# models.py
from django.db import models
class Person(models.Model):
first_name = models.CharField(max_length=30)
last_name = models.CharField(max_length=30)
text_blob = models.CharField(max_length=50000)
# tasks.py
import celery
@celery.task
def my_task(person):
# example operation: does something to person
# needs only a few of the attributes of person
# and not the entire bulky record
person.first_name = person.first_name.title()
person.last_name = person.last_name.title()
person.save()
In my application somewhere I have something like:
from models import Person
from tasks import my_task
import celery
g = celery.group([my_task.s(p) for p in Person.objects.all()])
g.apply_async()
How can I efficiently and evenly distribute the Person records to workers running on multiple machines?
Could this be a better idea? Wouldn't it overwhelm the db if Person has a few million records?
# tasks.py import celery from models import Person @celery.task def my_task(person_pk): # example operation that does not need text_blob person = Person.objects.get(pk=person_pk) person.first_name = person.first_name.title() person.last_name = person.last_name.title() person.save() #In my application somewhere from models import Person from tasks import my_task import celery g = celery.group([my_task.s(p.pk) for p in Person.objects.all()]) g.apply_async()
I believe it is better and safer to pass PK rather than the whole model object. Since PK is just a number, serialization is also much simpler. Most importantly, you can use a safer sarializer (json/yaml instead of pickle) and have a peace of mind that you won't have any problems with serializing your model.
As this article says:
Since Celery is a distributed system, you can't know in which process, or even on what machine the task will run. So you shouldn't pass Django model objects as arguments to tasks, its almost always better to re-fetch the object from the database instead, as there are possible race conditions involved.
Yes. If there are millions of records in the database then this probably isn't the best approach, but since you have to go through all many millions of the records, then pretty much no matter what you do, your DB is going to get hit pretty hard.
Here are some alternatives, none of which I'd call "better", just different.
./manage.py my_task a
would update all the records where the last name starts with "a". Obviously you'd have to run this several times to get through the whole database Keep in mind that the .save() is going to be the harder "hit" to the database then actually selecting the millions of records.
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