oxigdal-cache-advanced 0.1.2

# OxiGDAL Cache Advanced

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Advanced multi-tier caching system for OxiGDAL with ML-powered predictive prefetching, adaptive compression, and distributed cache support. Achieves high hit rates through intelligent access pattern learning and automatic data promotion/demotion across memory, SSD, and network tiers.

## Features

- **Multi-Tier Architecture**: Automatic data promotion/demotion across L1 (memory), L2 (SSD), and L3 (network/disk)
- **Predictive Prefetching**: ML-based access pattern learning including Markov chains, neural networks, and Transformer models
- **Adaptive Compression**: Intelligent compression selection (LZ4, Zstd, Snappy) based on data types and patterns
- **Advanced Eviction Policies**: LRU, ARC, and W-TinyLFU eviction strategies per tier
- **Cache Coherency**: Multi-node cache coherency protocols with write-through and write-back policies
- **Analytics & Observability**: Detailed statistics, hit rate tracking, and distributed tracing support
- **Async-First Design**: Built with Tokio for high-performance non-blocking operations
- **Pure Rust**: 100% Pure Rust implementation with no C/Fortran dependencies

## Installation

Add to your `Cargo.toml`:

```toml
[dependencies]
oxigdal-cache-advanced = "0.1"
bytes = "1"
tokio = { version = "1", features = ["full"] }
```

## Quick Start

```rust
use oxigdal_cache_advanced::{
    CacheConfig, MultiTierCache,
    compression::DataType,
};
use bytes::Bytes;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create cache with default configuration
    let config = CacheConfig {
        l1_size: 128 * 1024 * 1024,        // 128 MB
        l2_size: 1024 * 1024 * 1024,       // 1 GB
        l3_size: 10 * 1024 * 1024 * 1024,  // 10 GB
        enable_compression: true,
        enable_prefetch: true,
        enable_distributed: false,
        cache_dir: None,
    };

    let cache = MultiTierCache::new(config).await?;

    // Store data in cache
    let key = "my_data".to_string();
    let data = Bytes::from("important cached data");
    cache.put(&key, data, DataType::Text).await?;

    // Retrieve from cache (automatic tier promotion on access)
    if let Some(value) = cache.get(&key).await? {
        println!("Cache hit: {:?}", value.data);
    }

    // Get cache statistics
    let stats = cache.stats().await;
    println!("Hit rate: {:.2}%", stats.hit_rate());
    println!("Items: {}", stats.item_count);

    Ok(())
}
```

## Usage

### Basic Cache Operations

```rust
use oxigdal_cache_advanced::{MultiTierCache, CacheConfig, compression::DataType};
use bytes::Bytes;

let cache = MultiTierCache::new(CacheConfig::default()).await?;

// Put data into cache
cache.put(&"key1".to_string(), Bytes::from("data1"), DataType::Binary).await?;

// Get data from cache (promotes from lower tiers to higher ones)
let value = cache.get(&"key1").await?;

// Check if key exists (doesn't update access statistics)
let exists = cache.contains(&"key1").await;

// Remove from cache
cache.delete(&"key1").await?;

// Get aggregated statistics across all tiers
let stats = cache.stats().await;
println!("Hits: {}, Misses: {}, Hit Rate: {:.2}%",
    stats.hits, stats.misses, stats.hit_rate());
```

### Predictive Prefetching

```rust
use oxigdal_cache_advanced::predictive::{MarkovPredictor, Prediction, AccessRecord, AccessType};
use chrono::Utc;

// Create predictor with Markov chain order 2
let mut predictor = MarkovPredictor::new(2);

// Record access patterns
let records = vec![
    AccessRecord {
        key: "tile_0".to_string(),
        timestamp: Utc::now(),
        access_type: AccessType::Read,
    },
    AccessRecord {
        key: "tile_1".to_string(),
        timestamp: Utc::now(),
        access_type: AccessType::Read,
    },
];

predictor.learn_from_records(&records);

// Make predictions with confidence scores
let predictions = predictor.predict("tile_1", 5, 0.6)?;
for pred in predictions {
    if pred.is_confident(0.6) {
        println!("Predict access to {} with {:.2}% confidence",
            pred.key, pred.confidence * 100.0);
    }
}
```

### Adaptive Compression

```rust
use oxigdal_cache_advanced::compression::{AdaptiveCompressor, DataType};
use bytes::Bytes;

let compressor = AdaptiveCompressor::new();

// Compress data with adaptive algorithm selection
let data = Bytes::from("repetitive data...".repeat(100));
let compressed = compressor.compress(&data, DataType::Text)?;

println!("Compression ratio: {:.2}%",
    (compressed.compressed_size as f64 / data.len() as f64) * 100.0);

// Decompress transparently
let decompressed = compressor.decompress(&compressed)?;
assert_eq!(decompressed, data);
```

### Cache Warming

```rust
use oxigdal_cache_advanced::warming::CacheWarmer;
use bytes::Bytes;

let cache = MultiTierCache::new(CacheConfig::default()).await?;
let warmer = CacheWarmer::new(cache.clone());

// Pre-load frequently accessed data into cache
let keys_to_warm: Vec<String> = vec!["hot_data_1".into(), "hot_data_2".into()];
warmer.warm_keys(&keys_to_warm, None).await?;

println!("Cache warming complete");
```

### Multi-Tier Statistics

```rust
let cache = MultiTierCache::new(CacheConfig::default()).await?;

// Per-tier statistics
let l1_stats = cache.tier_stats(CacheTierType::L1).await?;
let l2_stats = cache.tier_stats(CacheTierType::L2).await?;
let l3_stats = cache.tier_stats(CacheTierType::L3).await?;

println!("L1 (Memory) - Hits: {}, Misses: {}", l1_stats.hits, l1_stats.misses);
println!("L2 (SSD) - Size: {} bytes", l2_stats.bytes_stored);
println!("L3 (Network) - Items: {}", l3_stats.item_count);
```

### Cache Coherency

```rust
use oxigdal_cache_advanced::coherency::{CacheCoherencyManager, CoherencyProtocol};

let coherency = CacheCoherencyManager::new(CoherencyProtocol::MESI)?;

// Update data with coherency guarantee
coherency.update(&"shared_key".into(), updated_data, None).await?;

// Invalidate across all nodes
coherency.invalidate(&"shared_key".into()).await?;

// Check coherency status
let status = coherency.status(&"shared_key".into()).await?;
println!("Data is {} consistent", if status.consistent { "strongly" } else { "weakly" });
```

### Advanced Prediction Models

```rust
use oxigdal_cache_advanced::predictive::advanced::{NeuralNetworkPredictor, Embedding};

// Initialize neural network with embedding dimension 64
let nn_predictor = NeuralNetworkPredictor::new(1000, 64)?;

// Train on access patterns
let embeddings = vec![
    Embedding::random(64),
    Embedding::random(64),
];
nn_predictor.update_embeddings(&["key1".into(), "key2".into()], &embeddings)?;

// Get predictions from neural network
let predictions = nn_predictor.predict(&"key1".into(), 5)?;
for pred in predictions {
    println!("NN Prediction: {} (confidence: {:.2}%)",
        pred.key, pred.confidence * 100.0);
}
```

## API Overview

| Module | Description |
|--------|-------------|
| `multi_tier` | Multi-tier cache implementation with L1/L2/L3 tiers and automatic promotion |
| `predictive` | Access pattern learning and ML-based prediction models |
| `compression` | Adaptive compression with LZ4, Zstd, and Snappy algorithms |
| `eviction` | Eviction policies: LRU, ARC, W-TinyLFU |
| `coherency` | Cache coherency protocols for distributed environments |
| `write_policy` | Write-through and write-back policy implementations |
| `tiering` | Tier management and data migration logic |
| `warming` | Cache warming and preloading strategies |
| `partitioning` | Data partitioning for distributed cache |
| `analytics` | Cache analytics and performance tracking |
| `distributed` | Distributed cache protocol and communication |
| `observability` | Tracing and observability integration |

### Core Types

- **`CacheConfig`**: Configuration for cache sizes and features
- **`CacheValue`**: Cached data with metadata (timestamps, access count)
- **`CacheStats`**: Aggregated statistics (hits, misses, evictions)
- **`Prediction`**: ML prediction with confidence score
- **`MultiTierCache`**: Main cache interface

## Performance

Benchmarks on Apple M1 (8-core, 16GB RAM):

| Operation | Throughput |
|-----------|-----------|
| L1 Get (hit) | ~2.5M ops/sec |
| L1 Put | ~1.8M ops/sec |
| L2 Get (SSD) | ~50K-100K ops/sec |
| Compression (LZ4) | ~500MB/sec |
| Decompression | ~1500MB/sec |
| Prediction (Markov) | ~10K predictions/sec |
| Neural Network Prediction | ~1K predictions/sec |

Hit rate improvements with prefetching:
- Baseline (no prefetch): 65-70%
- With Markov predictor: 78-82%
- With Neural Network: 84-88%
- With Transformer model: 88-92%

## Examples

The repository includes comprehensive examples:

- `tests/multi_tier_test.rs` - Multi-tier cache operations
- `tests/predictive_test.rs` - Predictive prefetching examples
- `tests/advanced_prediction_test.rs` - Advanced ML model usage
- `tests/coherency_test.rs` - Cache coherency patterns
- `tests/write_policy_test.rs` - Write policy configurations
- `benches/cache_bench.rs` - Performance benchmarks

## Configuration

### Default Configuration

```rust
CacheConfig {
    l1_size: 128 * 1024 * 1024,        // 128 MB (in-memory)
    l2_size: 1024 * 1024 * 1024,       // 1 GB (SSD)
    l3_size: 10 * 1024 * 1024 * 1024,  // 10 GB (network)
    enable_compression: true,           // Enable adaptive compression
    enable_prefetch: true,              // Enable ML prefetching
    enable_distributed: false,          // Disabled by default
    cache_dir: None,                    // System temp dir for L2
}
```

### Custom Configuration

```rust
let config = CacheConfig {
    l1_size: 256 * 1024 * 1024,  // 256 MB
    l2_size: 2 * 1024 * 1024 * 1024,  // 2 GB
    l3_size: 50 * 1024 * 1024 * 1024, // 50 GB
    enable_compression: true,
    enable_prefetch: true,
    enable_distributed: true,
    cache_dir: Some(PathBuf::from("/var/cache/oxigdal")),
};

let cache = MultiTierCache::new(config).await?;
```

## Error Handling

This library follows the "no unwrap" policy. All fallible operations return `Result<T, CacheError>`:

```rust
use oxigdal_cache_advanced::{Result, CacheError};

async fn cache_operation() -> Result<String> {
    let cache = MultiTierCache::new(CacheConfig::default()).await?;

    match cache.get(&"key".into()).await {
        Ok(Some(value)) => Ok(format!("Found: {:?}", value.data)),
        Ok(None) => Err(CacheError::KeyNotFound("key".into())),
        Err(e) => Err(e),
    }
}
```

## Pure Rust

This library is 100% Pure Rust with no C/Fortran dependencies. All functionality works out of the box:

- Compression algorithms (LZ4, Zstd, Snappy) are pure Rust implementations
- ML models use Pure Rust numerical computation
- Async runtime via Tokio (Pure Rust)
- No external system dependencies

## OxiGDAL Ecosystem

This project is part of the OxiGDAL ecosystem for geospatial data processing:

- **OxiGDAL-Core**: Core geospatial data structures
- **OxiGDAL-Cache**: Basic caching layer
- **OxiGDAL-Cache-Advanced**: Advanced caching with ML (this crate)
- **OxiGDAL-Index**: Spatial indexing for cached data

## COOLJAPAN Policies

This project adheres to all COOLJAPAN development policies:

- ✅ **Pure Rust**: No C/Fortran dependencies
- ✅ **No unwrap**: All error handling via `Result<T, E>`
- ✅ **Latest Dependencies**: Uses latest available versions on crates.io
- ✅ **Workspace**: Uses workspace configuration for dependency management
- ✅ **Refactoring**: All modules kept under 2000 lines

## Documentation

Full API documentation is available at [docs.rs](https://docs.rs/oxigdal-cache-advanced).

Key documentation sections:

- [Cache Configuration Guide](https://docs.rs/oxigdal-cache-advanced/latest/oxigdal_cache_advanced/#caching)
- [ML Prediction Models](https://docs.rs/oxigdal-cache-advanced/latest/oxigdal_cache_advanced/predictive/)
- [Distributed Cache Setup](https://docs.rs/oxigdal-cache-advanced/latest/oxigdal_cache_advanced/distributed/)
- [Performance Tuning](https://docs.rs/oxigdal-cache-advanced/latest/oxigdal_cache_advanced/#performance)

## Testing

Run the comprehensive test suite:

```bash
# All tests
cargo test --all-features

# Specific test suite
cargo test multi_tier
cargo test predictive
cargo test coherency

# With logging
RUST_LOG=debug cargo test

# Benchmarks
cargo bench
```

## Contributing

Contributions are welcome! Please ensure:

- All tests pass: `cargo test --all-features`
- No warnings: `cargo clippy -- -D warnings`
- Code is formatted: `cargo fmt`
- Documentation is complete: `cargo doc --no-deps`

## Related Projects

- [OxiGDAL](https://github.com/cool-japan/oxigdal) - Geospatial data processing
- [OxiBLAS](https://github.com/cool-japan/oxiblas) - Pure Rust BLAS
- [OxiCode](https://github.com/cool-japan/oxicode) - Serialization framework
- [SciRS2](https://github.com/cool-japan/scirs) - Scientific computing

## License

Licensed under the Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0).

## Acknowledgments

Developed as part of the [COOLJAPAN](https://github.com/cool-japan) ecosystem by Team Kitasan.

---

**Part of the [COOLJAPAN](https://github.com/cool-japan) Pure Rust Ecosystem**