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Build a search index across content from multiple SQLite database tables and run faceted searches against it using Datasette


A live example of this plugin is running at - configured using this YAML file.

Read more about how this example works in Building a search engine for


Install this tool like so:

$ pip install dogsheep-beta


Run the indexer using the dogsheep-beta command-line tool:

$ dogsheep-beta index dogsheep.db config.yml

The config.yml file contains details of the databases and document types that should be indexed:

        sql: |-
       as key,
                'Tweet by @' || users.screen_name as title,
                tweets.created_at as timestamp,
                tweets.full_text as search_1
            from tweets join users on tweets.user =
        sql: |-
                id as key,
                name || ' @' || screen_name as title,
                created_at as timestamp,
                description as search_1
            from users

This will create a search_index table in the dogsheep.db database populated by data from those SQL queries.

By default the search index that this tool creates will be configured for Porter stemming. This means that searches for words like run will match documents containing runs or running.

If you don't want to use Porter stemming, use the --tokenize none option:

$ dogsheep-beta index dogsheep.db config.yml --tokenize none

You can pass other SQLite tokenize argumenst here, see the SQLite FTS tokenizers documentation.


The columns that can be returned by our query are:

  • key - a unique (within that type) primary key
  • title - the title for the item
  • timestamp - an ISO8601 timestamp, e.g. 2020-09-02T21:00:21
  • search_1 - a larger chunk of text to be included in the search index
  • category - an integer category ID, see below
  • is_public - an integer (0 or 1, defaults to 0 if not set) specifying if this is public or not

Public records are things like your public tweets, blog posts and GitHub commits.


Indexed items can be assigned a category. Categories are integers that correspond to records in the categories table, which defaults to containing the following:

id name
1 created
2 saved
3 received

created is for items that have been created by the Dogsheep instance owner.

saved is for items that they have saved, liked or favourited.

received is for items that have been specifically sent to them by other people - incoming emails or direct messages for example.

Datasette plugin

Run datasette install dogsheep-beta (or use pip install dogsheep-beta in the same environment as Datasette) to install the Dogsheep Beta Datasette plugin.

Once installed, a custom search interface will be made available at /-/beta. You can use this interface to execute searches.

The Datasette plugin has some configuration options. You can set these by adding the following to your metadata.json configuration file:

    "plugins": {
        "dogsheep-beta": {
            "database": "beta",
            "config_file": "dogsheep-beta.yml",
            "template_debug": true

The configuration settings for the plugin are:

  • database - the database file that contains your search index. If the file is beta.db you should set database to beta.
  • config_file - the YAML file containing your Dogsheep Beta configuration.
  • template_debug - set this to true to enable debugging output if errors occur in your custom templates, see below.

Custom results display

Each indexed item type can define custom display HTML as part of the config.yml file. It can do this using a display key containing a fragment of Jinja template, and optionally a display_sql key with extra SQL to execute to fetch the data to display.

Here's how to define a custom display template for a tweet:

        sql: |-
       as key,
                'Tweet by @' || users.screen_name as title,
                tweets.created_at as timestamp,
                tweets.full_text as search_1
            from tweets join users on tweets.user =
        display: |-
            <p>{{ title }} - tweeted at {{ timestamp }}</p>
            <blockquote>{{ search_1 }}</blockquote>

This example reuses the value that were stored in the search_index table when the indexing query was run.

To load in extra values to display in the template, use a display_sql query like this:

        sql: |-
       as key,
                'Tweet by @' || users.screen_name as title,
                tweets.created_at as timestamp,
                tweets.full_text as search_1
            from tweets join users on tweets.user =
        display_sql: |-
                tweets join users on tweets.user =
       = :key
        display: |-
            <p>{{ display.screen_name }} - tweeted at {{ display.created_at }}</p>
            <blockquote>{{ display.full_text }}</blockquote>

The display_sql query will be executed for every search result, passing the key value from the search_index table as the :key parameter and the user's search term as the :q parameter.

This performs well because many small queries are efficient in SQLite.

If an error occurs while rendering one of your templates the search results page will return a 500 error. You can use the template_debug configuration setting described above to instead output debugging information for the search results item that experienced the error.

Displaying maps

This plugin will eventually include a number of useful shortcuts for rendering interesting content.

The first available shortcut is for displaying maps. Make your custom content output something like this:

    data-map-latitude="{{ display.latitude }}"
    data-map-longitude="{{ display.longitude }}"
    style="display: none; float: right; width: 250px; height: 200px; background-color: #ccc;"

JavaScript on the page will look for any elements with data-map-latitude and data-map-longitude and, if it finds any, will load Leaflet and convert those elements into maps centered on that location. The default zoom level will be 12, or you can set a data-map-zoom attribute to customize this.


To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd dogsheep-beta
python3 -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: