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airtable-export-0.7.1
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مشاهده بیشترتوضیحات
Export Airtable data to files on disk
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | airtable-export-0.7.1 |
| نام | airtable-export |
| نسخه کتابخانه | 0.7.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Simon Willison |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/simonw/airtable-export |
| آدرس اینترنتی | https://pypi.org/project/airtable-export/ |
| مجوز | Apache License, Version 2.0 |
# airtable-export
[](https://pypi.org/project/airtable-export/)
[](https://github.com/simonw/airtable-export/releases)
[](https://github.com/simonw/airtable-export/actions?query=workflow%3ATest)
[](https://github.com/simonw/airtable-export/blob/master/LICENSE)
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](http://ndjson.org/) 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](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:
<dl>
<dt>AIRTABLE_BASE_ID</dt>
<dd>The base ID, a string beginning `app...`</dd>
<dt>AIRTABLE_KEY</dt>
<dd>Your Airtable API key</dd>
<dt>AIRTABLE_TABLES</dt>
<dd>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. <samp>'My table with spaces in the name' OtherTableWithNoSpaces</samp></dd>
</dl>
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 $AIRTABLE_TABLES -v
- name: Commit and push if it changed
run: |-
git config user.name "Automated"
git config user.email "actions@users.noreply.github.com"
git add -A
timestamp=$(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](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
نیازمندی
| مقدار | نام |
|---|---|
| - | click |
| - | PyYAML |
| - | httpx |
| - | sqlite-utils |
| - | pytest |
| - | pytest-mock |
نحوه نصب
نصب پکیج whl airtable-export-0.7.1:
pip install airtable-export-0.7.1.whl
نصب پکیج tar.gz airtable-export-0.7.1:
pip install airtable-export-0.7.1.tar.gz