Developing a new enrichment#

Enrichments are implemented as Datasette plugins.

An enrichment plugin should implement the register_enrichments() plugin hook, which should return a list of instances of subclasses of the Enrichment base class.

The function can also return an awaitable function which returns that list of instances. This is useful if your plugin needs to do some asynchronous work before it can return the list of enrichments.

The plugin hook#

Your enrichment plugin should register new enrichments using the register_enrichments() plugin hook:

from datasette import hookimpl

def register_enrichments():
    return [MyEnrichment()]

register_enrichment() can optionally accept a datasette argument. This can then be used to read plugin configuration or run database queries.

The plugin hook can return an awaitable function if it needs to do some asynchronous work before it can return the list of enrichments, for example:

def register_enrichments(datasette):
    async def inner():
        db = datasette.get_database("mydb")
        settings = [
            for row in await db.execute(
                "select setting from special_settings"
        return [
            for setting in settings
    return inner

Enrichment subclasses#

Most of the code you write will be in a subclass of Enrichment:

from datasette_enrichments import Enrichment

class MyEnrichment(Enrichment):
    name = "Name of My Enrichment"
    slug = "my-enrichment"
    description = "One line description of what it does"

The name, slug and description attributes are required. They are used to display information about the enrichment to users.

Try to ensure your slug is unique among all of the other enrichments your users might have installed.

You can also set a batch_size attribute. This defaults to 100 but you can set it to another value to control how many rows are passed to your enrich_batch() method at a time. You may want to set it to 1 to process rows one at a time.


Your class can optionally implement an initialize() method. This will be called once at the start of each enrichment run.

This method is often used to prepare the database - for example, adding a new table column that the enrichment will then populate.

async def initialize(
    datasette: Datasette,
    db: Database,
    table: str,
    config: dict

The named parameters passed to initialize() are all optional. If you declare them they will be passed as follows:

  • datasette is the Datasette instance.

  • db is the Database instance for the database that the enrichment is being run against.

  • table is the name of the table.

  • config is a dictionary of configuration options that the user set for the enrichment, using the configuration form (if one was provided).


You must implement the following method:

async def enrich_batch(
    datasette: Datasette,
    db: Database,
    table: str,
    rows: List[dict],
    pks: List[str],
    config: dict,
    job_id: int,
    # Enrichment logic goes here

Again, you can use just the subset of the named parameters that you need.

This method will be called multiple times, each time with a different list of rows.

It should perform whatever enrichment logic is required, using the db object (documented here) to write any results back to the database.

enrich_batch() is an async def method, so you can use await within the method to perform asynchronous operations such as HTTP calls (using HTTPX) or database queries.

The parameters available to enrich_batch() are as follows:

  • datasette is the Datasette instance. You can use this to read plugin configuration, check permissions, render templates and more.

  • db is the Database instance for the database that the enrichment is being run against. You can use this to execute SQL queries against the database.

  • table is the name of the table that the enrichment is being run against.

  • rows is a list of dictionaries for the current batch, each representing a row from the table. These are the same shape as JSON dictionaries returned by the Datasette JSON API. The batch size defaults to 100 but can be customized by your class.

  • pks is a list of primary key column names for the table.

  • config is a dictionary of configuration options that the user set for the enrichment, using the configuration form (if one was provided).

  • job_id is a unique integer ID for the current job. This can be used to log additional information about the enrichment execution.


The get_config_form() method can optionally be implemented to return a WTForms form class that the user can use to configure the enrichment.

This example defines a form with two fields: a template text area field and an output_column single line input:

from wtforms import Form, StringField, TextAreaField
from wtforms.validators import DataRequired

# ...
    async def get_config_form(self):
        class ConfigForm(Form):
            template = TextAreaField(
                description='Template to use',
            output_column = StringField(
                "Output column name",
                description="The column to store the output in - will be created if it does not exist.",
                validators=[DataRequired(message="Column is required.")],
        return ConfigForm

The valid dictionary that is produced by filling in this form will be passed as config to both the initialize() and enrich_batch() methods.

The get_config_form() method can take the following optional named parameters:


Your class can optionally implement a finalize() method. This will be called once at the end of each enrichment run.

async def finalize(self, datasette, db, table, config):
    # ...

Again, these named parameters are all optional:

  • datasette is the [Datasette instance]

  • db is the Database instance

  • table is the name of the table (a string)

  • config is an optional dictionary of configuration options that the user set for the run

Tracking errors#

Errors that occur while running an enrichment are recorded in the _enrichment_errors table, with the following schema:

create table _enrichment_errors (
    id integer primary key,
    job_id integer references _enrichment_jobs(id),
    created_at text,
    row_pks text, -- JSON list of row primary keys
    error text

If your .enrich_batch() raises any exception, all of the IDs in that batch will be marked as errored in this table.

Alternatively you can catch errors for individual rows within your enrich_batch() method and record them yourself using the await self.log_error() method, which has the following signature:

async def log_error(
    self, db: Database, job_id: int, ids: List[IdType], error: str

Call this with a reference to the current database, the job ID, a list of row IDs (which can be strings, integers or tuples for compound primary key tables) and the error message string.

If you set log_traceback = True on your Enrichment class a full stacktrace for the most recent exception will be recorded in the database table in addition to the string error message. This is useful during plugin development:

class MyEnrichment(Enrichment):
    log_traceback = True

Writing tests for enrichments#

Take a look at the test suite for datasette-enrichments-re2 for an example of how to test an enrichment.

You can use the datasette_enrichments.wait_for_job() utility function to avoid having to run a polling loop in your tests to wait for the enrichment to complete.

Here’s an example test for an enrichment, using to start the enrichment running:

from import Datasette
from datasette_enrichments.utils import wait_for_job
import pytest

async def test_enrichment():
    # Create a Datasette instance with an in-memory database to tests against
    datasette = Datasette()
    db = datasette.add_memory_database("demo")
    await db.execute_write("create table if not exists news (body text)")
    for text in ("example a", "example b", "example c"):
        await db.execute_write("insert into news (body) values (?)", [text])

    # Obtain ds_actor and ds_csrftoken cookies
    cookies = {"ds_actor": datasette.sign({"a": {"id": "root"}}, "actor")}
    csrftoken = (
        await datasette.client.get(
    cookies["ds_csrftoken"] = csrftoken

    response = await
            "source_column": "body",
            "csrftoken": cookies["ds_csrftoken"],
    assert response.status_code == 302

    # Get the job_id so we can wait for it to complete
    job_id = response.headers["Location"].split("=")[-1]
    await wait_for_job(datasette, job_id, database="demo", timeout=1)
    db = datasette.get_database("demo")
    jobs = await db.execute("select * from _enrichment_jobs")
    job = dict(jobs.first())
    assert job["status"] == "finished"
    assert job["enrichment"] == "name-of-enrichment"
    assert job["done_count"] == 3
    results = await db.execute("select * from news order by body")
    rows = [dict(r) for r in results.rows]
    assert rows == [
        {"body": "example a transformed"},
        {"body": "example b transformed"},
        {"body": "example c transformed"}

The full signature for wait_for_job() is:

async def wait_for_job(
    datasette: Datasette,
    job_id: int,
    database: Optional[str] = None,
    timeout: Optional[int] = None,

The timeout argument to wait_for_job() is optional - this will cause the test to fail if the job does not complete within the specified number of seconds.

If you omit the database name the single first database will be used, which is often the right thing to do for tests.