] Task voter_rolls.tasks.task_sum_pages[a132f8cc-e3ba-49a9-aa8c-595842df9912] succeeded in 0.0030205770017346367s: None

Option 4: Assign all stations in parallel

python manage.py queue_task assign_all_stations_in_parallel

When this task is executed, you should see one instance of task_assign_stations_for_one_center get kicked off for every center in the database. While the performance implications aren't noticeable for our test database size, it will become inefficient with a larger database and more queries. As noted in the talk, this is a type of database-intensive work that is better optimized through the database queries themselves, rather than trying to do more things at once.

[2023-10-04 15:25:46,769: INFO/MainProcess] Task voter_rolls.tasks.task_assign_all_stations_in_parallel[6fcd7ce3-7784-4de5-9c1e-1dce39b905b8] received
[2023-10-04 15:25:46,826: INFO/MainProcess] Task voter_rolls.tasks.task_assign_stations_for_one_center[36b66ad9-f6b4-4472-a133-531f14c86b78] received
[2023-10-04 15:25:46,827: INFO/MainProcess] Task voter_rolls.tasks.task_assign_stations_for_one_center[596c4efd-afc9-4ea0-8b58-9ac70a266eb5] received

Option 5A: Assign stations and generate lists with starmap

python manage.py queue_task assign_stations_and_generate_lists_with_starmap

When this task is executed, you will see Celery queue and begin to execute a single task that iterates through all the (center_id, station_id) pairs in order:

[2023-10-04 15:28:52,035: INFO/ForkPoolWorker-7] Task voter_rolls.tasks.task_assign_stations_and_generate_lists_with_starmap[61616618-bb2a-4c0a-a09f-bf7c74e88821] succeeded in 0.37203644499822985s: None
[2023-10-04 15:28:52,040: WARNING/ForkPoolWorker-1] Writing center list for Mayville Middle (24915), station 1 with 500 voters
[2023-10-04 15:28:54,516: WARNING/ForkPoolWorker-1] Writing center list for Mayville Middle (24915), station 2 with 500 voters
[2023-10-04 15:28:56,834: WARNING/ForkPoolWorker-1] Writing center list for Mayville Middle (24915), station 3 with 186 voters
<snip>
[2023-10-04 15:29:40,687: INFO/ForkPoolWorker-1] Task celery.starmap[1142f715-66f5-4376-bbd6-58a1e23a3ec2] succeeded in 48.653234126992174s: [13, 13, 5, 13, 13, 13, 13, 4, 3, 9, 13, 2, 9, 13, 5, 13, 12, 13, 13, 13, 13, 13, 3, 13, 13, 3]

The performance is relatively similar to Option 1 (48 vs 49 seconds).

Option 5B: Assign stations and generate lists with group

python manage.py queue_task assign_stations_and_generate_lists_with_chord

With this option, you will see a task for each (center_id, station_id) pair queued immediately, which are executed in parallel by all configured workers.

[2023-10-04 15:31:19,003: INFO/MainProcess] Task voter_rolls.tasks.task_generate_lists_for_one_station[e7e738b1-92f8-449f-899b-9ccd6a8c3e10] received
<snip>
[2023-10-04 15:31:38,402: INFO/ForkPoolWorker-4] Task voter_rolls.tasks.task_generate_lists_for_one_station[f40367a2-27da-4b25-a013-d5551e7622e5] succeeded in 4.487195311012329s: 13

Since all the work is done in smaller tasks, there is not a total time taken printed anywhere, but you can compare the timestamps of the first and last task to calculate the time taken (in this case, about 19 seconds).

When all the tasks in the group have been completed, task_sum_pages is queued and executed again to tally the results:

[2023-10-04 15:31:38,403: INFO/MainProcess] Task voter_rolls.tasks.task_sum_pages[e09cba55-a1f4-45f9-9a4e-57739d149f93] received
[2023-10-04 15:31:38,405: WARNING/ForkPoolWorker-7] sum_pages: Total pages written: 263
[2023-10-04 15:31:38,407: INFO/ForkPoolWorker-7] Task voter_rolls.tasks.task_sum_pages[e09cba55-a1f4-45f9-9a4e-57739d149f93] succeeded in 0.002908542999648489s: None

Cleanup

To clean up processes left by this repo, after testing you can:

  • Stop the Celery worker with Control-C
  • Stop the rabbitmq container with: docker stop rabbitmq