sections
7 rows where breadcrumbs contains "Publishing data" sorted by content
This data as json, CSV (advanced)
id | page | ref | title | content ▼ | breadcrumbs | references |
---|---|---|---|---|---|---|
publish:cli-package | publish | cli-package | datasette package | 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 http://localhost:8081/ You can customize the port that is exposed by the container using the --port option: 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. | ["Publishing data"] | [{"href": "https://www.docker.com/docker-mac", "label": "Docker for Mac"}] |
publish:cli-publish | publish | cli-publish | datasette publish | 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. You will need a hosting account with Heroku or Google Cloud . Once you have created your account you will need to install and configure the heroku or gcloud command-line tools. | ["Publishing data"] | [{"href": "https://github.com/simonw/csvs-to-sqlite/", "label": "csvs-to-sqlite"}, {"href": "https://www.heroku.com/", "label": "Heroku"}, {"href": "https://cloud.google.com/", "label": "Google Cloud"}] |
publish:publish-heroku | publish | publish-heroku | Publishing to Heroku | To publish your data using Heroku , first create an account there and install and configure the Heroku CLI tool . 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 data", "datasette publish"] | [{"href": "https://www.heroku.com/", "label": "Heroku"}, {"href": "https://devcenter.heroku.com/articles/heroku-cli", "label": "Heroku CLI tool"}] |
publish:publish-vercel | publish | publish-vercel | 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 data", "datasette publish"] | [{"href": "https://vercel.com/", "label": "Vercel"}, {"href": "https://github.com/simonw/datasette-publish-vercel", "label": "datasette-publish-vercel"}, {"href": "https://github.com/simonw/datasette-publish-vercel/blob/main/README.md", "label": "datasette-publish-vercel README"}] |
publish:publish-custom-metadata-and-plugins | publish | publish-custom-metadata-and-plugins | 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 : 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 | ["Publishing data", "datasette publish"] | [{"href": "https://github.com/simonw/datasette-vega", "label": "datasette-vega"}, {"href": "https://github.com/simonw/datasette-auth-github", "label": "datasette-auth-github"}] |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [sections] ( [id] TEXT PRIMARY KEY, [page] TEXT, [ref] TEXT, [title] TEXT, [content] TEXT, [breadcrumbs] TEXT, [references] TEXT );