docs
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settings:using-setting | settings | using-setting | Using --setting | Datasette supports a number of settings. These can be set using the --setting name value option to datasette serve . You can set multiple settings at once like this: datasette mydatabase.db \ --setting default_page_size 50 \ --setting sql_time_limit_ms 3500 \ --setting max_returned_rows 2000 Settings can also be specified in the database.yaml configuration file . | ["Settings"] | [] |
settings:config-dir | settings | config-dir | Configuration directory mode | Normally you configure Datasette using command-line options. For a Datasette instance with custom templates, custom plugins, a static directory and several databases this can get quite verbose: datasette one.db two.db \ --metadata=metadata.json \ --template-dir=templates/ \ --plugins-dir=plugins \ --static css:css As an alternative to this, you can run Datasette in configuration directory mode. Create a directory with the following structure: # In a directory called my-app: my-app/one.db my-app/two.db my-app/datasette.yaml my-app/metadata.json my-app/templates/index.html my-app/plugins/my_plugin.py my-app/static/my.css Now start Datasette by providing the path to that directory: datasette my-app/ Datasette will detect the files in that directory and automatically configure itself using them. It will serve all *.db files that it finds, will load metadata.json if it exists, and will load the templates , plugins and static folders if they are present. The files that can be included in this directory are as follows. All are optional. *.db (or *.sqlite3 or *.sqlite ) - SQLite database files that will be served by Datasette datasette.yaml - Configuration for the Datasette instance metadata.json - Metadata for those databases - metadata.yaml or metadata.yml can be used as well inspect-data.json - the result of running datasette inspect *.db --inspect-file=inspect-data.json from the configuration directory - any database files listed here will be treated as immutable, so they should not be changed while Datasette is running templates/ - a directory containing Custom templates … | ["Settings"] | [] |
settings:id2 | settings | id2 | Settings | The following options can be set using --setting name value , or by storing them in the settings.json file for use with Configuration directory mode . | ["Settings"] | [] |
settings:setting-secret | settings | setting-secret | Configuring the secret | Datasette uses a secret string to sign secure values such as cookies. If you do not provide a secret, Datasette will create one when it starts up. This secret will reset every time the Datasette server restarts though, so things like authentication cookies and API tokens will not stay valid between restarts. You can pass a secret to Datasette in two ways: with the --secret command-line option or by setting a DATASETTE_SECRET environment variable. datasette mydb.db --secret=SECRET_VALUE_HERE Or: export DATASETTE_SECRET=SECRET_VALUE_HERE datasette mydb.db One way to generate a secure random secret is to use Python like this: python3 -c 'import secrets; print(secrets.token_hex(32))' cdb19e94283a20f9d42cca50c5a4871c0aa07392db308755d60a1a5b9bb0fa52 Plugin authors make use of this signing mechanism in their plugins using .sign(value, namespace="default") and .unsign(value, namespace="default") . | ["Settings"] | [] |
settings:setting-publish-secrets | settings | setting-publish-secrets | Using secrets with datasette publish | The datasette publish and datasette package commands both generate a secret for you automatically when Datasette is deployed. This means that every time you deploy a new version of a Datasette project, a new secret will be generated. This will cause signed cookies to become invalid on every fresh deploy. You can fix this by creating a secret that will be used for multiple deploys and passing it using the --secret option: datasette publish cloudrun mydb.db --service=my-service --secret=cdb19e94283a20f9d42cca5 | ["Settings"] | [] |