# cachebox
[**Releases**](https://github.com/awolverp/cachebox/releases) | [**Benchmarks**](https://github.com/awolverp/cachebox-benchmark) | [**Issues**](https://github.com/awolverp/cachebox/issues/new)
**The fastest caching Python library written in Rust**
### What does it do?
You can easily and powerfully perform caching operations in Python as fast as possible.
This can make your application very faster and it's a good choice in big applications.
- 🚀 10-50x faster than other caching libraries.
- 📊 Very low memory usage (1/2 of dictionary).
- 🔥 Full-feature and easy-to-use
- 🧶 Completely thread-safe
- 🔧 Tested and correct
- **\[R\]** written in Rust that has high-performance
- 🤝 Support Python 3.8+ (PyPy & CPython)
- 📦 Over 7 cache algorithms are supported
## Page Content
- [**When i need caching and cachebox?**](#when-i-need-caching-and-cachebox)
- [**Why `cachebox`?**](#why-cachebox)
- [**Installation**](#installation)
- [**Example**](#example)
- [**Learn**](#learn)
- [**Incompatible changes**](#incompatible-changes)
- [**Tips & Notes**](#tips-and-notes)
## When i need caching and cachebox?
**📈 Frequent Data Access** \
If your application frequently accesses the same data, caching can helps you.
**💎 Expensive Operations** \
When data retrieval involves costly operations such as database queries or API calls, caching can save time and resources.
**🚗 High Traffic Scenarios** \
In big applications with high user traffic caching can help by reducing the number of operations.
**#️⃣ Web Page Rendering** \
Caching HTML pages can speed up the delivery of static content.
**🚧 Rate Limiting** \
Caching can help you to manage rate limits imposed by third-party APIs by reducing the number of requests sent.
**🤖 Machine Learning Models** \
If your application frequently makes predictions using the same input data, caching the results can save computation time.
**And a lot of other situations ...**
## Why cachebox?
**⚡ Rust** \
It uses *Rust* language to has high-performance.
**🧮 SwissTable** \
It uses Google's high-performance SwissTable hash map. thanks to [hashbrown](https://github.com/rust-lang/hashbrown).
**✨ Low memory usage** \
It has very low memory usage.
**⭐ Zero Dependency** \
As we said, `cachebox` written in Rust so you don't have to install any other dependecies.
**🧶 Thread safe** \
It's completely thread-safe and uses locks to prevent problems.
**👌 Easy To Use** \
You only need to import it and choice your implementation to use and behave with it like a dictionary.
## Installation
cachebox is installable by `pip`:
```bash
pip3 install -U cachebox
```
> [!WARNING]\
> The new version v4 has some incompatible with v3, for more info please see [Incompatible changes](#incompatible-changes)
## Example
The simplest example of **cachebox** could look like this:
```python
import cachebox
# Like functools.lru_cache, If maxsize is set to 0, the cache can grow without bound and limit.
@cachebox.cached(cachebox.FIFOCache(maxsize=128))
def factorial(number: int) -> int:
fact = 1
for num in range(2, n + 1):
fact *= num
return fact
assert factorial(5) == 125
assert len(factorial.cache) == 1
# Async are also supported
@cachebox.cached(cachebox.LRUCache(maxsize=128))
async def make_request(method: str, url: str) -> dict:
response = await client.request(method, url)
return response.json()
```
> [!NOTE]\
> Unlike functools.lru_cache and other caching libraries, cachebox will copy `dict`, `list`, and `set`.
> ```python
> @cachebox.cached(cachebox.LRUCache(maxsize=128))
> def make_dict(name: str, age: int) -> dict:
> return {"name": name, "age": age}
>
> d = make_dict("cachebox", 10)
> assert d == {"name": "cachebox", "age": 10}
> d["new-key"] = "new-value"
>
> d2 = make_dict("cachebox", 10)
> # `d2` will be `{"name": "cachebox", "age": 10, "new-key": "new-value"}` if you use other libraries
> assert d2 == {"name": "cachebox", "age": 10}
> ```
## Learn
There are 2 decorators:
- [**cached**](#function-cached): a decorator that helps you to cache your functions and calculations with a lot of options.
- [**cachedmethod**](#function-cachedmethod): this is excatly works like `cached()`, but ignores `self` parameters in hashing and key making.
- [**is_cached**](#function-is_cached): check if a function/method cached by cachebox or not
There are 9 classes:
- [**BaseCacheImpl**](#class-basecacheimpl): base-class for all classes.
- [**Cache**](#class-cache): A simple cache that has no algorithm; this is only a hashmap.
- [**FIFOCache**](#class-fifocache): the FIFO cache will remove the element that has been in the cache the longest.
- [**RRCache**](#class-rrcache): the RR cache will choice randomly element to remove it to make space when necessary.
- [**TTLCache**](#class-ttlcache): the TTL cache will automatically remove the element in the cache that has expired.
- [**LRUCache**](#class-lrucache): the LRU cache will remove the element in the cache that has not been accessed in the longest time.
- [**LFUCache**](#class-lfucache): the LFU cache will remove the element in the cache that has been accessed the least, regardless of time.
- [**VTTLCache**](#class-vttlcache): the TTL cache will automatically remove the element in the cache that has expired when need.
- [**Frozen**](#class-frozen): you can use this class for freezing your caches.
Using this library is very easy and you only need to import cachebox and then use these classes like a dictionary (or use its decorator such as `cached` and `cachedmethod`).
There are some examples for you with different methods for introducing those. \
**All the methods you will see in the examples are common across all classes (except for a few of them).**
* * *
### *function* cached
Decorator to wrap a function with a memoizing callable that saves results in a cache.
**Parameters:**
- `cache`: Specifies a cache that handles and stores the results. if `None` or `dict`, `FIFOCache` will be used.
- `key_maker`: Specifies a function that will be called with the same positional and keyword
arguments as the wrapped function itself, and which has to return a suitable
cache key (must be hashable).
- `clear_reuse`: The wrapped function has a function named `clear_cache` that uses `cache.clear`
method to clear the cache. This parameter will be passed to cache's `clear` method.
- `callback`: Every time the `cache` is used, callback is also called.
The callback arguments are: event number (see `EVENT_MISS` or `EVENT_HIT` variables), key, and then result.
- `copy_level`: The wrapped function always copies the result of your function and then returns it.
This parameter specifies that the wrapped function has to copy which type of results.
`0` means "never copy", `1` means "only copy `dict`, `list`, and `set` results" and
`2` means "always copy the results".
**A simple example:**
```python
import cachebox
@cachebox.cached(cachebox.LRUCache(128))
def sum_as_string(a, b):
return str(a+b)
assert sum_as_string(1, 2) == "3"
assert len(sum_as_string.cache) == 1
sum_as_string.cache_clear()
assert len(sum_as_string.cache) == 0
```
**A key_maker example:**
```python
import cachebox
def simple_key_maker(args: tuple, kwds: dict):
return args[0].path
# Async methods are supported
@cachebox.cached(cachebox.LRUCache(128), key_maker=simple_key_maker)
async def request_handler(request: Request):
return Response("hello man")
```
**A typed key_maker example:**
```python
import cachebox
@cachebox.cached(cachebox.LRUCache(128), key_maker=cachebox.make_typed_key)
def sum_as_string(a, b):
return str(a+b)
sum_as_string(1.0, 1)
sum_as_string(1, 1)
print(len(sum_as_string.cache)) # 2
```
You have also manage functions' caches with `.cache` attribute as you saw in examples.
Also there're more attributes and methods you can use:
```python
import cachebox
@cachebox.cached(cachebox.LRUCache(0))
def sum_as_string(a, b):
return str(a+b)
print(sum_as_string.cache)
# LRUCache(0 / 9223372036854775807, capacity=0)
print(sum_as_string.cache_info())
# CacheInfo(hits=0, misses=0, maxsize=9223372036854775807, length=0, cachememory=8)
# `.cache_clear()` clears the cache
sum_as_string.cache_clear()
```
**callback example:** (Added in v4.2.0)
```python
import cachebox
def callback_func(event: int, key, value):
if event == cachebox.EVENT_MISS:
print("callback_func: miss event", key, value)
elif event == cachebox.EVENT_HIT:
print("callback_func: hit event", key, value)
else:
# unreachable code
raise NotImplementedError
@cachebox.cached(cachebox.LRUCache(0), callback=callback_func)
def func(a, b):
return a + b
assert func(1, 2) == 3
# callback_func: miss event (1, 2) 3
assert func(1, 2) == 3 # hit
# callback_func: hit event (1, 2) 3
assert func(1, 2) == 3 # hit again
# callback_func: hit event (1, 2) 3
assert func(5, 4) == 9
# callback_func: miss event (5, 4) 9
```
> [!NOTE]\
> Recommended use `cached` method for **@staticmethod**s and use [`cachedmethod`](#function-cachedmethod) for **@classmethod**s;
> And set `copy_level` parameter to `2` on **@classmethod**s.
> ```python
> class MyClass:
> def __init__(self, num: int) -> None:
> self.num = num
>
> @classmethod
> @cachedmethod({}, copy_level=2)
> def class_func(cls, num: int):
> return cls(num)
>
> @staticmethod
> @cached({})
> def static_func(num: int):
> return num * 5
> ```
> [!TIP]\
> There's a new feature **since `v4.1.0`** that you can tell to a cached function that don't use cache for a call:
> ```python
> # with `cachebox__ignore=True` parameter, cachebox does not use cache and only calls the function and returns its result.
> sum_as_string(10, 20, cachebox__ignore=True)
> ```
> [!NOTE]\
> You can see [LRUCache here](#class-lrucache).
* * *
### *function* cachedmethod
this is excatly works like `cached()`, but ignores `self` parameters in hashing and key making.
```python
import cachebox
class MyClass:
@cachebox.cachedmethod(cachebox.TTLCache(0, ttl=10))
def my_method(self, name: str):
return "Hello, " + name + "!"
c = MyClass()
c.my_method()
```
> [!NOTE]\
> You can see [TTLCache here](#class-ttlcache).
* * *
### *function* is_cached
Check if a function/method cached by cachebox or not
```python
import cachebox
@cachebox.cached(cachebox.FIFOCache(0))
def func():
pass
assert cachebox.is_cached(func)
```
> [!NOTE]\
> You can see [TTLCache here](#class-ttlcache).
* * *
### *class* BaseCacheImpl
This is the base class of all cache classes such as Cache, FIFOCache, ... \
Do not try to call its constructor, this is only for type-hint.
```python
import cachebox
class ClassName(cachebox.BaseCacheImpl):
# ...
def func(cache: BaseCacheImpl):
# ...
cache = cachebox.LFUCache(0)
assert isinstance(cache, cachebox.BaseCacheImpl)
```
* * *
### *class* Cache
A simple cache that has no algorithm; this is only a hashmap.
> [!TIP]\
> **`Cache` vs `dict`**:
> - it is thread-safe and unordered, while `dict` isn't thread-safe and ordered (Python 3.6+).
> - it uses very lower memory than `dict`.
> - it supports useful and new methods for managing memory, while `dict` does not.
> - it does not support `popitem`, while `dict` does.
> - You can limit the size of `Cache`, but you cannot for `dict`.
| | get | insert | delete | popitem |
| ------------ | ----- | ------- | ------ | ------- |
| Worse-case | O(1) | O(1) | O(1) | N/A |
```python
from cachebox import Cache
# These parameters are common in classes:
# By `maxsize` param, you can specify the limit size of the cache ( zero means infinity ); this is unchangable.
# By `iterable` param, you can create cache from a dict or an iterable.
# If `capacity` param is given, cache attempts to allocate a new hash table with at
# least enough capacity for inserting the given number of elements without reallocating.
cache = Cache(maxsize=100, iterable=None, capacity=100)
# you can behave with it like a dictionary
cache["key"] = "value"
# or you can use `.insert(key, value)` instead of that (recommended)
cache.insert("key", "value")
print(cache["key"]) # value
del cache["key"]
cache["key"] # KeyError: key
# cachebox.Cache does not have any policy, so will raise OverflowError if reached the bound.
cache.update({i:i for i in range(200)})
# OverflowError: The cache has reached the bound.
```
* * *
### *class* FIFOCache
FIFO Cache implementation - First-In First-Out Policy (thread-safe).
In simple terms, the FIFO cache will remove the element that has been in the cache the longest.
| | get | insert | delete(i) | popitem |
| ------------ | ----- | ------- | --------- | ------- |
| Worse-case | O(1) | O(1) | O(min(i, n-i)) | O(1) |
```python
from cachebox import FIFOCache
cache = FIFOCache(5, {i:i*2 for i in range(5)})
print(len(cache)) # 5
cache["new-key"] = "new-value"
print(len(cache)) # 5
print(cache.get(3, "default-val")) # 6
print(cache.get(6, "default-val")) # default-val
print(cache.popitem()) # (1, 2)
# insert method returns a value:
# - If the cache did not have this key present, None is returned.
# - If the cache did have this key present, the value is updated, and the old value is returned.
print(cache.insert(3, "val")) # 6
print(cache.insert("new-key", "val")) # None
# Returns the first key in cache; this is the one which will be removed by `popitem()`.
print(cache.first())
```
* * *
### *class* RRCache
RRCache implementation - Random Replacement policy (thread-safe).
In simple terms, the RR cache will choice randomly element to remove it to make space when necessary.
| | get | insert | delete | popitem |
| ------------ | ----- | ------- | ------ | ------- |
| Worse-case | O(1) | O(1) | O(1) | O(1)~ |
```python
from cachebox import RRCache
cache = RRCache(10, {i:i for i in range(10)})
print(cache.is_full()) # True
print(cache.is_empty()) # False
# Returns the number of elements the map can hold without reallocating.
print(cache.capacity()) # 28
# Shrinks the cache to fit len(self) elements.
cache.shrink_to_fit()
print(cache.capacity()) # 10
print(len(cache)) # 10
cache.clear()
print(len(cache)) # 0
```
* * *
### *class* TTLCache
TTL Cache implementation - Time-To-Live Policy (thread-safe).
In simple terms, the TTL cache will automatically remove the element in the cache that has expired.
| | get | insert | delete(i) | popitem |
| ------------ | ----- | ------- | --------- | ------- |
| Worse-case | O(1)~ | O(1)~ | O(min(i, n-i)) | O(n) |
```python
from cachebox import TTLCache
import time
# The `ttl` param specifies the time-to-live value for each element in cache (in seconds); cannot be zero or negative.
cache = TTLCache(0, ttl=2)
cache.update({i:str(i) for i in range(10)})
print(cache.get_with_expire(2)) # ('2', 1.99)
# Returns the oldest key in cache; this is the one which will be removed by `popitem()`
print(cache.first()) # 0
cache["mykey"] = "value"
time.sleep(2)
cache["mykey"] # KeyError
```
* * *
### *class* LRUCache
LRU Cache implementation - Least recently used policy (thread-safe).
In simple terms, the LRU cache will remove the element in the cache that has not been accessed in the longest time.
| | get | insert | delete(i) | popitem |
| ------------ | ----- | ------- | --------- | ------- |
| Worse-case | O(1)~ | O(1)~ | O(1)~ | O(1)~ |
```python
from cachebox import LRUCache
cache = LRUCache(0, {i:i*2 for i in range(10)})
# access `1`
print(cache[0]) # 0
print(cache.popitem()) # (1, 2)
# .peek() searches for a key-value in the cache and returns it without moving the key to recently used.
print(cache.peek(2)) # 4
print(cache.popitem()) # (3, 6)
# Does the `popitem()` `n` times and returns count of removed items.
print(cache.drain(5)) # 5
```
* * *
### *class* LFUCache
LFU Cache implementation - Least frequantly used policy (thread-safe).
In simple terms, the LFU cache will remove the element in the cache that has been accessed the least, regardless of time.
| | get | insert | delete(i) | popitem |
| ------------ | ----- | ------- | --------- | ------- |
| Worse-case | O(1)~ | O(1)~ | O(n) | O(n) |
```python
from cachebox import LFUCache
cache = cachebox.LFUCache(5)
cache.insert(1, 1)
cache.insert(2, 2)
# access 1 twice
cache[1]
cache[1]
# access 2 once
cache[2]
assert cache.least_frequently_used() == 2
assert cache.least_frequently_used(2) is None # 2 is out of range
for item in cache.items():
print(item)
# (2, '2')
# (1, '1')
```
> [!TIP]\
> `.items()`, `.keys()`, and `.values()` are ordered (v4.0+)
* * *
### *class* VTTLCache
VTTL Cache implementation - Time-To-Live Per-Key Policy (thread-safe).
In simple terms, the TTL cache will automatically remove the element in the cache that has expired when need.
| | get | insert | delete(i) | popitem |
| ------------ | ----- | ------- | --------- | ------- |
| Worse-case | O(1)~ | O(1)~ | O(n) | O(n) |
```python
from cachebox import VTTLCache
import time
# The `ttl` param specifies the time-to-live value for `iterable` (in seconds); cannot be zero or negative.
cache = VTTLCache(100, iterable={i:i for i in range(4)}, ttl=3)
print(len(cache)) # 4
time.sleep(3)
print(len(cache)) # 0
# The "key1" is exists for 5 seconds
cache.insert("key1", "value", ttl=5)
# The "key2" is exists for 2 seconds
cache.insert("key2", "value", ttl=2)
time.sleep(2)
# "key1" is exists for 3 seconds
print(cache.get("key1")) # value
# "key2" has expired
print(cache.get("key2")) # None
```
> [!TIP]
> **`VTTLCache` vs `TTLCache`:**
> - In `VTTLCache` each item has its own unique time-to-live, unlike `TTLCache`.
> - `VTTLCache` is generally slower than `TTLCache`.
* * *
### *class* Frozen
**This is not a cache.** this class can freeze your caches and prevents changes ❄️.
```python
from cachebox import Frozen, FIFOCache
cache = FIFOCache(10, {1:1, 2:2, 3:3})
# parameters:
# cls: your cache
# ignore: If False, will raise TypeError if anyone try to change cache. will do nothing otherwise.
frozen = Frozen(cache, ignore=True)
print(frozen[1]) # 1
print(len(frozen)) # 3
# Frozen ignores this action and do nothing
frozen.insert("key", "value")
print(len(frozen)) # 3
# Let's try with ignore=False
frozen = Frozen(cache, ignore=False)
frozen.insert("key", "value")
# TypeError: This cache is frozen.
```
> [!NOTE]\
> The **Frozen** class can't prevent expiring in [TTLCache](#ttlcache) or [VTTLCache](#vttlcache).
>
> For example:
> ```python
> cache = TTLCache(0, ttl=3, iterable={i:i for i in range(10)})
> frozen = Frozen(cache)
>
> time.sleep(3)
> print(len(frozen)) # 0
> ```
## Incompatible changes
These are changes that are not compatible with the previous version:
**You can see more info about changes in [Changelog](CHANGELOG.md).**
* * *
#### Pickle serializing changed!
If you try to load bytes that has dumped by pickle in previous version, you will get `TypeError` exception.
There's no way to fix that 💔, but it's worth it.
```python
import pickle
with open("old-version.pickle", "rb") as fd:
pickle.load(fd) # TypeError: ...
```
* * *
#### Iterators changed!
In previous versions, the iterators are not ordered; but now all of iterators are ordered.
this means all of `.keys()`, `.values()`, `.items()`, and `iter(cache)` methods are ordered now.
For example:
```python
from cachebox import FIFOCache
cache = FIFOCache(maxsize=4)
for i in range(4):
cache[i] = str(i)
for key in cache:
print(key)
# 0
# 1
# 2
# 3
```
* * *
#### `.insert()` method changed!
In new version, the `.insert()` method has a small change that can help you in coding.
`.insert()` equals to `self[key] = value`, but:
- If the cache did not have this key present, **None is returned**.
- If the cache did have this key present, the value is updated,
and **the old value is returned**. The key is not updated, though;
For example:
```python
from cachebox import LRUCache
lru = LRUCache(10, {"a": "b", "c": "d"})
print(lru.insert("a", "new-key")) # "b"
print(lru.insert("no-exists", "val")) # None
```
## Tips and Notes
#### How to save caches in files?
there's no built-in file-based implementation, but you can use `pickle` for saving caches in files. For example:
```python
import cachebox
import pickle
c = cachebox.LRUCache(100, {i:i for i in range(78)})
with open("file", "wb") as fd:
pickle.dump(c, fd)
with open("file", "rb") as fd:
loaded = pickle.load(fd)
assert c == loaded
assert c.capacity() == loaded.capacity()
```
> [!TIP]\
> For more, see this [issue](https://github.com/awolverp/cachebox/issues/8).
> [!NOTE]\
> Supported since version 3.1.0
* * *
#### How to copy the caches?
Use `copy.deepcopy` or `copy.copy` for copying caches. For example:
```python
import cachebox, copy
c = cachebox.LRUCache(100, {i:i for i in range(78)})
copied = copy.copy(c)
assert c == copied
assert c.capacity() == copied.capacity()
```
> [!NOTE]\
> Supported since version 3.1.0
## License
This repository is licensed under the [MIT License](LICENSE)
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"description": "# cachebox\n\n\n\n\n\n\n\n[**Releases**](https://github.com/awolverp/cachebox/releases) | [**Benchmarks**](https://github.com/awolverp/cachebox-benchmark) | [**Issues**](https://github.com/awolverp/cachebox/issues/new)\n\n**The fastest caching Python library written in Rust**\n\n### What does it do?\nYou can easily and powerfully perform caching operations in Python as fast as possible.\nThis can make your application very faster and it's a good choice in big applications.\n\n- \ud83d\ude80 10-50x faster than other caching libraries.\n- \ud83d\udcca Very low memory usage (1/2 of dictionary).\n- \ud83d\udd25 Full-feature and easy-to-use\n- \ud83e\uddf6 Completely thread-safe\n- \ud83d\udd27 Tested and correct\n- **\\[R\\]** written in Rust that has high-performance\n- \ud83e\udd1d Support Python 3.8+ (PyPy & CPython)\n- \ud83d\udce6 Over 7 cache algorithms are supported\n\n## Page Content\n- [**When i need caching and cachebox?**](#when-i-need-caching-and-cachebox)\n- [**Why `cachebox`?**](#why-cachebox)\n- [**Installation**](#installation)\n- [**Example**](#example)\n- [**Learn**](#learn)\n- [**Incompatible changes**](#incompatible-changes)\n- [**Tips & Notes**](#tips-and-notes)\n\n## When i need caching and cachebox?\n**\ud83d\udcc8 Frequent Data Access** \\\nIf your application frequently accesses the same data, caching can helps you.\n\n**\ud83d\udc8e Expensive Operations** \\\nWhen data retrieval involves costly operations such as database queries or API calls, caching can save time and resources.\n\n**\ud83d\ude97 High Traffic Scenarios** \\\nIn big applications with high user traffic caching can help by reducing the number of operations.\n\n**#\ufe0f\u20e3 Web Page Rendering** \\\nCaching HTML pages can speed up the delivery of static content.\n\n**\ud83d\udea7 Rate Limiting** \\\nCaching can help you to manage rate limits imposed by third-party APIs by reducing the number of requests sent.\n\n**\ud83e\udd16 Machine Learning Models** \\\nIf your application frequently makes predictions using the same input data, caching the results can save computation time.\n\n**And a lot of other situations ...**\n\n## Why cachebox?\n**\u26a1 Rust** \\\nIt uses *Rust* language to has high-performance.\n\n**\ud83e\uddee SwissTable** \\\nIt uses Google's high-performance SwissTable hash map. thanks to [hashbrown](https://github.com/rust-lang/hashbrown).\n\n**\u2728 Low memory usage** \\\nIt has very low memory usage.\n\n**\u2b50 Zero Dependency** \\\nAs we said, `cachebox` written in Rust so you don't have to install any other dependecies.\n\n**\ud83e\uddf6 Thread safe** \\\nIt's completely thread-safe and uses locks to prevent problems.\n\n**\ud83d\udc4c Easy To Use** \\\nYou only need to import it and choice your implementation to use and behave with it like a dictionary.\n\n## Installation\ncachebox is installable by `pip`:\n```bash\npip3 install -U cachebox\n```\n\n> [!WARNING]\\\n> The new version v4 has some incompatible with v3, for more info please see [Incompatible changes](#incompatible-changes)\n\n## Example\nThe simplest example of **cachebox** could look like this:\n```python\nimport cachebox\n\n# Like functools.lru_cache, If maxsize is set to 0, the cache can grow without bound and limit.\n@cachebox.cached(cachebox.FIFOCache(maxsize=128))\ndef factorial(number: int) -> int:\n fact = 1\n for num in range(2, n + 1):\n fact *= num\n return fact\n\nassert factorial(5) == 125\nassert len(factorial.cache) == 1\n\n# Async are also supported\n@cachebox.cached(cachebox.LRUCache(maxsize=128))\nasync def make_request(method: str, url: str) -> dict:\n response = await client.request(method, url)\n return response.json()\n```\n\n> [!NOTE]\\\n> Unlike functools.lru_cache and other caching libraries, cachebox will copy `dict`, `list`, and `set`.\n> ```python\n> @cachebox.cached(cachebox.LRUCache(maxsize=128))\n> def make_dict(name: str, age: int) -> dict:\n> return {\"name\": name, \"age\": age}\n>\n> d = make_dict(\"cachebox\", 10)\n> assert d == {\"name\": \"cachebox\", \"age\": 10}\n> d[\"new-key\"] = \"new-value\"\n> \n> d2 = make_dict(\"cachebox\", 10)\n> # `d2` will be `{\"name\": \"cachebox\", \"age\": 10, \"new-key\": \"new-value\"}` if you use other libraries\n> assert d2 == {\"name\": \"cachebox\", \"age\": 10}\n> ```\n\n## Learn\nThere are 2 decorators:\n- [**cached**](#function-cached): a decorator that helps you to cache your functions and calculations with a lot of options.\n- [**cachedmethod**](#function-cachedmethod): this is excatly works like `cached()`, but ignores `self` parameters in hashing and key making.\n- [**is_cached**](#function-is_cached): check if a function/method cached by cachebox or not\n\nThere are 9 classes:\n- [**BaseCacheImpl**](#class-basecacheimpl): base-class for all classes.\n- [**Cache**](#class-cache): A simple cache that has no algorithm; this is only a hashmap.\n- [**FIFOCache**](#class-fifocache): the FIFO cache will remove the element that has been in the cache the longest.\n- [**RRCache**](#class-rrcache): the RR cache will choice randomly element to remove it to make space when necessary.\n- [**TTLCache**](#class-ttlcache): the TTL cache will automatically remove the element in the cache that has expired.\n- [**LRUCache**](#class-lrucache): the LRU cache will remove the element in the cache that has not been accessed in the longest time.\n- [**LFUCache**](#class-lfucache): the LFU cache will remove the element in the cache that has been accessed the least, regardless of time.\n- [**VTTLCache**](#class-vttlcache): the TTL cache will automatically remove the element in the cache that has expired when need.\n- [**Frozen**](#class-frozen): you can use this class for freezing your caches.\n\nUsing this library is very easy and you only need to import cachebox and then use these classes like a dictionary (or use its decorator such as `cached` and `cachedmethod`).\n\nThere are some examples for you with different methods for introducing those. \\\n**All the methods you will see in the examples are common across all classes (except for a few of them).**\n\n* * *\n\n### *function* cached\n\nDecorator to wrap a function with a memoizing callable that saves results in a cache.\n\n**Parameters:**\n- `cache`: Specifies a cache that handles and stores the results. if `None` or `dict`, `FIFOCache` will be used.\n\n- `key_maker`: Specifies a function that will be called with the same positional and keyword\n arguments as the wrapped function itself, and which has to return a suitable\n cache key (must be hashable).\n\n- `clear_reuse`: The wrapped function has a function named `clear_cache` that uses `cache.clear`\n method to clear the cache. This parameter will be passed to cache's `clear` method.\n\n- `callback`: Every time the `cache` is used, callback is also called.\n The callback arguments are: event number (see `EVENT_MISS` or `EVENT_HIT` variables), key, and then result.\n\n- `copy_level`: The wrapped function always copies the result of your function and then returns it.\n This parameter specifies that the wrapped function has to copy which type of results.\n `0` means \"never copy\", `1` means \"only copy `dict`, `list`, and `set` results\" and\n `2` means \"always copy the results\".\n\n**A simple example:**\n```python\nimport cachebox\n\n@cachebox.cached(cachebox.LRUCache(128))\ndef sum_as_string(a, b):\n return str(a+b)\n\nassert sum_as_string(1, 2) == \"3\"\n\nassert len(sum_as_string.cache) == 1\nsum_as_string.cache_clear()\nassert len(sum_as_string.cache) == 0\n```\n\n**A key_maker example:**\n```python\nimport cachebox\n\ndef simple_key_maker(args: tuple, kwds: dict):\n return args[0].path\n\n# Async methods are supported\n@cachebox.cached(cachebox.LRUCache(128), key_maker=simple_key_maker)\nasync def request_handler(request: Request):\n return Response(\"hello man\")\n```\n\n**A typed key_maker example:**\n```python\nimport cachebox\n\n@cachebox.cached(cachebox.LRUCache(128), key_maker=cachebox.make_typed_key)\ndef sum_as_string(a, b):\n return str(a+b)\n\nsum_as_string(1.0, 1)\nsum_as_string(1, 1)\nprint(len(sum_as_string.cache)) # 2\n```\n\nYou have also manage functions' caches with `.cache` attribute as you saw in examples.\nAlso there're more attributes and methods you can use:\n```python\nimport cachebox\n\n@cachebox.cached(cachebox.LRUCache(0))\ndef sum_as_string(a, b):\n return str(a+b)\n\nprint(sum_as_string.cache)\n# LRUCache(0 / 9223372036854775807, capacity=0)\n\nprint(sum_as_string.cache_info())\n# CacheInfo(hits=0, misses=0, maxsize=9223372036854775807, length=0, cachememory=8)\n\n# `.cache_clear()` clears the cache\nsum_as_string.cache_clear()\n```\n\n**callback example:** (Added in v4.2.0)\n```python\nimport cachebox\n\ndef callback_func(event: int, key, value):\n if event == cachebox.EVENT_MISS:\n print(\"callback_func: miss event\", key, value)\n elif event == cachebox.EVENT_HIT:\n print(\"callback_func: hit event\", key, value)\n else:\n # unreachable code\n raise NotImplementedError\n\n@cachebox.cached(cachebox.LRUCache(0), callback=callback_func)\ndef func(a, b):\n return a + b\n\nassert func(1, 2) == 3\n# callback_func: miss event (1, 2) 3\n\nassert func(1, 2) == 3 # hit\n# callback_func: hit event (1, 2) 3\n\nassert func(1, 2) == 3 # hit again\n# callback_func: hit event (1, 2) 3\n\nassert func(5, 4) == 9\n# callback_func: miss event (5, 4) 9\n```\n\n> [!NOTE]\\\n> Recommended use `cached` method for **@staticmethod**s and use [`cachedmethod`](#function-cachedmethod) for **@classmethod**s;\n> And set `copy_level` parameter to `2` on **@classmethod**s.\n> ```python\n> class MyClass:\n> def __init__(self, num: int) -> None:\n> self.num = num\n>\n> @classmethod\n> @cachedmethod({}, copy_level=2)\n> def class_func(cls, num: int):\n> return cls(num)\n>\n> @staticmethod\n> @cached({})\n> def static_func(num: int):\n> return num * 5\n> ```\n\n> [!TIP]\\\n> There's a new feature **since `v4.1.0`** that you can tell to a cached function that don't use cache for a call:\n> ```python\n> # with `cachebox__ignore=True` parameter, cachebox does not use cache and only calls the function and returns its result.\n> sum_as_string(10, 20, cachebox__ignore=True)\n> ```\n\n> [!NOTE]\\\n> You can see [LRUCache here](#class-lrucache).\n\n* * *\n\n### *function* cachedmethod\n\nthis is excatly works like `cached()`, but ignores `self` parameters in hashing and key making.\n\n```python\nimport cachebox\n\nclass MyClass:\n @cachebox.cachedmethod(cachebox.TTLCache(0, ttl=10))\n def my_method(self, name: str):\n return \"Hello, \" + name + \"!\"\n\nc = MyClass()\nc.my_method()\n```\n\n> [!NOTE]\\\n> You can see [TTLCache here](#class-ttlcache).\n\n* * *\n\n### *function* is_cached\n\nCheck if a function/method cached by cachebox or not\n\n```python\nimport cachebox\n\n@cachebox.cached(cachebox.FIFOCache(0))\ndef func():\n pass\n\nassert cachebox.is_cached(func)\n```\n\n> [!NOTE]\\\n> You can see [TTLCache here](#class-ttlcache).\n\n* * *\n\n### *class* BaseCacheImpl\nThis is the base class of all cache classes such as Cache, FIFOCache, ... \\\nDo not try to call its constructor, this is only for type-hint.\n\n```python\nimport cachebox\n\nclass ClassName(cachebox.BaseCacheImpl):\n # ...\n\ndef func(cache: BaseCacheImpl):\n # ...\n\ncache = cachebox.LFUCache(0)\nassert isinstance(cache, cachebox.BaseCacheImpl)\n```\n\n* * *\n\n### *class* Cache\nA simple cache that has no algorithm; this is only a hashmap.\n\n> [!TIP]\\\n> **`Cache` vs `dict`**:\n> - it is thread-safe and unordered, while `dict` isn't thread-safe and ordered (Python 3.6+).\n> - it uses very lower memory than `dict`.\n> - it supports useful and new methods for managing memory, while `dict` does not.\n> - it does not support `popitem`, while `dict` does.\n> - You can limit the size of `Cache`, but you cannot for `dict`.\n\n| | get | insert | delete | popitem |\n| ------------ | ----- | ------- | ------ | ------- |\n| Worse-case | O(1) | O(1) | O(1) | N/A |\n\n```python\nfrom cachebox import Cache\n\n# These parameters are common in classes:\n# By `maxsize` param, you can specify the limit size of the cache ( zero means infinity ); this is unchangable.\n# By `iterable` param, you can create cache from a dict or an iterable.\n# If `capacity` param is given, cache attempts to allocate a new hash table with at\n# least enough capacity for inserting the given number of elements without reallocating.\ncache = Cache(maxsize=100, iterable=None, capacity=100)\n\n# you can behave with it like a dictionary\ncache[\"key\"] = \"value\"\n# or you can use `.insert(key, value)` instead of that (recommended)\ncache.insert(\"key\", \"value\")\n\nprint(cache[\"key\"]) # value\n\ndel cache[\"key\"]\ncache[\"key\"] # KeyError: key\n\n# cachebox.Cache does not have any policy, so will raise OverflowError if reached the bound.\ncache.update({i:i for i in range(200)})\n# OverflowError: The cache has reached the bound.\n```\n\n* * *\n\n### *class* FIFOCache\nFIFO Cache implementation - First-In First-Out Policy (thread-safe).\n\nIn simple terms, the FIFO cache will remove the element that has been in the cache the longest.\n\n| | get | insert | delete(i) | popitem |\n| ------------ | ----- | ------- | --------- | ------- |\n| Worse-case | O(1) | O(1) | O(min(i, n-i)) | O(1) |\n\n```python\nfrom cachebox import FIFOCache\n\ncache = FIFOCache(5, {i:i*2 for i in range(5)})\n\nprint(len(cache)) # 5\ncache[\"new-key\"] = \"new-value\"\nprint(len(cache)) # 5\n\nprint(cache.get(3, \"default-val\")) # 6\nprint(cache.get(6, \"default-val\")) # default-val\n\nprint(cache.popitem()) # (1, 2)\n\n# insert method returns a value:\n# - If the cache did not have this key present, None is returned.\n# - If the cache did have this key present, the value is updated, and the old value is returned.\nprint(cache.insert(3, \"val\")) # 6\nprint(cache.insert(\"new-key\", \"val\")) # None\n\n# Returns the first key in cache; this is the one which will be removed by `popitem()`.\nprint(cache.first())\n```\n\n* * *\n\n### *class* RRCache\nRRCache implementation - Random Replacement policy (thread-safe).\n\nIn simple terms, the RR cache will choice randomly element to remove it to make space when necessary.\n\n| | get | insert | delete | popitem |\n| ------------ | ----- | ------- | ------ | ------- |\n| Worse-case | O(1) | O(1) | O(1) | O(1)~ |\n\n```python\nfrom cachebox import RRCache\n\ncache = RRCache(10, {i:i for i in range(10)})\nprint(cache.is_full()) # True\nprint(cache.is_empty()) # False\n\n# Returns the number of elements the map can hold without reallocating.\nprint(cache.capacity()) # 28\n\n# Shrinks the cache to fit len(self) elements.\ncache.shrink_to_fit()\nprint(cache.capacity()) # 10\n\nprint(len(cache)) # 10\ncache.clear()\nprint(len(cache)) # 0\n```\n\n* * *\n\n### *class* TTLCache\nTTL Cache implementation - Time-To-Live Policy (thread-safe).\n\nIn simple terms, the TTL cache will automatically remove the element in the cache that has expired.\n\n| | get | insert | delete(i) | popitem |\n| ------------ | ----- | ------- | --------- | ------- |\n| Worse-case | O(1)~ | O(1)~ | O(min(i, n-i)) | O(n) |\n\n```python\nfrom cachebox import TTLCache\nimport time\n\n# The `ttl` param specifies the time-to-live value for each element in cache (in seconds); cannot be zero or negative.\ncache = TTLCache(0, ttl=2)\ncache.update({i:str(i) for i in range(10)})\n\nprint(cache.get_with_expire(2)) # ('2', 1.99)\n\n# Returns the oldest key in cache; this is the one which will be removed by `popitem()` \nprint(cache.first()) # 0\n\ncache[\"mykey\"] = \"value\"\ntime.sleep(2)\ncache[\"mykey\"] # KeyError\n```\n\n* * *\n\n### *class* LRUCache\nLRU Cache implementation - Least recently used policy (thread-safe).\n\nIn simple terms, the LRU cache will remove the element in the cache that has not been accessed in the longest time.\n\n| | get | insert | delete(i) | popitem |\n| ------------ | ----- | ------- | --------- | ------- |\n| Worse-case | O(1)~ | O(1)~ | O(1)~ | O(1)~ |\n\n```python\nfrom cachebox import LRUCache\n\ncache = LRUCache(0, {i:i*2 for i in range(10)})\n\n# access `1`\nprint(cache[0]) # 0\nprint(cache.popitem()) # (1, 2)\n\n# .peek() searches for a key-value in the cache and returns it without moving the key to recently used.\nprint(cache.peek(2)) # 4\nprint(cache.popitem()) # (3, 6)\n\n# Does the `popitem()` `n` times and returns count of removed items.\nprint(cache.drain(5)) # 5\n```\n\n* * *\n\n### *class* LFUCache\nLFU Cache implementation - Least frequantly used policy (thread-safe).\n\nIn simple terms, the LFU cache will remove the element in the cache that has been accessed the least, regardless of time.\n\n| | get | insert | delete(i) | popitem |\n| ------------ | ----- | ------- | --------- | ------- |\n| Worse-case | O(1)~ | O(1)~ | O(n) | O(n) |\n\n```python\nfrom cachebox import LFUCache\n\ncache = cachebox.LFUCache(5)\ncache.insert(1, 1)\ncache.insert(2, 2)\n\n# access 1 twice\ncache[1]\ncache[1]\n\n# access 2 once\ncache[2]\n\nassert cache.least_frequently_used() == 2\nassert cache.least_frequently_used(2) is None # 2 is out of range\n\nfor item in cache.items():\n print(item)\n# (2, '2')\n# (1, '1')\n```\n\n> [!TIP]\\\n> `.items()`, `.keys()`, and `.values()` are ordered (v4.0+)\n\n* * *\n\n### *class* VTTLCache\nVTTL Cache implementation - Time-To-Live Per-Key Policy (thread-safe).\n\nIn simple terms, the TTL cache will automatically remove the element in the cache that has expired when need.\n\n| | get | insert | delete(i) | popitem |\n| ------------ | ----- | ------- | --------- | ------- |\n| Worse-case | O(1)~ | O(1)~ | O(n) | O(n) |\n\n```python\nfrom cachebox import VTTLCache\nimport time\n\n# The `ttl` param specifies the time-to-live value for `iterable` (in seconds); cannot be zero or negative.\ncache = VTTLCache(100, iterable={i:i for i in range(4)}, ttl=3)\nprint(len(cache)) # 4\ntime.sleep(3)\nprint(len(cache)) # 0\n\n# The \"key1\" is exists for 5 seconds\ncache.insert(\"key1\", \"value\", ttl=5)\n# The \"key2\" is exists for 2 seconds\ncache.insert(\"key2\", \"value\", ttl=2)\n\ntime.sleep(2)\n# \"key1\" is exists for 3 seconds\nprint(cache.get(\"key1\")) # value\n\n# \"key2\" has expired\nprint(cache.get(\"key2\")) # None\n```\n\n> [!TIP]\n> **`VTTLCache` vs `TTLCache`:**\n> - In `VTTLCache` each item has its own unique time-to-live, unlike `TTLCache`.\n> - `VTTLCache` is generally slower than `TTLCache`.\n\n* * *\n\n### *class* Frozen\n**This is not a cache.** this class can freeze your caches and prevents changes \u2744\ufe0f.\n\n```python\nfrom cachebox import Frozen, FIFOCache\n\ncache = FIFOCache(10, {1:1, 2:2, 3:3})\n\n# parameters:\n# cls: your cache\n# ignore: If False, will raise TypeError if anyone try to change cache. will do nothing otherwise.\nfrozen = Frozen(cache, ignore=True)\nprint(frozen[1]) # 1\nprint(len(frozen)) # 3\n\n# Frozen ignores this action and do nothing\nfrozen.insert(\"key\", \"value\")\nprint(len(frozen)) # 3\n\n# Let's try with ignore=False\nfrozen = Frozen(cache, ignore=False)\n\nfrozen.insert(\"key\", \"value\")\n# TypeError: This cache is frozen.\n```\n\n> [!NOTE]\\\n> The **Frozen** class can't prevent expiring in [TTLCache](#ttlcache) or [VTTLCache](#vttlcache).\n>\n> For example:\n> ```python\n> cache = TTLCache(0, ttl=3, iterable={i:i for i in range(10)})\n> frozen = Frozen(cache)\n> \n> time.sleep(3)\n> print(len(frozen)) # 0\n> ```\n\n## Incompatible changes\nThese are changes that are not compatible with the previous version:\n\n**You can see more info about changes in [Changelog](CHANGELOG.md).**\n\n* * *\n\n#### Pickle serializing changed!\nIf you try to load bytes that has dumped by pickle in previous version, you will get `TypeError` exception.\nThere's no way to fix that \ud83d\udc94, but it's worth it.\n\n```python\nimport pickle\n\nwith open(\"old-version.pickle\", \"rb\") as fd:\n pickle.load(fd) # TypeError: ...\n```\n\n* * *\n\n#### Iterators changed!\nIn previous versions, the iterators are not ordered; but now all of iterators are ordered.\nthis means all of `.keys()`, `.values()`, `.items()`, and `iter(cache)` methods are ordered now.\n\nFor example:\n```python\nfrom cachebox import FIFOCache\n\ncache = FIFOCache(maxsize=4)\nfor i in range(4):\n cache[i] = str(i)\n\nfor key in cache:\n print(key)\n# 0\n# 1\n# 2\n# 3\n```\n\n* * *\n\n#### `.insert()` method changed!\nIn new version, the `.insert()` method has a small change that can help you in coding.\n\n`.insert()` equals to `self[key] = value`, but:\n- If the cache did not have this key present, **None is returned**.\n- If the cache did have this key present, the value is updated,\nand **the old value is returned**. The key is not updated, though;\n\nFor example:\n```python\nfrom cachebox import LRUCache\n\nlru = LRUCache(10, {\"a\": \"b\", \"c\": \"d\"})\n\nprint(lru.insert(\"a\", \"new-key\")) # \"b\"\nprint(lru.insert(\"no-exists\", \"val\")) # None\n```\n\n## Tips and Notes\n#### How to save caches in files?\nthere's no built-in file-based implementation, but you can use `pickle` for saving caches in files. For example:\n```python\nimport cachebox\nimport pickle\nc = cachebox.LRUCache(100, {i:i for i in range(78)})\n\nwith open(\"file\", \"wb\") as fd:\n pickle.dump(c, fd)\n\nwith open(\"file\", \"rb\") as fd:\n loaded = pickle.load(fd)\n\nassert c == loaded\nassert c.capacity() == loaded.capacity()\n```\n\n> [!TIP]\\\n> For more, see this [issue](https://github.com/awolverp/cachebox/issues/8).\n\n> [!NOTE]\\\n> Supported since version 3.1.0\n\n* * *\n\n#### How to copy the caches?\nUse `copy.deepcopy` or `copy.copy` for copying caches. For example:\n```python\nimport cachebox, copy\nc = cachebox.LRUCache(100, {i:i for i in range(78)})\n\ncopied = copy.copy(c)\n\nassert c == copied\nassert c.capacity() == copied.capacity()\n```\n\n> [!NOTE]\\\n> Supported since version 3.1.0\n\n## License\nThis repository is licensed under the [MIT License](LICENSE)\n\n",
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