django-fast-update

Faster db updates using UPDATE FROM VALUES sql variants.

Installation & Usage

Run pip install django-fast-update and place fast_update in INSTALLED_APPS.

With attaching FastUpdateManager as a manager to your model, fast_update can be used instead of bulk_update, e.g.:

from django.db import models
from fast_update.query import FastUpdateManager

class MyModel(models.Model):
    objects = FastUpdateManager()
    field_a = ...
    field_b = ...
    field_c = ...


# to update multiple instances at once:
MyModel.objects.fast_update(bunch_of_instances, ['field_a', 'field_b', 'field_c'])

Alternatively fast.fast_update can be used directly with a queryset as first argument (Warning - this skips most sanity checks with up to 30% speed gain, so make sure not to feed something totally off).

Compatibility

fast_update is known to work with these database versions:

• SQLite 3.15+
• PostgreSQL
• MariaDB 10.2+
• MySQL 5.7+

For unsupported database backends or outdated versions fast_update will fall back to flat_update. (It is possible to register fast update implementations for other db vendors with register_implementation. See fast_update/fast.py for more details.)

Note that with fast_update f-expressions cannot be used anymore. This is a design decision to not penalize update performance by some swiss-army-knife functionality. If you have f-expressions in your update data, consider re-grouping the update steps and update those fields with update or bulk_update instead.

Other than with bulk_update duplicates in a changeset are not allowed and will raise. This is mainly a safety guard to not let slip through duplicates, where the final update state would be undetermined or directly depend on the database's compatibility.

By default flat_update will execute another SELECT query if the queryset is prefiltered. Set unfiltered=True if you want to skip the prefiltering.

copy_update

This is a PostgreSQL only update implementation based on COPY FROM. This runs even faster than fast_update for medium to big changesets (but tends to be slower than fast_update for <100 objects).

copy_update follows the same interface idea as bulk_update and fast_update, minus a batch_size argument (data is always transferred in one big batch). It can be used likewise from the FastUpdateManager. copy_update also has no support for f-expressions, also duplicates will raise.

Note copy_update will probably never leave the alpha/PoC-state, as psycopg3 brings great COPY support, which does a more secure value conversion and has a very fast C-version.

Note Django 4.2 brings psycopg3 support, which is now supported by copy_update. It still uses the old implementation, the transition to the faster psycopg3 copy interface is still pending.

flat_update & merged_update

Since version 0.3.0 the package provides 2 new update implementations. Both use direct calls of the update method, which turns out to be much faster than bulk_update for local database installations (roughly 4x faster).

While flat_update simply loops over the objects and calls update once with all field values applied, merged_update tries to create field value groups to lower the update calls, which will perform much better with highly intersecting data.

Note that merged_update is currently ALPHA, as the merge details and the cost prediction still need some conceptual work and tests.

Status

Currently beta, still some TODOs left.

The package is tested with Django 3.2, 4.2 & 5.2 on Python 3.8 to 3.13.

Performance

There is a management command in the example app testing performance of updates on the FieldUpdate model (./manage.py perf).

fast_update is at least 8 times faster than bulk_update, and keeps making ground for bigger changesets. This indicates different runtime complexity. fast_update grows almost linear for very big numbers of rows (tested during some perf series against copy_update up to 10M), while bulk_update grows much faster (looks quadratic to me, which can be lowered to linear by applying a proper batch_size, but it stays very steep compared to fast_update).

For very big changesets copy_update is the clear winner, and even shows a substantial increase in updated rows/s (within my test range, as upper estimate this of course cannot grow slower than linear, as the data pumping will saturate to linear).

Changelog

• 0.3.0 new beta release
  • new update implementations:
    • flat_update
    • merged_update (ALPHA)
  • dropped bulk_update as fallback
  • psycopg3 support for copy_update