cachekit 0.8.0

Production-ready Redis caching for Python with intelligent reliability features and Rust-powered performance

cachekit

Python caching, batteries included

Production-ready caching for Python with intelligent reliability features and Rust-powered performance.

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[!WARNING] Alpha Software — cachekit is under active development. While we've been building and testing for ~6 months, the API is not yet stable and breaking changes may occur between releases. We're committed to making this library rock-solid, but we need your help!

🐛 Found a bug? Please open an issue — even small ones help us improve.

💡 Something feel off? We want to hear about rough edges, confusing APIs, or missing features.

Your feedback directly shapes the path to 1.0. Cheers!

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Why cachekit?

Simple to use, production-ready out of the box.

from cachekit import cache

@cache
def expensive_function():
    return fetch_data()

That's it. You get:

Feature	Description
Circuit breaker	Prevents cascading failures
Distributed locking	Multi-pod safety
Prometheus metrics	Built-in observability
MessagePack serialization	Efficient with optional compression
Zero-knowledge encryption	Client-side AES-256-GCM
Adaptive timeouts	Auto-tune to system load

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Quick Start

Installation

pip install cachekit

Or with uv (recommended):

uv add cachekit

Setup (Redis — recommended default)

# Run Redis locally or use your existing infrastructure
export REDIS_URL="redis://localhost:6379"

from cachekit import cache

@cache  # Uses Redis backend by default
def expensive_api_call(user_id: int):
    return fetch_user_data(user_id)

[!TIP] No Redis? No worries! Use @cache(backend=None) for L1-only in-memory caching, like lru_cache, but with all the bells and whistles.

More Backends

CachekitIO — Managed SaaS (Alpha)

import os
from cachekit import cache

# Set your CachekitIO API key
# export CACHEKIT_API_KEY="your-api-key"  # pragma: allowlist secret

@cache.io()  # Uses CachekitIO SaaS backend — no Redis to manage
def expensive_api_call(user_id: int):
    return fetch_user_data(user_id)

cachekit.io is in closed alpha — request access to get started.

Memcached — Optional

from cachekit import cache
from cachekit.backends.memcached import MemcachedBackend

# pip install cachekit[memcached]

backend = MemcachedBackend()  # Defaults to 127.0.0.1:11211

@cache(backend=backend)
def expensive_api_call(user_id: int):
    return fetch_user_data(user_id)

Requires: pip install cachekit[memcached] or uv add cachekit[memcached]

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CachekitIO Cloud (Alpha) Managed caching with zero infrastructure. L1+L2 caching, circuit breaker, and automatic failover — no Redis to manage. cachekit.io is in closed alpha — request access to get started.

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Intent-Based Optimization

cachekit provides preset configurations for different use cases:

# Speed-critical: trading, gaming, real-time
@cache.minimal
def get_price(symbol: str):
    return fetch_price(symbol)

# Reliability-critical: payments, APIs
@cache.production
def process_payment(amount):
    return payment_gateway.charge(amount)

# Security-critical: PII, medical, financial
@cache.secure
def get_user_profile(user_id: int):
    return db.fetch_user(user_id)

Feature	@cache.minimal	@cache.production	@cache.secure	@cache.io()	@cache.local()
Circuit Breaker	-	✅	✅	✅	-
Adaptive Timeouts	-	✅	✅	✅	-
Monitoring	-	✅ Full	✅ Full	✅ Full	✅ Basic
Integrity Checking	-	✅ Enabled	✅ Enforced	✅ Enabled	-
Encryption	-	-	✅ Required	-	-
Backend	Redis	Redis	Redis	CachekitIO SaaS	In-process
Use Case	High throughput	Production reliability	Compliance/security	Managed cloud	Opaque objects

Additional Presets

# Development: debugging with verbose output
@cache.dev
def debug_expensive_call():
    return complex_computation()

# Testing: deterministic, no randomness
@cache.test
def test_cached_function():
    return fixed_test_value()

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Application                          │
├─────────────────────────────────────────────────────────────┤
│                     @cache Decorator                        │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │   Circuit   │  │  Adaptive   │  │    Distributed      │  │
│  │   Breaker   │  │  Timeouts   │  │      Locking        │  │
│  └─────────────┘  └─────────────┘  └─────────────────────┘  │
├─────────────────────────────────────────────────────────────┤
│  L1 Cache (In-Memory)  │  L2 Cache (Pluggable Backend)     │
│       ~50ns            │  Redis / CachekitIO / File /      │
│                        │  Memcached    ~2-50ms             │
├─────────────────────────────────────────────────────────────┤
│                    Rust Core (PyO3)                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────┐  │
│  │    LZ4      │  │  xxHash3    │  │    AES-256-GCM      │  │
│  │ Compression │  │  Checksums  │  │    Encryption       │  │
│  └─────────────┘  └─────────────┘  └─────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

[!TIP] Building in Rust? The core compression, checksums, and encryption are available as a standalone crate: cachekit-core

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Features

Production Hardened

• Circuit breaker with graceful degradation
• Connection pooling with thread affinity (+28% throughput)
• Distributed locking prevents cache stampedes
• Pluggable backend abstraction (Redis, CachekitIO, File, Memcached, custom)

[!NOTE] All reliability features are enabled by default with @cache.production. Use @cache.minimal to disable them for maximum throughput.

Smart Serialization

Serializer	Speed	Use Case
StandardSerializer	★★★★☆	General Python types, NumPy, Pandas
OrjsonSerializer	★★★★★	JSON APIs (2-5x faster than stdlib)
ArrowSerializer	★★★★★	Large DataFrames (6-23x faster for 10K+ rows)
EncryptionWrapper	★★★★☆	Wraps any serializer with AES-256-GCM

Serializer Examples

from cachekit.serializers import OrjsonSerializer, ArrowSerializer, EncryptionWrapper

# Fast JSON for API responses
@cache.production(serializer=OrjsonSerializer())
def get_api_response(endpoint: str):
    return {"status": "success", "data": fetch_api(endpoint)}

# Zero-copy DataFrames for large datasets
@cache(serializer=ArrowSerializer())
def get_large_dataset(date: str):
    return pd.read_csv(f"data/{date}.csv")

# Encrypted DataFrames for sensitive data
@cache(serializer=EncryptionWrapper(serializer=ArrowSerializer()))
def get_patient_data(hospital_id: int):
    return pd.read_sql("SELECT * FROM patients WHERE hospital_id = ?", conn, params=[hospital_id])

Integrity Checking

[!IMPORTANT] All serializers support configurable checksums for corruption detection using xxHash3-64 (8 bytes). Enabled by default in @cache.production and @cache.secure.

Performance Impact (benchmark-proven):

Data Type	Latency Reduction (disabled)	Size Overhead
MessagePack (default)	60-90%	8 bytes
Arrow DataFrames	35-49%	8 bytes
JSON (orjson)	37-68%	8 bytes

Security

[!CAUTION] When handling PII, medical, or financial data, always use @cache.secure to enforce encryption.

cachekit employs comprehensive security tooling:

• Supply Chain Security: cargo-deny for license compliance + RustSec scanning
• Formal Verification: Kani proves correctness of compression, checksums, encryption
• Runtime Analysis: Miri + sanitizers for memory safety
• Fuzzing: Coverage-guided testing with >80% code coverage
• Zero CVEs: Continuous vulnerability scanning

Security Commands

make security-install  # Install security tools (one-time)
make security-fast     # Run fast checks (< 3 min)

Security Tiers:

Tier	Time	Coverage
Fast	< 3 min	Vulnerability scanning, license checks, linting
Medium	< 15 min	Unsafe code analysis, API stability, Miri subset
Deep	< 2 hours	Formal verification, extended fuzzing, full sanitizers

See SECURITY.md for vulnerability reporting and detailed documentation.

Built-in Monitoring

• Prometheus metrics - Production-ready observability
• Structured logging - Context-aware with correlation IDs
• Health checks - Comprehensive status endpoints
• Performance tracking - Built-in latency monitoring

Thread Safety Details

Per-Function Statistics:

• Statistics tracked per decorated function (shared across all calls)
• Thread-safe via RLock (all methods safe for concurrent access)

from concurrent.futures import ThreadPoolExecutor

@cache()
def expensive_func(x):
    return x ** 2

# All threads share same stats
with ThreadPoolExecutor(max_workers=10) as executor:
    results = list(executor.map(expensive_func, range(100)))

print(expensive_func.cache_info())
# CacheInfo(hits=90, misses=10, maxsize=None, currsize=10)

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Documentation

Start Here

Guide	Description
Comparison Guide	How cachekit compares to lru_cache, aiocache, cachetools
Getting Started	Progressive tutorial from basics to advanced
API Reference	Complete API documentation

Feature Deep Dives

Feature	Description
Serializer Guide	ArrowSerializer vs StandardSerializer benchmarks
Circuit Breaker	Prevent cascading failures
Distributed Locking	Cache stampede prevention
Prometheus Metrics	Built-in observability
Zero-Knowledge Encryption	Client-side security
Adaptive Timeouts	Auto-tune to infrastructure

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Configuration

Environment Variables

# Redis Connection (priority: CACHEKIT_REDIS_URL > REDIS_URL)
CACHEKIT_REDIS_URL="redis://localhost:6379"  # Primary (preferred)
REDIS_URL="redis://localhost:6379"           # Fallback

# CachekitIO SaaS Backend (closed alpha — request access at cachekit.io)
CACHEKIT_API_KEY="your-api-key"             # Required for @cache.io()  # pragma: allowlist secret
CACHEKIT_API_URL="https://api.cachekit.io"  # Default SaaS endpoint

# Memcached Backend (optional: pip install cachekit[memcached])
CACHEKIT_MEMCACHED_SERVERS='["mc1:11211", "mc2:11211"]'  # Default: 127.0.0.1:11211
CACHEKIT_MEMCACHED_CONNECT_TIMEOUT=2.0                   # Default: 2.0 seconds
CACHEKIT_MEMCACHED_TIMEOUT=1.0                            # Default: 1.0 seconds
CACHEKIT_MEMCACHED_KEY_PREFIX="myapp:"                    # Default: "" (none)

# Optional Configuration
CACHEKIT_DEFAULT_TTL=3600
CACHEKIT_MAX_CHUNK_SIZE_MB=100
CACHEKIT_ENABLE_COMPRESSION=true

[!NOTE] If both CACHEKIT_REDIS_URL and REDIS_URL are set, CACHEKIT_REDIS_URL takes precedence.

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Development

git clone https://github.com/cachekit-io/cachekit-py.git
cd cachekit-py
uv sync && make install
make quick-check  # format + lint + critical tests

See CONTRIBUTING.md for full development guidelines.

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Requirements

Component	Version
Python	3.9+

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License

MIT License - see LICENSE for details.

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