cachekit

Python caching, batteries included

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

[!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!

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

Quick Start

Installation

pip install cachekit

Or with uv (recommended):

uv add cachekit

Setup (choose a backend)

cachekit exposes one decorator API over a pluggable backend abstraction. Pick the backend that fits your infrastructure — they're peers behind the same @cache API:

Backend	Best for	Select with
Redis	Self-hosted, full control	`REDIS_URL` / `CACHEKIT_REDIS_URL`
CachekitIO	Managed, zero-ops (alpha)	`CACHEKIT_API_KEY`
Memcached	High-throughput, existing infra	`CACHEKIT_MEMCACHED_SERVERS`
File / L1-only	Local dev, tests, no external deps	`CACHEKIT_FILE_CACHE_DIR` / `backend=None`

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

from cachekit import cache

@cache  # Auto-detects backend (defaults to Redis at localhost)
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]

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.

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.dev`	`@cache.test`	`@cache.production`	`@cache.secure`
Circuit Breaker	-	✅	-	✅	✅
Backpressure	✅	✅	-	✅	✅
Integrity Checking	-	✅	-	✅	✅ 🔒
Encryption	-	-	-	-	✅ Required
L1 SWR	-	✅	-	✅	✅
L1 Invalidation	-	-	-	✅	✅
L1 Namespace Index	-	-	-	✅	✅
Prometheus Metrics	-	-	-	✅	✅
Tracing	-	✅	-	✅	✅
Structured Logging	-	✅	-	✅	✅
Use Case	High throughput	Local debugging	Deterministic tests	Production reliability	Compliance/security

🔒 @cache.secure forces integrity_checking=True — it cannot be overridden.

@cache.io() mirrors @cache.production (full reliability + observability) but routes to the managed CachekitIO SaaS backend instead of Redis. @cache.local() is a separate in-process path backed by ObjectCache (raw object references, entry-count LRU, no serialization) — the reliability and encryption features listed above do not apply to it.

Additional Presets: @cache.dev and @cache.test

See the comparison table above for the exact feature set of each preset.

# Development: verbose logging, integrity checks on, Prometheus off
@cache.dev
def debug_expensive_call():
    return complex_computation()

# Testing: deterministic, all protections off (no circuit breaker, no backpressure)
@cache.test
def test_cached_function():
    return fixed_test_value()

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

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) — requires `cachekit[json]`
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.

Monitoring & Observability

Per-function statistics - cache_info() on every decorated function, modelled on functools.lru_cache
Prometheus metrics - Recorded by default (your app owns exposition)
Structured logging - Context-aware with correlation IDs
Health checks - Comprehensive status endpoints

Every decorated function exposes cache_info(), returning a CacheInfo named tuple with hit/miss counts, the L1/L2 split, and average backend latency:

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

get_score(2)
get_score(2)  # served from cache

info = get_score.cache_info()
# CacheInfo has 9 fields: hits, misses, l1_hits, l2_hits, maxsize,
# currsize, l2_avg_latency_ms, last_operation_at, session_id
assert info.l1_hits + info.l2_hits == info.hits  # every hit is L1 or L2

maxsize and currsize are always None (the cache lives in an external store, not a bounded in-process dict); they exist only for lru_cache API parity. See the API Reference for the full field reference and a sample stats endpoint, and the Prometheus Metrics guide for metric names and exposition setup.

Thread Safety Details

Per-Function Statistics:

Statistics tracked per function identity (module.qualname), shared across all calls and across re-decorations of the same function
Thread-safe via RLock (all methods safe for concurrent access)
Fork-safe: a forked child starts with zeroed counters and its own session ID

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)))

info = expensive_func.cache_info()
# CacheInfo(hits=..., misses=..., l1_hits=..., l2_hits=...,
#           maxsize=None, currsize=None, l2_avg_latency_ms=...,
#           last_operation_at=..., session_id=...)

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

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_VALUE_SIZE=104857600
CACHEKIT_ARROW_COMPRESSION=zstd

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

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.

Requirements

Component	Version
Python	3.10+

License

MIT License - see LICENSE for details.

PyPI · GitHub · Issues

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Algorithm	Hash digest
SHA256	`3b75501accbd5ce6e50609bafca35d46c44f0ffdcb2edcc93bd0e88d8da8b448`
MD5	`b94af7ec2090db3c7844ff2c72e26259`
BLAKE2b-256	`f31bebb8c784199f8306a5ac5275f2200ebec8513f5418223096a04909f0f97e`

cachekit 0.17.0

cachekit

Why cachekit?

Quick Start

Installation

Setup (choose a backend)

More Backends

Intent-Based Optimization

Architecture

Features

Production Hardened

Smart Serialization

Integrity Checking

Security

Monitoring & Observability

Documentation

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1 maintainer

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