yaml-to-sqlite

Load the contents of a YAML file into a SQLite database table.

$ yaml-to-sqlite --help
Usage: yaml-to-sqlite [OPTIONS] DB_PATH TABLE YAML_FILE

  Convert YAML files to SQLite

Options:
  --version             Show the version and exit.
  --pk TEXT             Column to use as a primary key
  --single-column TEXT  If YAML file is a list of values, populate this column
  --help                Show this message and exit.

Usage

Given a news.yml file containing the following:

- date: 2021-06-05
  body: |-
    [Datasette 0.57](https://docs.datasette.io/en/stable/changelog.html#v0-57) is out with an important security patch.
- date: 2021-05-10
  body: |-
    [Django SQL Dashboard](https://simonwillison.net/2021/May/10/django-sql-dashboard/) is a new tool that brings a useful authenticated subset of Datasette to Django projects that are built on top of PostgreSQL.

Running this command:

$ yaml-to-sqlite news.db stories news.yml

Will create a database file with this schema:

$ sqlite-utils schema news.db
CREATE TABLE [stories] (
   [date] TEXT,
   [body] TEXT
);

The --pk option can be used to set a column as the primary key for the table:

$ yaml-to-sqlite news.db stories news.yml --pk date
$ sqlite-utils schema news.db
CREATE TABLE [stories] (
   [date] TEXT PRIMARY KEY,
   [body] TEXT
);

Single column YAML lists

The --single-column option can be used when the YAML file is a list of values, for example a file called dogs.yml containing the following:

- Cleo
- Pancakes
- Nixie

Running this command:

$ yaml-to-sqlite dogs.db dogs.yaml --single-column=name

Will create a single dogs table with a single name column that is the primary key:

$ sqlite-utils schema dogs.db
CREATE TABLE [dogs] (
   [name] TEXT PRIMARY KEY
);
$ sqlite-utils dogs.db 'select * from dogs' -t
name
--------
Cleo
Pancakes
Nixie

airtable-export

Export Airtable data to files on disk

Installation

Install this tool using pip:

$ pip install airtable-export

Usage

You will need to know the following information:

Your Airtable base ID - this is a string starting with app...
Your Airtable API key - this is a string starting with key...
The names of each of the tables that you wish to export

You can export all of your data to a folder called export/ by running the following:

airtable-export export base_id table1 table2 --key=key

This example would create two files: export/table1.yml and export/table2.yml.

Rather than passing the API key using the --key option you can set it as an environment variable called AIRTABLE_KEY.

Export options

By default the tool exports your data as YAML.

You can also export as JSON or as newline delimited JSON using the --json or --ndjson options:

airtable-export export base_id table1 table2 --key=key --ndjson

You can pass multiple format options at once. This command will create a .json, .yml and .ndjson file for each exported table:

airtable-export export base_id table1 table2 \
    --key=key --ndjson --yaml --json

SQLite database export

You can export tables to a SQLite database file using the --sqlite database.db option:

airtable-export export base_id table1 table2 \
    --key=key --sqlite database.db

This can be combined with other format options. If you only specify --sqlite the export directory argument will be ignored.

The SQLite database will have a table created for each table you export. Those tables will have a primary key column called airtable_id.

If you run this command against an existing SQLite database records with matching primary keys will be over-written by new records from the export.

Request options

By default the tool uses python-httpx's default configurations.

You can override the user-agent using the --user-agent option:

airtable-export export base_id table1 table2 --key=key --user-agent ""Airtable Export Robot""

You can override the timeout during a network read operation using the --http-read-timeout option. If not set, this defaults to 5s.

airtable-export export base_id table1 table2 --key=key --http-read-timeout 60

Running this using GitHub Actions

GitHub Actions is GitHub's workflow automation product. You can use it to run airtable-export in order to back up your Airtable data to a GitHub repository. Doing this gives you a visible commit history of changes you make to your Airtable data - like this one.

To run this for your own Airtable database you'll first need to add the following secrets to your GitHub repository:

AIRTABLE_BASE_ID: The base ID, a string beginning `app...`
AIRTABLE_KEY: Your Airtable API key
AIRTABLE_TABLES: A space separated list of the Airtable tables that you want to backup. If any of these contain spaces you will need to enclose them in single quotes, e.g. 'My table with spaces in the name' OtherTableWithNoSpaces

Once you have set those secrets, add the following as a file called .github/workflows/backup-airtable.yml:

name: Backup Airtable

on:
  workflow_dispatch:
  schedule:
  - cron: '32 0 * * *'

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
    - name: Check out repo
      uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.8
    - uses: actions/cache@v2
      name: Configure pip caching
      with:
        path: ~/.cache/pip
        key: ${{ runner.os }}-pip-
        restore-keys: |
          ${{ runner.os }}-pip-
    - name: Install airtable-export
      run: |
        pip install airtable-export
    - name: Backup Airtable to backups/
      env:
        AIRTABLE_BASE_ID: ${{ secrets.AIRTABLE_BASE_ID }}
        AIRTABLE_KEY: ${{ secrets.AIRTABLE_KEY }}
        AIRTABLE_TABLES: ${{ secrets.AIRTABLE_TABLES }}
      run: |-
        airtable-export backups $AIRTABLE_BASE_ID $AIRTABLE_TABLES -v
    - name: Commit and push if it changed
      run: |-
        git config user.name ""Automated""
        git config user.email ""actions@users.noreply.github.com""
        git add -A
        timestamp=$(date -u)
        git commit -m ""Latest data: ${timestamp}"" || exit 0
        git push

This will run once a day (at 32 minutes past midnight UTC) and will also run if you manually click the ""Run workflow"" button, see GitHub Actions: Manual triggers with workflow_dispatch.

Development

To contribute to this tool, first checkout the code. Then create a new virtual environment:

cd airtable-export
python -mvenv venv
source venv/bin/activate

Or if you are using pipenv:

pipenv shell

Now install the dependencies and tests:

pip install -e '.[test]'

To run the tests:

pytest

yaml-to-sqlite

Usage

Single column YAML lists

airtable-export

Installation

Usage

Export options

SQLite database export

Request options

Running this using GitHub Actions

Development

datasette-yaml

Installation

Usage

Demo

Development