psu-oit-arc / aol-backend

The backend API which supports the Atlas of Oregon Lakes

Atlas of Oregon Lakes

Technology stack

• PostgreSQL 11
• PostGIS 2.5
• Python 3.11
• Django 3.2.x
• ArcGIS (Cloud/SaaS)

Getting started

To prepare the database you may use, e.g., the import_database command to install a copy of production data.

To use the provided Docker container definitions:

docker compose up -d

To authenticate with the provided default user:

username: [email protected]
password: foobar

Deploying

This project using the Emcee tooling to define and orchestrate resource provisioning and deployment. See the AWS cloudformation templates in cloudformation and the command definitions in commands.py for more information.

General notes

It's very important to create an index like this:

CREATE INDEX observation_gist
ON observation
USING GIST (ST_BUFFER(ST_TRANSFORM(the_geom, 3644), 10));

This allows you to do intersection queries like:

ST_BUFFER(ST_TRANSFORM(the_geom, 3644), 10) && (SELECT the_geom FROM lake_geom WHERE reachcode = %s)

with very good performance. The magic '10' is arbitrary, but you should keep this consistent with DISTANCE_FROM_ITEM in lakes.models.

We cannot do this in a migration unfortunately, since the observation table is part of a different application/schema

Some custom migrations exist that add fields (like an hstore), and setup stored procs and triggers. So don't go blindly deleting them.