Skip to main content

Basic database persistance / connection pooling for Django + Postgres.

Project description

Django DB Pool

**Note that this code has not been rigorously tested in high-volume production systems! You should perform your own
load / concurrency tests prior to any deployment. And of course, patches are highly appreciated.**

Another connection pool "solution"?

Yes, alas. Django punts on the problem of pooled / persistant connections ([1][1]), generally telling folks to use a
dedicated application like PGBouncer (for Postgres.) However that's not always workable on app-centric platforms like
Heroku, where each application runs in isolation. Thus this package. There are others ([2][2]), but this one attempts
to provide connection persistance / pooling with as few dependencies as possible.

Currently only the Django's postgres_psycopg2 / postgis drivers are supported. Connection pooling is implemented by
thinly wrapping a psycopg2 connection object with a pool-aware class. The actual pool implementation is psycop2g's
built-in [ThreadedConnectionPool](, which handles thread safety for the pool
instance, as well as simple dead connection testing when connections are returned.

Because this implementation sits inside the python interpreter, in a multi-process app server environment the pool will
never be larger than one connection. However, you can still benefit from connection persistance (no connection creation
overhead, query plan caching, etc.) so the (minimal) additional overhead of the pool should be outweighed by these
benefits. TODO: back this up with some data!


* [Django 1.3 - 1.5](
* [PostgreSQL]( or [PostGIS]( for your database


pip install django-db-pool


* PostgreSQL
* Change your `DATABASES` -> `ENGINE` from `'django.db.backends.postgresql_psycopg2'` to `'dbpool.db.backends.postgresql_psycopg2'`.
* PostGIS
* Change your `DATABASES` -> `ENGINE` from `'django.contrib.gis.db.backends.postgis'` to `'dbpool.db.backends.postgis'`.

If you are in a multithreaded environment, also set `MAX_CONNS` and optionally `MIN_CONNS` in the `OPTIONS`,
like this:

'default': {
'ENGINE': 'dbpool.db.backends.postgresql_psycopg2',
# These options will be used to generate the connection pool instance
# on first use and should remain unchanged from your previous entries
'NAME': 'test',
'USER': 'test',
'PASSWORD': 'test123',
'HOST': 'localhost',
'PORT': '',

See the [code][base] for more information on settings `MAX_CONNS` and `MIN_CONNS`.

You can set `TEST_ON_BORROW` (also in the `OPTIONS`) to True if you would like a connection to be validated each time it is
checked out. If you enable this, any connection that fails a test query will be discarded from the pool and a new connection
fetched, retrying up to the largest size of the pool. Since this incurs some overhead you should weigh it against the
benefit of transparently recovering from database connection failures.

Lastly, if you use [South]( (and you should!) you'll want to make sure it knows that you're still
using Postgres:

'default': 'south.db.postgresql_psycopg2',


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

django-db-pool-0.0.10.tar.gz (8.4 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page