KotobaDB Memory Optimization

Advanced memory management and optimization features for KotobaDB, providing intelligent memory pooling, caching strategies, and garbage collection optimization.

🚀 Features

• Memory Pooling: Efficient object pooling and slab allocation
• Intelligent Caching: Multi-strategy caching with LRU, LFU, and adaptive policies
• Memory Profiling: Real-time memory usage analysis and leak detection
• GC Optimization: Garbage collection tuning and performance optimization
• Custom Allocators: Jemalloc, Mimalloc, and custom arena allocators
• Performance Monitoring: Comprehensive memory performance metrics

📊 Memory Optimization Components

Memory Pooling

• Slab Allocation: Fixed-size object allocation for reduced fragmentation
• Arena Allocation: Temporary allocation arenas for bulk operations
• Object Pooling: Reuse of frequently allocated objects
• Fragmentation Control: Memory layout optimization to reduce fragmentation

Intelligent Caching

• Multiple Policies: LRU, LFU, FIFO, and adaptive cache eviction
• Multi-level Caching: Memory and disk-based caching strategies
• Access Pattern Analysis: Learning from usage patterns for optimal caching
• TTL and Size Management: Configurable cache expiration and size limits

Memory Profiling

• Real-time Monitoring: Live memory usage tracking
• Leak Detection: Automatic identification of memory leaks
• Allocation Hotspots: Analysis of high-allocation code paths
• Temporal Analysis: Memory usage patterns over time

GC Optimization

• Adaptive Tuning: Dynamic GC parameter adjustment
• Pause Time Optimization: Minimizing GC-induced application pauses
• Efficiency Analysis: Measuring GC effectiveness and overhead
• Collection Strategy: Optimal GC algorithm selection and configuration

🏃 Quick Start

Basic Memory Optimization

use kotoba_memory::{MemoryOptimizer, MemoryConfig};

let config = MemoryConfig {
    enable_pooling: true,
    pool_size_mb: 256,
    enable_caching: true,
    cache_size_mb: 512,
    cache_policy: kotoba_memory::CachePolicy::Adaptive,
    enable_custom_allocators: false,
    ..Default::default()
};

let mut optimizer = MemoryOptimizer::new(config);
optimizer.start().await?;

// Your application code here
run_my_database_operations().await;

let report = optimizer.stop().await?;
println!("{}", report.summary());

Memory Pooling

use kotoba_memory::memory_pool::{MemoryPool, MemoryBlock};

// Create a memory pool
let pool = MemoryPool::new(64 * 1024 * 1024); // 64MB pool

// Allocate from pool
let block = pool.allocate(1024)?; // 1KB allocation
assert_eq!(block.size(), 1024);

// Use the memory
let slice = block.as_slice();
// ... use slice ...

// Automatic deallocation when block goes out of scope
drop(block);

Intelligent Caching

use kotoba_memory::cache_manager::{CacheManager, CachePolicy, CachedValue};
use std::time::{Duration, Instant};

let cache = CacheManager::new(100 * 1024 * 1024, CachePolicy::Adaptive); // 100MB cache

let value = CachedValue {
    data: vec![1, 2, 3, 4, 5],
    metadata: CacheMetadata {
        content_type: "binary".to_string(),
        size_bytes: 5,
        compression_ratio: None,
        checksum: None,
    },
    access_count: 0,
    last_access: Instant::now(),
    created_at: Instant::now(),
    ttl: Some(Duration::from_secs(3600)), // 1 hour TTL
};

// Store in cache
cache.put("my_key".to_string(), value);

// Retrieve from cache
if let Some(cached_value) = cache.get("my_key") {
    println!("Cache hit! Data: {:?}", cached_value.data);
}

Custom Allocators

use kotoba_memory::allocators::{create_custom_allocator, create_monitored_allocator};

// Create a custom arena allocator
let arena_allocator = create_custom_allocator();

// Wrap with monitoring
let monitored_allocator = create_monitored_allocator(arena_allocator);

// Use allocator
let layout = std::alloc::Layout::from_size_align(1024, 8)?;
let ptr = monitored_allocator.allocate(layout)?;

// Check statistics
let stats = monitored_allocator.stats();
println!("Allocations: {}, Peak usage: {} bytes",
    stats.allocations, stats.peak_usage);

GC Optimization

use kotoba_memory::gc_optimizer::GcOptimizer;
use std::time::Duration;

let mut gc_optimizer = GcOptimizer::new();
gc_optimizer.start().await?;

// Record GC events (in real usage, this would be automatic)
gc_optimizer.record_collection(
    Duration::from_millis(45), // pause time
    10_000_000,               // bytes reclaimed
    0                         // generation
);

// Analyze GC performance
let analysis = gc_optimizer.analyze().await?;
println!("GC Performance Score: {:.2}", analysis.performance_score);

// Apply optimizations
gc_optimizer.optimize().await?;

📈 Configuration Options

Memory Configuration

let config = MemoryConfig {
    enable_pooling: true,
    pool_size_mb: 256,
    enable_caching: true,
    cache_size_mb: 512,
    cache_policy: CachePolicy::Adaptive,
    enable_custom_allocators: true,
    allocator_type: AllocatorType::Custom,
    enable_gc_optimization: true,
    target_memory_usage_percent: 75.0,
    monitoring_interval_ms: 1000,
};

Cache Policies

• LRU (Least Recently Used): Evicts least recently accessed items
• LFU (Least Frequently Used): Evicts least frequently accessed items
• FIFO (First In, First Out): Evicts oldest items first
• Adaptive: Learns from access patterns to choose optimal eviction

Allocator Types

• System: Standard system allocator
• Jemalloc: Facebook's jemalloc (needs jemalloc feature)
• Mimalloc: Microsoft mimalloc (needs mimalloc feature)
• Custom: Arena-based custom allocator

🔍 Analysis and Monitoring

Memory Usage Analysis

let stats = optimizer.memory_stats().await;
println!("Current memory: {:.1} MB", stats.current_memory_mb);
println!("Peak memory: {:.1} MB", stats.peak_memory_mb);
println!("Memory efficiency: {:.1}%", stats.memory_efficiency * 100.0);

Cache Performance Analysis

let cache_analysis = cache.analyze();
println!("Cache hit rate: {:.1}%", cache_analysis.stats.hit_rate * 100.0);
println!("Cache effectiveness: {:.1}%", cache_analysis.cache_effectiveness * 100.0);

for recommendation in &cache_analysis.recommendations {
    println!("💡 {}", recommendation);
}

GC Performance Analysis

let gc_analysis = gc_optimizer.analyze().await?;
println!("GC bottlenecks: {}", gc_analysis.bottlenecks.len());

for bottleneck in &gc_analysis.bottlenecks {
    println!("🚧 {} (Severity: {:.1})",
        bottleneck.description, bottleneck.severity);
}

🎛️ Advanced Usage

Custom Memory Pools

// Create specialized pools for different object sizes
let small_pool = MemoryPool::new(16 * 1024 * 1024);  // 16MB for small objects
let large_pool = MemoryPool::new(128 * 1024 * 1024); // 128MB for large objects

// Use appropriate pool based on size
let block = if size <= 4096 {
    small_pool.allocate(size)?
} else {
    large_pool.allocate(size)?
};

Multi-Level Caching

// L1 cache (fast, small)
let l1_cache = CacheManager::new(64 * 1024 * 1024, CachePolicy::Lru);

// L2 cache (slower, larger)
let l2_cache = CacheManager::new(512 * 1024 * 1024, CachePolicy::Adaptive);

// Implement cache hierarchy
fn get_with_hierarchy(key: &str) -> Option<CachedValue> {
    // Try L1 first
    if let Some(value) = l1_cache.get(key) {
        return Some(value);
    }

    // Try L2
    if let Some(value) = l2_cache.get(key) {
        // Promote to L1 for faster future access
        l1_cache.put(key.to_string(), value.clone());
        return Some(value);
    }

    None
}

Memory Leak Detection

let profiler = memory_profiler::MemoryProfiler::new();
profiler.start().await?;

// Run application workload
run_workload().await;

// Analyze for leaks
let analysis = profiler.analyze().await?;
for leak in &analysis.memory_leaks {
    println!("🚨 Memory leak detected:");
    println!("  Size: {} bytes", leak.size);
    println!("  Location: {}", leak.allocation_site);
    println!("  Age: {:.1} seconds", leak.age_seconds);
}

GC Tuning Recommendations

let recommendations = gc_optimizer.analyze().await?
    .optimization_opportunities;

for rec in recommendations {
    if rec.expected_benefit > 0.3 && matches!(rec.risk_level, RiskLevel::Low) {
        println!("🎯 {}: {}", rec.optimization_type, rec.description);
        println!("   Expected benefit: {:.0}%", rec.expected_benefit * 100.0);
        for action in &rec.actions {
            println!("   • {}", action);
        }
    }
}

📊 Performance Metrics

Memory Pooling Metrics

• Allocation Efficiency: Ratio of used to allocated memory
• Fragmentation Ratio: Measure of memory fragmentation
• Hit Rate: Percentage of allocations served from pool
• Average Allocation Time: Time spent in allocation operations

Caching Metrics

• Hit Rate: Percentage of cache lookups that succeed
• Hit Latency: Time to retrieve cached items
• Miss Latency: Time to fetch uncached items
• Eviction Rate: Rate at which items are evicted from cache

GC Metrics

• Pause Time: Time application is paused for GC
• Collection Frequency: How often GC runs
• Efficiency: Memory reclaimed per unit GC time
• Generational Statistics: Performance by GC generation

Memory Profiling Metrics

• Allocation Rate: Objects allocated per second
• Deallocation Rate: Objects deallocated per second
• Memory Growth Rate: Rate of memory usage increase
• Leak Detection Accuracy: Effectiveness of leak detection

🔬 Technical Details

Memory Pool Implementation

• Slab Allocation: Pre-allocated memory chunks for fixed sizes
• Buddy System: Efficient allocation of variable-sized blocks
• Arena Allocation: Bulk allocation for temporary data
• Defragmentation: Periodic memory reorganization

Cache Architecture

• Concurrent Access: Thread-safe cache operations
• Size Management: Automatic eviction based on size limits
• TTL Support: Time-based cache expiration
• Compression: Optional data compression for storage efficiency

GC Optimization Strategies

• Concurrent GC: Run GC concurrently with application
• Generational Collection: Different strategies for different object ages
• Heap Tuning: Optimal heap size and generation ratios
• Allocation Site Optimization: Improve object allocation patterns

Custom Allocators

• Jemalloc: Low fragmentation, good multithreading performance
• Mimalloc: Microsoft allocator with good overall performance
• Arena Allocator: Fast allocation for temporary objects
• Monitoring: Performance tracking for all allocators

🚀 Performance Targets

Memory Pooling

• Allocation Speed: <10ns for pooled allocations
• Fragmentation: <5% internal fragmentation
• Memory Overhead: <1% metadata overhead
• Concurrency: Lock-free allocation for most cases

Caching

• Hit Latency: <1μs for memory cache hits
• Hit Rate: >80% for well-tuned caches
• Memory Efficiency: <10% cache metadata overhead
• Scalability: Linear scaling with cache size

GC Optimization

• Pause Times: <50ms for 95th percentile
• Throughput Impact: <5% application throughput reduction
• Memory Reclamation: >90% of unreachable objects collected
• Tuning Time: <1 second for parameter optimization

Memory Profiling

• Overhead: <2% CPU and memory overhead
• Leak Detection: >95% accuracy for leak identification
• Real-time Analysis: <100ms for memory snapshot analysis
• Historical Tracking: Continuous monitoring with minimal retention

🎖️ Optimization Impact Examples

Memory Pooling Benefits

Before: 50,000 allocations/sec, 15% fragmentation
After:  200,000 allocations/sec, 2% fragmentation
Impact: 4x allocation throughput, 87% fragmentation reduction

Intelligent Caching

Before: 40% cache hit rate, 25ms avg response time
After:  85% cache hit rate, 8ms avg response time
Impact: 2.1x cache efficiency, 68% response time improvement

GC Optimization

Before: 150ms max pause time, 25 GC/min
After:   35ms max pause time, 8 GC/min
Impact: 4.3x pause time reduction, 68% GC frequency reduction

Memory Leak Prevention

Before: 500MB memory growth over 1 hour, OOM crashes
After:  Stable 200MB usage, no OOM events
Impact: 60% memory usage reduction, eliminated OOM crashes

Remember: Measure, analyze, optimize, repeat! 📊⚡🧠

🔧 Build Features

Enable optional features in your Cargo.toml:

[dependencies]
kotoba-memory = { version = "0.1.0", features = ["jemalloc", "mimalloc"] }

Available features:

• jemalloc: Enable jemalloc allocator support
• mimalloc: Enable mimalloc allocator support
• cluster: Enable cluster-aware memory optimization