cachebox 4.1.1

The fastest memoizing and caching Python library written in Rust

cachebox

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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?
• Why cachebox?
• Installation
• Example
• Learn
• Incompatible changes
• Tips & 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.

✨ Low memory usage
It has very low memory usage.

⭐ Zero-Dependecy
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:

pip3 install -U cachebox

[!WARNING]
The new version v4 has some incompatible with v3, for more info please see Incompatible changes

Example

The simplest example of cachebox could look like this:

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.

@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: a decorator that helps you to cache your functions and calculations with a lot of options.
• cachedmethod: this is excatly works like cached(), but ignores self parameters in hashing and key making.

There are 9 classes:

• BaseCacheImpl: base-class for all classes.
• Cache: A simple cache that has no algorithm; this is only a hashmap.
• FIFOCache: the FIFO cache will remove the element that has been in the cache the longest.
• RRCache: the RR cache will choice randomly element to remove it to make space when necessary.
• TTLCache: the TTL cache will automatically remove the element in the cache that has expired.
• LRUCache: the LRU cache will remove the element in the cache that has not been accessed in the longest time.
• LFUCache: the LFU cache will remove the element in the cache that has been accessed the least, regardless of time.
• VTTLCache: the TTL cache will automatically remove the element in the cache that has expired when need.
• 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

a decorator that helps you to cache your functions and calculations with a lot of options.

A simple example:

import cachebox

# Parameters:
#   - `cache`: your cache and cache policy.
#   - `key_maker`: you can set your key maker, see examples below.
#   - `clear_cache`: will be passed to cache's `clear` method when clearing cache.
@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:

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:

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:

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()

[!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:

# 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.

---

function cachedmethod

this is excatly works like cached(), but ignores self parameters in hashing and key making.

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 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.

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

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)

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)~

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)

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)~

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)

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)

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.

---

Frozen

This is not a cache. this class can freeze your caches and prevents changes ❄️.

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 or VTTLCache.

For example:

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.

---

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.

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:

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:

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:

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.

[!NOTE]
Supported since version 3.1.0

---

How to copy the caches?

Use copy.deepcopy or copy.copy for copying caches. For example:

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