I've rarely been able to take advantage of Django's bulk_create / bulk_update APIs in production applications; especially in the cases where I need to create or update multiple complex objects with a script. Often time, these complex objects trigger a chain of signals or need non-trivial setups before any operations can be performed on each of them.

The issue is, bulk_create / bulk_update doesn't trigger these signals or expose any hooks to run any setup code. The Django doc mentions these caveates in detail. Here are a few of them:

  • The model’s save() method will not be called, and the pre_save and post_save signals will not be sent.
  • It does not work with child models in a multi-table inheritance scenario.
  • If the model's primary key is an AutoField, the primary key attribute can only be retrieved on certain databases (currently PostgreSQL, MariaDB 10.5+, and SQLite 3.35+). On other databases, it will not be set.
  • It does not work with many-to-many relationships.
  • It casts objs to a list, which fully evaluates objs if it’s a generator. Here, obj is the iterable that passes the information necessary to create the database objects in a single go.

To solve this, I wanted to take advantage of Python's concurrent.futures module. It exposes a similar API for both thread-based and process-based concurrency. The snippet below creates ten thousand user objects in the database and runs some setup code before creating each object.

# script.py

from __future__ import annotations

import os
from typing import Iterable

import django

os.environ["DJANGO_SETTINGS_MODULE"] = "mysite.settings"

from concurrent.futures import ProcessPoolExecutor

from django.contrib.auth.models import User
from tqdm import tqdm


def create_user_setup() -> None:
    # ... Run some heavy weight setup code here.

def create_user(username: str, email: str) -> None:
    # ... Call complex setup code here. This allows the
    # setup code to run concurrently.
    User.objects.create(username=username, email=email)

def bulk_create_users(users: Iterable[dict[str, str]]) -> None:
    # A container for the pending future objects.
    futures = []

    # With PostgreSQL, Psycopg2 often complains about closed cursors
    # and this fixes that.

    with ProcessPoolExecutor(max_workers=MAX_WORKERS) as executor:
        for user in users:
            future = executor.submit(create_user, **user)

        # Wait for all the futures to complete and give the
        # user a visual feedback with tqdm progressbar.
        for future in tqdm(futures):


if __name__ == "__main__":
    users = (
            "username": f"{i}",
            "email": f"{i}@{i}.com",
        for i in range(10_000)


Here, the create_user_setup function runs some complex setup code before the creation of each user object. We wrap the user creation process in a function named create_user and call the setup code in that. This allows us to run the complex setup code concurrently. The magic happens in the bulk_create_users function. It takes in an iterable containing the information to create the users and runs the create_user functions concurrently.

The ProcessPoolExecutor forks 4 processes and starts consuming the iterable. We use the executor.submit method for maximum flexibility. This allows us to further process the returned value from the create_user function (in this case it's None). Running this snippet will also show a progress bar as the processes start chewing through the work.

You can also try experimenting with ThreadPoolExecutor, executor.map, and chunksize. I didn't choose executor.map because it's tricky to show the progress bar with map. Also, I encountered some psycopg2 errors in a PostgreSQL database whenever I switched to the ThreadPoolExecutor. Another gotcha is that psycopg can complain about closed cursors and closing the database connection before running each process is a way to avoid that. Notice that the script above runs django.db.connections.close_all() before entering into the ProcessPoolExecutor context manager.

This appoach will run the pre_save and post_save signals which allows me to take advantage of these hooks without losing the ability of being able to perform concurrent row operations.

Example shown here performs a trivial task of creating 10k user objects. In cases like this, you might find that a simple for-loop might be faster. Always run at least a rudimentary benchmark before adding concurrency to your workflow.

Also, this approach primarily targets ad-hoc scripts and tasks. I don't recommend forking multiple processes in your views or forms since Python processes aren't cheap.






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