cache_house 1.0.0

Caching tool for python

Caching tool for python, working with Redis single instance and Redis cluster mode

PyPi link

Features

• ✅ Automatic reconnection: Redis client handles reconnection automatically
• ✅ Graceful degradation: Falls back to in-memory cache when Redis is unavailable
• ✅ No crashes: All operations handle errors gracefully
• ✅ Async & Sync support: Works with both async and sync functions
• ✅ Redis Cluster support: Works with single Redis instance and Redis Cluster
• ✅ Custom encoders/decoders: Support for custom serialization

Installation

 $ pip install cache-house 

or with poetry

poetry add cache-house

---

Quick Start

---

Cache decorator works with both async and sync functions. The library automatically handles Redis reconnection and falls back to in-memory cache when Redis is unavailable.

from cache_house.backends import RedisFactory
from cache_house.cache import cache
import asyncio

# Initialize Redis with fallback enabled (recommended for production)
RedisFactory.init(fallback_to_memory=True)

@cache()  # default expire time is 180 seconds
async def test_cache(a: int, b: int):
    print("async cached - this only prints on cache miss")
    return [a, b]

@cache()
def test_cache_1(a: int, b: int):
    print("cached - this only prints on cache miss")
    return [a, b]


if __name__ == "__main__":
    print("First call (cache miss):")
    print(test_cache_1(3, 4))
    print("\nSecond call (cache hit):")
    print(test_cache_1(3, 4))  # This will use cache, print won't execute
    
    print("\nAsync function:")
    print(asyncio.run(test_cache(1, 2)))
    print(asyncio.run(test_cache(1, 2)))  # Cached

Output:

First call (cache miss):
cached - this only prints on cache miss
[3, 4]

Second call (cache hit):
[3, 4]  # No print - served from cache

Async function:
async cached - this only prints on cache miss
[1, 2]
[1, 2]  # Cached

Check stored cache keys:

➜ $ rdcli KEYS "*"
1) cachehouse:main:8f65aed1010f0062a783c83eb430aca0
2) cachehouse:main:f665833ea64e4fc32653df794257ca06

---

Setup Redis cache instance

---

You can pass all redis-py arguments to RedisFactory.init method and additional arguments:

def RedisFactory.init(
        host: str = "localhost",
        port: int = 6379,
        encoder: Callable[..., Any] = ...,
        decoder: Callable[..., Any] = ...,
        namespace: str = ...,
        key_prefix: str = ...,
        key_builder: Callable[..., Any] = ...,
        password: str = ...,
        db: int = ...,
        cluster_mode: bool = False,        # Force cluster mode (skip auto-detection)
        autodetect_cluster: bool = True,   # Auto-detect if Redis is running in cluster mode
        fallback_to_memory: bool = True,   # Enable in-memory fallback when Redis is unavailable
        **redis_kwargs
    )

Cluster auto-detection

By default (autodetect_cluster=True), RedisFactory.init will:

• Try to send CLUSTER INFO to the target Redis node
• If the command succeeds → cluster mode is detected, and RedisClusterCache is used internally
• If the command fails with a Redis error → standalone mode is assumed, and RedisCache is used

This means you can usually just call:

from cache_house.backends import RedisFactory

RedisFactory.init(
    host="localhost",
    port=6379,
    fallback_to_memory=True,
    # autodetect_cluster=True by default
)

and cache-house will automatically choose the correct backend (standalone or cluster) based on the Redis server configuration.

Explicit modes (optional)

• Force standalone Redis (no detection):

RedisFactory.init(
    host="localhost",
    port=6379,
    cluster_mode=False,
    autodetect_cluster=False,  # Always use standalone RedisCache
)

• Force Redis Cluster (no detection):

RedisFactory.init(
    host="localhost",
    port=6379,
    cluster_mode=True,         # Always use RedisClusterCache
    autodetect_cluster=False,  # Optional, explicit
)

Best Practice: Initialize Redis with fallback enabled

from cache_house.backends import RedisFactory

# Initialize with fallback to memory cache (default: True)
# Your application will continue working even if Redis is temporarily unavailable
RedisFactory.init(
    host="localhost",
    port=6379,
    password="your_password",  # Optional
    db=0,
    fallback_to_memory=True  # Falls back to in-memory cache when Redis is down
)

Custom encoder and decoder

from cache_house.backends import RedisFactory
import json

def custom_encoder(data):
    return json.dumps(data)

def custom_decoder(data):
    return json.loads(data)

RedisFactory.init(
    encoder=custom_encoder, 
    decoder=custom_decoder,
    fallback_to_memory=True
)

Default encoder and decoder is pickle module.

---

Setup Redis Cluster cache instance

---

All manipulation with RedisCache is the same with RedisClusterCache

from cache_house.backends import RedisFactory
from cache_house.cache import cache

# Initialize Redis Cluster with fallback enabled
RedisFactory.init(
    cluster_mode=True,
    startup_nodes=[
        {"host": "127.0.0.1", "port": "7000"},
        {"host": "127.0.0.1", "port": "7001"},
    ],
    fallback_to_memory=True  # Falls back to in-memory cache when cluster is unavailable
)

@cache()
async def test_cache(a: int, b: int):
    print("cached")
    return [a, b]

Redis Cluster parameters (all redis-py cluster arguments are supported):

RedisFactory.init(
    cluster_mode=True,
    startup_nodes=[{"host": "127.0.0.1", "port": "7000"}],
    cluster_error_retry_attempts: int = 3,
    require_full_coverage: bool = True,
    skip_full_coverage_check: bool = False,
    reinitialize_steps: int = 10,
    read_from_replicas: bool = False,
    fallback_to_memory: bool = True,
    **redis_kwargs
)

---

Setup cache instance with FastAPI

---

Best Practice: Initialize Redis in startup event with fallback enabled. Your application will continue working even if Redis is temporarily unavailable.

import logging
import uvicorn
from fastapi.applications import FastAPI
from cache_house.backends import RedisFactory
from cache_house.cache import cache

app = FastAPI()


@app.on_event("startup")
async def startup():
    # Initialize with fallback - app won't crash if Redis is unavailable
    RedisFactory.init(
        host="localhost",
        port=6379,
        fallback_to_memory=True  # Enable in-memory fallback
    )
    print("App started - Redis cache initialized")


@app.on_event("shutdown")
async def shutdown():
    # Gracefully close connections
    RedisFactory.close_connections()
    print("App shutdown - Redis connections closed")


@app.get("/notcached")
async def test_route():
    print("notcached")
    return {"hello": "world"}


@app.get("/cached")
@cache(expire=60)  # Cache for 60 seconds
async def test_route():
    print("cached")  # This print only runs on cache miss
    return {"hello": "world"}


@app.get("/cached-with-custom-expire")
@cache(expire=300, namespace="api")  # Cache for 5 minutes with custom namespace
async def expensive_operation():
    # Simulate expensive operation
    import time
    time.sleep(1)
    return {"result": "expensive computation"}


if __name__ == "__main__":
    uvicorn.run(app, port=8033)

---

Cache decorator options

---

You can set expire time (seconds or timedelta), namespace, and key prefix in the cache decorator:

from datetime import timedelta
from cache_house.cache import cache

# Using seconds
@cache(expire=30, namespace="app", key_prefix="test") 
async def test_cache(a: int, b: int):
    print("cached")
    return [a, b]

# Using timedelta
@cache(expire=timedelta(minutes=5), namespace="app", key_prefix="test")
def test_cache_sync(a: int, b: int):
    print("cached")
    return [a, b]

if __name__ == "__main__":
    print(asyncio.run(test_cache(1, 2)))
    print(test_cache_sync(3, 4))

Check stored cache:

rdcli KEYS "*"
1) test:app:f665833ea64e4fc32653df794257ca06

---

Understanding Namespaces and Key Builders

---

Namespaces

Namespaces help organize your cache keys and make it easier to manage different parts of your application. The default namespace is "main".

Key Format: {prefix}:{namespace}:{hash}

Example with different namespaces:

from cache_house.backends import RedisFactory
from cache_house.cache import cache

RedisFactory.init(fallback_to_memory=True)

# API endpoints namespace
@cache(expire=60, namespace="api")
def get_user(user_id: int):
    return {"id": user_id, "name": f"User {user_id}"}

# Database queries namespace
@cache(expire=300, namespace="database")
def get_user_posts(user_id: int):
    return [{"id": 1, "title": "Post 1"}]

# Configuration namespace
@cache(expire=3600, namespace="config")
def get_app_config():
    return {"setting": "value"}

# Default namespace (if not specified)
@cache(expire=180)
def default_function():
    return "default"

Cache keys will be:

cachehouse:api:abc123...
cachehouse:database:def456...
cachehouse:config:ghi789...
cachehouse:main:jkl012...  # default namespace

Benefits of using namespaces:

• Organization: Group related cache entries together
• Easy cleanup: Clear all keys in a specific namespace
• Multi-tenancy: Separate cache for different applications/services
• Debugging: Easier to identify cache keys in Redis

Global Namespace Configuration

You can set a default namespace for all cache operations:

from cache_house.backends import RedisFactory

# Set default namespace for all cache operations
RedisFactory.init(
    namespace="myapp",  # All cache keys will use "myapp" namespace by default
    key_prefix="app",  # Change default prefix from "cachehouse" to "app"
    fallback_to_memory=True
)

# This will use "myapp" namespace
@cache(expire=60)
def my_function():
    return "data"

# Override namespace for specific function
@cache(expire=60, namespace="special")
def special_function():
    return "special data"

Resulting keys:

app:myapp:abc123...  # default namespace
app:special:def456...  # overridden namespace

Key Prefix

The key prefix is the first part of every cache key. Default is "cachehouse".

# Global prefix
RedisFactory.init(key_prefix="myapp", namespace="v1")

# Per-decorator prefix (overrides global)
@cache(expire=60, key_prefix="api", namespace="users")
def get_user(id: int):
    return {"id": id}

Key format: {key_prefix}:{namespace}:{hash}

Custom Key Builder

You can create your own key builder function for complete control over cache key generation:

import hashlib
from cache_house.backends import RedisFactory
from cache_house.cache import cache

def custom_key_builder(module, name, args, kwargs, prefix="cachehouse", namespace="main"):
    """
    Custom key builder function
    
    Args:
        module: Function's module name
        name: Function name
        args: Function positional arguments
        kwargs: Function keyword arguments
        prefix: Key prefix
        namespace: Namespace
    """
    # Example: Create a more readable key
    # Format: prefix:namespace:module.function:arg1:arg2:kwarg1=value1
    key_parts = [prefix, namespace, f"{module}.{name}"]
    
    # Add positional arguments
    for arg in args:
        key_parts.append(str(arg))
    
    # Add keyword arguments
    for k, v in sorted(kwargs.items()):
        key_parts.append(f"{k}={v}")
    
    # Join and create hash for long keys
    key_string = ":".join(key_parts)
    if len(key_string) > 200:  # Redis key length limit
        key_hash = hashlib.md5(key_string.encode()).hexdigest()
        return f"{prefix}:{namespace}:{key_hash}"
    
    return key_string

# Use custom key builder globally
RedisFactory.init(
    key_builder=custom_key_builder,
    namespace="custom",
    fallback_to_memory=True
)

@cache(expire=60)
def my_function(a: int, b: int, name: str = "test"):
    return {"result": a + b, "name": name}

Or use custom key builder per decorator:

def simple_key_builder(module, name, args, kwargs, prefix="cache", namespace="app"):
    # Simple key: just use function name and first argument
    first_arg = args[0] if args else "default"
    return f"{prefix}:{namespace}:{name}:{first_arg}"

@cache(expire=60, key_builder=simple_key_builder, namespace="simple")
def get_item(item_id: int):
    return {"id": item_id}

Key builder function signature:

def key_builder(
    module: str,      # Function's module (e.g., "__main__" or "myapp.services")
    name: str,       # Function name
    args: tuple,     # Positional arguments
    kwargs: dict,     # Keyword arguments
    prefix: str,      # Key prefix
    namespace: str    # Namespace
) -> str:
    # Return the cache key as a string
    return "your:custom:key:format"

Clearing Cache by Namespace

You can clear all cache keys in a specific namespace:

from cache_house.backends import RedisCache

# Clear all keys in a namespace
RedisCache.clear_keys("cachehouse:api")  # Clears all keys starting with "cachehouse:api"

# Or with custom prefix
RedisCache.clear_keys("myapp:database")  # Clears all keys in "database" namespace

Example: Clear cache for a specific namespace

from cache_house.backends import RedisFactory, RedisCache

RedisFactory.init(namespace="myapp", fallback_to_memory=True)

# Cache some data
@cache(expire=300, namespace="users")
def get_user(id: int):
    return {"id": id}

@cache(expire=300, namespace="posts")
def get_post(id: int):
    return {"id": id}

# Later, clear only "users" namespace
RedisCache.clear_keys("cachehouse:users")  # Only clears users cache
# Posts cache remains intact

Best Practices for Namespaces

1. Use descriptive namespaces:

   @cache(namespace="api.users")  # Good
   @cache(namespace="x")          # Bad - not descriptive
   

2. Organize by feature or service:

   namespace="api.users"
   namespace="api.products"
   namespace="database.queries"
   namespace="external.api"
   

3. Use consistent naming:

   # Good - consistent pattern
   namespace="v1.api"
   namespace="v1.database"
   namespace="v2.api"
   

4. Set global namespace for multi-tenant apps:

   # Different namespace per tenant
   tenant_id = get_current_tenant()
   RedisFactory.init(namespace=f"tenant_{tenant_id}")
   

5. Use namespaces for cache invalidation:

   # When user data changes, clear user namespace
   def update_user(user_id):
       # ... update logic ...
       RedisCache.clear_keys("cachehouse:users")  # Clear all user cache
   

---

Custom encoder and decoder in decorator

---

If your function works with non-standard data types, you can pass custom encoder and decoder functions to the cache decorator:

import asyncio
import json
from cache_house.backends import RedisFactory
from cache_house.cache import cache

RedisFactory.init(fallback_to_memory=True)

def custom_encoder(data):
    return json.dumps(data)

def custom_decoder(data):
    return json.loads(data)

@cache(expire=30, encoder=custom_encoder, decoder=custom_decoder, namespace="custom")
async def test_cache(a: int, b: int):
    print("async cached")
    return {"a": a, "b": b}


@cache(expire=30)
def test_cache_1(a: int, b: int):
    print("cached")
    return [a, b]


if __name__ == "__main__":
    print(asyncio.run(test_cache(1, 2)))
    print(test_cache_1(3, 4))

Check stored cache:

rdcli KEYS "*"
1) cachehouse:main:8f65aed1010f0062a783c83eb430aca0
2) cachehouse:custom:f665833ea64e4fc32653df794257ca06

---

Error Handling and Resilience

---

cache-house is designed to be resilient and won't crash your application:

Automatic Reconnection

Redis client handles reconnection automatically. You don't need to manage connections manually.

In-Memory Fallback

When Redis is unavailable, cache operations automatically fall back to in-memory cache:

from cache_house.backends import RedisFactory
from cache_house.cache import cache

# Initialize with fallback enabled (default: True)
RedisFactory.init(
    host="localhost",
    port=6379,
    fallback_to_memory=True  # Falls back to in-memory cache when Redis is down
)

@cache(expire=60)
def expensive_operation(data):
    # This function will work even if Redis is unavailable
    # Results will be cached in memory temporarily
    return process_data(data)

Graceful Error Handling

All cache operations handle errors gracefully:

from cache_house.backends import RedisFactory
from cache_house.cache import cache

# Even if Redis is not initialized, your code won't crash
cache_instance = RedisFactory.get_instance()
if cache_instance is None:
    print("Cache not available, but app continues running")

@cache(expire=60)
def my_function():
    # If Redis fails, this function still executes normally
    # Cache errors are logged but don't crash the app
    return expensive_computation()

Best Practices

1. Always enable fallback for production:

   RedisFactory.init(fallback_to_memory=True)
   

2. Handle cache as optional:

   @cache(expire=60)
   def my_function():
       # Function works with or without cache
       return compute_result()
   

3. Use appropriate expiration times:

   @cache(expire=300)  # 5 minutes for stable data
   def get_stable_data():
       return fetch_data()
   
   @cache(expire=30)  # 30 seconds for frequently changing data
   def get_dynamic_data():
       return fetch_data()
   

4. Close connections on shutdown (e.g., in FastAPI):

   @app.on_event("shutdown")
   async def shutdown():
       RedisFactory.close_connections()
   

---

Complete Example: Production-Ready Setup

---

Here's a complete example showing best practices for using cache-house in production:

import asyncio
import logging
from datetime import timedelta
from cache_house.backends import RedisFactory
from cache_house.cache import cache

# Configure logging to see cache operations
logging.basicConfig(level=logging.INFO)

# Initialize Redis with fallback enabled
# Your app will work even if Redis is temporarily unavailable
RedisFactory.init(
    host="localhost",
    port=6379,
    password=None,  # Set if your Redis requires authentication
    db=0,
    fallback_to_memory=True,  # Enable in-memory fallback
    # You can pass any redis-py connection arguments here
    socket_connect_timeout=5,
    socket_timeout=5,
)

# Example 1: Cache expensive computation
@cache(expire=300)  # Cache for 5 minutes
def expensive_computation(n: int):
    """This expensive operation will be cached"""
    result = sum(i * i for i in range(n))
    print(f"Computed result for {n}: {result}")
    return result

# Example 2: Cache API response
@cache(expire=60, namespace="api")  # Cache for 1 minute with namespace
async def fetch_user_data(user_id: int):
    """Simulate API call - will be cached"""
    print(f"Fetching user {user_id} from API...")
    await asyncio.sleep(0.1)  # Simulate network delay
    return {"user_id": user_id, "name": f"User {user_id}"}

# Example 3: Cache with custom expiration
@cache(expire=timedelta(hours=1), namespace="long_term")
def get_configuration():
    """Configuration that changes rarely"""
    print("Loading configuration...")
    return {"setting1": "value1", "setting2": "value2"}

# Example 4: Cache database query result
@cache(expire=180, namespace="database")
async def get_user_posts(user_id: int):
    """Simulate database query"""
    print(f"Querying database for user {user_id} posts...")
    await asyncio.sleep(0.05)
    return [{"id": 1, "title": "Post 1"}, {"id": 2, "title": "Post 2"}]

async def main():
    print("=== Example 1: Expensive computation ===")
    print(expensive_computation(1000000))  # First call - computes
    print(expensive_computation(1000000))  # Second call - from cache
    
    print("\n=== Example 2: API response caching ===")
    print(await fetch_user_data(1))  # First call - fetches
    print(await fetch_user_data(1))  # Second call - from cache
    
    print("\n=== Example 3: Configuration caching ===")
    print(get_configuration())  # First call - loads
    print(get_configuration())  # Second call - from cache
    
    print("\n=== Example 4: Database query caching ===")
    print(await get_user_posts(1))  # First call - queries
    print(await get_user_posts(1))  # Second call - from cache
    
    # Clean up
    RedisFactory.close_connections()

if __name__ == "__main__":
    asyncio.run(main())

Output:

INFO:cache_house.backends.redis_backend:redis initialized (Redis will handle reconnections automatically)
=== Example 1: Expensive computation ===
Computed result for 1000000: 333333333333500000
Computed result for 1000000: 333333333333500000
=== Example 2: API response caching ===
Fetching user 1 from API...
{'user_id': 1, 'name': 'User 1'}
{'user_id': 1, 'name': 'User 1'}
...

Note: If Redis is unavailable, all operations will still work using the in-memory fallback cache. Your application won't crash!

---

All examples work with both Redis Cluster and single Redis instance.

---

Contributing

Free to open issue and send PR

cache-house supports Python >= 3.10