# Python static type checker (mypy) [mypy](http://mypy-lang.org/) is a compile-time static type checker for Python, allowing optional, gradual typing of Python code. Zulip was fully annotated with mypy's Python 2 syntax in 2016, before our migration to Python 3 in late 2017. In 2018 and 2020, we migrated essentially the entire codebase to the nice PEP 484 (Python 3 only) and PEP 526 (Python 3.6) syntax for static types: ```python user_dict: Dict[str, UserProfile] = {} def get_user(email: str, realm: Realm) -> UserProfile: ... # Actual code of the function here ``` You can learn more about it at: - The [mypy cheat sheet for Python 3](https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html) is the best resource for quickly understanding how to write the PEP 484 type annotations used by mypy correctly. - The [Python type annotation spec in PEP 484](https://www.python.org/dev/peps/pep-0484/). - Our [blog post on being an early adopter of mypy][mypy-blog-post] from 2016. - Our [best practices](#best-practices) section below. The mypy type checker is run automatically as part of Zulip's Travis CI testing process in the `backend` build. [mypy-blog-post]: https://blog.zulip.org/2016/10/13/static-types-in-python-oh-mypy/ ## Installing mypy mypy is installed by default in the Zulip development environment. ## Running mypy on Zulip's code locally To run mypy on Zulip's python code, you can run the command: ```bash tools/run-mypy ``` Mypy outputs errors in the same style as a compiler would. For example, if your code has a type error like this: ```python foo = 1 foo = '1' ``` you'll get an error like this: ```console test.py: note: In function "test": test.py:200: error: Incompatible types in assignment (expression has type "str", variable has type "int") ``` ## Mypy is there to find bugs in Zulip before they impact users For the purposes of Zulip development, you can treat `mypy` like a much more powerful linter that can catch a wide range of bugs. If, after running `tools/run-mypy` on your Zulip branch, you get mypy errors, it's important to get to the bottom of the issue, not just do something quick to silence the warnings, before we merge the changes. Possible explanations include: - A bug in any new type annotations you added. - A bug in the existing type annotations. - A bug in Zulip! - Some Zulip code is correct but confusingly reuses variables with different types. - A bug in mypy (though this is increasingly rare as mypy is now fairly mature as a project). Each explanation has its own solution, but in every case the result should be solving the mypy warning in a way that makes the Zulip codebase better. If you're having trouble, silence the warning with an `Any` or `# type: ignore[code]` so you're not blocked waiting for help, add a `# TODO: ` comment so it doesn't get forgotten in code review, and ask for help in chat.zulip.org. ## Mypy stubs for third-party modules For the Python standard library and some popular third-party modules, the [typeshed project](https://github.com/python/typeshed) has [stubs](https://github.com/python/mypy/wiki/Creating-Stubs-For-Python-Modules), basically the equivalent of C header files defining the types used in these Python APIs. For other third-party modules that we call from Zulip, one either needs to add an `ignore_missing_imports` entry in `pyproject.toml` in the root of the project, letting `mypy` know that it's third-party code, or add type stubs to the `stubs/` directory, which has type stubs that mypy can use to type-check calls into that third-party module. It's easy to add new stubs! Just read the docs, look at some of existing examples to see how they work, and remember to remove the `ignore_missing_imports` entry in `pyproject.toml` when you add them. For any third-party modules that don't have stubs, `mypy` treats everything in the third-party module as an `Any`, which is the right model (one certainly wouldn't want to need stubs for everything just to use `mypy`!), but means the code can't be fully type-checked. ## Working with types from django-stubs For features that are difficult to be expressed with static type annotations, type analysis is supplemented with mypy plugins. Zulip's Python codebases uses the Django web framework, and such a plugin is required in order for `mypy` to correctly infer the types of most code interacting with Django model classes (i.e. code that accesses the database). We use the `mypy_django_plugin` plugin from the [django-stubs](https://github.com/typeddjango/django-stubs) project, which supports accurate type inference for classes like `QuerySet`. For example, `Stream.objects.filter(realm=realm)` is simple Django code to fetch all the streams in a realm. With this plugin, mypy will correctly determine its type is `QuerySet[Stream]`, aka a standard, lazily evaluated Django query object that can be iterated through to access `Stream` objects, without the developer needing to do an explicit annotation. When declaring the types for functions that accept a `QuerySet` object, you should always supply the model type that it accepts as the type parameter. ```python def foo(user: QuerySet[UserProfile]) -> None: ... ``` In cases where you need to type the return value from `.values_list` or `.values` on a `QuerySet`, you can use the special `django_stubs_ext.ValuesQuerySet` type. For `.values_list`, the second type parameter will be the type of the column. ```python from django_stubs_ext import ValuesQuerySet def get_book_page_counts() -> ValuesQuerySet[Book, int]: return Book.objects.filter().values_list("page_count", flat=True) ``` For `.values`, we prefer to define a `TypedDict` containing the key-value pairs for the columns. ```python from django_stubs_ext import ValuesQuerySet class BookMetadata(TypedDict): id: int name: str def get_book_meta_data( book_ids: List[int], ) -> ValuesQuerySet[Book, BookMetadata]: return Book.objects.filter(id__in=book_ids).values("name", "id") ``` When writing a helper function that returns the response from a test client, it should be typed as `TestHttpResponse` instead of `HttpResponse`. This type is only defined in the Django stubs, so it has to be conditionally imported only when type checking. Conventionally, we alias it as `TestHttpResponse`, which is internally named `_MonkeyPatchedWSGIResponse` within django-stubs. ```python from typing import TYPE_CHECKING from zerver.lib.test_classes import ZulipTestCase if TYPE_CHECKING: from django.test.client import _MonkeyPatchedWSGIResponse as TestHttpResponse class FooTestCase(ZulipTestCase): def helper(self) -> "TestHttpResponse": return self.client_get("/bar") ``` We sometimes encounter innaccurate type annotations in the Django stubs project. We prefer to address these by [submitting a pull request](https://github.com/typeddjango/django-stubs/pulls) to fix the issue in the upstream project, just like we do with `typeshed` bugs. ## Using @overload to accurately describe variations Sometimes, a function's type is most precisely expressed as a few possibilities, and which possibility can be determined by looking at the arguments. You can express that idea in a way mypy understands using `@overload`. For example, `check_list` returns a `Validator` function that verifies that an object is a list, raising an exception if it isn't. It supports being passed a `sub_validator`, which will verify that each element in the list has a given type as well. One can express the idea "If `sub_validator` validates that something is a `ResultT`, `check_list(sub_validator)` validators that something is a `List[ResultT]` as follows: ```python @overload def check_list(sub_validator: None, length: Optional[int]=None) -> Validator[List[object]]: ... @overload def check_list(sub_validator: Validator[ResultT], length: Optional[int]=None) -> Validator[List[ResultT]]: ... def check_list(sub_validator: Optional[Validator[ResultT]]=None, length: Optional[int]=None) -> Validator[List[ResultT]]: ``` The first overload expresses the types for the case where no `sub_validator` is passed, in which case all we know is that it returns a `Validator[List[object]]`; whereas the second defines the type logic for the case where we are passed a `sub_validator`. **Warning:** Mypy only checks the body of an overloaded function against the final signature and not against the more restrictive `@overload` signatures. This allows some type errors to evade detection by mypy: ```python @overload def f(x: int) -> int: ... @overload def f(x: str) -> int: ... # oops def f(x: Union[int, str]) -> Union[int, str]: return x x: int = f("three!!") ``` Due to this potential for unsafety, we discourage overloading unless it's absolutely necessary. Consider writing multiple functions with different names instead. See the [mypy overloading documentation][mypy-overloads] for more details. [mypy-overloads]: https://mypy.readthedocs.io/en/stable/more_types.html#function-overloading ## Best practices ### When is a type annotation justified? Usually in fully typed code, mypy will protect you from writing a type annotation that isn't justified by the surrounding code. But when you need to write annotations at the border between untyped and typed code, keep in mind that **a type annotation should always represent a guarantee,** not an aspiration. If you have validated that some value is an `int`, it can go in an `int` annotated variable. If you are going to validate it later, it should not. When in doubt, an `object` annotation is always safe. Mypy understands many Python constructs like `assert`, `if`, `isinstance`, and logical operators, and uses them to automatically narrow the type of validated objects in many cases. ```python def f(x: object, y: Optional[str]) -> None: if isinstance(x, int): # Within this if block, mypy infers that x: int print(x + 1) assert y is not None # After that assert statement, mypy infers that y: str print(y.strip()) ``` It won't be able do this narrowing if the validation is hidden behind a function call, so sometimes it's helpful for a validation function to return the type-narrowed value back to the caller even though the caller already has it. (The validators in `zerver/lib/validator.py` are examples of this pattern.) ### Avoid the `Any` type Mypy provides the [`Any` type](https://mypy.readthedocs.io/en/stable/dynamic_typing.html) for interoperability with untyped code, but it is completely unchecked. You can put an value of an arbitrary type into an expression of type `Any`, and get an value of an arbitrary type out, and mypy will make no effort to check that the input and output types match. So using `Any` defeats the type safety that mypy would otherwise provide. ```python x: Any = 5 y: str = x # oops print(y.strip()) # runtime error ``` If you think you need to use `Any`, consider the following safer alternatives first: - To annotate a dictionary where different keys correspond to values of different types, instead of writing `Dict[str, Any]`, try declaring a [**`dataclass`**](https://mypy.readthedocs.io/en/stable/additional_features.html#dataclasses) or a [**`TypedDict`**](https://mypy.readthedocs.io/en/stable/more_types.html#typeddict). - If you're annotating a class or function that might be used with different data types at different call sites, similar to the builtin `List` type or the `sorted` function, [**generic types**](https://mypy.readthedocs.io/en/stable/generics.html) with `TypeVar` might be what you need. - If you need to accept data of several specific possible types at a single site, you may want a [**`Union` type**](https://mypy.readthedocs.io/en/stable/kinds_of_types.html#union-types). `Union` is checked: before using `value: Union[str, int]` as a `str`, mypy requires that you validate it with an `instance(value, str)` test. - If you really have no information about the type of a value, use the **`object` type**. Since every type is a subtype of `object`, you can correctly annotate any value as `object`. The [difference between `Any` and `object`](https://mypy.readthedocs.io/en/stable/dynamic_typing.html#any-vs-object) is that mypy will check that you safely validate an `object` with `isinstance` before using it in a way that expects a more specific type. - A common way for `Any` annotations to sneak into your code is the interaction with untyped third-party libraries. Mypy treats any value imported from an untyped library as annotated with `Any`, and treats any type imported from an untyped library as equivalent to `Any`. Consider providing real type annotations for the library by [**writing a stub file**](#mypy-stubs-for-third-party-modules). ### Avoid `cast()` The [`cast` function](https://mypy.readthedocs.io/en/stable/type_narrowing.html#casts) lets you provide an annotation that Mypy will not verify. Obviously, this is completely unsafe in general. ```python x = cast(str, 5) # oops print(x.strip()) # runtime error ``` Instead of using `cast`: - You can use a [variable annotation](https://mypy.readthedocs.io/en/stable/type_inference_and_annotations.html#explicit-types-for-variables) to be explicit or to disambiguate types that mypy can check but cannot infer. ```python l: List[int] = [] ``` - You can use an [`isinstance` test](https://mypy.readthedocs.io/en/stable/common_issues.html#complex-type-tests) to safely verify that a value really has the type you expect. ### Avoid `# type: ignore` comments Mypy allows you to ignore any type checking error with a [`# type: ignore` comment](https://mypy.readthedocs.io/en/stable/common_issues.html#spurious-errors-and-locally-silencing-the-checker), but you should avoid this in the absence of a very good reason, such as a bug in mypy itself. If there are no safe options for dealing with the error, prefer an unchecked `cast`, since its unsafety is somewhat more localized. Our linter requires all `# type: ignore` comments to be [scoped to the specific error code](https://mypy.readthedocs.io/en/stable/error_codes.html) being ignored, and followed by an explanation such as a link to a GitHub issue. ### Avoid other unchecked constructs - As mentioned [above](#using-overload-to-accurately-describe-variations), we **discourage writing overloaded functions** because their bodies are not checked against the `@overload` signatures. - **Avoid `Callable[..., T]`** (with literal ellipsis `...`), since mypy cannot check the types of arguments passed to it. Provide the specific argument types (`Callable[[int, str], T]`) in simple cases, or use [callback protocols](https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols) in more complex cases. ### Use `Optional` and `None` correctly The [`Optional` type](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html#built-in-types) is for optional values, which are values that could be `None`. For example, `Optional[int]` is equivalent to `Union[int, None]`. The `Optional` type is **not for optional parameters** (unless they are also optional values as above). This signature does not use the `Optional` type: ```python def func(flag: bool = False) -> str: ... ``` A collection such as `List` should only be `Optional` if `None` would have a different meaning than the natural meaning of an empty collection. For example: - An include list where the default is to include everything should be `Optional` with default `None`. - An exclude list where the default is to exclude nothing should be non-`Optional` with default `[]`. Don't test an `Optional` value using truthiness (`if value:`, `not value`, `value or default_value`), especially when the type might have falsy values other than `None`. ```python s: Optional[str] if not s: # bad: are we checking for None or ""? ... if s is None: # good ... ``` ### Read-only types The basic Python collections [`List`](https://docs.python.org/3/library/typing.html#typing.List), [`Dict`](https://docs.python.org/3/library/typing.html#typing.Dict), and [`Set`](https://docs.python.org/3/library/typing.html#typing.Set) are mutable, but it's confusing for a function to mutate a collection that was passed to it as an argument, especially by accident. To avoid this, prefer annotating function parameters with read-only types: - [`Sequence`](https://docs.python.org/3/library/typing.html#typing.Sequence) instead of `List`, - [`Mapping`](https://docs.python.org/3/library/typing.html#typing.Mapping) instead of `Dict`, - [`AbstractSet`](https://docs.python.org/3/library/typing.html#typing.AbstractSet) instead of `Set`. This is especially important for parameters with default arguments, since a mutable default argument is confusingly shared between all calls to the function. ```python def f(items: Sequence[int] = []) -> int: items.append(1) # mypy catches this mistake return sum(items) ``` In some cases the more general [`Collection`](https://docs.python.org/3/library/typing.html#typing.Collection) or [`Iterable`](https://docs.python.org/3/library/typing.html#typing.Iterable) types might be appropriate. (But don’t use `Iterable` for a value that might be iterated multiple times, since a one-use iterator is `Iterable` too.) For example, if a function gets called with either a `list` or a `QuerySet`, and it only iterates the object once, the parameter can be typed as `Iterable`. ```python def f(items: Iterable[Realm]) -> None: for item in items: ... realms_list: List[Realm] = [zulip, analytics] realms_queryset: QuerySet[Realm] = Realm.objects.all() f(realms_list) # OK f(realms_queryset) # Also OK ``` A function's return type can be mutable if the return value is always a freshly created collection, since the caller ends up with the only reference to the value and can freely mutate it without risk of confusion. But a read-only return type might be more appropriate for a function that returns a reference to an existing collection. Read-only types have the additional advantage of being [covariant rather than invariant](https://mypy.readthedocs.io/en/latest/common_issues.html#invariance-vs-covariance): if `B` is a subtype of `A`, then `List[B]` may not be converted to `List[A]`, but `Sequence[B]` may be converted to `Sequence[A]`. ### Typing decorators A simple decorator that operates on functions of a fixed signature works with no issues: ```python def fancy(func: Callable[[str], str]) -> Callable[[int], str]: def wrapped_func(n: int) -> str: print("so fancy") return func(str(n)) return wrapped_func @fancy def f(s: str) -> str: return s ``` A decorator with an argument also works: ```python def fancy(message: str) -> Callable[[Callable[[str], str]], Callable[[int], str]]: def wrapper(func: Callable[[str], str]) -> Callable[[int], str]: def wrapped_func(n: int) -> str: print(message) return func(str(n)) return wrapped_func return wrapper @fancy("so fancy") def f(s: str) -> str: return s ``` And a [generic decorator](https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators) that operates on functions of arbitrary signatures can be written [with a `cast`](https://github.com/python/mypy/issues/1927) if the output signature is always the same as the input signature: ```python FuncT = TypeVar("FuncT", bound=Callable[..., object]) def fancy(func: FuncT) -> FuncT: def wrapped_func(*args: object, **kwargs: object) -> object: print("so fancy") return func(*args, **kwargs) return cast(FuncT, wrapped_func) # https://github.com/python/mypy/issues/1927 @fancy def f(s: str) -> str: return s ``` (A generic decorator with an argument would return `Callable[[FuncT], FuncT]`.) But Mypy doesn't yet support the advanced type annotations that would be needed to correctly type generic signature-changing decorators, such as `zerver.decorator.authenticated_json_view`, which passes an extra argument to the inner function. For these decorators we must unfortunately give up some type safety by falling back to `Callable[..., T]`. ## Troubleshooting advice All of our linters, including mypy, are designed to only check files that have been added in Git (this is by design, since it means you have untracked files in your Zulip checkout safely). So if you get a `mypy` error like this after adding a new file that is referenced by the existing codebase: ```console mypy | zerver/models.py:1234: note: Import of 'zerver.lib.markdown_wrappers' ignored mypy | zerver/models.py:1234: note: (Using --follow-imports=error, module not passed on command line) ``` The problem is that you need to `git add` the new file.