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Structure unstructured data for the purpose of static type checking

Project description

typecats

Structure unstructured data for the purpose of static type checking. An opinionated wrapper for attrs and cattrs.

In many web services it is common to consume or generate JSON or some JSON-like representation of data. JSON translates quite nicely to core Python objects such as dicts and lists. However, if your data is structured, it is nice to be able to work with it in a structured manner, i.e. with Python objects. Python objects give you better code readability, and in more recent versions of Python they are also capable of being statically type-checked with a tool like mypy.

attrs is an excellent library for defining boilerplate-free Python classes that are easy to work with and that make static type-checking with mypy a breeze. You define your attributes and their types with a very clean syntax, attrs gives you constructors and dunder methods, and mypy brings the static type-checking.

Throwing cattrs into the mix, you can have pleasant and simple conversions to and from unstructured data with extremely low boilerplate as well.

typecats, and its core decorator Cat, is a thin opinionated layer on top of these two runtime libraries (attrs and cattrs) and the develop-time mypy. It defines an attrs class with a few additional features. The 3 core features are:

Features

  1. Built-in struc and unstruc.

    Static class function struc and object method unstruc added to every class type defined as a Cat, which pass directly through to their underlying structure and unstructure implementations in cattrs.

    @Cat
    class TestCat:
        name: str
        age: int
    
    TestCat.struc(dict(name='Tom', age=9)) == TestCat(name='Tom', age=9)
    
    TestCat.struc(dict(name='Tom', age=9)).unstruc() == dict(name='Tom', age=9)
    

    Rationale

    Make your code easier to read, create a common pattern for defining, structuring, and unstructuring pure data objects, and require fewer imports - just import your defined type and go! Abbreviated forms of the verbs structure and unstructure were chosen to underscore the difference between the built-in cattrs verbs and to reduce code clutter slightly for what is intended to be a common and idiomatic operation.

    Considerations

    Note that a mypy plugin is provided to inform the type checker that these dynamically-added methods are real and provide the intended result types. Add to your mypy.ini:

    plugins = typecats.cats_mypy_plugin
    

    Additionally, struc and unstruc first-class functions are provided if you strongly prefer a functional approach. struc reverses the order of the cattrs function signature to make it suitable for the common case of partial application:

    TestCat_struc = functools.partial(struc, TestCat)
    TestCat_struc(dict(name='Tom', age=2))
    
  2. Non-empty validators defined for all attributes with no default provided.

    @Cat
    class TestCat:
        name: str
        age: int
        neutered: bool = True
        owner: Optional[Owner] = None
    
    works = TestCat.struc(dict(name='Tom', age=0))
    assert works.neutered == True
    
    try:
        TestCat.struc(dict(name='', age=0))
    except ValueError as ve:
        print(ve)
        # Attribute "name" on class <class 'TestCat'> with type <class 'str'> cannot have empty value ''!
    

    Rationale

    For many types of data, a default value such as an empty string, empty list/set, or missing complex type is perfectly valid, and typecats takes the approach that such attributes should have a defined default value in order to simplify the use of those objects. This has been found to be particularly useful in the context of structuring data from APIs, where the API contract may not require all keys to be provided for a given type, and where new attributes/keys may be defined later on that old clients would not know about (backwards compatibility). In these cases, not defining a default value would complicate the code, by forcing developers to remember which keys needed to be added to a raw data dict before structuring it.

    On the other hand, there are some facets of the data that are absolutely required. A common example would be a database ID - without a defined ID, the object/data is meaningless. typecats allows you to enforce the most basic level of compliance by not defining defaults, which forces clients to provide not simply a value of the proper type, but a non-empty value of that type - for instance, the empty string would never be a valid database ID.

  3. Wildcats - partial/gradual types via classes.

    Objects may subclass dict in order to transparently retain untyped key/value pairs for a roundtrip structure-unstructure. These are called Wildcats, since they allow a significant amount of extra functionality at the cost of not fully enforcing type-checking.

    @Cat
    class TestWildcat(dict):
        name: str
        age: int
    
    cat_from_db = dict(name='Tom', age=8, gps_tracker=True)
    wc = TestWildcat.struc(cat_from_db)
    assert wc.name == Tom
    assert wc.age == 8
    assert wc['gps_tracker'] == True  # cattrs would normally drop this key at structure time
    assert wc.unstruc() == cat_from_db  # `gps_tracker` survived the roundtrip
    

    Rationale

    Effectively provides a partially-typed overlay on top of existing data, as gradual/partial typing within a specific data format can be very useful.

    In other static type-checking systems such as Flow for JavaScript, you may define a type as being a simple overlay on top of an object which does not prevent that object from containing other data for keys outside the typed set. A Cat is an attrs class with a defined set of attributes that will be structured from raw data, and as of cattrs 1.0.0rc0, unexpected keys are silently dropped in order to prevent users from needing to sanitize their data before structuring (as opposed to being a runtime error). This behavior means that a structured object is not suitable for being passed between different parts of a program if there may be other parts to the data that the structuring class does not know about. This is an unfortunately common requirement, for instance when operating a roundtrip read/write transaction to/from a database. Since the alternative of passing around the raw data and performing many separate structuring/unstructuring roundtrips can be prohibitively expensive, and additionally it is arguably (e.g., the design philosophy behind Clojure's Maps, or simply duck/structural typing in general) better software design in many cases to allow code to operate on a limited subset of attributes without preventing objects with a superset of their functionality from being used, typecats provides the Wildcat functionality to mimic these more expressive and flexible type/data systems.

    Considerations

    Note that, as with the rest of typecats, this is a local optimum designed for specific, though arguably common, usecases. You don't need to use the Wildcat functionality to take advantage of features 1 and 2, and since it is presumably (for good reason) quite rare to explicity subclass dict for normal Python classes, it seems unlikely that this implementation choice to require inheritance would prevent most practical use cases of Cat even if the functionality of preserving unknown data was specifically not desirable for a given application.

    If an application attempts to get or set items within a Wildcat which are defined attributes on the class, this will (as of v1.1) be allowed but a warning will be logged. This seems to be a better in-practice balance for evolving codebases than the v1.0 behavior of raising an error. A future version could potentially allow this to be toggled globally or per Wildcat class, but the default will remain permissive for backwards compatiblity.

    A further design note on Wildcats: A non-inheriting implementation was considered and rejected (so far) for two reasons: first, that this would require major additional work in order to support pylint and mypy understanding that dict-like access was legal for these objects; and second, that not inheriting dict but overriding __getitem__ and __setitem__ would be even more likely to conflict with existing class hierarchies, since any object that already inherited from dict would appear to 'work' as a Wildcat but its underlying dict would be overlaid and inaccessible as a Wildcat.

Notes on intent, compatibility, and dependencies

typecats and Cat are explictly intended to solve a few specific but common uses, and though they do not intentionally override or replace attrs or cattrs features, any complex use of those underlying features may or may not be fully operational. If you want to write complex validator or constructor/builder logic of your own, this library may not be for you.

That said, it is common in our experience to register a number of specific structure and unstructure hooks with cattrs to make certain specific scenarios work ideally with your data, and typecats provides convenient wrappers to allow adding your hooks to its internal cattrs Converter instance. By defining its own converter instance, typecats does not interfere in any way with an existing application's usage of attrs or cattrs, and may be used in addition to, rather than as a replacement for, those libraries.

Use register_struc_hook and register_unstruc_hook to register on the built-in converter instance.

typecats uses newer-style static typing within its own codebase, and is therefore currently only compatible with Python 3.6 and up.

As core parts of the implementation, both attrs and cattrs are runtime dependencies.

mypy plugin

typecats provides a mypy plugin that tells mypy how to interpret the dynamically-generated struc and unstruc methods on Cat-annotated classes and objects.

This plugin was most recently updated to account for plugin API changes in mypy 0.750.

Users/Stability

typecats has been used in production in the Vision system at XOi Technologies since early 2019.

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