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* infra: exit sceptre, enter terraform infra: ignores, vars * infra: tweaks * ci: remove sceptre validation * ci: bootstrap * build: deps * ci: py setup * docs: add docs to tasks * docs: update |
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| infrastructure | ||
| script | ||
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| .python-version | ||
| docker-compose.yml | ||
| Dockerfile | ||
| LICENSE | ||
| README.md | ||
| requirements.in | ||
| requirements.txt | ||
| requirements_dev.in | ||
| requirements_dev.txt | ||
| tasks.py | ||
lambda-boilerplate
🛠 Skip the boilerplate and start building fun λ things 🛠
Overview
AWS Lambdas are fun, but often the amount of boilerplate involved in getting a project off the ground hinders the fun. From setting up a local environment to writing out infrastructure templates, the overhead of Lambda-based greenfield projects can be daunting. No more. Just use this repository as a template or clone it and jump straight into the action! This repository offers a quick template your can use, full with a Docker setup for local development and invocation commands that you can use to package and deploy Lambda-based apps.
Local development
To get started, hit the bootstrap script with . script/bootstrap. This will set up a Python 3.8 virtualenv set up with some basic tools that will make your life easier.
The base Lambda handler is at src/base.py and all the Terraform configurations are in infrastructure.
Invocations
This template uses PyInvoke, all commands are of the format inv <command> <parameters>.
Use inv --list for the full list of commands.
Deployment
Deployment is in three steps: on first setup, you will need to make sure that your bootstrap environment is ready via inv cloud.apply bootstrap. Then, you should upload your lambdas' source with inv cloud.pack and inv cloud.push. Finally, you can deploy your application resources with inv cloud.deploy app.
Contributing
Got suggestions or improvements you'd like to make? Open a PR or an issue!
Feature requests should keep in mind that the goal of this boilerplate is to be general enough so that the cost of tailoring it to specific use cases is low.