Django Unique Bulk Inserts - django

I need to be able to quickly bulk insert large amounts of records quickly, while still ensuring uniqueness in the database. The new records to be inserted have already been parsed, and are unique. I'm hoping there is a way to enforce uniqueness at the database level, and not in the code itself.
I'm using MySQL as the database backend. If django supports this functionality in any other database, I am flexible in changing the backend, as this is a requirement.
Bulk inserts in Django don't use the save method, so how can I insert several hundred to several thousand records at a time, while still respecting unique fields and unique together fields?
My model structures, simplified, look something like this:
class Example(models.Model):
Meta:
unique_together = (('name', 'number'),)
name = models.CharField(max_length = 50)
number = models.CharField(max_length = 10)
...
fk = models.ForeignKey(OtherModel)
Edit:
The records that aren't already in the database should be inserted, and the records that already existed should be ignored.

As miki725's mentioned you don't have a problem with your current code.
I'm assuming you are using the bulk_create method. It is true that the save() method is not called when using bulk_create, but the uniqueness of fields is not enforced inside the save() method. When you use unique_together a unique constraint is added to the underlying table in mysql when creating the table:
Django:
unique_together = (('name', 'number'),)
MySQL:
UNIQUE KEY `name` (`name`,`number`)
So if you insert a value into the table using any method (save, bulk_insert or even raw sql) you will get this exception from mysql:
Duplicate entry 'value1-value2' for key 'name'
UPDATE:
What bulk_insert does is that it creates one big query that inserts all the data at once with one query. So if one of the entries is duplicate, it throws an exception and none of the data is inserted.
1- One option is to use batch_size parameter of bulk_insert and make it insert the data in a number of batches so that if one of them fails you only miss rest of the data of that batch. (depends how important it is to insert all the data and how frequent the duplicate entries are)
2- Another option is to write a for loop over the bulk data and insert the bulk data one by one. This way the exception is thrown for that one row only and the rest of the data is inserted. This is gonna query the db every time and is of course a lot slower.
3- Third option is to lift the unique constraint, insert the data using bulk_create and then write a simple query that deletes the duplicate rows.

Django itself does not enforce the unique_together meta attribute. This is enforced by the database using the UNIQUE clause. You can insert as much data as you want and you are guaranteed that the specified fields will be unique. If not, then an exception will be raised (not sure which one). More about unique_together in the docs.

Related

How can I use Django's update_or_create function for a model with multiple fields?

I have a model with many fields. In order to update my model, I make a query for my DB and using the dictfetchall() from Django I get a list of dicts containing the results from the query (each key in each dict is the column name for 1 object).
class Enterprise(models.Model):
--- primary key here ---
...
--- many other fields here ---
I want to use the Django's function update_or_create() for updating the existing rows with new information or creating new rows if the object already not exists (based on its pk).
But I don't know how to implement this, due to the large number of fields. Furthermore, the dict keys are not equal to the name of the field in my model.
How can I do this?
Than you!
If I simplify the question a little, you have a list of many objects as dicts you and to add to your database. Some are new, and some already exist. You want to update the existing and create the new ones.
You can use django-bulk-update-or-create to do that.
For the example, I'll use inputs as the list of dictionaries containing your new information.
Enterprise.objects.bulk_update_or_create([
Enterprise(**fields)
for fields in input
], ["all_your_fields_here"], match_field="pk")

Django queryset behind the scenes

**
Difference between creating a foreign key for consistency and for joins
**
I am fine to use Foreignkey and Queryset API with Django.
I just want to understand little bit more deeply how it works behind the scenes.
In Django manual, it says
a database index is automatically created on the ForeignKey. You can
disable this by setting db_index to False. You may want to avoid the
overhead of an index if you are creating a foreign key for consistency
rather than joins, or if you will be creating an alternative index
like a partial of multiple column index.
creating for a foreign key for consistency rather than joins
this part is confusing me.
I expected that you use Join keyword if you do query with Foreign key like below.
SELECT
*
FROM
vehicles
INNER JOIN users ON vehicles.car_owner = users.user_id
For example,
class Place(models.Model):
name = models.Charfield(max_length=50)
address = models.Charfield(max_length=50)
class Comment(models.Model):
place = models.ForeignKeyField(Place)
content = models.Charfield(max_length=50)
if you use queryset like Comment.objects.filter(place=1), i expected using Join Keyword in low level SQL command.
but, when I checked it by printing out queryset.query in console, it showed like below.
(I simplified with Model just to explains. below, it shows all attributes in my model. you can ignore attributes)
SELECT
"bfm_comment"."id", "bfm_comment"."content", "bfm_comment"."user_id", "bfm_comment"."place_id", "bfm_comment"."created_at"
FROM "bfm_comment" WHERE "bfm_comment"."place_id" = 1
creating a foreign key for consistency vs creating a foreign key for joins
simply, I thought if you use any queryset, it means using foreign key for joins. Because you can get parent's table data by c = Comment.objects.get(id=1) c.place.name easily. I thought it joins two tables behind scenes. But result of Print(queryset.query) didn't how Join Keyword but Find it by Where keyword.
The way I understood from an answer
Case 1:
Comment.objects.filter(place=1)
result
SELECT
"bfm_comment"."id", "bfm_comment"."content", "bfm_comment"."user_id", "bfm_comment"."place_id", "bfm_comment"."created_at"
FROM "bfm_comment"
WHERE "bfm_comment"."id" = 1
Case 2:
Comment.objects.filter(place__name="df")
result
SELECT "bfm_comment"."id", "bfm_comment"."content", "bfm_comment"."user_id", "bfm_comment"."place_id", "bfm_comment"."created_at"
FROM "bfm_comment" INNER JOIN "bfm_place" ON ("bfm_comment"."place_id" = "bfm_place"."id")
WHERE "bfm_place"."name" = df
Case1 is searching rows which has comment.id column is 1 in just Comment table.
But in Case 2, it needs to know Place table's attribute 'name', so It has to use JOIN keyword to check values in column of Place table. Right?
So Is it alright to think that I create a foreign key for joins if i use queryset like Case2 and that it is better to create index on the Foreign Key?
for above question, I think I can take the answer from Django Manual
Consider adding indexes to fields that you frequently query using
filter(), exclude(), order_by(), etc. as indexes may help to speed up
lookups. Note that determining the best indexes is a complex
database-dependent topic that will depend on your particular
application. The overhead of maintaining an index may outweigh any
gains in query speed
In conclusion, it really depends on how my application work with it.
If you execute the following command the mystery will be revealed
./manage.py sqlmigrate myapp 0001
Take care to replace myapp with your app name (bfm I think) and 0001 with the actual migration where the Comment model is created.
The generated sql will reveal that the actual table is created with place_id int rather than a place Place that is because the RDBMS doesn't know anything about models, the models are only in the application level. It's the job of the django orm to fetch the data from the RDBMS and convert them into model instances. That's why you always get a place member in each of your Comment instances and that place member gives you access to the members of the related Place instance in turn.
So what happens when you do?
Comment.objects.filter(place=1)
Django is smart enough to know that you are referring to a place_id because 1 is obviously not an instance of a Place. But if you used a Place instance the result would be the same. So there is no join here. The above query would definitely benefit from having an index on the place_id, but it wouldn't benefit from having a foreign key constraint!! Only the Comment table is queried.
If you want a join, try this:
Comment.objects.filter(place__name='my home')
Queries of this nature with the __ often result in joins, but sometimes it results in a sub query.
Querysets are lazy.
https://docs.djangoproject.com/en/1.10/topics/db/queries/#querysets-are-lazy
QuerySets are lazy – the act of creating a QuerySet doesn’t involve
any database activity. You can stack filters together all day long,
and Django won’t actually run the query until the QuerySet is
evaluated. Take a look at this example:

How to create an auto increment field for a django model, with two keys which are unique_together?

I have a model like
class Item(models.Model):
site = Site()
id_on_site = PositiveIntegerField()
Now i want to create an instance Item(current_site, next_id_on_site) with
next_id_on_site = Item.objects.filter(site=current_site).aggregate(current_id=Max("id_on_site"))['current_id']+1
The problem is, that the operation of generating the ID and creating the Item is not atomic, so there is a race condition which creates duplicate IDs, so .get(site=current_site, id_on_site=someid) will raise a MultipleObjectsReturned exception.
Using unique_together in the model does not help with the generation of the auto increment ID and doesn't seem to be implemented at the DB level at all.
unique_together is definitely implemented in the database, but since it generates a unique database index, you may need to run a migration to see its effects.
If all you want is for id_on_site to be some unique identifier for the item at the site, it might be easier to use something like a UUIDField(default=uuid.uuid4), which has a near-certain guarantee of uniqueness. If you need the ID to be an auto-incrementing integer, that's a bit harder.
One option to avoid the race condition would be to lock the row with the highest id_on_site value. (This will only work on certain database backends, e.g. postgres):
from django.db.transaction import atomic
with atomic():
next_id_on_site = (
Item.objects
.filter(site=current_site)
.select_for_update(nowait=False)
.latest('id_on_site').id_on_site)
Item.objects.create(current_site, next_id_on_site)
This should cause other transactions to block if they're trying to get the highest id_on_site, and once the transaction is committed, the item you just inserted will be returned to the other transaction. This could be problematic if the transaction is long-lived for some reason.

Remove duplicates in Django ORM -- multiple rows

I have a model that has four fields. How do I remove duplicate objects from my database?
Daniel Roseman's answer to this question seems appropriate, but I'm not sure how to extend this to situation where there are four fields to compare per object.
Thanks,
W.
def remove_duplicated_records(model, fields):
"""
Removes records from `model` duplicated on `fields`
while leaving the most recent one (biggest `id`).
"""
duplicates = model.objects.values(*fields)
# override any model specific ordering (for `.annotate()`)
duplicates = duplicates.order_by()
# group by same values of `fields`; count how many rows are the same
duplicates = duplicates.annotate(
max_id=models.Max("id"), count_id=models.Count("id")
)
# leave out only the ones which are actually duplicated
duplicates = duplicates.filter(count_id__gt=1)
for duplicate in duplicates:
to_delete = model.objects.filter(**{x: duplicate[x] for x in fields})
# leave out the latest duplicated record
# you can use `Min` if you wish to leave out the first record
to_delete = to_delete.exclude(id=duplicate["max_id"])
to_delete.delete()
You shouldn't do it often. Use unique_together constraints on database instead.
This leaves the record with the biggest id in the DB. If you want to keep the original record (first one), modify the code a bit with models.Min. You can also use completely different field, like creation date or something.
Underlying SQL
When annotating django ORM uses GROUP BY statement on all model fields used in the query. Thus the use of .values() method. GROUP BY will group all records having those values identical. The duplicated ones (more than one id for unique_fields) are later filtered out in HAVING statement generated by .filter() on annotated QuerySet.
SELECT
field_1,
…
field_n,
MAX(id) as max_id,
COUNT(id) as count_id
FROM
app_mymodel
GROUP BY
field_1,
…
field_n
HAVING
count_id > 1
The duplicated records are later deleted in the for loop with an exception to the most frequent one for each group.
Empty .order_by()
Just to be sure, it's always wise to add an empty .order_by() call before aggregating a QuerySet.
The fields used for ordering the QuerySet are also included in GROUP BY statement. Empty .order_by() overrides columns declared in model's Meta and in result they're not included in the SQL query (e.g. default sorting by date can ruin the results).
You might not need to override it at the current moment, but someone might add default ordering later and therefore ruin your precious delete-duplicates code not even knowing that. Yes, I'm sure you have 100% test coverage…
Just add empty .order_by() to be safe. ;-)
https://docs.djangoproject.com/en/3.2/topics/db/aggregation/#interaction-with-default-ordering-or-order-by
Transaction
Of course you should consider doing it all in a single transaction.
https://docs.djangoproject.com/en/3.2/topics/db/transactions/#django.db.transaction.atomic
If you want to delete duplicates on single or multiple columns, you don't need to iterate over millions of records.
Fetch all unique columns (don't forget to include the primary key column)
fetch = Model.objects.all().values("id", "skuid", "review", "date_time")
Read the result using pandas (I did using pandas instead ORM query)
import pandas as pd
df = pd.DataFrame.from_dict(fetch)
Drop duplicates on unique columns
uniq_df = df.drop_duplicates(subset=["skuid", "review", "date_time"])
## Dont add primary key in subset you dumb
Now, you'll get the unique records from where you can pick the primary key
primary_keys = uniq_df["id"].tolist()
Finally, it's show time (exclude those id's from records and delete rest of the data)
records = Model.objects.all().exclude(pk__in=primary_keys).delete()

django orm - How to use select_related() on the Foreign Key of a Subclass from its Super Class

I've always found the Django orm's handling of subclassing models to be pretty spiffy. That's probably why I run into problems like this one.
Take three models:
class A(models.Model):
field1 = models.CharField(max_length=255)
class B(A):
fk_field = models.ForeignKey('C')
class C(models.Model):
field2 = models.CharField(max_length=255)
So now you can query the A model and get all the B models, where available:
the_as = A.objects.all()
for a in the_as:
print a.b.fk_field.field2 #Note that this throws an error if there is no B record
The problem with this is that you are looking at a huge number of database calls to retrieve all of the data.
Now suppose you wanted to retrieve a QuerySet of all A models in the database, but with all of the subclass records and the subclass's foreign key records as well, using select_related() to limit your app to a single database call. You would write a query like this:
the_as = A.objects.select_related("b", "b__fk_field").all()
One query returns all of the data needed! Awesome.
Except not. Because this version of the query is doing its own filtering, even though select_related is not supposed to filter any results at all:
set_1 = A.objects.select_related("b", "b__fk_field").all() #Only returns A objects with associated B objects
set_2 = A.objects.all() #Returns all A objects
len(set_1) > len(set_2) #Will always be False
I used the django-debug-toolbar to inspect the query and found the problem. The generated SQL query uses an INNER JOIN to join the C table to the query, instead of a LEFT OUTER JOIN like other subclassed fields:
SELECT "app_a"."field1", "app_b"."fk_field_id", "app_c"."field2"
FROM "app_a"
LEFT OUTER JOIN "app_b" ON ("app_a"."id" = "app_b"."a_ptr_id")
INNER JOIN "app_c" ON ("app_b"."fk_field_id" = "app_c"."id");
And it seems if I simply change the INNER JOIN to LEFT OUTER JOIN, then I get the records that I want, but that doesn't help me when using Django's ORM.
Is this a bug in select_related() in Django's ORM? Is there any work around for this, or am I simply going to have to do a direct query of the database and map the results myself? Should I be using something like Django-Polymorphic to do this?
It looks like a bug, specifically it seems to be ignoring the nullable nature of the A->B relationship, if for example you had a foreign key reference to B in A instead of the subclassing, that foreign key would of course be nullable and django would use a left join for it. You should probably raise this in the django issue tracker. You could also try using prefetch_related instead of select_related that might get around your issue.
I found a work around for this, but I will wait a while to accept it in hopes that I can get some better answers.
The INNER JOIN created by the select_related('b__fk_field') needs to be removed from the underlying SQL so that the results aren't filtered by the B records in the database. So the new query needs to leave the b__fk_field parameter in select_related out:
the_as = A.objects.select_related('b')
However, this forces us to call the database everytime a C object is accessed from the A object.
for a in the_as:
#Note that this throws an DoesNotExist error if a doesn't have an
#associated b
print a.b.fk_field.field2 #Hits the database everytime.
The hack to work around this is to get all of the C objects we need from the database from one query and then have each B object reference them manually. We can do this because the database call that accesses the B objects retrieved will have the fk_field_id that references their associated C object:
c_ids = [a.b.fk_field_id for a in the_as] #Get all the C ids
the_cs = C.objects.filter(pk__in=c_ids) #Run a query to get all of the needed C records
for c in the_cs:
for a in the_as:
if a.b.fk_field_id == c.pk: #Throws DoesNotExist if no b associated with a
a.b.fk_field = c
break
I'm sure there's a functional way to write that without the nested loop, but this illustrates what's happening. It's not ideal, but it provides all of the data with the absolute minimum number of database hits - which is what I wanted.