# datasette-publish-fly
[](https://pypi.org/project/datasette-publish-fly/)
[](https://github.com/simonw/datasette-publish-fly/releases)
[](https://github.com/simonw/datasette-publish-fly/actions?query=workflow%3ATest)
[](https://github.com/simonw/datasette-publish-fly/blob/main/LICENSE)
[Datasette](https://datasette.io/) plugin for deploying Datasette instances to [Fly.io](https://fly.io/).
Project background: [Using SQLite and Datasette with Fly Volumes](https://simonwillison.net/2022/Feb/15/fly-volumes/)
## Installation
Install this plugin in the same environment as Datasette.
$ datasette install datasette-publish-fly
## Deploying read-only data
First, install the `flyctl` command-line tool by [following their instructions](https://fly.io/docs/getting-started/installing-flyctl/).
Run `flyctl auth signup` to create an account there, or `flyctl auth login` if you already have one.
You can now use `datasette publish fly` to publish one or more SQLite database files:
datasette publish fly my-database.db --app="my-data-app"
The argument you pass to `--app` will be used for the URL of your application: `my-data-app.fly.dev`.
To update an application, run the publish command passing the same application name to the `--app` option.
Fly have [a free tier](https://fly.io/docs/about/pricing/#free-allowances), beyond which they will charge you monthly for each application you have live. Details of their pricing can be [found on their site](https://fly.io/docs/pricing/).
Your application will be deployed at `https://your-app-name.fly.io/` - be aware that it may take several minutes to start working the first time you deploy it.
## Using Fly volumes for writable databases
Fly [Volumes](https://fly.io/docs/reference/volumes/) provide persistant disk storage for Fly applications. Volumes can be 1GB or more in size and the Fly free tier includes 3GB of volume space.
Datasette plugins such as [datasette-uploads-csvs](https://datasette.io/plugins/datasette-upload-csvs) and [datasette-tiddlywiki](https://datasette.io/plugins/datasette-tiddlywiki) can be deployed to Fly and store their mutable data in a volume.
> :warning: **You should only run a single instance of your application** if your database accepts writes. Fly has excellent support for running multiple instances in different geographical regions, but `datasette-publish-fly` with volumes is not yet compatible with that model. You should probably [use Fly PostgreSQL instead](https://fly.io/blog/globally-distributed-postgres/).
Here's how to deploy `datasette-tiddlywiki` with authentication provided by `datasette-auth-passwords`.
First, you'll need to create a root password hash to use to sign into the instance.
You can do that by installing the plugin and running the `datasette hash-password` command, or by using [this hosted tool](https://datasette-auth-passwords-demo.datasette.io/-/password-tool).
The hash should look like `pbkdf2_sha256$...` - you'll need this for the next step.
In this example we're also deploying a read-only database called `content.db`.
Pick a name for your new application, then run the following:
datasette publish fly \
content.db \
--app your-application-name \
--create-volume 1 \
--create-db tiddlywiki \
--install datasette-auth-passwords \
--install datasette-tiddlywiki \
--plugin-secret datasette-auth-passwords root_password_hash 'pbkdf2_sha256$...'
This will create the new application, deploy the `content.db` read-only database, create a 1GB volume for that application, create a new database in that volume called `tiddlywiki.db`, then install the two plugins and configure the password you specified.
### Updating applications that use a volume
Once you have deployed an application using a volume, you can update that application without needing the `--create-volume` or `--create-db` options. To add the [datasette-graphq](https://datasette.io/plugins/datasette-graphql) plugin to your deployed application you would run the following:
datasette publish fly \
content.db \
--app your-application-name \
--install datasette-auth-passwords \
--install datasette-tiddlywiki \
--install datasette-graphql \
--plugin-secret datasette-auth-passwords root_password_hash 'pbkdf2_sha256$...' \
Since the application name is the same you don't need the `--create-volume` or `--create-db` options - these are persisted automatically between deploys.
You do need to specify the full list of plugins that you want to have installed, and any plugin secrets.
You also need to include any read-only database files that are part of the instance - `content.db` in this example - otherwise the new deployment will not include them.
### Advanced volume usage
`datasette publish fly` will add a volume called `datasette` to your Fly application. You can customize the name using the `--volume name custom_name` option.
Fly can be used to scale applications to run multiple instances in multiple regions around the world. This works well with read-only Datasette but is not currently recommended using Datasette with volumes, since each Fly replica would need its own volume and data stored in one instance would not be visible in others.
If you want to use multiple instances with volumes you will need to switch to using the `flyctl` command directly. The `--generate-dir` option, described below, can help with this.
## Generating without deploying
Use the `--generate-dir` option to generate a directory that can be deployed to Fly rather than deploying directly:
datasette publish fly my-database.db \
--app="my-generated-app" \
--generate-dir /tmp/deploy-this
You can then manually deploy your generated application using the following:
cd /tmp/deploy-this
flyctl apps create my-generated-app
flyctl deploy
## datasette publish fly --help
<!-- [[[cog
import cog
from datasette import cli
from click.testing import CliRunner
runner = CliRunner()
result = runner.invoke(cli.cli, ["publish", "fly", "--help"])
help = result.output.replace("Usage: cli", "Usage: datasette")
cog.out(
"```\n{}```".format(help)
)
]]] -->
```
Usage: datasette publish fly [OPTIONS] [FILES]...
Deploy an application to Fly that runs Datasette against the provided database
files.
Usage example:
datasette publish fly my-database.db --app="my-data-app"
Full documentation: https://datasette.io/plugins/datasette-publish-fly
Options:
-m, --metadata FILENAME Path to JSON/YAML file containing metadata to
publish
--extra-options TEXT Extra options to pass to datasette serve
--branch TEXT Install datasette from a GitHub branch e.g.
main
--template-dir DIRECTORY Path to directory containing custom templates
--plugins-dir DIRECTORY Path to directory containing custom plugins
--static MOUNT:DIRECTORY Serve static files from this directory at
/MOUNT/...
--install TEXT Additional packages (e.g. plugins) to install
--plugin-secret <TEXT TEXT TEXT>...
Secrets to pass to plugins, e.g. --plugin-
secret datasette-auth-github client_id xxx
--version-note TEXT Additional note to show on /-/versions
--secret TEXT Secret used for signing secure values, such as
signed cookies
--title TEXT Title for metadata
--license TEXT License label for metadata
--license_url TEXT License URL for metadata
--source TEXT Source label for metadata
--source_url TEXT Source URL for metadata
--about TEXT About label for metadata
--about_url TEXT About URL for metadata
--spatialite Enable SpatialLite extension
--region TEXT Fly region to deploy to, e.g sjc - see
https://fly.io/docs/reference/regions/
--create-volume INTEGER RANGE Create and attach volume of this size in GB
[x>=1]
--create-db TEXT Names of read-write database files to create
--volume-name TEXT Volume name to use
-a, --app TEXT Name of Fly app to deploy [required]
-o, --org TEXT Name of Fly org to deploy to
--generate-dir DIRECTORY Output generated application files and stop
without deploying
--show-files Output the generated Dockerfile, metadata.json
and fly.toml
--setting SETTING... Setting, see
docs.datasette.io/en/stable/settings.html
--crossdb Enable cross-database SQL queries
--help Show this message and exit.
```
<!-- [[[end]]] -->
## Development
To contribute to this tool, first checkout the code. Then create a new virtual environment:
cd datasette-publish-fly
python -m venv venv
source venv/bin/activate
Or if you are using `pipenv`:
pipenv shell
Now install the dependencies and test dependencies:
pip install -e '.[test]'
To run the tests:
pytest
### Integration tests
The tests in `tests/test_integration.py` make actual calls to Fly to deploy a test application.
These tests are skipped by default. If you have `flyctl` installed and configured, you can run the integration tests like this:
pytest --integration -s
The `-s` option here ensures that output from the deploys will be visible to you - otherwise it can look like the tests have hung.
The tests will create applications on Fly that start with the prefix `publish-fly-temp-` and then delete them at the end of the run.
Raw data
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"description": "# datasette-publish-fly\n\n[](https://pypi.org/project/datasette-publish-fly/)\n[](https://github.com/simonw/datasette-publish-fly/releases)\n[](https://github.com/simonw/datasette-publish-fly/actions?query=workflow%3ATest)\n[](https://github.com/simonw/datasette-publish-fly/blob/main/LICENSE)\n\n[Datasette](https://datasette.io/) plugin for deploying Datasette instances to [Fly.io](https://fly.io/).\n\nProject background: [Using SQLite and Datasette with Fly Volumes](https://simonwillison.net/2022/Feb/15/fly-volumes/)\n\n## Installation\n\nInstall this plugin in the same environment as Datasette.\n\n $ datasette install datasette-publish-fly\n\n## Deploying read-only data\n\nFirst, install the `flyctl` command-line tool by [following their instructions](https://fly.io/docs/getting-started/installing-flyctl/).\n\nRun `flyctl auth signup` to create an account there, or `flyctl auth login` if you already have one.\n\nYou can now use `datasette publish fly` to publish one or more SQLite database files:\n\n datasette publish fly my-database.db --app=\"my-data-app\"\n\nThe argument you pass to `--app` will be used for the URL of your application: `my-data-app.fly.dev`.\n\nTo update an application, run the publish command passing the same application name to the `--app` option.\n\nFly have [a free tier](https://fly.io/docs/about/pricing/#free-allowances), beyond which they will charge you monthly for each application you have live. Details of their pricing can be [found on their site](https://fly.io/docs/pricing/).\n\nYour application will be deployed at `https://your-app-name.fly.io/` - be aware that it may take several minutes to start working the first time you deploy it.\n\n## Using Fly volumes for writable databases\n\nFly [Volumes](https://fly.io/docs/reference/volumes/) provide persistant disk storage for Fly applications. Volumes can be 1GB or more in size and the Fly free tier includes 3GB of volume space.\n\nDatasette plugins such as [datasette-uploads-csvs](https://datasette.io/plugins/datasette-upload-csvs) and [datasette-tiddlywiki](https://datasette.io/plugins/datasette-tiddlywiki) can be deployed to Fly and store their mutable data in a volume.\n\n> :warning: **You should only run a single instance of your application** if your database accepts writes. Fly has excellent support for running multiple instances in different geographical regions, but `datasette-publish-fly` with volumes is not yet compatible with that model. You should probably [use Fly PostgreSQL instead](https://fly.io/blog/globally-distributed-postgres/).\n\nHere's how to deploy `datasette-tiddlywiki` with authentication provided by `datasette-auth-passwords`.\n\nFirst, you'll need to create a root password hash to use to sign into the instance.\n\nYou can do that by installing the plugin and running the `datasette hash-password` command, or by using [this hosted tool](https://datasette-auth-passwords-demo.datasette.io/-/password-tool).\n\nThe hash should look like `pbkdf2_sha256$...` - you'll need this for the next step.\n\nIn this example we're also deploying a read-only database called `content.db`.\n\nPick a name for your new application, then run the following:\n\n datasette publish fly \\\n content.db \\\n --app your-application-name \\\n --create-volume 1 \\\n --create-db tiddlywiki \\\n --install datasette-auth-passwords \\\n --install datasette-tiddlywiki \\\n --plugin-secret datasette-auth-passwords root_password_hash 'pbkdf2_sha256$...'\n\nThis will create the new application, deploy the `content.db` read-only database, create a 1GB volume for that application, create a new database in that volume called `tiddlywiki.db`, then install the two plugins and configure the password you specified.\n\n### Updating applications that use a volume\n\nOnce you have deployed an application using a volume, you can update that application without needing the `--create-volume` or `--create-db` options. To add the [datasette-graphq](https://datasette.io/plugins/datasette-graphql) plugin to your deployed application you would run the following:\n\n datasette publish fly \\\n content.db \\\n --app your-application-name \\\n --install datasette-auth-passwords \\\n --install datasette-tiddlywiki \\\n --install datasette-graphql \\\n --plugin-secret datasette-auth-passwords root_password_hash 'pbkdf2_sha256$...' \\\n\nSince the application name is the same you don't need the `--create-volume` or `--create-db` options - these are persisted automatically between deploys.\n\nYou do need to specify the full list of plugins that you want to have installed, and any plugin secrets.\n\nYou also need to include any read-only database files that are part of the instance - `content.db` in this example - otherwise the new deployment will not include them.\n\n### Advanced volume usage\n\n`datasette publish fly` will add a volume called `datasette` to your Fly application. You can customize the name using the `--volume name custom_name` option.\n\nFly can be used to scale applications to run multiple instances in multiple regions around the world. This works well with read-only Datasette but is not currently recommended using Datasette with volumes, since each Fly replica would need its own volume and data stored in one instance would not be visible in others.\n\nIf you want to use multiple instances with volumes you will need to switch to using the `flyctl` command directly. The `--generate-dir` option, described below, can help with this.\n\n## Generating without deploying\n\nUse the `--generate-dir` option to generate a directory that can be deployed to Fly rather than deploying directly:\n\n datasette publish fly my-database.db \\\n --app=\"my-generated-app\" \\\n --generate-dir /tmp/deploy-this\n\nYou can then manually deploy your generated application using the following:\n\n cd /tmp/deploy-this\n flyctl apps create my-generated-app\n flyctl deploy\n\n## datasette publish fly --help\n\n<!-- [[[cog\nimport cog\nfrom datasette import cli\nfrom click.testing import CliRunner\nrunner = CliRunner()\nresult = runner.invoke(cli.cli, [\"publish\", \"fly\", \"--help\"])\nhelp = result.output.replace(\"Usage: cli\", \"Usage: datasette\")\ncog.out(\n \"```\\n{}```\".format(help)\n)\n]]] -->\n```\nUsage: datasette publish fly [OPTIONS] [FILES]...\n\n Deploy an application to Fly that runs Datasette against the provided database\n files.\n\n Usage example:\n\n datasette publish fly my-database.db --app=\"my-data-app\"\n\n Full documentation: https://datasette.io/plugins/datasette-publish-fly\n\nOptions:\n -m, --metadata FILENAME Path to JSON/YAML file containing metadata to\n publish\n --extra-options TEXT Extra options to pass to datasette serve\n --branch TEXT Install datasette from a GitHub branch e.g.\n main\n --template-dir DIRECTORY Path to directory containing custom templates\n --plugins-dir DIRECTORY Path to directory containing custom plugins\n --static MOUNT:DIRECTORY Serve static files from this directory at\n /MOUNT/...\n --install TEXT Additional packages (e.g. plugins) to install\n --plugin-secret <TEXT TEXT TEXT>...\n Secrets to pass to plugins, e.g. --plugin-\n secret datasette-auth-github client_id xxx\n --version-note TEXT Additional note to show on /-/versions\n --secret TEXT Secret used for signing secure values, such as\n signed cookies\n --title TEXT Title for metadata\n --license TEXT License label for metadata\n --license_url TEXT License URL for metadata\n --source TEXT Source label for metadata\n --source_url TEXT Source URL for metadata\n --about TEXT About label for metadata\n --about_url TEXT About URL for metadata\n --spatialite Enable SpatialLite extension\n --region TEXT Fly region to deploy to, e.g sjc - see\n https://fly.io/docs/reference/regions/\n --create-volume INTEGER RANGE Create and attach volume of this size in GB\n [x>=1]\n --create-db TEXT Names of read-write database files to create\n --volume-name TEXT Volume name to use\n -a, --app TEXT Name of Fly app to deploy [required]\n -o, --org TEXT Name of Fly org to deploy to\n --generate-dir DIRECTORY Output generated application files and stop\n without deploying\n --show-files Output the generated Dockerfile, metadata.json\n and fly.toml\n --setting SETTING... Setting, see\n docs.datasette.io/en/stable/settings.html\n --crossdb Enable cross-database SQL queries\n --help Show this message and exit.\n```\n<!-- [[[end]]] -->\n\n## Development\n\nTo contribute to this tool, first checkout the code. Then create a new virtual environment:\n\n cd datasette-publish-fly\n python -m venv venv\n source venv/bin/activate\n\nOr if you are using `pipenv`:\n\n pipenv shell\n\nNow install the dependencies and test dependencies:\n\n pip install -e '.[test]'\n\nTo run the tests:\n\n pytest\n\n### Integration tests\n\nThe tests in `tests/test_integration.py` make actual calls to Fly to deploy a test application.\n\nThese tests are skipped by default. If you have `flyctl` installed and configured, you can run the integration tests like this:\n\n pytest --integration -s\n\nThe `-s` option here ensures that output from the deploys will be visible to you - otherwise it can look like the tests have hung.\n\nThe tests will create applications on Fly that start with the prefix `publish-fly-temp-` and then delete them at the end of the run.\n",
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