Get Started with Code Examples

Decide and enforce withdrawal limits with custom hold times.

Compute income from Plaid transactions.

Bypass the cache with a max-staleness of 0.

Compute streaming window aggregate functions on sensor data.

Deploy to Chalk (either as a preview deployment or to production).

Detect changes in user behavior over time.

Resolvers are just Python functions, so they are easy to unit test.

Resolvers are shared between all models.

Use pydantic, attrs, dataclasses.dataclass, or custom types as feature values.

Chain a DataFrame resolver with a scalar resolver.

Combine batch, caching, and DataFrames to create a powerful predictive maintenance pipeline.

Create features directly from messages on a stream.

Keep the cache warm by scheduling a resolver to run more frequently than the max-staleness.

Resolvers chain together through their required dependencies and declared outputs.

Access and override the time at which a feature should be recorded.

Integrate data from credit bureaus like Transunion.

Filter the rows of a DataFrame by supplying conditions to the __getitem__() method.

Tag related features.

Cache intermediate feature values.

Set up preview deployments for all PRs.

Aggregate failed logins over a Kafka stream.

Supply a feature value in the input to skip the cache and any resolver entirely.

Describe features at a feature class or feature level.

Describe features at a feature class or feature level.

Compute aggregates over a DataFrame.

Define a has-many relationship between feature classes.

Define a has-many relationship between feature classes.

Define a has-one relationship between feature classes.

Install the Chalk CLI in a GitHub Action.

Query scalars with SQL files or strings.

Cache the last computed example of the feature.

Compute an aggregation on windows using DataFrames.

Scope down the set of rows available in a DataFrame.

Create a namespaced set of features.

Assign owners to features for monitoring and alerting.

Make use of vendor APIs to verify identities, control costs with Chalk's platform.

Trigger special behavior with tags.

Test interactions between resolvers with preview deployments.

Create a resolver that returns a single feature.

Create a resolver that returns many features.

Set the primary key for an entity.

Compose projections and filters to create a new DataFrame.

Easily listen to streaming data and parse messages with custom logic.

Access historical sensor data as-of any time in the past.

Compute a streaming window aggregate function using DataFrames.

Compute a streaming window aggregate function using DataFrames.

Assigning tags & owners to features.

Set max-staleness per-request.

Pick exactly the examples that you’d like to run.

Identify transactions returned for non-sufficient funds.

Create sets of features from your feature classes.

Cache feature values rather than computing them realtime.

Aggregate user statistics across tradelines.

Identify users with multiple accounts.

Run resolvers on a schedule and filter down which examples to consider.

Run resolvers on a schedule with all possible arguments.

Serve many end-customers with differentiated behavior.

Query many rows and take advantage of push down filters.