Inputs

Resolvers can depend on other features to compute their features. These dependencies are declared through the type signature of the arguments to the resolver function.

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Scalar dependencies

To depend on a feature from a Feature Set, you give your resolver an argument with that feature as the type. You can then use that argument in the body of your resolver to compute your output features. If you’re running our editor plugin, your editor will see the type of each variable as the type of the underlying scalar.

from chalk.features import features, online

@features
class User:
    id: str
    email: str
    email_domain: str

@online
def get_domain(a: User.email) -> User.email_domain:
    # type(email) == str
    return email.split('@')[1].lower()

You can require multiple features in a resolver. However, all feature dependencies in a single resolver need to originate at the same root namespace:

@online
def fn(a: User.email, b: User.name) -> User.email_name_match:
    return ...

Here, we incorrectly request features from the root namespaces of Transfer and User:

@online
def fn(a: User.email, b: Transfer.memo) -> Transfer.email_in_memo:
    return email in memo

If you want to require features from namespaces, you can use has-one or has-many relationships.

@online
def fn(email: Transfer.user.email, memo: Transfer.memo) -> Transfer.email_in_memo:
    return email in memo

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Has-one dependencies

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Scalar has-one

You can also require features joined to a Feature Set through has-one relationships. For example, if users in your system have bank accounts, and you wanted to compare the name on the user’s bank account to the user’s name, you could require the user’s name and the account’s title through the user:

@online
def name_sim(title: User.account.title, name: User.full_name) -> ...

You can also require all scalars on the user’s profile:

@online
def fn(profile: User.profile) -> ...:
    profile.signup_date

Chalk will materialize all scalar features on the profile before calling this function. If you want to pull only a few features from the profile, require each directly:

@online
def fn(signup_date: User.profile.signup_date, age: User.profile.age) -> ...

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Optional has-one

Has-one relationships can also be declared as optional. You may also require feature through optional relationships, but the types for all of those optional features will become optional. Consider the below example:

@features
class Account:
    id: int
    user_id: int
    balance: float  # Non-optional balance

@features
class User:
    id: int
    # Optional relationship
    account: Account | None = has_one(lambda: Account.user_id == User.id)
    has_high_balance: bool

@online
def has_high_bal(balance: User.account.balance) -> User.has_high_balance:
    # Balance will be "float | None"
    if balance is None:
        return False
    return balance > 1000

The resolver in this example receives an optional float, even though balance is not an optional field on Account. The optional is added because the user may not have an account, in which case the resolver will receive None for the balance.

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Nested has-one

You can also traverse nested has-one relationships in the same manner as requiring a single has-one.

Consider a schema where users have a feature set of profile information, and the user’s profile has an identity feature set, which in turn has the age of the user’s email. You can require the email age feature as below:

@online
def fn(email_age: User.profile.identity.email_age) -> ...

However, you cannot access nested relationships without explicit asking for them.

@online
def fn(acct: User.account) -> ...:
    acct.balance           # Ok
    acct.institution.name  # Error!

Instead, you can require the nested relationship directly and access any of its scalar features.

@online
def fn(ins: User.account.institution, acct: User.account) -> ...:
    acct.balance  # Ok
    ins.name      # Ok

The semantics of optional has-one dependencies carry over to nested has-one dependencies. If you traverse an optional relationship, then all downstream attributes will become optional.

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Has-many dependencies

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Scalar has-many

You can also require has-many relationships as inputs to your resolver:

@online
def fn(transfers: User.transfers) -> ...:

You receive a Chalk DataFrame, which supports projections, filtering, and aggregations, among other operations.

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Limiting data fetching

By default, Chalk will materialize all scalar features on the Transfer feature set before calling your resolver. As an optimization hint, you can specify which features from the transfers that you’d like Chalk to materialize before calling the function. For example, if there were expensive features to compute on the transfer, you could scope the features to only the set you need:

@online
def fn(transfers: User.transfers[Transfer.amount, Transfer.memo]) -> ...:
    transfers[Transfer.amount].sum()      # Ok
    transfers[Transfer.from_institution]  # Error: filtered out above

The error above is surfaced statically by our editor plugin.

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Filtering

You can apply filters to the has-many inputs of resolvers:

@online
def fn(transfers: User.transfers[Transfer.amount > 100]) -> ...:

Filters can be composed with projections following the semantics of the Chalk DataFrame.

@online
def fn(transfers: User.transfers[Transfer.amount > 100, Transfer.memo]) -> ...:

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Has-many through has-one

Has-many relationships can be required through has-one relationships:

@online
def fn(transfers: User.account.transfers) -> ...:

As with scalar has-many dependencies, you can scope down the scalar features on the transfer to only those required:

@online
def fn(transfers: User.account.transfers[Transfer.amount]) -> ...:
    transfers[Transfer.amount].sum()      # Ok
    transfers[Transfer.from_institution]  # Error: filtered out above

​

Has-many through optional-has-one

If the has-one relationship that you’re traversing is optional, then the transfers argument in the example above will either be None or a Chalk DataFrame.

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Has-one through has-many

@online
def fn(ts: User.transfers[Transfer.account, Transfer.amount]) -> ...:
    ts[Transfer.account.balance].sum()  # Ok
    ts[Transfer.amount].sum()           # Ok
    ts[Transfer.memo]                   # Error! Filtered out

You can also refine the types pulled from the nested has-one:

@online
def fn(ts: User.transfers[Transfer.account.balance]) -> ...:
    ts[Transfer.account.balance].sum()  # Ok
    ts[Transfer.account.title]          # Error! Filtered out

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Has-many through has-many

Has-many relationships can be required through other has-many relationships.

For example, consider the following feature definitions for User, Account, and Transaction, where a user can have many accounts, each with many transactions.

from chalk.features import features, has_many, DataFrame


@features
class Transaction:
    id: str
    account_id: str
    amount: float


@features
class Account:
    id: str
    user_id: str
    transactions: DataFrame[Transaction] = has_many(
        lambda: Transaction.account_id == Account.id
    )


@features
class User:
    id: str
    total_spent: float
    accounts: DataFrame[Account] = has_many(lambda: Account.user_id == User.id)

We can resolve the total_spent feature on User by computing the sum of transaction amounts across all of a user’s accounts, as shown below.

@online
def get_total_spent(
    txns: User.accounts.transactions[Transaction.amount]
) -> User.total_spent:
    return txns.sum()