impress

History

Emi Matchu e1a5eaeb68 Install cron job to run `rails public_data:commit` weekly in production The Sunday 1:15am time was chosen pretty arbitrarily; I think having it happen at a "start of week" kind of weekday is clarifying for weekly tasks, but I chose ":15" mostly to mitigate that thing where cron jobs all run on the hour at the same time, while still feeling normal :p		2024-03-01 13:20:59 -08:00
..
files	Create `rails public_data:commit` task, to share public data dumps	2024-02-29 14:30:33 -08:00
deploy.yml	Create `rails public_data:commit` task, to share public data dumps	2024-02-29 14:30:33 -08:00
inventory.cfg	Remove beta.impress.openneo.net from deploy setup	2023-10-25 15:22:50 -07:00
README	Create setup.yml deploy script	2023-10-23 19:05:09 -07:00
setup.yml	Install cron job to run `rails public_data:commit` weekly in production	2024-03-01 13:20:59 -08:00

README

Dress to Impress is deployed to a VPS server. We use this Ansible Playbook to
automate the environment setup!

We expect to be deploying to Ubuntu 20.04 LTS, initially with nothing
installed. The user you deploy with should have sudoers access. That should be
all it takes!

First, run `yarn deploy:setup` in the app root, to run the `setup.yml`
playbook. This will prompt you for your root password, to set up system
dependencies. It should be safe to re-run this, including if you add a new
dependency to the playbook, because the steps are non-destructive and Ansible
will skip steps that are already satisfied.

Then, to deploy a new version of the app, run `yarn deploy`. This will build
the app from the code on your machine, then send the source and build output
to the remote machine, and switch it to be the new production version. Nice!

Note that the setup script references a file named `production.env`, which is
gitignored because it contains sensitive information, like database passwords.
You should create a `production.env` file in the local `deploy/files`
directory, to be copied to the remote server and used as its environment
variables.