Skip to content

Latest commit

 

History

History
73 lines (43 loc) · 2.82 KB

README.md

File metadata and controls

73 lines (43 loc) · 2.82 KB
Raw

kettle

Kettle is a command line tool for creating and deploying serverless services, starting from from simple templates.

This CLI has two primary commands:

  • kettle create <name> creates a directory containing all the boiler plate code that you need to get going.
  • kettle deploy <path> deploys the code in that directory to the cloud. Deploy currently supports serverless functions on AWS and GCP.

Templates

Kettle supports three types of templates:

  1. Templates that are already on your computer, at a given path.
  2. Templates that are git repositories
  3. Templates that are in the kettle-templates repository; browse that repo's README to see the templates that it contains spanning AWS Lambda, GCP Functions, and GCP Run.

Installing with brew

You can install kettle using brew and this tap.

❯ brew tap nlathia/tap
❯ brew install kettle-cli

# You can see that this works by running
❯ kettle version

Usage

Here's an example that takes you from a template to a deployed AWS Lambda.

Example from kettle-templates

In the example below, we use the pyenv-aws-lambda template in the kettle-templates repository. Since we're using a kettle-templates template, we just need to use kettle create <name>, where <name> is the directory name in the templates repo:

❯ kettle create pyenv-aws-lambda
Project name: hello-world

✅  Created:  <path>/hello-world

This will prompt you for a project name, and will then create that directory and add all the boiler plate you need to get going. This particular template comes with a Makefile, that we can use to set up the local environment:

❯ cd hello-world

❯ make install

Kettle deploy

Kettle deploy is the command to deploy your project as a serverless function. It currently supports:

AWS Lambdas

You must have the aws cli installed.

For Python, kettle supports Lambdas where Python is managed with pyenv or conda.

Google Cloud Functions

You must have the gcloud SDK installed. You also need to have enabled the Cloud Functions API in the GCP console.

Google Cloud Run

You must have the gcloud SDK installed, and optionally Docker to build and run Cloud Run containerized applications locally. You also need to have enabled the Cloud Run API in the GCP console.

Bug Reports

Please report any bugs or issues to me (neal.lathia@gmail.com) or by raising an issue in this repo.