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cacheable-iterators-0.1.1
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Python decorators for caching functions returning iterators
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cacheable-iterators-0.1.1 |
| نام | cacheable-iterators |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://gitlab.com/Hares-Lab/cacheable-iterators |
| آدرس اینترنتی | https://pypi.org/project/cacheable-iterators/ |
| مجوز | BSD 2-Clause License |
# Cacheable Iterators
A simple tool to assist caching iterators response (lazy computations remain lazy).
Supports computations for the following return types:
- `Iterator[T]`
- `Awaitable[Iterator[T]]`
- `AsyncIterator[T]`
Read [full documentation] online.
For simple or asynchronous iterators it can use either built-in
`functools.cache` (or its replacement `cacheable_iter.helpers.simple_cache`),
`functools.lru_cache`, or any other appropriate cache engine.
For awaitable iterators it should use coroutine-compatible caches,
the default (bundled) solution is `async_lru.alru_cache` (PyPI: [async_lru]).
Also works for methods and/or class methods, as well as with bound methods,
as long as the caching engine supports them.
## Usage
To make generator-like function be cacheable, simply decorate it with one of the following functions:
- `cacheable_iter.core.iter_cache` or `cacheable_iter.core.lru_iter_cache` - for functions those return `Iterator[T]`
- `cacheable_iter.core.alru_iter_cache` - for functions those return `Awaitable[Iterator[T]]`
- `cacheable_iter.core.lru_async_iter_cache` - for functions those return `AsyncIterator[T]`
### Caching Simple Iterator
```python
from typing import *
from cacheable_iter import iter_cache
@iter_cache
def iterator_function(n: int) -> Iterator[int]:
yield from range(n)
```
### Caching Awaitable Iterator
```python
import asyncio
from typing import *
from cacheable_iter import alru_iter_cache
@alru_iter_cache
async def awaitable_iterator_function(n: int) -> Iterator[int]:
gen = iterator_function(n)
await asyncio.sleep(0.5)
return gen
```
### Caching Asynchronous Iterator
```python
import asyncio
from typing import *
from cacheable_iter import lru_async_iter_cache
@lru_async_iter_cache
async def async_iterator_function(n: int) -> AsyncIterator[int]:
for _ in await awaitable_iterator_function(n):
yield _
await asyncio.sleep(0.5)
```
### Example
This package provides a few decorators to wrap iterators.
They all support lazy computations,
so if an iterator is not iterated, the values are not computed.
(This is safe to use with infinite or endless iterators like counters.)
```python
from typing import *
from cacheable_iter import iter_cache
@iter_cache
def my_iter(n: int) -> Iterator[int]:
print(" * my_iter called")
for i in range(n):
print(f" * my_iter step {i}")
yield i
gen1 = my_iter(4)
print("Creating an iterator...")
print(f"The first value of gen1 is {next(gen1)}")
print(f"The second value of gen1 is {next(gen1)}")
gen2 = my_iter(4)
print("Creating an iterator...")
print(f"The first value of gen2 is {next(gen2)}")
print(f"The second value of gen2 is {next(gen2)}")
print(f"The third value of gen2 is {next(gen2)}")
```
The code snippet above would print the following:
```
Creating an iterator...
* my_iter called
* my_iter step 0
The first value of gen1 is 0
* my_iter step 1
The second value of gen1 is 1
Creating an iterator...
The first value of gen2 is 0
The second value of gen2 is 1
* my_iter step 2
The third value of gen2 is 2
```
## Principe of Work
Like in caching, the function is wrapped around with the new one
which, however, instead of checking the function arguments,
transforms the result into a special helper class
(either `CachedIterable[T]` or `CachedAsyncIterable[T]`).
Then the caching happens -- instead of storing the iterator itself it stores wrapper over it.
When the cache value is extracted, both `CachedIterable[T]` and `CachedAsyncIterable[T]`
are transformed into `CachedIterator[T]` or `CachedAsyncIterator[T]` respectively.
(This is done by calling their `__iter__` and `__aiter__` methods.)
So, the client always receive an `Iterator[T]` (or analogue) rather then `Iterable[T]`.
When the client reads from the iterator wrapper,
the iterator checks the internal `CachedIterable`/`CachedAsyncIterable` cache
and, if nothing found, asks the next value of the parent iterator which is then saved.
`CachedIterable`/`CachedAsyncIterable` classes also take note when the iterator is ended to prevent ask an ended stream.
### For Simple Iterators
- Call function => `Iterator[T]`
- Wrap result to `CachedIterable[T]`
- Save result to the cache
- Transform `CachedIterable[T]` to `CachedIterator[T]`
- Iterate
Decorate with: `cacheable_iter.core.iter_cache` or `cacheable_iter.core.lru_iter_cache`.
### For Awaitable Iterators
- Call function => `Awaitable[Iterator[T]]`
- Wrap result to `Awaitable[CachedIterable[T]]`
- Save result to async cache
- Transform `Awaitable[CachedIterable[T]]` to `Awaitable[CachedIterator[T]]`
- Await
- Iterate
Decorate with: `cacheable_iter.core.alru_iter_cache`.
### For Asynchronous Iterators
- Call function => `AsyncIterator[T]`
- Wrap result to `CachedAsyncIterable[T]`
- Save result to async cache
- Transform `CachedAsyncIterable[T]` to `CachedAsyncIterator[T]`
- Asynchronously iterate
Decorate with: `cacheable_iter.core.lru_async_iter_cache`.
<!-- Region: Links -->
[async_lru]: https://pypi.org/project/async_lru/
[Full Documentation]: https://hares-lab.gitlab.io/cacheable-iterators
نیازمندی
| مقدار | نام |
|---|---|
| ~=1.0.2 | async-lru |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.0 | Python |
نحوه نصب
نصب پکیج whl cacheable-iterators-0.1.1:
pip install cacheable-iterators-0.1.1.whl
نصب پکیج tar.gz cacheable-iterators-0.1.1:
pip install cacheable-iterators-0.1.1.tar.gz