👉🏽 👉🏽 👉🏽 Full writeup: Flat Data Project 👈🏽 👈🏽 👈🏽
Flat Data is a GitHub action which makes it easy to fetch data and commit it to your repository as flatfiles. The action is intended to be run on a schedule, retrieving data from any supported target and creating a commit if there is any change to the fetched data. Flat Data builds on the “git scraping” approach pioneered by Simon Willison to offer a simple pattern for bringing working datasets into your repositories and versioning them, because developing against local datasets is faster and easier than working with data over the wire.
✨ Best used in tandem with the Flat Editor VS Code Extension.
Flat Data aims to simplify everyday data acquisition and cleanup tasks. It runs on GitHub Actions, so there's no infrastructure to provision and monitor. Each Flat workflow fetches the data you specify, and optionally executes a postprocessing script on the fetched data. The resulting data is committed to your repository if the new data is different, with a commit message summarizing the changes. Flat workflows usually run on a periodic timer, but can be triggered by a variety of stimuli, like changes to your code, or manual triggers. That's it! No complicated job dependency graphs or orchestrators. No dependencies, libraries, or package managers. No new mental model to learn and incorporate. Just evergreen data, right in your repo.
Check out our example repositories.
The easiest way to get a Flat Data action up and running is with the accompanying Flat Editor VSCode Extension which helps you author Flat yml files.
To use it, install the extension and then invoke Flat Editor from the command palette within VSCode (Mac: ⌘⇧P, Others:ctrl-shift-P).
In the repository where you wish to fetch data, create .github/workflows/flat.yml. The following example will fetch a URL every thirty minutes and commit the response, but only if the response has changed since the last commit.
name: Flat
on:
push:
branches:
- main
workflow_dispatch:
schedule:
- cron: '*/30 * * * *'
jobs:
scheduled:
runs-on: ubuntu-latest
steps:
# This step installs Deno, which is a new Javascript runtime that improves on Node. Can be used for an optional postprocessing step
- name: Setup deno
uses: denoland/setup-deno@main
with:
deno-version: v1.10.x
# Check out the repository so it can read the files inside of it and do other operations
- name: Check out repo
uses: actions/checkout@v2
# The Flat Action step. We fetch the data in the http_url and save it as downloaded_filename
- name: Fetch data
uses: githubocto/flat@v3
with:
http_url: # THE URL YOU WISH TO FETCH GOES HERE
downloaded_filename: # The http_url gets saved and renamed in our repository. Example: data.json, data.csv, image.pngNote that the schedule parameter affects the overall workflow, which may contain other jobs and steps beyond Flat.
The schedule parameter uses crontab format. There's a library of useful examples and an interactive playground on Crontab guru.
The action currently has two fetching modes:
http: GETs a supplied URLsql: Queries a SQL datastore
These two modes are exclusive; you cannot mix settings for these two in one Flat step for a workflow job.
A URL from which to fetch data. Specifying this input puts Flat into http mode.
This can be any endpoint: a json, csv, png, zip, xlsx, etc.
A string used for authorizing the HTTP request. The value of this field is passed in as a header w/ the authorization key.
For example, if this field is set to Bearer abc123 then the following header is sent with each request:
{
"Authorization": "Bearer abc123"
}Under the hood, the http backend uses Axios for data fetching. By default, Flat assumes you're interested in using the GET method to fetch data, but if you'd like to POST (e.g., sending a GraphQL query), the axios_config option allows you to override this behavior.
Specifically, the axios_config parameter should reflect a relative path to a .json file in your repository. This JSON file should mirror the shape of Axios' request config parameters, with a few notable exceptions.
urlandbaseURLwill both be ignored, as thehttp_urlspecified above will take precedence.headerswill be merged in with the authorization header described by theauthorizationparameter above. Please do not put secret keys here, as they will be stored in plain text!- All
functionparameters will be ignored (e.g.,transformRequest). - The response type is always set to
responseType: 'stream'in the background.
An example axios_config might look thusly if you were interested in hitting GitHub's GraphQL API (here is a demo) 👇
{
"method": "post",
"data": {
"query": "query { repository(owner:\"octocat\", name:\"Hello-World\") { issues(last:20, states:CLOSED) { edges { node { title url labels(first:5) { edges { node { name } } } } } } } }"
}
}We advise escaping double quotes like \" in your JSON file.
The name of the file to store data fetched by Flat.
In http mode this can be anything. This can be any endpoint: a json, csv, txt, png, zip, xlsx, etc. file
A path to a local Deno javascript or typescript file for postprocessing the downloaded_filename file. Read more in the "Postprocessing section".
If your http_url string contains secrets, you can choose to mask it from the commit message. You have two options:
Option 1: use a string boolean
mask: true # removes the source entirely from the commit message, defaults to false
Option 2: use a string array with each secret to mask
mask: '["${{ secrets.SECRET1 }}", "${{ secrets.SECRET2 }}"]'
A URI-style database connection string. Flat will use this connection string to connect to the database and issue the query.
⚠️ Don't write secrets into your workflow.yml!Most connection strings contain an authentication secret like a username and password. GitHub provides an encrypted vault for secrets like these which can be used by the action when it runs. Create a secret on the repository where the Flat action will run, and use that secret in your workflow.yaml like so:
sql_connstring: ${{secrets.NAME_OF_THE_CREATED_SECRET}}If you're using the flat-vscode extension, this is handled for you.
The pathname of the file containing the SQL query that will be issued to the database. Defaults to .github/workflows/query.sql. This path is relative to the root of your repo.
The name of the file to store data fetched by Flat.
In sql mode this should be one of csv or json. SQL query results will be serialized to disk in the specified format.
⚠️ While the JSON is not pretty-printed, CSV is often a more efficient serialization for tabular data.
A JSON string representing a configuration passed to TypeORMs createConnection function.
A common use case for this value is connecting your Flat action to a Heroku database.
For instance, you can pass the following configuration string to your Flat action in order to connect to a Heroku Postgres database.
typeorm_config: '{"ssl":true,"extra":{"ssl":{"rejectUnauthorized":false}}}'A path to a local Deno javascript or typescript file for postprocessing the downloaded_filename file. Read more in the "Postprocessing section".
A signed number describing the number of bytes that changed in this run. If the new data is smaller than the existing, committed data, this will be a negative number.
You can add a postprocess input in the Action which is path to a deno Javascript or Typescript script that will be invoked to postprocess your data after it is fetched. This path is relative to the root of your repo.
The script can use either Deno.args[0] or the name of the downloaded_filename to access the file fetched by Flat Data.
import { readJSON, writeJSON } from 'https://deno.land/x/flat/mod.ts'
// The filename is the first invocation argument
const filename = Deno.args[0] // Same name as downloaded_filename
const data = await readJSON(filename)
// Pluck a specific key off
// and write it out to a different file
// Careful! any uncaught errors and the workflow will fail, committing nothing.
const newfile = `subset_of_${filename}`
await writeJSON(newfile, data.path.to.something)You can use console.log() as much as you like within your postprocessing script; the results should show up in your actions log.
Deno's import-by-url model makes it easy to author lightweight scripts that can include dependencies without forcing you to set up a bundler.
The postprocessing script is invoked with deno run -q -A --unstable {your script} {your fetched data file}. Note that the -A grants your script full permissions to access network, disk — everything! Make sure you trust any dependencies you pull in, as they aren't restricted. We will likely revisit this in the future with another setting that specifies which permissions to grant deno.
The learn more about the possibilities for postprocessing check out our helper and examples postprocessing repo.
npm run distand commit the built output (yes, you read that right)- Bump whatever you want to bump in the
package.jsonversion field - Merge
mainintovMAJORbranch.git checkout vMAJOR && git merge main
- If this is a new major version, create the branch.
git checkout -b vMAJOR - Push the branch.
git push --set-upstream origin vMAJOR
- Create a new tag for the version:
git tag -f vMAJOR.MINOR.PATCH - Push main
git checkout main && git push - Navigate to https://github.com/githubocto/flat/tags and cut a new release from the tag you just pushed!
If you run into any trouble or have questions, feel free to open an issue.
❤️ GitHub OCTO