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atools-0.9.2
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مشاهده بیشترتوضیحات
Python 3.6+ async/sync memoize and rate decorators
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | atools-0.9.2 |
| نام | atools |
| نسخه کتابخانه | 0.9.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | cevans |
| ایمیل نویسنده | c.d.evans87@gmail.com |
| آدرس صفحه اصلی | https://github.com/cevans87/atools |
| آدرس اینترنتی | https://pypi.org/project/atools/ |
| مجوز | mit |
[](https://coveralls.io/github/cevans87/atools?branch=master)
# atools
Python 3.6+ decorators including
- `@memoize` - a function decorator for sync and async functions that memoizes results.
- `@rate` - a function decorator for sync and async functions that rate limits calls.
## @memoize
Decorates a function call and caches return value for given inputs.
- If `db_path` is provided, memos will persist on disk and reloaded during initialization.
- If `duration` is provided, memos will only be valid for given `duration`.
- If `keygen` is provided, memo hash keys will be created with given `keygen`.
- If `pickler` is provided, persistent memos will (de)serialize using given `pickler`.
- If `size` is provided, LRU memo will be evicted if current count exceeds given `size`.
### Examples
- Body will run once for unique input `bar` and result is cached.
```python3
@memoize
def foo(bar) -> Any: ...
foo(1) # Function actually called. Result cached.
foo(1) # Function not called. Cached result returned.
foo(2) # Function actually called. Result cached.
```
- Same as above, but async.
```python3
@memoize
async def foo(bar) -> Any: ...
# Concurrent calls from the same event loop are safe. Only one call is generated. The
# other nine calls in this example wait for the result.
await asyncio.gather(*[foo(1) for _ in range(10)])
```
- Classes may be memoized.
```python3
@memoize
Class Foo:
def init(self, _): ...
Foo(1) # Instance is actually created.
Foo(1) # Instance not created. Cached instance returned.
Foo(2) # Instance is actually created.
```
- Calls `foo(1)`, `foo(bar=1)`, and `foo(1, baz='baz')` are equivalent and only cached once.
```python3
@memoize
def foo(bar, baz='baz'): ...
```
- Only 2 items are cached. Acts as an LRU.
```python3
@memoize(size=2)
def foo(bar) -> Any: ...
foo(1) # LRU cache order [foo(1)]
foo(2) # LRU cache order [foo(1), foo(2)]
foo(1) # LRU cache order [foo(2), foo(1)]
foo(3) # LRU cache order [foo(1), foo(3)], foo(2) is evicted to keep cache size at 2
```
- Items are evicted after 1 minute.
```python3
@memoize(duration=datetime.timedelta(minutes=1))
def foo(bar) -> Any: ...
foo(1) # Function actually called. Result cached.
foo(1) # Function not called. Cached result returned.
sleep(61)
foo(1) # Function actually called. Cached result was too old.
```
- Memoize can be explicitly reset through the function's `.memoize` attribute
```python3
@memoize
def foo(bar) -> Any: ...
foo(1) # Function actually called. Result cached.
foo(1) # Function not called. Cached result returned.
foo.memoize.reset()
foo(1) # Function actually called. Cache was emptied.
```
- Current cache length can be accessed through the function's `.memoize` attribute
```python3
@memoize
def foo(bar) -> Any: ...
foo(1)
foo(2)
len(foo.memoize) # returns 2
```
- Alternate memo hash function can be specified. The inputs must match the function's.
```python3
Class Foo:
@memoize(keygen=lambda self, a, b, c: (a, b, c)) # Omit 'self' from hash key.
def bar(self, a, b, c) -> Any: ...
a, b = Foo(), Foo()
# Hash key will be (a, b, c)
a.bar(1, 2, 3) # LRU cache order [Foo.bar(a, 1, 2, 3)]
# Hash key will again be (a, b, c)
# Be aware, in this example the returned result comes from a.bar(...), not b.bar(...).
b.bar(1, 2, 3) # Function not called. Cached result returned.
```
- If part of the returned key from keygen is awaitable, it will be awaited.
```python3
async def awaitable_key_part() -> Hashable: ...
@memoize(keygen=lambda bar: (bar, awaitable_key_part()))
async def foo(bar) -> Any: ...
```
- If the memoized function is async and any part of the key is awaitable, it is awaited.
```python3
async def morph_a(a: int) -> int: ...
@memoize(keygen=lambda a, b, c: (morph_a(a), b, c))
def foo(a, b, c) -> Any: ...
```
- Properties can be memoized.
```python3
Class Foo:
@property
@memoize
def bar(self) -> Any: ...
a = Foo()
a.bar # Function actually called. Result cached.
a.bar # Function not called. Cached result returned.
b = Foo() # Memoize uses 'self' parameter in hash. 'b' does not share returns with 'a'
b.bar # Function actually called. Result cached.
b.bar # Function not called. Cached result returned.
```
- Be careful with eviction on instance methods. Memoize is not instance-specific.
```python3
Class Foo:
@memoize(size=1)
def bar(self, baz) -> Any: ...
a, b = Foo(), Foo()
a.bar(1) # LRU cache order [Foo.bar(a, 1)]
b.bar(1) # LRU cache order [Foo.bar(b, 1)], Foo.bar(a, 1) is evicted
a.bar(1) # Foo.bar(a, 1) is actually called and cached again.
```
- Values can persist to disk and be reloaded when memoize is initialized again.
```python3
@memoize(db_path=Path.home() / '.memoize')
def foo(a) -> Any: ...
foo(1) # Function actually called. Result cached.
# Process is restarted. Upon restart, the state of the memoize decorator is reloaded.
foo(1) # Function not called. Cached result returned.
```
- If not applied to a function, calling the decorator returns a partial application.
```python3
memoize_db = memoize(db_path=Path.home() / '.memoize')
@memoize_db(size=1)
def foo(a) -> Any: ...
@memoize_db(duration=datetime.timedelta(hours=1))
def bar(b) -> Any: ...
```
- Comparison equality does not affect memoize. Only hash equality matters.
```python3
# Inherits object.__hash__
class Foo:
# Don't be fooled. memoize only cares about the hash.
def __eq__(self, other: Foo) -> bool:
return True
@memoize
def bar(foo: Foo) -> Any: ...
foo0, foo1 = Foo(), Foo()
assert foo0 == foo1
bar(foo0) # Function called. Result cached.
bar(foo1) # Function called again, despite equality, due to different hash.
```
### A warning about arguments that inherit `object.__hash__`:
It doesn't make sense to keep a memo if it's impossible to generate the same input again. Inputs
that inherit the default `object.__hash__` are unique based on their id, and thus, their
location in memory. If such inputs are garbage-collected, they are gone forever. For that
reason, when those inputs are garbage collected, `memoize` will drop memos created using those
inputs.
- Memo lifetime is bound to the lifetime of any arguments that inherit `object.__hash__`.
```python3
# Inherits object.__hash__
class Foo:
...
@memoize
def bar(foo: Foo) -> Any: ...
bar(Foo()) # Memo is immediately deleted since Foo() is garbage collected.
foo = Foo()
bar(foo) # Memo isn't deleted until foo is deleted.
del foo # Memo is deleted at the same time as foo.
```
- Types that have specific, consistent hash functions (int, str, etc.) won't cause problems.
```python3
@memoize
def foo(a: int, b: str, c: Tuple[int, ...], d: range) -> Any: ...
foo(1, 'bar', (1, 2, 3), range(42)) # Function called. Result cached.
foo(1, 'bar', (1, 2, 3), range(42)) # Function not called. Cached result returned.
```
- Classmethods rely on classes, which inherit from `object.__hash__`. However, classes are
almost never garbage collected until a process exits so memoize will work as expected.
```python3
class Foo:
@classmethod
@memoize
def bar(cls) -> Any: ...
foo = Foo()
foo.bar() # Function called. Result cached.
foo.bar() # Function not called. Cached result returned.
del foo # Memo not cleared since lifetime is bound to class Foo.
foo = Foo()
foo.bar() # Function not called. Cached result returned.
foo.bar() # Function not called. Cached result returned.
```
- Long-lasting object instances that inherit from `object.__hash__`.
```python3
class Foo:
@memoize
def bar(self) -> Any: ...
foo = Foo()
foo.bar() # Function called. Result cached.
# foo instance is kept around somewhere and used later.
foo.bar() # Function not called. Cached result returned.
```
- Custom pickler may be specified for persistent memo (de)serialization.
```python3
import dill
@memoize(db_path='~/.memoize`, pickler=dill)
def foo() -> Callable[[], None]:
return lambda: None
```
## rate
Function decorator that rate limits the number of calls to function.
- `size` must be provided. It specifies the maximum number of calls that may be made
concurrently and optionally within a given `duration` time window.
- If `duration` is provided it limits the maximum call count to `size` in any given `duration`
time window.
### Examples
- Only 2 concurrent calls allowed.
```python3
@rate(size=2)
def foo(): ...
```
- Only 2 calls allowed per minute.
```python3
@rate(size=2, duration=60)
def foo(): ...
```
- Same as above, but duration specified with a timedelta.
```python3
@rate(size=2, duration=datetime.timedelta(minutes=1))
def foo(): ...
```
- Same as above, but async.
```python3
@rate(size=2, duration=datetime.timedelta(minutes=1))
async def foo(): ...
```
- More advanced rate limiting is possible by composing multiple rate decorators.
```python3
# Up to 100 calls per minute, but only 10 concurrent.
@rate(size=100, duration=60)
@rate(size=10)
def foo(): ...
```
نیازمندی
| مقدار | نام |
|---|---|
| - | frozendict |
| >=0.6 | dataclasses |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl atools-0.9.2:
pip install atools-0.9.2.whl
نصب پکیج tar.gz atools-0.9.2:
pip install atools-0.9.2.tar.gz