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coveo-functools-2.0.9
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Generic function tooling helpers
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | coveo-functools-2.0.9 |
| نام | coveo-functools |
| نسخه کتابخانه | 2.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jonathan Piché |
| ایمیل نویسنده | tools@coveo.com |
| آدرس صفحه اصلی | https://github.com/coveooss/coveo-python-oss/tree/main/coveo-functools |
| آدرس اینترنتی | https://pypi.org/project/coveo-functools/ |
| مجوز | Apache-2.0 |
# `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:
```json
[
{"Name": "John", "SocialNumber": 123},
{"Name": "Jean", "SocialNumber": 123}
]
```
Explicit usage example:
```python
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])
```
Automatic usage example:
```python
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:
```python
@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)*
- 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:
```python
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:
```python
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
1. Being able to specify a delegate for a specific kind of payload
1. 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.
1. Make your code more obscure and more likely to investigate the dark arts.
1. Break your IDE's autocompletion features.
1. 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.
```python
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)
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._
1. 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:
```python
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)
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](#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)
1. By exact name (str)
1. By flexed value (str)
1. 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:
```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:
```python
# 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:
```python
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)
all_transactions = flex.deserialize([payload, payload], hint=List[Transaction])
```
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:
```python
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:
```python
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:
```python
@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:
```python
@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:
```python
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:
```python
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`:
```python
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:
```python
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:
```python
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:
```python
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`:
```python
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](https://docs.python.org/3.8/library/functools.html#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:
```python
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.
```python
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
```python
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:
```python
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:
```python
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()
```
نیازمندی
| مقدار | نام |
|---|---|
| - | inflection |
| - | typing_extensions |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl coveo-functools-2.0.9:
pip install coveo-functools-2.0.9.whl
نصب پکیج tar.gz coveo-functools-2.0.9:
pip install coveo-functools-2.0.9.tar.gz