coveo-functools 2.0.33

Generic function tooling helpers

coveo-functools

Introspection, finalizers, delegates, dispatchers, waiters... These utilities aim at increasing productivity.

annotations

Introspect classes and callables at runtime.

Can convert string annotations into their actual type reference.

flex

Overview

Flex works with annotations to adjust and convert input data to match your target structure.

It was originally done as a mean to fit CamelCase payloads from external APIs into snake_case classes.

Take for example this payload that we'd like to fit into a pep8 context:

[
    {"Name": "John", "SocialNumber": 123},
    {"Name": "Jean", "SocialNumber": 123}
]

Explicit usage example:

from coveo_functools import flex

@dataclass
class Person:
    name: str
    social_number: Optional[int] = None

# the deserializer is used directly to receive a list of Person instances
response = flex.deserialize(json.load(), hint=List[Person], errors='raise')

Automatic usage example:

from coveo_functools.flex import flex

@flex
@dataclass
class SomeObject:
    """ I am decorated with @flex, so you can always give me some trouble. """
    name: str

response = [SomeObject(**data) for data in json.load()]

When remapping keys, Flex will ignore:

• Casing
• Underscores
• Hyphens
• Dots
• Spaces

For instance, it will happily accept {"__NaM e._": "John"} as valid input for the Person class.

It can also create instances of custom classes:

@dataclass
class Address:
  street: str

@dataclass
class Person:
    name: str
    address: List[Address]
    social_number: Optional[int] = None

You could then feed it a payload like {"name": "Lucy", address: [{"street": ...}, {...}]}. Flex will create an instance of Person, that has a list of 2 Address instances.

Note: The basic types str, bool, int, float, dict, list, None are ignored (no conversion occurs). This is because json.load() already returns these values in the proper type. This may change in the future.

Supported objects and annotations

Flex can be used with:

• Classes and dataclasses
• Abstract classes (new in 2.0.9) (requires adapter or serialization; explained below)
• Enums (new in 2.0.6) (Enum data-type support added in 2.0.26)
• Literals (new in 2.0.21)
• Functions
• Methods
• Union[str, bool, int, float, list, dict, None] (or any combination of these basic json-compatible types)
• These typing constructs, where T is your custom class:
  • List[T]
  • Dict[str, T] (in 2.0.23 and below)
  • Dict[K, V] (new in 2.0.24; dict keys are no longer limited to strings)
  • Union[T, List[T]] (for APIs that may return a thing-or-list-of-things)
  • Optional[T]
  • InitVar[T] (new in 2.0.25)

Limitations

• Variable positional args (such as def fn(*args): ...) are left untouched.
• Basic json-compatible types will be left untouched. This is determined by the annotation, not the actual value.
• If None is given as a value to deserialize into anything, None is given back. Absolutely no validation occurs in this case.
• An Abstract class requires additional metadata or a subclass adapter.
• No support for additional typing and collections objects other than the ones mentioned in this documentation.
• You can only Union basic json-compatible types, or List[T], T.
• All referenced types must be importable from the module it is defined in. This means that you cannot use inline and dynamic classes.

These are subject to change.

Features and FAQs

Subclass Adapters

new in 2.0.9

You can direct and override how to instantiate a payload by registering a callback adapter.

The adapter is a Callable[[Any], TypeHint] that you provide. It will be called with the payload value as Any, so you can inspect the content. It must return a TypeHint that tells flex which class to use.

new in 2.0.10: TypeHint may also be a callable (previously, it had to be a class).

With subclass adapters, you can selectively decide the implementation class based on the payload to deserialize. While this is necessary when annotating structures with Abstract classes, it can be used for any other class as well.

For this to work, you must register the annotated class with a callback:

from coveo_functools.flex.subclass_adapter import register_subclass_adapter

class Abstract:
  @abstractmethod
  def api(self) -> None:
    ...

  
class ThisImplementation(Abstract):
  def api(self) -> None:
    ...
  
  
class OtherImplementation(Abstract):
  def api(self) -> None:
    ...
  
  
def adapter(payload: Any) -> Type:
  assert isinstance(payload, dict)  # actual type depends on payload
  return ThisImplementation if 'this' in payload else OtherImplementation
  
  
register_subclass_adapter(Abstract, adapter)

Thanks to the adapter, this is now possible:

assert isinstance(deserialize({'this': {}}, hint=Abstract), ThisImplementation)
assert isinstance(deserialize({}, hint=Abstract), OtherImplementation)


@dataclass
class Payload:
    owner: Abstract
    

instance = deserialize({"owner": {"this": {}}}, hint=Payload)
assert isinstance(instance.owner, ThisImplementation)

The intended use of subclass adapters is to:

1. Support Abstract classes as annotations
2. Being able to specify a delegate for a specific kind of payload
3. To enhance/clean a payload before it is used

Any other use will generally:

1. Mess up your type annotation game because types are altered dynamically at runtime.
2. Make your code more obscure and more likely to investigate the dark arts.
3. Break your IDE's autocompletion features.
4. Linters which rely on static analysis will not be as powerful as they could be.

That being said, it's a powerful and potentially game-breaking feature that can be used to bend the framework if you accept bearing the consequences:

• There are no validations (to allow duck typing and stuff)
• This means you don't have to return an actual subclass; just something that can handle that payload
• You can register a callback for Any (or anything else really)
• You're not limited to return custom classes: you can return things like Dict[str, int] or List[Implementation] and the flex machinery will handle it just as if it was statically annotated that way.
• The payload value received by the adapter is not a copy, modifications will be honored.

Factory Adapters

Some types don't play well with keyword arguments. For instance, using the datetime class is much more convenient using strings, isoformat() and fromisoformat() than having to parse it into the year/month/etc component.

To serialize such types, you can use a factory adapter, which is expected to return the instance instead of the type.

from dataclasses import dataclass
from datetime import datetime
from coveo_functools import flex
from coveo_functools.flex.factory_adapter import register_factory_adapter

# Implementation of the deserialization:

def _datetime_factory(value: str) -> datetime:
  return datetime.fromisoformat(value)

register_factory_adapter(datetime, _datetime_factory)



# Demonstration:

@dataclass
class WithDateTime:
  timestamp: datetime
  

timestamp = datetime.utcnow()
instance = flex.deserialize({"timestamp": timestamp.isoformat()}, hint=WithDateTime, errors='raise')
assert instance.timestamp == timestamp
assert isinstance(instance.timestamp, datetime)

About Abstract classes

There are two ways to deal with abstract classes:

1. If you control the serialization aspect, flex can inspect a custom instance and generate metadata information that can be used during the deserialization process. This is the way.
2. If you don't control the serialization (e.g.: it's a json payload from an api), you can attach callbacks to inspect the payload and return the non-abstract class to use.

Abstract using SerializationMetadata

new in 2.0.10

Note: This is a dumbed down / magic version of the functionality offered by pickle and yaml, with the difference that the metadata and the payload are kept separate and readable.

The SerializationMetadata class inspects an instance and stores the type of the objects within (not their value!). Think of it as a header that must accompany your serialized data, so you can rebuild it later using the same subclasses.

This allows you use abstract classes in annotations, but deserialize into concrete ones:

from coveo_functools.flex.deserializer import deserialize
from coveo_functools.flex.serializer import SerializationMetadata

class Abstract:
    @abstractmethod
    def api(self) -> None:
        ...

    
@dataclass
class Concrete(Abstract):
    def api(self) -> None:
      ...


@dataclass    
class Parent:
    nested: Optional[Abstract] = None


meta = SerializationMetadata.from_instance(Parent(Concrete()))
parent = deserialize({"nested": {}}, hint=meta, errors='raise')
assert isinstance(parent.nested, Concrete)

Serialization metadata ties to a payload/instance and none other. It keeps the type information of your instance, and thus may be different on a different instance. For each payload you want to store, you must generate a new SerializationMetadata instance.

Example use cases:

• Serialize objects into a cloud-based queue
• Store objects into a no-SQL database

Limitation: The concrete implementation must be importable using the normal python mechanisms. A class returned from a function is not importable, and will not deserialize correctly.

Abstract using Subclass adapters

new in 2.0.9

The other, more involved way to use Abstract classes as annotations is to register subclass adapters.

See the Subclass Adapters section for more info.

About Enums

Enums will resolve by value or name, in this order:

1. By exact value (str/int/etc)
2. By exact name (str)
3. By flexed value (str)
4. By flexed name (str)

flex.deserialize

This is where the magic happens, and is the recommended usage whenever it meets your use case.

TL;DR: Given that payload is a dict,flex.deserialize(payload, hint=Job) will convert payload into an instance of Job.

Here's an example puzzle! An uncanny API returns a messy "transaction" JSON:

{
    "Sold_To": {"Name": "Jon"},
    "Items": [
        {"sku": 123, "price": 19.99},
        {"sku": 234, "price": 13.99},
        {"sku": 0, "price": 0.50, "NOTE": "Forgot the reusable bag at home!!"}
    ],
    "Rebates": {
        "airmiles": {"Flat": 10.0},
        "coupon": [{"Flat": 0.79}, {"Flat":  1.50}],
        "senior": {"Percentage": 2.5}
    },
    "Id": "GgfhAs89876yh.z"
}

Wouldn't it be convenient if you could create simple classes/dataclasses around them without any boilerplate?

You can solve it with flex. In one line, too!

Start by designing a hierarchy of classes with annotations that closely follow the API reference.

Remember, casing and underscore are ignored in flex, so you could use pep8 if you want:

# models.py

from dataclasses import dataclass
from typing import List, Dict, Union, Optional


class SkuItem:
    def __init__(self, sku: int, price: float) -> None:
        self.sku = sku
        self.price = price


@dataclass
class Rebate:
    percentage: Optional[float] = None
    flat: Optional[float] = None


@dataclass
class Customer:
    name: str


@dataclass
class Transaction:
    sold_to: Customer
    items: List[SkuItem]
    rebates: Dict[str, Union[Rebate, List[Rebate]]]

Did you notice any flex-related boilerplate in the snippet above? No? Good! :)

Here's how you can use the flex deserializer to bend the furious API response into your perfect python classes:

payload = {
    "Sold_To": {"Name": "Jon"},
    "Items": [
        {"sku": 123, "price": 19.99},
        {"sku": 234, "price": 13.99},
        {"sku": 0, "price": 0.50, "NOTE": "Forgot the reusable bag at home!!"}
    ],
    "Rebates": {
        "airmiles": {"Flat": 10.0},
        "coupon": [{"Flat": 0.79}, {"Flat":  1.50}],
        "senior": {"Percentage": 2.5}
    },
    "Id": "GgfhAs89876yh.z"
}

transaction = flex.deserialize(payload, hint=Transaction, errors='raise')
all_transactions = flex.deserialize([payload, payload], hint=List[Transaction], errors='raise')

Interesting details:

• Well, the casing worked! :shrug:
• Id and NOTE were dropped because they were excluded from the Transaction model. Time saver; some APIs return tons of data.
• The rebates actually kept the keys, and created Rebate instances as the values.
• The value type of the rebates dict is either a single Rebate instance or a list of them. See the "thing or list of things" section below for considerations.
• In the all_transactions variable, List[Annotation] was used directly as the hint. Nifty!

@flex and flex(obj)

There is a decorator version of deserialize.

from coveo_functools.flex import flex

It returns a function, method or class wrapped in flex.deserialize magic. When called, the wrapper will automatically adjust the call arguments to match the wrapped object, call the wrapped object with them, and return the response.

flex can be used:

• as a decorator over classes, methods and functions
• inline to call a function or to create flexible factories

When used inline, you can adjust a payload for any callable:

from some_3rd_party import calculate_price

price = flex(calculate_price)(**payload)

You can also generate "flexible" factories, for instance to be used as a delegate:

from some_3rd_party import ThisClass

factory: Callable[..., T] = flex(ThisClass)
instance1 = factory(**payload1)
instance2 = factory(**payload2)

When used as a decorator, all invocations are automatically handled for all callers:

@flex
def calculate_price(sold_to: Customer, items: Union[SkuItems, List[SkuItems]]) -> float:
    ...

# breaks static analysis; wrong argument shown for demonstration purposes
price = calculate_price(SoldTo=dict(Name="Marie"), items={"sku": 123, "price": 19.99})

You could adjust the Transaction from earlier class like this:

@flex
@dataclass
class Transaction:
    sold_to: Customer
    items: List[SkuItem]
    rebates: Dict[str, Union[Rebate, List[Rebate]]]

So that you can drop the explicit calls to flex.deserialize and use them directly:

one_transaction = Transaction(**payload)
list_transactions = [Transaction(**t) for t in [payload, payload]]

flex or deserialize?

Favor flex.deserialize over the decorator pattern:

• This will make the usages explicit rather than implicit.
• The additional wrappers created by the decorator may affect performance in the presence of huge structures.
• You can flex.deserialize([], hint=List[T]) and get a list, but you cannot flex(List[T]) directly (both methods demonstrated below)

Generally, it leads to a better design because you end up wiring the flex.deserialize call next to the json.load() call in a generic manner, and that's 100% of the flex code you'll ever need:

class ApiWrapper:
    def get_transaction(self, id: int) -> Transaction:
        return self._do_request("GET", f"api/transactions/{id}", hint=Transaction)

    def get_all_transactions(self) -> List[Transaction]:
        return self._do_request("GET", "api/transactions", hint=List[Transaction])
  
    def _do_request(self, method: str, url: str, hint: T) -> T:
        response = self._session.request(method=method, url=url)
        return flex.deserialize(response.json, hint=hint)

Because explicit is better than implicit, the above design is generally easier to understand than the one below, where Transaction is assumed to be decorated with @flex:

class ApiWrapper:
    def get_transaction(self, id: int) -> Transaction:
        return Transaction(**self._do_request("GET", f"api/transactions/{id}"))

    def get_all_transactions(self) -> List[Transaction]:
        return [Transaction(**data) for data in self._do_request("GET", "api/transactions")]
  
    def _do_request(self, method: str, url: str) -> Any:
        response = self._session.request(method=method, url=url)
        return response.json

Consideration for mypy

There is one annotation case worth mentioning. Consider this code:

class Inner:
    ...

@flex
def fn(inner: Inner) -> ...:
    ...

_ = fn(**{'inner': {...}})

In this case, mypy will infer that you're doing **Dict[str, Dict] and complain that Dict is not compatible with Inner.

To solve this without an ignore statement, explicitly annotate/cast your payloads with Any:

payload: Dict[str, Any] = {"inner": {}}
_ = fn(**payload)

unflex

Unflex is one of the utilities used by flex.deserializer.

It is responsible for adjusting the keyword arguments of a dictionary, so that they match the argument names of a target function.

It does not perform any conversion; all it does is matching keys. Extra keys are dropped by default:

from coveo_functools.flex import unflex

def fn(arg1: str, arg2: str) -> None:
    ...

assert unflex(fn, {"ARG1": ..., "ArG_2": ..., "extra": ...}) == {"arg1": ..., "arg2": ...}

Note: To target classes, you need to unflex(cls.__init__, ...)

@flexcase

flexcase is the decorator version of unflex:

from coveo_functools.flex import flexcase

@flexcase
def fn(arg1: str, arg2: str) -> str:
    return f"{arg1} {arg2}"


assert fn(ARG1="hello", _arg2="world", extra=...) == "hello world"

dispatch

An enhanced version of functools.singledispatch:

• Adds support for Type[] annotations (singledispatch only works on instances)
• You are no longer limited to the first argument of the method
• You can target an argument by its name too, regardless of its position

finalizer

A classic and simple try/finally context manager that launches a delegate once a block of code has completed.

A common trick is to "cook" the finalizer arguments through a mutable type such as a list or dict:

from typing import List
from coveo_functools.finalizer import finalizer

def clean_up(container_names: List[str]) -> None:
    for _ in container_names:
        ...
    
def test_spawning_containers() -> None:
    containers: List[str] = []
    with finalizer(clean_up, containers):
        containers.append('some-container-1')
        containers.append('some-container-2')
        containers.append('some-container-3')

wait.until()

Waits for a condition to happen. Can be configured with exceptions to ignore.

from coveo_functools import wait
import requests

def _ready() -> bool:
    return requests.get('/ping').status_code == 200

wait.until(_ready, timeout_s=30, retry_ms=100, handle_exceptions=ConnectionError,
           failure_message="The service failed to respond in time.")

wait.Backoff

A customizable class to assist in the creation of backoff retry strategies.

• Customizable growth factor
• Jitter
• Backoff progress % (want to fire some preliminary alarms at 50% backoff maybe?)
• Supports infinite backoff
• Can be configured to raise after too many attempts
• Can be configured to raise after a set amount of time

e.g.: Worker loop failure management by catching RetriesExhausted

from coveo_functools.wait import Backoff

backoff = Backoff()
while my_loop:
    try:
        do_stuff()
    except Exception as exception:
        try:
            quit_flag.wait(next(backoff))
        except backoff.RetriesExhausted:
            raise exception

e.g.: Worker loop failure management without the nested try/catch:

from coveo_functools.wait import Backoff

backoff = Backoff()
while my_loop:
    try:
        do_stuff()
    except Exception as exception:
        wait_time = next(backoff, None)
        if wait_time is None:
            raise exception
        quit_flag.wait(wait_time)

e.g.: You can generate the wait times without creating a Backoff instance, too:

import time
from coveo_functools.wait import Backoff

wait_times = list(Backoff.generate_backoff_stages(first_wait, growth, max_backoff))
for sleep_time in wait_times:
    try:
        do_stuff()
        break
    except:
        time.sleep(sleep_time)
else:
    raise ImSickOfTrying()