Performance Optimization Guide

/// Core performance optimization principles
pub struct OptimizationPrinciples {
    /// Measure before optimizing
    pub measure_first: bool,
    /// Focus on bottlenecks
    pub focus_on_bottlenecks: bool,
    /// Consider trade-offs
    pub consider_tradeoffs: bool,
    /// Test thoroughly
    pub test_thoroughly: bool,
}

let principles = OptimizationPrinciples {
    measure_first: true,
    focus_on_bottlenecks: true,
    consider_tradeoffs: true,
    test_thoroughly: true,
};

use nerve::memory::pool::MemoryPool;

/// Optimized memory pool configuration
let memory_pool = MemoryPool::new(1024 * 1024 * 100) // 100MB total
    .with_chunk_size(4096)          // 4KB chunks
    .with_max_chunks_per_size(1000) // Limit per size
    .with_reuse_threshold(0.8)      // Reuse at 80%
    .with_compaction(true)          // Enable compaction
    .with_stats_collection(true);   // Collect statistics

// Use pool for message buffers
let buffer = MessageBuffer::new(1000, QoSPolicy::BestEffort)
    .with_memory_pool(memory_pool);

use nerve::memory::reuse::ObjectPool;

/// Object pool for message reuse
let message_pool = ObjectPool::<Message>::new()
    .with_initial_capacity(1000)
    .with_max_capacity(10000)
    .with_reset_function(|msg| {
        msg.headers.clear();
        msg.payload.clear();
        msg.correlation_id = None;
    });

// Reuse messages instead of creating new ones
let mut message = message_pool.get().await;
message.topic = "optimized_topic".into();
message.payload = b"reused_payload".to_vec();

// Return to pool when done
message_pool.return_object(message).await;

use nerve::memory::cache::LruCache;

/// Optimized cache configuration
let cache = LruCache::new(1000) // 1000 entries
    .with_ttl(Duration::from_secs(300)) // 5 minutes
    .with_eviction_policy(EvictionPolicy::Lru)
    .with_metrics(true) // Track hit/miss ratios
    .with_compression(true); // Compress large values

// Cache frequently accessed data
cache.put("routing_table", routing_data).await;
let cached = cache.get("routing_table").await;

use nerve::thread::ThreadPool;
use std::sync::atomic::{AtomicUsize, Ordering};

/// Optimized thread pool configuration
let thread_pool = ThreadPool::new()
    .with_core_threads(num_cpus::get()) // Match CPU cores
    .with_max_threads(num_cpus::get() * 2) // Allow oversubscription
    .with_keep_alive(Duration::from_secs(60)) // Keep threads alive
    .with_queue_size(10000) // Large work queue
    .with_thread_name("nerve-worker") // Descriptive names
    .with_affinity(true) // CPU affinity
    .with_metrics(true); // Performance metrics

// Submit work to optimized pool
thread_pool.submit(|| {
    // CPU-intensive work
    process_message_batch(messages)
}).await?;

use nerve::memory::lockfree::LockFreeQueue;
use crossbeam::epoch;

/// Lock-free queue for high-concurrency scenarios
let queue = LockFreeQueue::new(1000)
    .with_backoff_strategy(BackoffStrategy::Exponential)
    .with_spin_count(1000) // Spin before blocking
    .with_batching(true) // Batch operations
    .with_metrics(true); // Performance tracking

// Concurrent push operations
for i in 0..100 {
    queue.push(Message::new("topic", format!("msg_{}", i))).await?;
}

// Concurrent pop operations
while let Some(message) = queue.pop().await {
    process_message(message).await?;
}

use tokio::runtime::Builder;

/// Optimized async runtime configuration
let runtime = Builder::new_multi_thread()
    .worker_threads(num_cpus::get()) // Match CPU cores
    .max_blocking_threads(num_cpus::get() * 2) // Blocking threads
    .thread_name("nerve-async") // Descriptive names
    .thread_stack_size(2 * 1024 * 1024) // 2MB stack
    .enable_all() // Enable all features
    .build()?;

// Use optimized runtime
runtime.spawn(async {
    // Async work
    process_async_messages().await
});

use nerve::io::batcher::IoBatcher;

/// I/O batching for optimization
let io_batcher = IoBatcher::new()
    .with_batch_size(100) // Batch 100 operations
    .with_max_wait_time(Duration::from_millis(10)) // 10ms max wait
    .with_compression(true) // Compress batches
    .with_parallel_processing(true); // Process in parallel

// Batch file operations
io_batcher.batch_write("log_file", log_entries).await?;

// Batch network operations
io_batcher.batch_send(messages).await?;

use nerve::io::buffer::BufferedWriter;

/// Buffered writer for I/O optimization
let buffered_writer = BufferedWriter::new(file)
    .with_buffer_size(64 * 1024) // 64KB buffer
    .with_auto_flush(true) // Auto-flush on buffer full
    .with_compression(true) // Compress before writing
    .with_metrics(true); // Track I/O performance

// Write with buffering
buffered_writer.write(&data).await?;
buffered_writer.flush().await?; // Manual flush if needed

use nerve::communication::pool::ConnectionPool;

/// Optimized connection pooling
let connection_pool = ConnectionPool::new()
    .with_max_connections(100) // Maximum connections
    .with_idle_timeout(Duration::from_secs(300)) // 5 minutes idle
    .with_connection_timeout(Duration::from_secs(30)) // 30s connect timeout
    .with_health_check_interval(Duration::from_secs(60)) // Health check every minute
    .with_metrics(true); // Connection metrics

// Get connection from pool
let connection = connection_pool.get_connection("endpoint").await?;

// Use connection
connection.send(&data).await?;

// Return to pool
connection_pool.return_connection(connection).await;

use nerve::data_structures::optimized::OptimizedHashMap;

/// Optimized hash map for performance
let optimized_map = OptimizedHashMap::new()
    .with_capacity(10000) // Pre-allocate capacity
    .with_load_factor(0.75) // Optimal load factor
    .with_hash_algorithm(HashAlgorithm::XXHash) // Fast hash
    .with_cache_locality(true); // Improve cache hits

// Fast insertions and lookups
optimized_map.insert("key", "value");
let value = optimized_map.get("key");

use nerve::algorithms::streaming::StreamProcessor;

/// Streaming algorithm for real-time processing
let stream_processor = StreamProcessor::new()
    .with_window_size(1000) // Process in windows
    .with_slide_interval(Duration::from_secs(1)) // Slide every second
    .with_parallel_processing(true) // Parallel window processing
    .with_watermark(Duration::from_secs(5)); // Event time watermark

// Process streaming data
stream_processor.process_stream(|window| {
    // Process window of data
    aggregate_window(window)
}).await?;

use nerve::algorithms::cache::AdaptiveCache;

/// Adaptive cache with automatic tuning
let adaptive_cache = AdaptiveCache::new(1000) // 1000 entries
    .with_adaptive_ttl(true) // Auto-adjust TTL
    .with_prefetch(true) // Prefetch likely accesses
    .with_compression(true) // Compress values
    .with_metrics(true); // Adaptive tuning metrics

// Smart caching
adaptive_cache.put_with_priority("high_priority", data, Priority::High);
let cached = adaptive_cache.get_with_fallback("key", fallback_fn).await;

use nerve::thread::affinity::CpuAffinity;

/// CPU affinity for performance
let cpu_affinity = CpuAffinity::new()
    .with_cpu_set(vec![0, 1, 2, 3]) // Use specific cores
    .with_numa_aware(true) // NUMA awareness
    .with_load_balancing(true) // Load balancing
    .with_isolation(true); // Isolate from other processes

// Set affinity for critical threads
cpu_affinity.set_affinity(thread_id, cpu_set).await?;

use nerve::memory::alloc::CustomAllocator;

/// Custom memory allocator for performance
#[global_allocator]
static ALLOCATOR: CustomAllocator = CustomAllocator::new()
    .with_pool_sizes(vec![64, 256, 1024, 4096]) // Common sizes
    .with_thread_local_caches(true) // Thread-local caches
    .with_large_page_support(true) // Large pages
    .with_metrics(true); // Allocation metrics

// Optimized allocations with custom allocator
let optimized_vec: Vec<u8> = Vec::with_capacity(1024);

use nerve::config::RuntimeConfig;

/// Optimized runtime configuration
let runtime_config = RuntimeConfig::new()
    .with_memory_limit(1024 * 1024 * 1024) // 1GB memory limit
    .with_cpu_limit(0.8) // 80% CPU usage limit
    .with_io_limit(100 * 1024 * 1024) // 100MB/s I/O limit
    .with_concurrency_limit(1000) // 1000 concurrent operations
    .with_adaptive_scaling(true) // Auto-scale based on load
    .with_health_checks(true); // Health monitoring

// Apply optimized configuration
nerve_system.configure(runtime_config).await?;

use nerve::communication::qos::OptimizedQoS;

/// Optimized QoS configuration
let optimized_qos = OptimizedQoS::new()
    .with_priority_levels(5) // 5 priority levels
    .with_bandwidth_allocation(vec![0.5, 0.2, 0.15, 0.1, 0.05]) // Bandwidth distribution
    .with_latency_targets(vec![
        Duration::from_millis(1),   // Highest priority
        Duration::from_millis(5),   // High priority
        Duration::from_millis(10),  // Medium priority
        Duration::from_millis(50),  // Low priority
        Duration::from_millis(100), // Lowest priority
    ])
    .with_starvation_prevention(true) // Prevent starvation
    .with_adaptive_queuing(true); // Adaptive queue management

// Apply optimized QoS
communication_system.set_qos_config(optimized_qos).await?;

use nerve::monitoring::PerformanceMonitor;

/// Performance monitoring for optimization
let performance_monitor = PerformanceMonitor::new()
    .with_metrics_interval(Duration::from_secs(1)) // 1-second intervals
    .with_alert_thresholds(vec![
        ("latency", Duration::from_millis(100)),
        ("memory_usage", 0.8), // 80% memory usage
        ("cpu_usage", 0.9),    // 90% CPU usage
        ("error_rate", 0.01),  // 1% error rate
    ])
    .with_auto_tuning(true) // Automatic performance tuning
    .with_anomaly_detection(true) // Detect performance anomalies
    .with_reporting(true); // Generate optimization reports

// Start monitoring
performance_monitor.start().await?;

use nerve::tuning::AutoTuner;

/// Automated performance tuning
let auto_tuner = AutoTuner::new()
    .with_tuning_interval(Duration::from_secs(60)) // Tune every minute
    .with_parameter_ranges(parameter_ranges) // Tuning parameter ranges
    .with_objective_function(|metrics| {
        // Objective: maximize throughput, minimize latency
        metrics.throughput - metrics.average_latency.as_millis() as f64
    })
    .with_safety_limits(safety_limits) // Safe operating limits
    .with_rollback_strategy(RollbackStrategy::Automatic); // Auto-rollback on failure

// Enable auto-tuning
auto_tuner.enable().await?;

use nerve::testing::benchmark::OptimizationBenchmark;

/// Benchmark for optimization validation
let benchmark = OptimizationBenchmark::new()
    .with_workloads(optimization_workloads)
    .with_metrics(optimization_metrics)
    .with_comparison_baseline(baseline_performance)
    .with_statistical_significance(true)
    .with_report_generation(true);

// Run optimization benchmark
let results = benchmark.run().await?;

// Validate optimization effectiveness
assert!(results.throughput_improvement > 0.1); // 10% improvement
assert!(results.latency_improvement > 0.05);   // 5% improvement

use nerve::testing::ab_testing::OptimizationABTest;

/// A/B testing for optimization validation
let ab_test = OptimizationABTest::new()
    .with_variant_a("baseline", baseline_config)
    .with_variant_b("optimized", optimized_config)
    .with_traffic_split(0.5) // 50/50 split
    .with_test_duration(Duration::from_secs(300)) // 5 minutes
    .with_metrics_comparison(true);

// Run A/B test
let test_results = ab_test.run().await?;

// Check if optimization is statistically significant
if test_results.is_significant() {
    println!("Optimization validated with {}% confidence",
             test_results.confidence_level * 100.0);
}