Table of Contents
pip install django-orm-performance-enhancersPlease see demo_project/data_models/models.py for the models used in the examples below.
For extra samples beyond what's shown in the README, see the tests.
Usage:
from django.db import models
from django_orm_performance_enhancers import ExtendedQuerySet
class MyModelQuerySet(ExtendedQuerySet):
pass
class MyModel(models.Model):
objects = MyModelQuerySet.as_manager()
# or objects = ExtendedQuerySet.as_manager()As the name suggests, adds callbacks to be evaluated when the queryset is evaluated.
Think of it as a python-based .annotate method.
API:
with_evaluation_callbacks(self, *evaluation_callbacks: EvaluationCallbackType)
EvaluationCallbackType = Callable[[Sequence[TModel]], Union[Sequence[TModel], None]]
Useful for:
- creating custom annotations
- transforming/processing data after evaluation
- prefetching related objects which is tricky or not doable in ORM way
- filtering prefetches in a way not achievable by ORM
- prefetching from tables which don't have a direct relation to current model
- re-using data fetched elsewhere in the queryset
- doing custom joins, which are not possible in ORM way
See a sample:
from data_models.models import User, Vehicle
def prefetch_vehicles_parked_on_user_address(results: list[User]):
address_ids = {user.address_id for user in results}
for user in results:
vehicles_by_address_id = {
vehicle.address_id: vehicle
for vehicle in Vehicle.objects.filter(address_id__in=address_ids)
}
user.vehicles_parked_on_user_address = vehicles_by_address_id.get(user.address_id, [])
class UserQuerySet:
def with_vehicles_parked_on_user_address(self):
return self.with_evaluation_callbacks(prefetch_vehicles_parked_on_user_address)
qs = (
User
.objects
.with_vehicles_parked_on_user_address() # no evaluation
)
qs = qs.annotate(...) # no evaluation
[x.vehicles_parked_on_user_address for x in qs] # evaluated in a single query to Vehicles parked on the user address.A drop-in replacement for prefetch_related which uses a pool of prefetched results.
This has 3 benefits:
- reduces CPU and memory usage while fetching particularly large datasets by re-using instances of models with the same id By default, django creates a new instance of a model for each row even if they have the same id. Here related objs are shared by reference instead. There's a catch: if you modify a shared object, it will get modified everywhere. This might or might not be what you wanted.
- reduces the number of queries by combining multiple prefetch_related targets into one query
- helps saving time on joining the same table multiple times via
select_relatedand re-using the same annotations, which might or might not increase performance. Always benchmark when in doubt.
⤵️ Reducing the number of queries:
from data_models.models import User
regular_results = list(
User
.objects
.select_related('address') # Query1: User + Address
# Query2: rides_starting_here
# Query3: rides_ending_here
# Query4: transactions for rides_starting_here
# Query5: transactions for rides_ending_here
.prefetch_related('address__rides_starting_here__transactions',
'address__rides_ending_here__transactions')
)
first_ride_starting_at_user_address = regular_results[0].address.rides_starting_here.all()[0]
first_ride_ending_at_user_address = regular_results[0].address.rides_ending_here.all()[0]
first_ride_starting_at_user_address.id == first_ride_ending_at_user_address.id # True
first_ride_starting_at_user_address is first_ride_ending_at_user_address # False
pooled_results = list(
User
.objects
.select_related('address') # Query1: User + address
.pool_related('address__rides_starting_here',
'address__rides_ending_here') # Query2: all rides
.pool_related('address__rides_starting_here__transactions',
'address_rides_ending_here__transactions') # Query3: all transactions
)
first_ride_starting_at_user_address = pooled_results[0].address.rides_starting_here.all()[0]
first_ride_ending_at_user_address = pooled_results[0].address.rides_ending_here.all()[0]
first_ride_starting_at_user_address.id == first_ride_ending_at_user_address.id # True
first_ride_starting_at_user_address is first_ride_ending_at_user_address # TrueA drop-in replacement for select_related which re-uses model instances with the same id.
By default, django duplicates instances of models with the same id.
⤵️ Sharing by reference:
from data_models.models import User
regular_results = list(
User
.objects
.select_related('address')
)
regular_results[0].address.id == regular_results[1].address.id # True
regular_results[0].address is regular_results[1].address # False
pooled_results = list(
User
.objects
.select_related_pooled('address') # Query1: User + address
)
regular_results[0].address.id == regular_results[1].address.id # True
regular_results[0].address is regular_results[1].address # Trueif limits is not provided, assumes LIMIT 1 for all fields and uses DISTINCT ON to get only one result.
If limits is provided, manages limiting in python to achieve necessary result,
because it's not trivial to do limit 2 in SQL without access to SELECT * from ... in ORM
API:
prefetch_related_with_limit(
self,
*fields_or_prefetches: Union[str, Prefetch],
limit: Optional[int] = None
)
Lets you batch-load related objects, then sort them to different attributes on the parent object based on SQL Case-When statements. API:
map_related(
self,
map_through: Union[str, MapRelatedCall],
*mapping_conditions: MapRelatedCondition
)
@dataclass
class MapRelatedCondition:
case_q: Q
to_attr: str
⤵️ Mapping vehicles by make:
from data_models.models import User, Vehicle
from django.db.models import Q, Prefetch
from django_orm_performance_enhancers.evaluation_callbacks import MapRelatedCondition
# regular django-orm way
(
User
.objects
.prefetch_related(
# Query1:
Prefetch('vehicles',
Vehicle.objects.filter(make=Vehicle.Makes.BMW),
to_attr='bmw_vehicles'),
# Query2:
Prefetch('vehicles',
Vehicle.objects.filter(make=Vehicle.Makes.TOYOTA),
to_attr='toyota_vehicles'),
# Query3:
Prefetch('vehicles',
Vehicle.objects.filter(make=Vehicle.Makes.FORD),
to_attr='ford_vehicles'),
)
)
users_with_grouped_vehicles = (
User
.objects
.map_related(
'vehicles', # One SQL query to fetch all vehicles
MapRelatedCondition(Q(make=Vehicle.Makes.BMW), 'bmw_vehicles'),
MapRelatedCondition(Q(make=Vehicle.Makes.TOYOTA), 'toyota_vehicles'),
MapRelatedCondition(Q(make=Vehicle.Makes.FORD), 'ford_vehicles'),
)
)
assert users_with_grouped_vehicles[0].bmw_vehicles[0].make == Vehicle.Makes.BMW
assert users_with_grouped_vehicles[0].toyota_vehicles[0].make == Vehicle.Makes.TOYOTA
assert users_with_grouped_vehicles[0].ford_vehicles[0].make == Vehicle.Makes.FORD⤵️ Popular author-book example:
Author
.objects
.map_related(
MapRelatedCall(
qs=Book.objects.all(),
child_field_name='author_id',
parent_field_name='id'
)
MapRelatedCondition(Q(genre='fantasy'), 'fantasy_books'),
MapRelatedCondition(Q(genre='sci-fi'), 'sci_fi_books'),
MapRelatedCondition(Q(genre='horror'), 'horror_books'),
)Lets you prefetch something with a to_attr without direct relation to the parent.
API:
prefetch_unrelated(self, *unrelated_prefetches: PrefetchUnrelatedCall)
@dataclass
class PrefetchUnrelatedCall:
qs: QuerySetType
group_by_attr: str
map_by_attr: str
map_to_attr: strFills these usecases:
Prefetch('some__relation__many_levels__deep', to_attr='some_attr_on_first_level')Prefetch('...no relation to parent...', to_attr='some_attr_on_parent')
API:
prefetch_unrelated(self, *unrelated_prefetches: PrefetchUnrelatedCall)
⤵️ Prefetching unrelated objects:
from data_models.models import User, Transaction
from django_orm_performance_enhancers.querysets import PrefetchUnrelatedCall
users_with_transactions = (
User
.objects
.prefetch_unrelated(
PrefetchUnrelatedCall(
# Transaction doesn't have any relation to user, just integer user_id column
Transaction.objects.all(),
'user_id',
'id',
'unrelated_transactions'
)
)
)
assert users_with_transactions[0].unrelated_transactions[0].user_id == users_with_transactions[0].idLets you create an easily re-usable attribute to hold either query_set cache of an annotation, or compute that annotation on the fly.
from typing_extensions import Self
from django.db import models
from django_orm_performance_enhancers.descriptors import PrefetchedProperty
from django_orm_performance_enhancers.querysets import ExtendedQuerySet, PrefetchUnrelatedCall
class Address(models.Model):
objects = ExtendedQuerySet.as_manager()
class UserQuerySet(ExtendedQuerySet):
def with_vehicles_parked_on_user_address(self) -> Self:
# use prefetch_unrelated to assign prefetched objects to user, not to address
return self.prefetch_unrelated(
PrefetchUnrelatedCall(
qs=Vehicle.objects.all(),
group_by_attr='parking_address_id',
map_by_attr='address_id',
map_to_attr='vehicles_parked_on_user_address' # map to the PrefetchedProperty
)
)
def with_amount_passenger_rides(self) -> Self:
return self.annotate(pass_rides_cnt=models.Count('passenger_rides'))
class User(models.Model):
address = models.ForeignKey(Address, on_delete=models.CASCADE, related_name='users')
vehicles_parked_on_user_address = PrefetchedProperty(
queryset_method=UserQuerySet.with_vehicles_parked_on_user_address,
single_instance_getter=lambda user: Vehicle.objects.filter(parking_address_id=user.address_id)
)
amount_passenger_rides = PrefetchedProperty(
queryset_method=UserQuerySet.with_amount_passenger_rides,
single_instance_getter=lambda user: user.passenger_rides.count(),
cache_attr_name='pass_rides_cnt' # use custom attr name from queryset method
)
objects = UserQuerySet.as_manager()
# 2 SQL queries # 1 SQL query
assert User.objects.first().amount_passenger_rides == User.objects.with_amount_passenger_rides().first().amount_passenger_ridesdjango-orm-performance-enhancers is distributed under the terms of the MIT license.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent