# Cachebox
 
[**v2 Changelog**](https://github.com/awolverp/cachebox/blob/main/CHANGELOG.md#200---2024-03-09) | [**Releases**](https://github.com/awolverp/cachebox/releases)
The fastest caching library with different implementations, written in Rust.
- 🚀 3-21x faster than other libraries (like cachetools and cacheout)
- 📊 Very very low memory usage (1/3 of dictionary)
- **(R)** written in Rust
- 🤝 Support Python 3.8 and above
- 📦 Over 7 cache algorithms are supported
- 🧶 Completely thread-safe
**(@)** decorator example:
```python
from cachebox import cached, cachedmethod, TTLCache, LRUCache
# Keep coin price for no longer than a minute
@cached(TTLCache(maxsize=126, ttl=60))
def get_coin_price(coin_name):
return web3_client.get_price(coin_name)
# Async functions are supported
@cached(LRUCache(maxsize=126))
async def get_coin_price(coin_name):
return await async_web3_client.get_price(coin_name)
# You can pass `capacity` parameter.
# If `capacity` specified, the cache will be able to hold at
# least capacity elements without reallocating.
@cached(LRUCache(maxsize=126, capacity=100))
def fib(n):
return n if n < 2 else fib(n - 1) + fib(n - 2)
# methods are supported
class APIResource:
@cachedmethod(
TTLCache(126, ttl=10),
# You can detemine how caching is done using `key_maker` parameter.
key_maker=lambda args, kwds: args[0].client_ip
)
def get_information(self, request):
...
```
## Page Contents
- ⁉️ [When i need caching?](#when-i-need-caching)
- 🤷♂️ [Why `cachebox`?](#why-cachebox)
- 🎯 [Features](#features)
- 🛠️ [Installation](#installation)
- 🎓 [Usage](#API)
- 🚀 [Performance table](#performance-table)
- ⁉️ [Frequently Asked Questions](#frequently-asked-questions)
- 🆕 [*CHANGELOG*](CHANGELOG.md)
- ⏱️ [*BENCHMARK*](https://github.com/awolverp/cachebox-benchmark)
## When i need caching?
1. **Sometimes you have functions that take a long time to execute, and you need to call them each time.**
```python
@cached(LRUCache(260))
def function(np_array):
# big operations
...
```
2. **Sometimes you need to temporarily store data in memory for a short period.**
3. **When dealing with remote APIs, Instead of making frequent API calls, store the responses in a cache.**
```python
@cached(TTLCache(0, ttl=10))
def api_call(key):
return api.call(key)
```
4. **Caching query results from databases can enhance performance.**
```python
@cached(TTLCache(0, ttl=1))
def select_user(id):
return db.execute("SELECT * FROM users WHERE id=?", (id,))
```
and ...
## Why `cachebox`?
**`cachebox`** library uses *Rust* language to has high-performance.
**Low memory usage** - It uses very low memory usage; let's have a simple compare to dictionary:
```python
>>> import sys, cachebox
>>> sys.getsizeof(cachebox.Cache(0, {i:i for i in range(100000)}))
1835032
>>> sys.getsizeof({i:i for i in range(100000)})
5242960
```
**High-speed** - Is speed important for you? It's is here for you; see [**here**](https://github.com/awolverp/cachebox-benchmark).
**Zero-dependecy** - As we said, `cachebox` written in Rust so you have not to install any other dependecies.
**Thread-safe** - It's completely thread-safe and uses read-writer locks to prevent problems.
## Installation
You can install **cachebox** from PyPi:
```sh
pip3 install -U cachebox
```
To verify that the library is installed correctly, run the following command:
```sh
python -c "import cachebox; print(cachebox.__version__)"
```
## API
All the implementations are support **mutable-mapping** methods (e.g `__setitem__`, `get`, `popitem`),
and there are some new methods for each implemetation.
These methods are available for all classes:
- `insert(key, value)`: an aliases for `__setitem__`
```python
>>> cache.insert(1, 1) # it equals to cache[1] = 1
```
- `capacity()`: Returns the number of elements the cache can hold without reallocating.
```python
>>> cache.update((i, i) for i in range(1000))
>>> cache.capacity()
1432
```
- `drain(n)`: According to cache algorithm, deletes and returns how many items removed from cache.
```python
>>> cache = LFUCache(10, {i:i for i in range(10)})
>>> cache.drain(8)
8
>>> len(cache)
2
>>> cache.drain(10)
2
>>> len(cache)
0
```
- `shrink_to_fit()`: Shrinks the capacity of the cache as much as possible.
```python
>>> cache = LRUCache(0, {i:i for i in range(10)})
>>> cache.capacity()
27
>>> cache.shrink_to_fit()
>>> cache.capacity()
11
```
### Cache
Fixed-size (or can be not) cache implementation without any policy,
So only can be fixed-size, or unlimited size cache.
```python
>>> from cachebox import Cache
>>> cache = Cache(100) # fixed-size cache
>>> cache = Cache(0) # unlimited-size cache
>>> cache = Cache(100, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
>>> cache = Cache(2, {i:i for i in range(10)})
...
OverflowError: maximum size limit reached
```
**There're no new methods for this class.**
### FIFOCache
FIFO Cache implementation (First-In First-Out policy, very useful).
In simple terms, the FIFO cache will remove the element that has been in the cache the longest;
It behaves like a Python dictionary.
```python
>>> from cachebox import FIFOCache
>>> cache = FIFOCache(100) # fixed-size cache
>>> cache = FIFOCache(0) # unlimited-size cache
>>> cache = FIFOCache(100, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
```
**There're new methods:**
- `first`: returns the first inserted key (the oldest)
- `last`: returns the last inserted key (the newest)
### LFUCache
LFU Cache implementation (Least frequantly used policy).
In simple terms, the LFU cache will remove the element in the cache that has been accessed the least,
regardless of time.
```python
>>> from cachebox import LFUCache
>>> cache = LFUCache(100) # fixed-size cache
>>> cache = LFUCache(0) # unlimited-size cache
>>> cache = LFUCache(100, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
```
**There's a new method:**
- `least_frequently_used`: returns the key that has been accessed the least.
### RRCache
RRCache implementation (Random Replacement policy).
In simple terms, the RR cache will choice randomly element to remove it to make space when necessary.
```python
>>> from cachebox import RRCache
>>> cache = RRCache(100) # fixed-size cache
>>> cache = RRCache(0) # unlimited-size cache
>>> cache = RRCache(100, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
```
**There're no new methods for this class.**
### LRUCache
LRU Cache implementation (Least recently used policy).
In simple terms, the LRU cache will remove the element in the cache that has not been accessed in the longest time.
```python
>>> from cachebox import LRUCache
>>> cache = LRUCache(100) # fixed-size cache
>>> cache = LRUCache(0) # unlimited-size cache
>>> cache = LRUCache(100, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
```
**There're new methods:**
- `least_recently_used`: returns the key that has not been accessed in the longest time.
- `most_recently_used`: returns the key that has been accessed in the longest time.
### TTLCache
TTL Cache implementation (Time-to-live policy).
In simple terms, The TTL cache is one that evicts items that are older than a time-to-live.
```python
>>> from cachebox import TTLCache
>>> cache = TTLCache(100, 2) # fixed-size cache, 2 ttl value
>>> cache = TTLCache(0, 10) # unlimited-size cache, 10 ttl value
>>> cache = TTLCache(100, 5, {"key1": "value1", "key2": "value2"}) # initialize from dict or any iterable object
```
**There're new methods:**
- `get_with_expire`: Works like `.get()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2})
>>> cache.get_with_expire(1)
(1, 1.23445675)
>>> cache.get_with_expire("no-exists")
(None, 0.0)
```
- `pop_with_expire`: Works like `.pop()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2})
>>> cache.pop_with_expire(1)
(1, 1.23445675)
>>> cache.pop_with_expire(1)
(None, 0.0)
```
- `popitem_with_expire`: Works like `.popitem()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2})
>>> cache.popitem_with_expire()
(1, 1, 1.23445675)
>>> cache.popitem_with_expire()
(2, 2, 1.94389545)
>>> cache.popitem_with_expire()
...
KeyError
```
### VTTLCache
VTTL Cache implementation (Time-to-live per-key policy)
Works like TTLCache, with this different that each key has own time-to-live value.
```python
>>> cache = VTTLCache(100) # fixed-size cache
>>> cache = VTTLCache(0) # unlimited-size cache
# initialize from dict or any iterable object;
# also these items will expire after 5 seconds
>>> cache = VTTLCache(100, {"key1": "value1", "key2": "value2"}, 5)
# initialize from dict or any iterable object;
# but these items never expire, because we pass None as them ttl value
>>> cache = VTTLCache(100, {"key1": "value1", "key2": "value2"}, None)
```
**There're new methods:**
- `insert(key, value, ttl)`: is different here. if you use `cache[key] = value` way, you cannot set ttl value for those item, but here you can.
```python
>>> cache.insert("key", "value", 10) # this item will expire after 10 seconds
>>> cache.insert("key", "value", None) # but this item never expire.
```
- `setdefault(key, default, ttl)`: Returns the value of the specified key.
If the key does not exist, insert the key, with the specified value.
- `update(iterable, ttl)`: inserts the specified items to the cache. The `iterable` can be a dictionary,
or an iterable object with key-value pairs.
```python
>>> cache = VTTLCache(20)
>>> cache.insert("key", "value", 10)
>>> cache.update({i:i for i in range(12)}, 2)
>>> len(cache)
13
>>> time.sleep(2)
>>> len(cache)
1
```
- `get_with_expire`: Works like `.get()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2}, 2)
>>> cache.get_with_expire(1)
(1, 1.9934)
>>> cache.get_with_expire("no-exists")
(None, 0.0)
```
- `pop_with_expire`: Works like `.pop()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2}, 2)
>>> cache.pop_with_expire(1)
(1, 1.99954)
>>> cache.pop_with_expire(1)
(None, 0.0)
```
- `popitem_with_expire`: Works like `.popitem()`, but also returns the remaining expiration.
```python
>>> cache.update({1: 1, 2: 2}, 2)
>>> cache.popitem_with_expire()
(1, 1, 1.9786564)
>>> cache.popitem_with_expire()
(2, 2, 1.97389545)
>>> cache.popitem_with_expire()
...
KeyError
```
## Performance table
> [!NOTE]\
> Operations which have an amortized cost are suffixed with a `*`. Operations with an expected cost are suffixed with a `~`.
| | get(i) | insert(i) | delete(i) | update(m) | popitem |
| ------------ | ------ | --------------- | -------------- | ---------------- | ------- |
| Cache | O(1)~ | O(1)~* | O(1)~ | O(m)~ | N/A |
| FIFOCache | O(1)~ | O(min(i, n-i))* | O(min(i, n-i)) | O(m*min(i, n-i)) | O(1) |
| LFUCache | O(1)~ | O(n)~* | O(1)~ | O(m*n)~ | O(n)~* |
| RRCache | O(1)~ | O(1)~* | O(1)~ | O(m)~ | O(1)~ |
| LRUCache | O(1)~ | ? | O(1)~ | ? | O(1) |
| TTLCache | O(1)~ | O(min(i, n-i))* | O(min(i, n-i)) | O(m*min(i, n-i)) | O(1) |
| VTTLCache | O(1)~ | ? | O(1)~ | ? | O(1)~ |
## Frequently asked questions
#### What is the difference between TTLCache and VTTLCache?
In `TTLCache`, you set an expiration time for all items, but in `VTTLCache`,
you can set a unique expiration time for each item.
| | TTL | Speed |
| ------------ | ----------- | ------- |
| TTLCache | One ttl for all items | TTLCache is very faster than VTTLCache |
| VTTLCache | Each item has unique expiration time | VTTLCache is slow in inserting |
#### Can we set maxsize to zero?
Yes, if you pass zero to maxsize, means there's no limit for items.
#### Migrate from cachetools to cachebox
*cachebox* syntax is very similar to *cachetools*.
Just change these:
```python
# If you pass infinity to a cache implementation, change it to zero.
cachetools.Cache(math.inf) -> cachebox.Cache(0)
# If you use `isinstance` for cachetools classes, change those.
isinstance(cache, cachetools.Cache) -> isinstance(cache, cachebox.BaseCacheImpl)
```
## License
Copyright (c) 2024 aWolverP - **MIT License**
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"description": "# Cachebox\n \n\n[**v2 Changelog**](https://github.com/awolverp/cachebox/blob/main/CHANGELOG.md#200---2024-03-09) | [**Releases**](https://github.com/awolverp/cachebox/releases)\n\nThe fastest caching library with different implementations, written in Rust.\n\n- \ud83d\ude80 3-21x faster than other libraries (like cachetools and cacheout)\n- \ud83d\udcca Very very low memory usage (1/3 of dictionary)\n- **(R)** written in Rust\n- \ud83e\udd1d Support Python 3.8 and above\n- \ud83d\udce6 Over 7 cache algorithms are supported\n- \ud83e\uddf6 Completely thread-safe\n\n**(@)** decorator example:\n```python\nfrom cachebox import cached, cachedmethod, TTLCache, LRUCache\n\n# Keep coin price for no longer than a minute\n@cached(TTLCache(maxsize=126, ttl=60))\ndef get_coin_price(coin_name):\n return web3_client.get_price(coin_name)\n\n# Async functions are supported\n@cached(LRUCache(maxsize=126))\nasync def get_coin_price(coin_name):\n return await async_web3_client.get_price(coin_name)\n\n# You can pass `capacity` parameter.\n# If `capacity` specified, the cache will be able to hold at\n# least capacity elements without reallocating.\n@cached(LRUCache(maxsize=126, capacity=100))\ndef fib(n):\n return n if n < 2 else fib(n - 1) + fib(n - 2)\n\n# methods are supported\nclass APIResource:\n @cachedmethod(\n TTLCache(126, ttl=10),\n # You can detemine how caching is done using `key_maker` parameter.\n key_maker=lambda args, kwds: args[0].client_ip\n )\n def get_information(self, request):\n ...\n```\n\n## Page Contents\n- \u2049\ufe0f [When i need caching?](#when-i-need-caching)\n- \ud83e\udd37\u200d\u2642\ufe0f [Why `cachebox`?](#why-cachebox)\n- \ud83c\udfaf [Features](#features)\n- \ud83d\udee0\ufe0f [Installation](#installation)\n- \ud83c\udf93 [Usage](#API)\n- \ud83d\ude80 [Performance table](#performance-table)\n- \u2049\ufe0f [Frequently Asked Questions](#frequently-asked-questions)\n- \ud83c\udd95 [*CHANGELOG*](CHANGELOG.md)\n- \u23f1\ufe0f [*BENCHMARK*](https://github.com/awolverp/cachebox-benchmark)\n\n## When i need caching?\n1. **Sometimes you have functions that take a long time to execute, and you need to call them each time.**\n\n```python\n@cached(LRUCache(260))\ndef function(np_array):\n # big operations\n ...\n```\n\n2. **Sometimes you need to temporarily store data in memory for a short period.**\n\n3. **When dealing with remote APIs, Instead of making frequent API calls, store the responses in a cache.**\n\n```python\n@cached(TTLCache(0, ttl=10))\ndef api_call(key):\n return api.call(key)\n```\n\n4. **Caching query results from databases can enhance performance.**\n\n```python\n@cached(TTLCache(0, ttl=1))\ndef select_user(id):\n return db.execute(\"SELECT * FROM users WHERE id=?\", (id,))\n```\n\nand ...\n\n## Why `cachebox`?\n**`cachebox`** library uses *Rust* language to has high-performance.\n\n**Low memory usage** - It uses very low memory usage; let's have a simple compare to dictionary:\n```python\n>>> import sys, cachebox\n>>> sys.getsizeof(cachebox.Cache(0, {i:i for i in range(100000)}))\n1835032\n>>> sys.getsizeof({i:i for i in range(100000)})\n5242960\n```\n\n**High-speed** - Is speed important for you? It's is here for you; see [**here**](https://github.com/awolverp/cachebox-benchmark).\n\n**Zero-dependecy** - As we said, `cachebox` written in Rust so you have not to install any other dependecies.\n\n**Thread-safe** - It's completely thread-safe and uses read-writer locks to prevent problems.\n\n## Installation\nYou can install **cachebox** from PyPi:\n```sh\npip3 install -U cachebox\n```\n\nTo verify that the library is installed correctly, run the following command:\n```sh\npython -c \"import cachebox; print(cachebox.__version__)\"\n```\n\n## API\nAll the implementations are support **mutable-mapping** methods (e.g `__setitem__`, `get`, `popitem`),\nand there are some new methods for each implemetation.\n\nThese methods are available for all classes:\n- `insert(key, value)`: an aliases for `__setitem__`\n\n```python\n>>> cache.insert(1, 1) # it equals to cache[1] = 1\n```\n\n- `capacity()`: Returns the number of elements the cache can hold without reallocating.\n\n```python\n>>> cache.update((i, i) for i in range(1000))\n>>> cache.capacity()\n1432\n```\n\n- `drain(n)`: According to cache algorithm, deletes and returns how many items removed from cache.\n\n```python\n>>> cache = LFUCache(10, {i:i for i in range(10)})\n>>> cache.drain(8)\n8\n>>> len(cache)\n2\n>>> cache.drain(10)\n2\n>>> len(cache)\n0\n```\n\n- `shrink_to_fit()`: Shrinks the capacity of the cache as much as possible.\n\n```python\n>>> cache = LRUCache(0, {i:i for i in range(10)})\n>>> cache.capacity()\n27\n>>> cache.shrink_to_fit()\n>>> cache.capacity()\n11\n```\n\n### Cache\nFixed-size (or can be not) cache implementation without any policy,\nSo only can be fixed-size, or unlimited size cache.\n\n```python\n>>> from cachebox import Cache\n>>> cache = Cache(100) # fixed-size cache\n>>> cache = Cache(0) # unlimited-size cache\n>>> cache = Cache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n>>> cache = Cache(2, {i:i for i in range(10)})\n...\nOverflowError: maximum size limit reached\n```\n\n**There're no new methods for this class.**\n\n### FIFOCache\nFIFO Cache implementation (First-In First-Out policy, very useful).\n\nIn simple terms, the FIFO cache will remove the element that has been in the cache the longest;\nIt behaves like a Python dictionary.\n\n```python\n>>> from cachebox import FIFOCache\n>>> cache = FIFOCache(100) # fixed-size cache\n>>> cache = FIFOCache(0) # unlimited-size cache\n>>> cache = FIFOCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n```\n\n**There're new methods:**\n- `first`: returns the first inserted key (the oldest)\n- `last`: returns the last inserted key (the newest)\n\n\n### LFUCache\nLFU Cache implementation (Least frequantly used policy).\n\nIn simple terms, the LFU cache will remove the element in the cache that has been accessed the least,\nregardless of time.\n\n```python\n>>> from cachebox import LFUCache\n>>> cache = LFUCache(100) # fixed-size cache\n>>> cache = LFUCache(0) # unlimited-size cache\n>>> cache = LFUCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n```\n\n**There's a new method:**\n- `least_frequently_used`: returns the key that has been accessed the least.\n\n\n### RRCache\nRRCache implementation (Random Replacement policy).\n\nIn simple terms, the RR cache will choice randomly element to remove it to make space when necessary.\n\n```python\n>>> from cachebox import RRCache\n>>> cache = RRCache(100) # fixed-size cache\n>>> cache = RRCache(0) # unlimited-size cache\n>>> cache = RRCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n```\n\n**There're no new methods for this class.**\n\n\n### LRUCache\nLRU Cache implementation (Least recently used policy).\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```python\n>>> from cachebox import LRUCache\n>>> cache = LRUCache(100) # fixed-size cache\n>>> cache = LRUCache(0) # unlimited-size cache\n>>> cache = LRUCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n```\n\n**There're new methods:**\n- `least_recently_used`: returns the key that has not been accessed in the longest time.\n- `most_recently_used`: returns the key that has been accessed in the longest time.\n\n\n### TTLCache\nTTL Cache implementation (Time-to-live policy).\n\nIn simple terms, The TTL cache is one that evicts items that are older than a time-to-live.\n\n```python\n>>> from cachebox import TTLCache\n>>> cache = TTLCache(100, 2) # fixed-size cache, 2 ttl value\n>>> cache = TTLCache(0, 10) # unlimited-size cache, 10 ttl value\n>>> cache = TTLCache(100, 5, {\"key1\": \"value1\", \"key2\": \"value2\"}) # initialize from dict or any iterable object\n```\n**There're new methods:**\n- `get_with_expire`: Works like `.get()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2})\n>>> cache.get_with_expire(1)\n(1, 1.23445675)\n>>> cache.get_with_expire(\"no-exists\")\n(None, 0.0)\n```\n\n- `pop_with_expire`: Works like `.pop()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2})\n>>> cache.pop_with_expire(1)\n(1, 1.23445675)\n>>> cache.pop_with_expire(1)\n(None, 0.0)\n```\n\n- `popitem_with_expire`: Works like `.popitem()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2})\n>>> cache.popitem_with_expire()\n(1, 1, 1.23445675)\n>>> cache.popitem_with_expire()\n(2, 2, 1.94389545)\n>>> cache.popitem_with_expire()\n...\nKeyError\n```\n\n### VTTLCache\nVTTL Cache implementation (Time-to-live per-key policy)\n\nWorks like TTLCache, with this different that each key has own time-to-live value.\n\n```python\n>>> cache = VTTLCache(100) # fixed-size cache\n>>> cache = VTTLCache(0) # unlimited-size cache\n\n# initialize from dict or any iterable object;\n# also these items will expire after 5 seconds\n>>> cache = VTTLCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}, 5)\n\n# initialize from dict or any iterable object;\n# but these items never expire, because we pass None as them ttl value\n>>> cache = VTTLCache(100, {\"key1\": \"value1\", \"key2\": \"value2\"}, None)\n```\n\n**There're new methods:**\n- `insert(key, value, ttl)`: is different here. if you use `cache[key] = value` way, you cannot set ttl value for those item, but here you can.\n\n```python\n>>> cache.insert(\"key\", \"value\", 10) # this item will expire after 10 seconds\n>>> cache.insert(\"key\", \"value\", None) # but this item never expire.\n```\n\n- `setdefault(key, default, ttl)`: Returns the value of the specified key.\nIf the key does not exist, insert the key, with the specified value.\n\n- `update(iterable, ttl)`: inserts the specified items to the cache. The `iterable` can be a dictionary,\nor an iterable object with key-value pairs.\n\n```python\n>>> cache = VTTLCache(20)\n>>> cache.insert(\"key\", \"value\", 10)\n>>> cache.update({i:i for i in range(12)}, 2)\n>>> len(cache)\n13\n>>> time.sleep(2)\n>>> len(cache)\n1\n```\n\n- `get_with_expire`: Works like `.get()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2}, 2)\n>>> cache.get_with_expire(1)\n(1, 1.9934)\n>>> cache.get_with_expire(\"no-exists\")\n(None, 0.0)\n```\n\n- `pop_with_expire`: Works like `.pop()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2}, 2)\n>>> cache.pop_with_expire(1)\n(1, 1.99954)\n>>> cache.pop_with_expire(1)\n(None, 0.0)\n```\n\n- `popitem_with_expire`: Works like `.popitem()`, but also returns the remaining expiration.\n\n```python\n>>> cache.update({1: 1, 2: 2}, 2)\n>>> cache.popitem_with_expire()\n(1, 1, 1.9786564)\n>>> cache.popitem_with_expire()\n(2, 2, 1.97389545)\n>>> cache.popitem_with_expire()\n...\nKeyError\n```\n\n## Performance table\n\n> [!NOTE]\\\n> Operations which have an amortized cost are suffixed with a `*`. Operations with an expected cost are suffixed with a `~`.\n\n| | get(i) | insert(i) | delete(i) | update(m) | popitem |\n| ------------ | ------ | --------------- | -------------- | ---------------- | ------- |\n| Cache | O(1)~ | O(1)~* | O(1)~ | O(m)~ | N/A |\n| FIFOCache | O(1)~ | O(min(i, n-i))* | O(min(i, n-i)) | O(m*min(i, n-i)) | O(1) |\n| LFUCache | O(1)~ | O(n)~* | O(1)~ | O(m*n)~ | O(n)~* |\n| RRCache | O(1)~ | O(1)~* | O(1)~ | O(m)~ | O(1)~ |\n| LRUCache | O(1)~ | ? | O(1)~ | ? | O(1) |\n| TTLCache | O(1)~ | O(min(i, n-i))* | O(min(i, n-i)) | O(m*min(i, n-i)) | O(1) |\n| VTTLCache | O(1)~ | ? | O(1)~ | ? | O(1)~ |\n\n## Frequently asked questions\n#### What is the difference between TTLCache and VTTLCache?\nIn `TTLCache`, you set an expiration time for all items, but in `VTTLCache`,\nyou can set a unique expiration time for each item.\n\n| | TTL | Speed |\n| ------------ | ----------- | ------- |\n| TTLCache | One ttl for all items | TTLCache is very faster than VTTLCache |\n| VTTLCache | Each item has unique expiration time | VTTLCache is slow in inserting |\n\n\n#### Can we set maxsize to zero?\nYes, if you pass zero to maxsize, means there's no limit for items.\n\n#### Migrate from cachetools to cachebox\n*cachebox* syntax is very similar to *cachetools*.\nJust change these:\n```python\n# If you pass infinity to a cache implementation, change it to zero.\ncachetools.Cache(math.inf) -> cachebox.Cache(0)\n# If you use `isinstance` for cachetools classes, change those.\nisinstance(cache, cachetools.Cache) -> isinstance(cache, cachebox.BaseCacheImpl)\n```\n\n## License\nCopyright (c) 2024 aWolverP - **MIT License**\n\n",
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