Outputs

Resolvers declare the features that they resolve through a Python type annotation on the return value of the function.

​

Scalar output

​

Single output

To return a single feature from a resolver, set the return type annotation to the feature you want to resolve:

from chalk.features import features

@features
class User:
    id: int
    name: str
    employer: str

@online
def resolve(u: User.id) -> User.name:
    return "Jennifer Doudna"

Equivalently, you can wrap the return value in the User class:

@online
def resolve(u: User.id) -> User.name:
  return "Jennifer Doudna"
  return User(name="Jennifer Doudna")

​

Multiple outputs

To return multiple features, return an instance of the feature class. In the type signature, specify the Features[...] class, parameterized by the features that you pass to the feature class.

@online
def resolve(u: User.id) -> Features[User.name, User.employer]:
  return User(
      name="Jennifer Doudna",
      employer="University of California, Berkeley"
  )

You only need to pass a subset of the features to the constructor for the feature class.

The editor plugin will check that the type annotation you assign to the resolver matches subset of features passed to the constructor of the feature class.

​

All features

To return all features of a class, use Features[...] around the feature class.

@online
def get_user(u: User.id) -> Features[User]:
    return User(
        name="Jennifer Doudna",
        employer="University of California, Berkeley"
    )

If your resolver takes input features, those features are not considered as part of the output features.

Note that the id feature is not returned from the function.

This definition is equivalent to:

@online
def get_user(u: User.id) -> Features[User]:
def get_user(u: User.id) -> Features[User.name, User.employer]:
  return User(
      name="Jennifer Doudna",
      employer="University of California, Berkeley"
  )

However, you may want to return almost all features of a class. Writing out all the features can be tedious and error-prone. You can subtract features from a feature class using the - operator:

from chalk.features import Features, ...

@online
def get_all_users(id: User.id) -> Features[User] - User.name:
    return User(employer="University of California, Berkeley")

Here, both the id feature and the name feature are not returned, which leaves only the employer feature.

​

DataFrame output

You can also output many instances of a feature class from a resolver by specifying a DataFrame as the return type of the function:

@offline
def get_events() -> DataFrame[Transfer.uuid, Transfer.amount, Transfer.ts]:
    return DataFrame.read_csv(...)

For more info on how to load batch data, see the Data Sources sections. DataFrame-returning resolvers don’t need inputs.

​

All features

To return all features of a class in a DataFrame, use DataFrame[...] class around the feature class:

@online
def get_all_users() -> DataFrame[User]:
    return DataFrame([
        User(
            name="Jennifer Doudna",
            employer="University of California, Berkeley"
        )
    ])

​

Other DataFrame-returning resolvers

Imagine a scalar feature you’d like to backfill over many thousands of pkeys and historical times. DataFrame-returning resolvers can dramatically reduce the computation time due to its vectorized handling.

@offline
def get_new_feature_as_dataframe(
    df: DataFrame[Transaction.id, ...]
) -> DataFrame[Transaction.id, Transaction.new_feature]:

The above resolver runs faster on a thousand rows than the equivalent scalar resolver ran a thousand times.

Chalk also supports relationship-returning resolvers that enable users to return a DataFrame belonging to a has-many relationship.

@offline
def relationship_returning_resolver(
    df: User.transactions[Transaction.id, Transaction.amount, Transaction.description],
    user_type: User.type
) -> User.transactions[Transaction.id, Transaction.transaction_type]:

Just make sure that the return DataFrames do not have duplicate rows. That means no two rows should have the same primary key, or primary key & timestamp combinations if the feature time is also returned.