oxcache 0.1.4

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Oxcache is a high-performance, production-grade two-level caching library for Rust, providing L1 (Moka in-memory cache) + L2 (Redis distributed cache) architecture.

✨ Key Features

• 🚀 Extreme Performance: L1 nanosecond response (P99 < 100ns), L1 millisecond response (P99 < 5ms)
• 🎯 Zero-Code Changes: Enable caching with a single #[cached] macro
• 🔄 Auto Recovery: Automatic degradation on Redis failure, WAL replay on recovery
• 🌐 Multi-Instance Sync: Pub/Sub + version-based invalidation synchronization
• ⚡ Batch Optimization: Intelligent batch writes for significantly improved throughput
• 🛡️ Production Grade: Complete observability, health checks, chaos testing verified

📦 Quick Start

1. Add Dependency

Add oxcache to your Cargo.toml:

[dependencies]
oxcache = "0.1.3"

Note: tokio and serde are already included by default. If you need minimal dependencies, you can use oxcache = { version = "0.1.3", default-features = false } and add them manually.

Features: To use #[cached] macro, enable macros feature: oxcache = { version = "0.1.3", features = ["macros"] }

Feature Tiers

# Full features (recommended)
oxcache = { version = "0.1.3", features = ["full"] }

# Core functionality only
oxcache = { version = "0.1.3", features = ["core"] }

# Minimal - L1 cache only
oxcache = { version = "0.1.3", features = ["minimal"] }

# Custom selection
oxcache = { version = "0.1.3", features = ["core", "macros", "metrics"] }

# Development with specific features
oxcache = { version = "0.1.3", features = [
    "l1-moka",      # L1 cache (Moka)
    "l2-redis",     # L2 cache (Redis)
    "macros",       # #[cached] macro
    "batch-write",  # Optimized batch writing
    "metrics",      # Basic metrics
] }

Advanced Features (included in full):

• macros - #[cached] attribute macro
• batch-write - Optimized batch writing
• wal-recovery - Write-ahead log for durability
• bloom-filter - Cache penetration protection
• rate-limiting - DoS protection
• database - Database integration
• cli - Command-line interface
• full-metrics - OpenTelemetry integration

2. Configuration

Create a config.toml file:

Important: To initialize from a config file, you need to enable both config-toml and confers features:

oxcache = { version = "0.1.3", features = ["config-toml", "confers"] }

[global]
default_ttl = 3600
health_check_interval = 30
serialization = "json"
enable_metrics = true

# Two-level cache (L1 + L2)
[services.user_cache]
cache_type = "two-level"  # "l1" | "l2" | "two-level"
ttl = 600

  [services.user_cache.l1]
  max_capacity = 10000
  ttl = 300  # L1 TTL must be <= L2 TTL
  tti = 180
  initial_capacity = 1000

  [services.user_cache.l2]
  mode = "standalone"  # "standalone" | "sentinel" | "cluster"
  connection_string = "redis://127.0.0.1:6379"

  [services.user_cache.two_level]
  write_through = true
  promote_on_hit = true
  enable_batch_write = true
  batch_size = 100
  batch_interval_ms = 50

# L1-only cache (memory only)
[services.session_cache]
cache_type = "l1"
ttl = 300

  [services.session_cache.l1]
  max_capacity = 5000
  ttl = 300
  tti = 120

# L2-only cache (Redis only)
[services.shared_cache]
cache_type = "l2"
ttl = 7200

  [services.shared_cache.l2]
  mode = "standalone"
  connection_string = "redis://127.0.0.1:6379"

3. Usage

Using Macros (Recommended)

use oxcache::macros::cached;
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize, Clone, Debug)]
struct User {
    id: u64,
    name: String,
}

// One-line cache enable
#[cached(service = "user_cache", ttl = 600)]
async fn get_user(id: u64) -> Result<User, String> {
    // Simulate slow database query
    tokio::time::sleep(std::time::Duration::from_millis(100)).await;
    Ok(User {
        id,
        name: format!("User {}", id),
    })
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize cache (from config file)
    oxcache::init_from_file("config.toml").await?;
    
    // First call: execute function logic + cache result (~100ms)
    let user = get_user(1).await?;
    println!("First call: {:?}", user);
    
    // Second call: return directly from cache (~0.1ms)
    let cached_user = get_user(1).await?;
    println!("Cached call: {:?}", cached_user);
    
    Ok(())
}

Manual Client Usage

use oxcache::{get_client, CacheOps};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    oxcache::init_from_file("config.toml").await?;
    
    let client = get_client("user_cache")?;
    
    // Standard operation: write to both L1 and L2
    client.set("key", &my_data, Some(300)).await?;
    let data: MyData = client.get("key").await?.unwrap();
    
    // Write to L1 only (temporary data)
    client.set_l1_only("temp_key", &temp_data, Some(60)).await?;
    
    // Write to L2 only (shared data)
    client.set_l2_only("shared_key", &shared_data, Some(3600)).await?;
    
    // Delete
    client.delete("key").await?;
    
    Ok(())
}

🎨 Use Cases

Scenario 1: User Information Cache

#[cached(service = "user_cache", ttl = 600)]
async fn get_user_profile(user_id: u64) -> Result<UserProfile, Error> {
    database::query_user(user_id).await
}

Scenario 2: API Response Cache

#[cached(
    service = "api_cache",
    ttl = 300,
    key = "api_{endpoint}_{version}"
)]
async fn fetch_api_data(endpoint: String, version: u32) -> Result<ApiResponse, Error> {
    http_client::get(&format!("/api/{}/{}", endpoint, version)).await
}

Scenario 3: L1-Only Hot Data Cache

#[cached(service = "session_cache", cache_type = "l1", ttl = 60)]
async fn get_user_session(session_id: String) -> Result<Session, Error> {
    session_store::load(session_id).await
}

🏗️ Architecture

graph TD
    A[Application Code<br/>#[cached] Macro] --> B[Cache Manager<br/>Service Registry + Health Monitor]
    
    B --> C[TwoLevelClient]
    B --> D[L1OnlyClient]
    B --> E[L2OnlyClient]
    
    C --> F[L1 Cache<br/>Moka]
    C --> G[L2 Cache<br/>Redis]
    
    D --> F
    E --> G
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style D fill:#fff3e0
    style E fill:#fce4ec
    style F fill:#f1f8e9
    style G fill:#fdf2e9

L1: In-process high-speed cache using LRU/TinyLFU eviction strategy
L2: Distributed shared cache supporting Sentinel/Cluster modes

📊 Performance Benchmarks

Test environment: M1 Pro, 16GB RAM, macOS, Redis 7.0

Note: Performance varies based on hardware, network conditions, and data size.

xychart-beta
    title "Single-thread Latency Test (P99)"
    x-axis ["L1 Cache", "L2 Cache", "Database"]
    y-axis "Latency (ms)" 0 --> 60
    bar [0.05, 3, 30]
    line [0.05, 3, 30]

xychart-beta
    title "Throughput Test (batch_size=100)"
    x-axis ["L1 Operations", "L2 Single Write", "L2 Batch Write"]
    y-axis "Ops/sec" 0 --> 600
    bar [7500, 75, 350]

Performance Summary:

• L1 Cache: 50-100ns (in-memory)
• L2 Cache: 1-5ms (Redis, localhost)
• Database: 10-50ms (typical SQL query)
• L1 Operations: 5-10M ops/sec
• L2 Single Write: 50-100K ops/sec
• L2 Batch Write: 200-500K ops/sec

🛡️ Reliability

• ✅ Single-Flight (prevent cache stampede)
• ✅ WAL (Write-Ahead Log) persistence
• ✅ Automatic degradation on Redis failure
• ✅ Graceful shutdown mechanism
• ✅ Health checks and auto-recovery

🔐 Security

Oxcache implements multiple security measures to protect against common attacks:

Input Validation

All user inputs are validated before being passed to Redis:

• Key Validation: Keys cannot be empty, exceed 512KB, or contain dangerous characters (\r, \n, \0) that could enable Redis protocol injection attacks.
• Lua Script Validation: Scripts are validated for:
  • Maximum length of 10KB
  • Maximum of 100 keys
  • Blocking dangerous commands: FLUSHALL, FLUSHDB, KEYS, SHUTDOWN, DEBUG, CONFIG, SAVE, BGSAVE, MONITOR
• SCAN Pattern Validation: Patterns are validated to prevent ReDoS attacks:
  • Maximum length of 256 characters
  • Maximum of 10 wildcard (*) characters
  • Count parameter clamped to safe range (1-1000)

Timeout Protection

Long-running operations have timeout protection:

• Lua Scripts: 30-second timeout prevents Redis blocking
• SCAN Operations: 30-second timeout prevents hanging scans

Secure Lock Values

Distributed locks use cryptographically secure UUID v4 values automatically generated by the library, eliminating the risk of lock value prediction attacks.

Connection String Redaction

Passwords in connection strings are redacted in logs by default to prevent credential leakage. Use normalize_connection_string_with_redaction() for secure logging.

Best Practices

1. Use the library's key validation - Don't bypass the validate_redis_key() function
2. Avoid custom Lua scripts - Use the built-in cache operations when possible
3. Set appropriate timeouts - Don't disable the 30-second default timeout
4. Rotate lock values - The library handles this automatically
5. Never log connection strings - Use the redaction utility for debugging

For more details, see Security Documentation.

📚 Documentation

• 📖 User Guide
• 📘 API Documentation
• 💻 Examples

🤝 Contributing

Pull Requests and Issues are welcome!

📝 Changelog

See CHANGELOG.md

📄 License

This project is licensed under MIT License. See LICENSE file.

If this project helps you, please give a ⭐ Star to show support!

Made with ❤️ by Kirky.X