Datasette includes tools for publishing and deploying your data to the internet. The
datasette publish command will deploy a new Datasette instance containing your databases directly to a Heroku or Google Cloud hosting account. You can also use
datasette package to create a Docker image that bundles your databases together with the datasette application that is used to serve them.
Once you have created a SQLite database (e.g. using csvs-to-sqlite) you can deploy it to a hosting account using a single command.
Publishing to Google Cloud Run#
Google Cloud Run allows you to publish data in a scale-to-zero environment, so your application will start running when the first request is received and will shut down again when traffic ceases. This means you only pay for time spent serving traffic.
Cloud Run is a great option for inexpensively hosting small, low traffic projects - but costs can add up for projects that serve a lot of requests.
Be particularly careful if your project has tables with large numbers of rows. Search engine crawlers that index a page for every row could result in a high bill.
The datasette-block-robots plugin can be used to request search engine crawlers omit crawling your site, which can help avoid this issue.
You will first need to install and configure the Google Cloud CLI tools by following these instructions.
You can then publish one or more SQLite database files to Google Cloud Run using the following command:
datasette publish cloudrun mydatabase.db --service=my-database
A Cloud Run service is a single hosted application. The service name you specify will be used as part of the Cloud Run URL. If you deploy to a service name that you have used in the past your new deployment will replace the previous one.
If you omit the
--service option you will be asked to pick a service name interactively during the deploy.
You may need to interact with prompts from the tool. Many of the prompts ask for values that can be set as properties for the Google Cloud SDK if you want to avoid the prompts.
For example, the default region for the deployed instance can be set using the command:
gcloud config set run/region us-central1
You should replace
us-central1 with your desired region. Alternately, you can specify the region by setting the
CLOUDSDK_RUN_REGION environment variable.
Once it has finished it will output a URL like this one:
Service [my-service] revision [my-service-00001] has been deployed and is serving traffic at https://my-service-j7hipcg4aq-uc.a.run.app
Cloud Run provides a URL on the
.run.app domain, but you can also point your own domain or subdomain at your Cloud Run service - see mapping custom domains in the Cloud Run documentation for details.
See datasette publish cloudrun for the full list of options for this command.
Publishing to Heroku#
You can publish one or more databases to Heroku using the following command:
datasette publish heroku mydatabase.db
This will output some details about the new deployment, including a URL like this one:
https://limitless-reef-88278.herokuapp.com/ deployed to Heroku
You can specify a custom app name by passing
-n my-app-name to the publish command. This will also allow you to overwrite an existing app.
Rather than deploying directly you can use the
--generate-dir option to output the files that would be deployed to a directory:
datasette publish heroku mydatabase.db --generate-dir=/tmp/deploy-this-to-heroku
See datasette publish heroku for the full list of options for this command.
Publishing to Vercel#
Vercel - previously known as Zeit Now - provides a layer over AWS Lambda to allow for quick, scale-to-zero deployment. You can deploy Datasette instances to Vercel using the datasette-publish-vercel plugin.
pip install datasette-publish-vercel datasette publish vercel mydatabase.db --project my-database-project
Not every feature is supported: consult the datasette-publish-vercel README for more details.
Publishing to Fly#
Fly is a competitively priced Docker-compatible hosting platform that supports running applications in globally distributed data centers close to your end users. You can deploy Datasette instances to Fly using the datasette-publish-fly plugin.
pip install datasette-publish-fly datasette publish fly mydatabase.db --app="my-app"
Consult the datasette-publish-fly README for more details.
Custom metadata and plugins#
datasette publish accepts a number of additional options which can be used to further customize your Datasette instance.
You can define your own Metadata and deploy that with your instance like so:
datasette publish cloudrun --service=my-service mydatabase.db -m metadata.json
If you just want to set the title, license or source information you can do that directly using extra options to
datasette publish cloudrun mydatabase.db --service=my-service \ --title="Title of my database" \ --source="Where the data originated" \ --source_url="http://www.example.com/"
You can also specify plugins you would like to install. For example, if you want to include the datasette-vega visualization plugin you can use the following:
datasette publish cloudrun mydatabase.db --service=my-service --install=datasette-vega
If a plugin has any Secret configuration values you can use the
--plugin-secret option to set those secrets at publish time. For example, using Heroku with datasette-auth-github you might run the following command:
datasette publish heroku my_database.db \ --name my-heroku-app-demo \ --install=datasette-auth-github \ --plugin-secret datasette-auth-github client_id your_client_id \ --plugin-secret datasette-auth-github client_secret your_client_secret
If you have docker installed (e.g. using Docker for Mac) you can use the
datasette package command to create a new Docker image in your local repository containing the datasette app bundled together with one or more SQLite databases:
datasette package mydatabase.db
Here's example output for the package command:
datasette package parlgov.db --extra-options="--setting sql_time_limit_ms 2500" Sending build context to Docker daemon 4.459MB Step 1/7 : FROM python:3.11.0-slim-bullseye ---> 79e1dc9af1c1 Step 2/7 : COPY . /app ---> Using cache ---> cd4ec67de656 Step 3/7 : WORKDIR /app ---> Using cache ---> 139699e91621 Step 4/7 : RUN pip install datasette ---> Using cache ---> 340efa82bfd7 Step 5/7 : RUN datasette inspect parlgov.db --inspect-file inspect-data.json ---> Using cache ---> 5fddbe990314 Step 6/7 : EXPOSE 8001 ---> Using cache ---> 8e83844b0fed Step 7/7 : CMD datasette serve parlgov.db --port 8001 --inspect-file inspect-data.json --setting sql_time_limit_ms 2500 ---> Using cache ---> 1bd380ea8af3 Successfully built 1bd380ea8af3
You can now run the resulting container like so:
docker run -p 8081:8001 1bd380ea8af3
This exposes port 8001 inside the container as port 8081 on your host machine, so you can access the application at
You can customize the port that is exposed by the container using the
datasette package mydatabase.db --port 8080
A full list of options can be seen by running
datasette package --help:
See datasette package for the full list of options for this command.