Scheduled Query

When features are coming from a single SQL file, or a single resolver, you can use resolver crons to keep your online and offline stores up to date.

However, when features are chained together, or when you need to run a feature pipeline on a schedule, you can use scheduled queries.

Scheduled queries let you run an offline query on a schedule, and persist the results in the online and/or offline feature stores.

​

Create a Scheduled Query

To create a scheduled query, make a ScheduledQuery object somewhere in your code.

from chalk import ScheduledQuery

ScheduledQuery(
    name="enrich-transactions",
    schedule="0 0 * * *",
    output=[Transaction.clean_name, Transaction.category],
    online=True,
    offline=True,
)

At the time of chalk apply, the scheduled query will be created.

In the web, you can see the list of scheduled queries in Runs > Scheduled Runs tab.

​

Incrementalization

By default, scheduled queries use incrementalization to only ingest data which has been updated since the last run. You can also set a resolver to use as the source of the incrementalization. For example, if you were enriching financial transaction data, you might use the transactions.chalk.sql resolver as the source of incrementalization.

from chalk import ScheduledQuery

ScheduledQuery(
    name="enrich-transactions",
    schedule="0 0 * * *",
    output=[Transaction.clean_name, Transaction.category],
    online=True,
    offline=True,
    incremental_resolvers="transactions",
)