dcache 0.0.2

Distributed Cache for Humans

dcache

Distributed Cache for Humans

• Documentation: https://dcache.readthedocs.io.

Installation

pip install dcache

How to Use

• TODO

Contributing

Contributions are welcome, feel free to open an Issue or Pull Request.

Pull requests must be for the develop branch.

git clone https://github.com/HBN3tw0rk/dcache
cd dcache
git checkout develop
python -m venv .venv
pip install -r requirements_dev.txt
pre-commit install
pytest

Pitch (Portuguese)

• Part 1: https://www.loom.com/share/ee553d6915ca4dc5ba8609d48b59bd55

• Part 2: https://www.loom.com/share/50cb4ff9560943879d78864f5fa1b4e0

What is

• distributed cache for humans

• simple API like lru_cache

• multiple backends

• easy to switch the backend

• good documentation

API

dcache

from dcache import dcache

@dcache
def slow_function(n):
    return n ** 1000

dcache vs redis

import redis
redis = redis.Redis(host='localhost', port=6379, db=0)

def slow_function(n):
    cached = redis.get(n)
    if cached:
        return cached
    value = n ** 1000
    redis.set(n, value)
    return value

def slow_function2(n):
    cached = redis.get(n)
    if cached:
        return cached
    value = n ** 1000
    redis.set(n, value)
    return value

from dcache import cache
from dcache.backends import RedisBackend

cache = dcache(RedisBackend(host='localhost', port=6379, db=0))

@cache
def slow_function(n):
    return n ** 1000

@cache
def slow_function2(n):
    return n ** 1000

real example

def process(id, input):
    cache_path = get_content_cache_path(id, input)

    if resource.file_exist(cache_path):
        return resource.get_json(cache_path)

    response = slow_function(id, input)
      resource.put_json(body=response, file_path=cache_path)
    return response

from dcache import dcache
from dcache.backends import S3Backend

@dcache(S3Backend())
def process(id, input):
    return slow_function(id, input)

Ideas

• integration tests using containers

multiple backends

from dcache import dcache
from dcache.backends import InMemoryBackend, RedisBackend

@dcache(multiple=[
    InMemoryBackend(),
    RedisBackend(host='localhost', port=6379, db=0),
])
def slow_function(n):
        return n ** 1000

1. search on the in-memory cache;

2. if exists, return, if not, search on Redis;

3. • if exists on Redis, save in memory and return;

4. • if not, exists on Redis, run the slow_function, save on Redis, save in-memory and return;

• doesn’t run if already returned

MVP

• in memory

Roadmap

• backends: Redis, Memcached, Filesystem, database, S3, etc.

• multiple backends

• plugins

Changelog

(unreleased)

• Add InMemory backend

0.0.1 (2022-07-30)

• First release on PyPI.