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Python static type checker (mypy)
mypy 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.
As a result, Zulip is in the process of migrating from using mypy's
Python 2 compatible syntax for type annotations (in which type
annotations are written inside comments that start with # type: ) to
the Python 3 syntax. Here's a brief example of the mypy syntax we're
using in Zulip:
user_dict = {} # type: 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 is the best resource for quickly understanding how to write the PEP 484 type annotations used by mypy correctly.
-
Our blog post on being an early adopter of mypy from 2016.
The mypy type checker is run automatically as part of Zulip's Travis
CI testing process in the backend build.
Installing mypy
mypy is installed by default in the Zulip development environment. If
you'd like to install just the version of mypy that we're using
(useful if e.g. you want mypy installed on your laptop outside the
Vagrant guest), you can do that with pip install -r requirements/mypy.txt.
Running mypy on Zulip's code locally
To run mypy on Zulip's python code, you can run the command:
tools/run-mypy
This will take a while to start running, since it runs mypy as a long-running daemon (server) process and send type-checking requests to the server; this makes checking mypy about 100x faster. But if you're debugging or for whatever reason don't want the daemon, you can use:
tools/run-mypy --no-daemon
Mypy outputs errors in the same style as a compiler would. For example, if your code has a type error like this:
foo = 1
foo = '1'
you'll get an error like this:
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 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 in production scripts
While in most of the Zulip codebase, we can consistently use the
typing module (Part of the standard library in Python 3.5, but
present as an installable module with older Python), in our installer
and other production scripts that might run outside a Zulip
virtualenv, we cannot rely on the typing module being present on the
system.
To solve this problem, we use the following (semi-standard in the mypy community) hack in those scripts:
if False:
# See https://zulip.readthedocs.io/en/latest/testing/mypy.html#mypy-in-production-scripts
from typing import List
and then use the Python 2 style type comment syntax for annotating
those files. This way, the Python interpreters for Python 2.7 and 3.4
will ignore this line, and thus not crash. But we can still get all
the benefits of type annotations in that codebase, since the mypy
type checker ignores the if False and thus still is able to
type-check the file using those imports.
The exception to this rule is that any scripts which use
setup_path_on_import before they import from the typing module are
safe. These, we generally declare in the relevant exclude line in
tools/linter_lib/custom_check.py
mypy stubs for third-party modules.
For the Python standard library and some popular third-party modules, the typeshed project has stubs, 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 mypy.ini 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 mypy.ini 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.
Note: When editing stubs, we recommend using
tools/run-mypy --no-daemon, because the mypy daemon's caching
system has some bugs around editing stubs that can be confusing.
type_debug.py
zerver/lib/type_debug.py has a useful decorator print_types. It
prints the types of the parameters of the decorated function and the
return type whenever that function is called. This can help find out
what parameter types a function is supposed to accept, or if
parameters with the wrong types are being passed to a function.
Here is an example using the interactive console:
>>> from zerver.lib.type_debug import print_types
>>>
>>> @print_types
... def func(x, y):
... return x + y
...
>>> func(1.0, 2)
func(float, int) -> float
3.0
>>> func('a', 'b')
func(str, str) -> str
'ab'
>>> func((1, 2), (3,))
func((int, int), (int,)) -> (int, int, int)
(1, 2, 3)
>>> func([1, 2, 3], [4, 5, 6, 7])
func([int, ...], [int, ...]) -> [int, ...]
[1, 2, 3, 4, 5, 6, 7]
print_all prints the type of the first item of lists. So [int, ...] represents
a list whose first element's type is int. Types of all items are not printed
because a list can have many elements, which would make the output too large.
Similarly in dicts, one key's type and the corresponding value's type are printed.
So {1: 'a', 2: 'b', 3: 'c'} will be printed as {int: str, ...}.