We have a Django application that uses Django-river for workflow management. For performance improvement, we had to use bulk_create. We need to insert data into a couple of tables and several rows in each.
Initially, we were using the normal .save() method and the workflow was working as expected (as the post save() signals were creating properly). But once we moved to the bulk_create, the performance was improved from minutes to seconds. But the Django_river stopped working and there was no default post save signals. We had to implement the signals based on the documentation available.
class CustomManager(models.Manager):
def bulk_create(items,....):
super().bulk_create(...)
for i in items:
[......] # code to send signal
And
class Task(models.Model):
objects = CustomManager()
....
This got the workflow working again, but the generation of signals is taking time and this destroys all the performance improvement gained with bulk_create.
So is there a way to improve the signal creation?
More details
def post_save_fn(obj):
post_save.send(obj.__class__, instance=obj, created=True)
class CustomManager(models.Manager):
def bulk_create(self, objs, **kwargs):
#Your code here
data_obj = super(CustomManager, self).bulk_create(objs,**kwargs)
for i in data_obj:
# t1 = threading.Thread(target=post_save_fn, args=(i,))
# t1.start()
post_save.send(i.__class__, instance=i, created=True)
return data_obj
class Test(Base):
test_name = models.CharField(max_length=100)
test_code = models.CharField(max_length=50)
objects = CustomManager()
class Meta:
db_table = "test_db"
What is the problem?
As others have mentioned in the comments, the problem is that the functions that are getting called via the post_save are taking a long time. (Remember that signals are not async!! - this is a common misconception).
I'm not familiar with django-river but taking a quick look at the functions that will get called post-save (see here and here) we can see that they involve additional calls to the database.
Whilst you save a lot of individual db hits by using bulk_create you are still doing calling the database again multiple times for each post_save signal.
What can be done about it?
In short. Not much!! For the vast majority of django requests, the slow part will be calling the database. This is why we try and minimise the number of calls to the db (using things like bulk_create).
Reading through the first few paragraphs of django-river the whole idea is to move things that would normally be in code to the database. The big advantage here is that you don't need to re-write code and re-deploy so often. But the disadvantage is that you're inevitably going to have to refer to the database more, which is going to slow things down. This will be fine for some use-cases, but not all.
There are two things I can think of which might help:
Does all of this currently happen as part of the request/response cycle. And if it is, does it need to be? If the answers to these two questions are 'yes' and 'no' respectively, then you could move this work to a separate task queue. This will still be slow, but at least it won't slow down your site.
Depending on exactly what your workflows are and the nature of the data you are creating, it might be the case that you can do everything that the post_save signals are doing in your own function, and do it more efficiently. But this will definitely depend upon your data, and your app, and will move away from the philosophy of django-river.
Use a separated worker if the "signal" logic allows you to be executed after the bulk save.
You can create an additional queue table and put the metadata about what to do for your future worker.
Create a separated worker (Django module) with needed logic and data from the queue table. You can do it as management command, this will allow you to run the worker in the main flow (you can run management commands from regular Django code) or you can run it by crontab based on a schedule.
How to run such a worker?
If you need something to be done as closely as you've created records - run it in a separate thread using the threading module. So your request-response lifecycle will be done right after you've started a new thread.
Else if you can do it later - make a schedule and run it by crontab using the management command framework.
I want to start using django-reversion. It seems the easiest way is to use their middleware. But it gives the following warning:
Warning: Due to changes in the Django 1.6 transaction handling, revision data will be saved in a separate database transaction to the one used to save your models, even if you set ATOMIC_REQUESTS = True.
What are the caveats if the requests are not atomic? It seems to indicate that there might be some kind of race conditions. How could they look like? What do I need to watch out for?
Thank you for your time. Sorry for spelling mistakes I'm not a native speaker.
As mentioned in the warning, due to some changes in the way django handles transactions since 1.6, the middlewares are no longer wrapped in the same transaction as the view function.
This is discussed at the following issue at django-reversion.
In practice, since the RevisionMiddleware runs outside of the transaction where the models are saved, no strict guarantee can be provided at the database level that reversion data will also be saved.
The usage of RevisionMiddleware was then discouraged. The following practice is advised:
If you need to ensure that your models and revisions are saved in the
save transaction, please use the reversion.create_revision() context
manager or decorator in combination with transaction.atomic()
This way, you can be sure that reversion_data will always be saved alongside model data. I hope this helps.
Using Django 1.3 with PostgreSQL 9.0, I have a multi-step object creation function/view, where:
The main object is created (have tried both MyModel.objects.create() and manually using object.save() methods) and,
Then m2m relationships are setup (they must follow the main object creation so that said object has an id to relate to).
Some of those relationships may fail, or some other problem may arise, thus I need the entire function to behave atomically.
I've tried wrapping the function with the transaction.commit_on_success decorator, as well as tried using commit_manually (and setting the commit point at the end of the function); but neither works. That is, the main object is created and saved in the database, even when an exception is raised later on in the function. This leaves the database in an inconsistent state, to put it politely. So, how to debug this? I've seen similar questions, but they had to do with using MySQL, whereas this kind of broken transaction is not supposed to happen with Postgres. There were tickets on the Django Trac about this issue from years back, but they were supposedly fixed/resolved. Could any Djangonauts out there provide enlightenment please?
See this ticket: https://code.djangoproject.com/ticket/6669
I think for now you'll just need to call transaction.rollback() explicitly when you get an IntegrityError
I don't know if this applies to you, but the problem that brought me here was a failure to read the manual with regard to Django testing.
If you are testing code with transactions in it you need to use TransactionTestCase instead of TestCase, failure to do so will result in the tests seeing the behavior you describe.
I have a django project in which database values need to be updated fairly often on their own. There's a cronjob that runs to update these values in the database, but some of the operations require atomic transactions. Does anybody know how to make a model method be a complete transaction in django without going through views?
Ideally, I'd like to be able to start a transaction at the beginning of a method and commit it at the end, and then just be able to call that method from anywhere (a view or a cronjob) with the guarantee that the method is atomic.
If you know how to do this, I also need to know whether or not, should the commit fail (due to a simultaneous write or something), the transaction is automatically re-attempted, or if I would have to manually catch a failure exception and re-call the method.
Thank you.
Did you have a look at Django's transaction docs? Especially the #transaction.commit_on_success (source code) decorator. It commits the transaction if the decorated function returns without raising an exception. If an exception occurs, it does a rollback.
If there a way to protect against concurrent modifications of the same data base entry by two or more users?
It would be acceptable to show an error message to the user performing the second commit/save operation, but data should not be silently overwritten.
I think locking the entry is not an option, as a user might use the "Back" button or simply close his browser, leaving the lock for ever.
This is how I do optimistic locking in Django:
updated = Entry.objects.filter(Q(id=e.id) && Q(version=e.version))\
.update(updated_field=new_value, version=e.version+1)
if not updated:
raise ConcurrentModificationException()
The code listed above can be implemented as a method in Custom Manager.
I am making the following assumptions:
filter().update() will result in a single database query because filter is lazy
a database query is atomic
These assumptions are enough to ensure that no one else has updated the entry before. If multiple rows are updated this way you should use transactions.
WARNING Django Doc:
Be aware that the update() method is
converted directly to an SQL
statement. It is a bulk operation for
direct updates. It doesn't run any
save() methods on your models, or emit
the pre_save or post_save signals
This question is a bit old and my answer a bit late, but after what I understand this has been fixed in Django 1.4 using:
select_for_update(nowait=True)
see the docs
Returns a queryset that will lock rows until the end of the transaction, generating a SELECT ... FOR UPDATE SQL statement on supported databases.
Usually, if another transaction has already acquired a lock on one of the selected rows, the query will block until the lock is released. If this is not the behavior you want, call select_for_update(nowait=True). This will make the call non-blocking. If a conflicting lock is already acquired by another transaction, DatabaseError will be raised when the queryset is evaluated.
Of course this will only work if the back-end support the "select for update" feature, which for example sqlite doesn't. Unfortunately: nowait=True is not supported by MySql, there you have to use: nowait=False, which will only block until the lock is released.
Actually, transactions don't help you much here ... unless you want to have transactions running over multiple HTTP requests (which you most probably don't want).
What we usually use in those cases is "Optimistic Locking". The Django ORM doesn't support that as far as I know. But there has been some discussion about adding this feature.
So you are on your own. Basically, what you should do is add a "version" field to your model and pass it to the user as a hidden field. The normal cycle for an update is :
read the data and show it to the user
user modify data
user post the data
the app saves it back in the database.
To implement optimistic locking, when you save the data, you check if the version that you got back from the user is the same as the one in the database, and then update the database and increment the version. If they are not, it means that there has been a change since the data was loaded.
You can do that with a single SQL call with something like :
UPDATE ... WHERE version = 'version_from_user';
This call will update the database only if the version is still the same.
Django 1.11 has three convenient options to handle this situation depending on your business logic requirements:
Something.objects.select_for_update() will block until the model become free
Something.objects.select_for_update(nowait=True) and catch DatabaseError if the model is currently locked for update
Something.objects.select_for_update(skip_locked=True) will not return the objects that are currently locked
In my application, which has both interactive and batch workflows on various models, I found these three options to solve most of my concurrent processing scenarios.
The "waiting" select_for_update is very convenient in sequential batch processes - I want them all to execute, but let them take their time. The nowait is used when an user wants to modify an object that is currently locked for update - I will just tell them it's being modified at this moment.
The skip_locked is useful for another type of update, when users can trigger a rescan of an object - and I don't care who triggers it, as long as it's triggered, so skip_locked allows me to silently skip the duplicated triggers.
For future reference, check out https://github.com/RobCombs/django-locking. It does locking in a way that doesn't leave everlasting locks, by a mixture of javascript unlocking when the user leaves the page, and lock timeouts (e.g. in case the user's browser crashes). The documentation is pretty complete.
You should probably use the django transaction middleware at least, even regardless of this problem.
As to your actual problem of having multiple users editing the same data... yes, use locking. OR:
Check what version a user is updating against (do this securely, so users can't simply hack the system to say they were updating the latest copy!), and only update if that version is current. Otherwise, send the user back a new page with the original version they were editing, their submitted version, and the new version(s) written by others. Ask them to merge the changes into one, completely up-to-date version. You might try to auto-merge these using a toolset like diff+patch, but you'll need to have the manual merge method working for failure cases anyway, so start with that. Also, you'll need to preserve version history, and allow admins to revert changes, in case someone unintentionally or intentionally messes up the merge. But you should probably have that anyway.
There's very likely a django app/library that does most of this for you.
Another thing to look for is the word "atomic". An atomic operation means that your database change will either happen successfully, or fail obviously. A quick search shows this question asking about atomic operations in Django.
The idea above
updated = Entry.objects.filter(Q(id=e.id) && Q(version=e.version))\
.update(updated_field=new_value, version=e.version+1)
if not updated:
raise ConcurrentModificationException()
looks great and should work fine even without serializable transactions.
The problem is how to augment the deafult .save() behavior as to not have to do manual plumbing to call the .update() method.
I looked at the Custom Manager idea.
My plan is to override the Manager _update method that is called by Model.save_base() to perform the update.
This is the current code in Django 1.3
def _update(self, values, **kwargs):
return self.get_query_set()._update(values, **kwargs)
What needs to be done IMHO is something like:
def _update(self, values, **kwargs):
#TODO Get version field value
v = self.get_version_field_value(values[0])
return self.get_query_set().filter(Q(version=v))._update(values, **kwargs)
Similar thing needs to happen on delete. However delete is a bit more difficult as Django is implementing quite some voodoo in this area through django.db.models.deletion.Collector.
It is weird that modren tool like Django lacks guidance for Optimictic Concurency Control.
I will update this post when I solve the riddle. Hopefully solution will be in a nice pythonic way that does not involve tons of coding, weird views, skipping essential pieces of Django etc.
To be safe the database needs to support transactions.
If the fields is "free-form" e.g. text etc. and you need to allow several users to be able to edit the same fields (you can't have single user ownership to the data), you could store the original data in a variable.
When the user committs, check if the input data has changed from the original data (if not, you don't need to bother the DB by rewriting old data),
if the original data compared to the current data in the db is the same you can save, if it has changed you can show the user the difference and ask the user what to do.
If the fields is numbers e.g. account balance, number of items in a store etc., you can handle it more automatically if you calculate the difference between the original value (stored when the user started filling out the form) and the new value you can start a transaction read the current value and add the difference, then end transaction. If you can't have negative values, you should abort the transaction if the result is negative, and tell the user.
I don't know django, so I can't give you teh cod3s.. ;)
From here:
How to prevent overwriting an object someone else has modified
I'm assuming that the timestamp will be held as a hidden field in the form you're trying to save the details of.
def save(self):
if(self.id):
foo = Foo.objects.get(pk=self.id)
if(foo.timestamp > self.timestamp):
raise Exception, "trying to save outdated Foo"
super(Foo, self).save()