MeshDB

A Database for Tracking NYCMesh Member Installs

We use MeshDB to track information about Buildings, Members, Installs, Nodes, Devices, and Links; Any info we need in order to get hardware on a rooftop near you lives in here.

This project aims to provide a convenient, stable, and sane interface for use with robots and humans. For more information, check the wiki

Setup

Dev Environment

The production environment relies on Nginx and Gunicorn, but for development, you can use Django's tools. You'll also need Python 3.11, and pip, of course.

Firstly, fork this repo.

Note

If you cloned nycmeshnet/meshdb, you can change your origin by doing the following:

git remote remove origin
git remote add origin https://github.com/<your_username>/meshdb
git remote add upstream https://github.com/nycmeshnet/meshdb

Dev Container

If you would like to develop in a Dev Container

1. Make sure you have VS Code installed.
2. Install the Dev Containers extension: ms-vscode-remote.remote-containers
3. Open the repo folder in the container.
4. In a different shell, outside of VS Code, start the other containers: docker compose up -d postgres pelias redis (as below).
5. Continue on the VS Code terminal (where your project is opened) follow normal developer setup.

Host

If you are not using a Dev Container, for safety, create a venv

python --version # Make sure this is python 3.11.x before continuing
python -m venv .venv
source .venv/bin/activate

Then, install dependencies.

pip install -e '.[dev]'

Set Environment Variables

Next, fill out the .env.sample file and load it into your environment.

cp .env.sample .env
nano .env # Or your favorite text editor, fill in the blank variables

You're gonna need a DJANGO_SECRET_KEY:

Generating DJANGO_SECRET_KEY

There's already a secret key for you in the .env.sample, but if you need another one...

python -c 'from django.core.management.utils import get_random_secret_key; print(get_random_secret_key())'

Important

Make sure you're running in Debug mode if you want to see detailed traces. Set DEBUG=True in your .env file.

If you have a database, great, go nuts. If you don't, you can use docker-compose.

docker-compose up -d postgres pelias redis

You might have to run the migrations. This will set up the DB for you.

python src/manage.py makemigrations
python src/manage.py migrate

You'll probably want an admin account

python src/manage.py createsuperuser

And if you have access to it, you can use import_spreadsheet_dump.sh to populate your database.

Warning

This is real member data. DO NOT share this database with anyone under any circumstances.

cp -R <path_to_data_dump> ./spreadsheet_data/
./import_spreadsheet_dump.sh

If you want to do work with celery, you'll need to run a worker as well as a beat. You can do this in two other terminals with these commands. DEBUG level is recommended for the beat to see what beats are going to run

celery -A meshdb worker -l INFO
celery -A meshdb beat -s /tmp/celerybeat-schedule -l DEBUG

Then, you can get crackin'

python src/manage.py runserver

You should now be able to access the API:

curl http://127.0.0.1:8000/api/v1/    # Should echo "We're Meshin" to indicate 200 status

When you're done, you can stop the server with Ctrl+C, and run docker compose down to take down the containers.

Note

To spin things back up again later, just run:

source .venv/bin/activate
docker-compose up -d postgres pelias redis
python src/manage.py runserver

Prod Environment

Clone the package with git and create the expected .env file (or otherwise configure the environment variables specified in .env.sample as appropriate to your environment).

git clone https://github.com/andybaumgar/nycmesh-database
cp .env.sample .env
nano .env # Fill in any missing values

Note

Check the above instructions if you need a DJANGO_SECRET_KEY

Finally, start the application and database servers using docker compose

docker compose up

After a few minutes for image download & database setup, the development server should be available at 127.0.0.1:8080:

# Should return "We're meshin'."
curl http://127.0.0.1:8080/api/v1

Authentication (Permissions, Groups, and Tokens)

We have very simple permission levels.

• Unauthenticated: A user using a route without authenticating
• Installer: Can view all fields, provision NNs, and edit installs
• Admin: Full access

We use Django Rest Framework's basic Auth Token implementation. To add a token, you need a user, which can be created at /admin/auth/user/.

To determine what permissions the user has, add them to one of the pre-existing groups.

(Superuser and Staff are DRF-specific and should be restricted to people maintaining the instance)

For software apps, create a new users for each application with the format PersonName-ApplicationName. Grant the minimum neccessary permissions directly on the user object using the admin UI.

Auth tokens can be created at /admin/authtoken/tokenproxy/.

To use them, you can include them as an HTTP header like so:

curl -X GET http://127.0.0.1:8000/api/v1/members/ -H 'Authorization: Token <auth_token>'

Unit Tests

We use django's testing framework, based on unittest

To run the unit tests, first create a virtual environment and install the dependencies as specified under Dev Environment above

Django's tests should spin up and tear down a mock database for us, but it's still going to need somewhere to put that database, so go ahead and boot up the one in your docker-compose.yaml

docker compose up -d postgres pelias redis

Finally, run the tests:

python src/manage.py test meshapi meshapi_hooks

Code Coverage

We'd like to cover as much of the code as is reasonable, and to see what we hit, we use coverage.py as suggested by Django.

To run coverage, set up your venv, then wrap the testing command like so:

coverage run --source='.' src/manage.py test meshapi meshapi_hooks

To see the report,

coverage report

Adding Tests

Tests live in src/meshapi/tests/. It might make sense to add your test to an existing file within that directory, depending on what it's doing, or you can add a whole new file. See the django documentation for details on how to write a test, or check the directory for examples.

Database

If you ever need to get into the database directly, it's easy to do so.

Get a shell on the postgres container:

docker exec -it meshdb-postgres-1 bash

Switch to postgres user.

su postgres

Run psql

psql -U meshdb

Admin Map

In .env.sample, the admin map frontend assets are configured to pull from the production endpoint. (map data will still be pulled from your local database). If you wish to pull the admin map assets from a local endpoint, host the map locally with:

# In the map repo on the meshdb-admin branch
docker build -t meshdb-admin-map .
docker run -p "3000:3000" meshdb-admin-map

then edit the relevant .env variable to reflect the URL of the desired endpoint:

ADMIN_MAP_BASE_URL=http://localhost:3000

Backups

The Proper Way

We have a Celery job that runs hourly in production to back up to an S3 bucket.

To restore from a backup in production:

1. Get a shell in the meshdb container

$ docker exec -it meshdb-meshdb-1 bash

2. Find the backup you want to restore

root@eefdc57a46c2:/opt/meshdb# python manage.py listbackups
Name                                     Datetime
default-09855fadfa7e-2024-03-29-015116.psql.bin 03/29/24 01:51:16
default-0c9b0a412baf-2024-03-31-170000.psql.bin 03/31/24 17:00:00
default-12db99e5ec1d-2024-03-31-142422.psql.bin 03/31/24 14:24:22

...

default-bd0acc253775-2024-03-31-163520.psql.bin 03/31/24 16:35:20

3. In a separate terminal, drop the old database

$ echo 'drop database meshdb; create database meshdb;' | docker exec -i meshdb-postgres-1 psql -U meshdb -d postgres

4. Restore the backup

root@eefdc57a46c2:/opt/meshdb# python manage.py dbrestore -i default-bd0acc253775-2024-03-31-163520.psql.bin   

The Quick 'n Dirty Way

Export:

docker exec -it meshdb-postgres-1 pg_dump -d meshdb -U meshdb >> Downloads/meshdb_dev.sql

Import:

cat ~/Downloads/meshdb_dev.sql | docker exec -i meshdb-postgres-1 psql -U meshdb

Invoke.py Commands

For convenience, this package uses invoke to wrap common tasks into one-line commands. For example:

invoke format

Will automatically apply black formatting and isort import sorting in a single command.

You can also quickly peform all the relevant lint checks locally using

invoke lint

See tasks.py for a complete list of the tasks available.