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https://github.com/simonw/airtable-export

Export Airtable data to YAML, JSON or SQLite files on disk
https://github.com/simonw/airtable-export

airtable airtable-api datasette-io datasette-tool yaml

Last synced: 19 days ago
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Export Airtable data to YAML, JSON or SQLite files on disk

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README 

        
# airtable-export



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Export Airtable data to files on disk



## Installation



Install this tool using `pip`:



    $ pip install airtable-export



## Usage



You will need to the following information:



- Your Airtable base ID - this is a string starting with `app...`

- Your Airtable personal access token - this is a string starting with `pat...`



If you just want to export a subset of your tables you also need to know the names of those tables.



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



    airtable-export export base_id --key=key



This example would files for each of your tables, for example: `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`.



To export only specified tables, pass their names as additional arguments:



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



## Export options



By default the tool exports your data as YAML.



You can also export as JSON or as [newline delimited JSON](http://ndjson.org/) using the `--json` or `--ndjson` options:



    airtable-export export base_id --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 \

        --key=key --ndjson --yaml --json



If you import all tables, or if you add the `--schema` option, a JSON schema for the base will be written to `output-dir/_schema.json`.



### SQLite database export



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



    airtable-export export base_id \

        --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](https://www.python-httpx.org)'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](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) 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](https://github.com/features/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](https://github.com/natbat/rockybeaches/commits/main/airtable).



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`:

```yaml

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 "[email protected]"

        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](https://github.blog/changelog/2020-07-06-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