Bend/docs/defining-data-types.md

# Defining data types

It is possible to easily define complex data types using the `type` keyword.

```py
# A Boolean is either True or False
type Bool = True | False
```

If a constructor has any arguments, parentheses are necessary around it:
```py
# An option either contains some value, or None
type Option = (Some val) | None
```

If the data type has a single constructor, it can be destructured using `let`:
```py
# A Box is a wrapper around a value.
type Boxed = (Box val)

let (Box value) = boxed; value
```

The fields of the constructor that is being destructured with the `match` are bound to the matched variable plus `.` and the field names.
```py
Option.map = λoption λf
  match option {
    Some: (Some (f option.val))
    None: None
  }
```

Rules can also have patterns.
They work like match expressions with explicit bindings:

```py
(Option.map (Some value) f) = (Some (f value))
(Option.map None f) = None
```

However, they also allow matching on multiple values at once, which is something that regular `match` can't do:

```py
type Boolean = True | False

(Option.is_both_some (Some lft_val) (Some rgt_val)) = True
(Option.is_both_some lft rgt) = False
```

You can read more about pattern matching rules in [Pattern matching](/docs/pattern-matching.md).

In conclusion, the `type` keyword is very useful as it allows you to easily create data types and deconstruct them.