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), 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), 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!

Requirements

• Django 1.3 - 1.5
• PostgreSQL or PostGIS for your database

Installation

pip install django-db-pool

Usage

• 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',          
    'OPTIONS': {'MAX_CONNS': 1},
    # 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 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:

SOUTH_DATABASE_ADAPTERS = {
    'default': 'south.db.postgresql_psycopg2',
}