Dispatch

Warning

This feature is experimental. It works and the API is stable but the behavior in some corner cases may change in the future.

The decorator deal.dispatch allows combining multiple implementations of a function into one. When the combined function is called, deal will try to execute every implementation and return the result of the first one that hasn’t raised PreContractError.

@deal.dispatch
def age2stage(age: int) -> str:
    raise NotImplementedError

@age2stage.register
@deal.pre(lambda age: age < 12)
def _(age: int) -> int:
    return 'kid'

@age2stage.register
@deal.pre(lambda age: age < 18)
def _(age: int) -> int:
    return 'teen'

age2stage(10)  # 'kid'
age2stage(14)  # 'teen'

If the given arguments passed pre-conditions for none of the implementations, NoMatchError is raised:

age2stage(20)
# NoMatchError: expected age < 12 (where age=20); expected age < 18 (where age=20)

To avoid it, just add a default implementation:

@age2stage.register
def _(age: int) -> int:
    return 'adult'

age2stage(20)  # 'adult'

The initially decorated function (which you directly pass into @deal.dispatch) is never executed. It is used only to provide the name, docstring, and type annotations for the combined function. However, we specify raise NotImplementedError instead of just pass as the function body, so type checkers won’t complain about invalid return type.

Since dispatch requires contracts to be enabled, when you call a dispatched function, contracts get forcefully enabled for the function duration. It may be changed in the future to enable only needed deal.pre contracts. So, keep it in mind: if you want to disable all contracts on the production all together, deal.dispatch could be a bad fit for your application.

Motivation

The decorator was introduced as a way to do the same as functools.singledispatch but using pre-conditions instead of types. It gives you much more flexibility, allowing you to implement anything you could do in some other languages with the combination of pattern matching, guards, and function overloading. A classic example is recursively calculating factorial.

In Elixir:

defmodule Math do
    @spec fac(integer) :: integer
    @doc """
    Calculate factorial of the given number.
    """
    def fac(0), do: 1
    def fac(n) when n > 0, do: n * fac(n - 1)
end

Math.fac(5)   # 120
Math.fac(-1)  # FunctionClauseError

And the same in Python using deal.dispatch:

@deal.dispatch
def fac(n: int) -> int:
    """Calculate factorial of the given number.
    """
    raise NotImplementedError

@fac.register
@deal.pre(lambda n: n == 0)
def _(n):
    return 1

@fac.register
@deal.pre(lambda n: n > 0)
def _(n):
    return n * fac(n - 1)

fac(5)   # 120
fac(-1)  # NoMatchError

The implementation based on deal.dispatch is more verbose. However, it pays back when each implementation is complex. For example, reading a config in different formats.

Simple solution:

from pathlib import Path

def read_config(path: Path) -> Config:
    if path.suffix == '.json':
        ...
    if path.suffix in {'.yml', '.yaml'}:
        ...
    raise ValueError

And solution with dispatch:

@deal.dispatch
def read_config(path: Path) -> Config:
    raise NotImplementedError

@read_config.register
@deal.pre(lambda path: path.suffix == '.json')
def read_json(path):
    ...

@read_config.register
@deal.pre(lambda path: path.suffix in {'.yml', '.yaml'})
def read_yaml(path):
    ...

The latter gives multiple benefits:

  1. Each implementation is isolated from others, and so it is easier to read and maintain.

  2. Each implementation can be called directly, by users of from tests.

  3. Pre-conditions are attached to implementations rather than the read_config function, so even if an implementation is called directly, we still can be sure that it is used correctly.

  4. Users can register new implementations. So, you have a plugins system out of the box.