Performance Optimization in TuskLang for Rust

Performance optimization in TuskLang for Rust leverages Rust's zero-cost abstractions, memory safety, and concurrency features to achieve maximum performance while maintaining the power and flexibility of TuskLang's configuration and scripting capabilities.

Memory Management Optimization

// Optimized memory management with TuskLang
pub struct OptimizedMemoryManager {
    pools: std::collections::HashMap<usize, ObjectPool>,
    cache: lru::LruCache<String, Box<dyn std::any::Any + Send + Sync>>,
}
pub struct ObjectPool {
    objects: Vec<Box<dyn std::any::Any + Send + Sync>>,
    object_size: usize,
    max_size: usize,
}
impl OptimizedMemoryManager {
    pub fn new() -> Self {
        Self {
            pools: std::collections::HashMap::new(),
            cache: lru::LruCache::new(1000), // 1000 items max
        }
    }
    
    pub fn get_object<T: 'static + Send + Sync + Default>(&mut self, size: usize) -> T {
        // Try to get from pool first
        if let Some(pool) = self.pools.get_mut(&size) {
            if let Some(obj) = pool.objects.pop() {
                if let Ok(obj) = obj.downcast::<T>() {
                    return *obj;
                }
            }
        }
        
        // Create new object if pool is empty
        T::default()
    }
    
    pub fn return_object<T: 'static + Send + Sync>(&mut self, obj: T, size: usize) {
        let pool = self.pools.entry(size).or_insert_with(|| ObjectPool {
            objects: Vec::new(),
            object_size: size,
            max_size: 100,
        });
        
        if pool.objects.len() < pool.max_size {
            pool.objects.push(Box::new(obj));
        }
    }
    
    pub fn cache_get<T: 'static + Send + Sync>(&mut self, key: &str) -> Option<&T> {
        self.cache.get(key)?.downcast_ref::<T>()
    }
    
    pub fn cache_set<T: 'static + Send + Sync>(&mut self, key: String, value: T) {
        self.cache.put(key, Box::new(value));
    }
}
// Zero-copy string handling
pub struct StringPool {
    strings: std::collections::HashMap<String, std::sync::Arc<String>>,
}
impl StringPool {
    pub fn new() -> Self {
        Self {
            strings: std::collections::HashMap::new(),
        }
    }
    
    pub fn intern(&mut self, s: String) -> std::sync::Arc<String> {
        if let Some(arc) = self.strings.get(&s) {
            arc.clone()
        } else {
            let arc = std::sync::Arc::new(s.clone());
            self.strings.insert(s, arc.clone());
            arc
        }
    }
    
    pub fn get(&self, s: &str) -> Option<std::sync::Arc<String>> {
        self.strings.get(s).cloned()
    }
}
// Usage with TuskLang
let mut memory_manager = OptimizedMemoryManager::new();
let mut string_pool = StringPool::new();
// Optimized configuration loading
let config_string = @file.read("config.tsk")?;
let interned_config = string_pool.intern(config_string);
// Cache parsed configuration
let parsed_config = @config.parse(&interned_config)?;
memory_manager.cache_set("parsed_config".to_string(), parsed_config);

Compile-Time Optimization

// Compile-time configuration optimization
pub struct CompileTimeConfig {
    values: std::collections::HashMap<String, ConfigValue>,
}
#[derive(Clone)]
pub enum ConfigValue {
    String(&'static str),
    Integer(i64),
    Float(f64),
    Boolean(bool),
    Array(Vec<ConfigValue>),
    Object(std::collections::HashMap<String, ConfigValue>),
}
impl CompileTimeConfig {
    pub const fn new() -> Self {
        Self {
            values: std::collections::HashMap::new(),
        }
    }
    
    pub const fn with_value(mut self, key: &'static str, value: ConfigValue) -> Self {
        // Note: This is a simplified version. In practice, you'd use const generics
        // or a macro-based approach for true compile-time evaluation
        self
    }
    
    pub fn get<T>(&self, key: &str) -> Option<T>
    where
        T: From<ConfigValue>,
    {
        self.values.get(key).cloned().map(T::from)
    }
}
// Compile-time optimized TuskLang operations
#[macro_export]
macro_rules! const_tusk_config {
    ($($key:ident = $value:expr),*) => {{
        let mut config = std::collections::HashMap::new();
        $(
            config.insert(stringify!($key).to_string(), $value);
        )*
        config
    }};
}
// Usage
const APP_CONFIG: std::collections::HashMap<&str, &str> = const_tusk_config! {
    app_name = "OptimizedApp",
    version = "1.0.0",
    debug_mode = "false"
};
// Compile-time evaluated expressions
const MAX_CONNECTIONS: usize = 1000;
const CACHE_TTL: u64 = 3600;
const API_TIMEOUT: u64 = 30;
// Compile-time string interning
const INTERNED_STRINGS: &[&str] = &[
    "GET", "POST", "PUT", "DELETE",
    "application/json", "text/plain",
    "Content-Type", "Authorization",
];

Concurrency and Parallelism

// Optimized concurrent processing with TuskLang
pub struct ConcurrentProcessor {
    thread_pool: rayon::ThreadPool,
    work_queue: crossbeam_channel::Sender<WorkItem>,
    result_receiver: crossbeam_channel::Receiver<WorkResult>,
}
#[derive(Clone)]
pub struct WorkItem {
    pub id: u64,
    pub operation: String,
    pub data: serde_json::Value,
    pub priority: u8,
}
pub struct WorkResult {
    pub id: u64,
    pub result: Result<serde_json::Value, Box<dyn std::error::Error + Send + Sync>>,
    pub duration: std::time::Duration,
}
impl ConcurrentProcessor {
    pub fn new(thread_count: usize) -> Self {
        let thread_pool = rayon::ThreadPoolBuilder::new()
            .num_threads(thread_count)
            .build()
            .unwrap();
        
        let (work_sender, work_receiver) = crossbeam_channel::unbounded();
        let (result_sender, result_receiver) = crossbeam_channel::unbounded();
        
        // Start worker threads
        for _ in 0..thread_count {
            let work_receiver = work_receiver.clone();
            let result_sender = result_sender.clone();
            
            thread_pool.spawn(move || {
                while let Ok(work_item) = work_receiver.recv() {
                    let start_time = std::time::Instant::now();
                    
                    let result = Self::process_work_item(work_item.clone());
                    
                    let duration = start_time.elapsed();
                    let work_result = WorkResult {
                        id: work_item.id,
                        result,
                        duration,
                    };
                    
                    let _ = result_sender.send(work_result);
                }
            });
        }
        
        Self {
            thread_pool,
            work_queue: work_sender,
            result_receiver,
        }
    }
    
    fn process_work_item(item: WorkItem) -> Result<serde_json::Value, Box<dyn std::error::Error + Send + Sync>> {
        match item.operation.as_str() {
            "database_query" => {
                let query = item.data["query"].as_str().unwrap();
                @db.execute(query)
            }
            "api_request" => {
                let url = item.data["url"].as_str().unwrap();
                @http.get(url)
            }
            "file_operation" => {
                let path = item.data["path"].as_str().unwrap();
                @file.read(path)
            }
            "cache_operation" => {
                let key = item.data["key"].as_str().unwrap();
                @cache.get(key)
            }
            _ => Err("Unknown operation".into()),
        }
    }
    
    pub fn submit_work(&self, item: WorkItem) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        self.work_queue.send(item)?;
        Ok(())
    }
    
    pub fn get_results(&self) -> Vec<WorkResult> {
        let mut results = Vec::new();
        while let Ok(result) = self.result_receiver.try_recv() {
            results.push(result);
        }
        results
    }
    
    pub fn process_batch(&self, items: Vec<WorkItem>) -> Vec<WorkResult> {
        // Submit all work items
        for item in items {
            self.submit_work(item).unwrap();
        }
        
        // Collect results
        let mut results = Vec::new();
        let expected_count = items.len();
        
        while results.len() < expected_count {
            if let Ok(result) = self.result_receiver.recv() {
                results.push(result);
            }
        }
        
        results
    }
}
// Async/await optimized processing
pub struct AsyncProcessor {
    runtime: tokio::runtime::Runtime,
    semaphore: std::sync::Arc<tokio::sync::Semaphore>,
}
impl AsyncProcessor {
    pub fn new(max_concurrent: usize) -> Self {
        let runtime = tokio::runtime::Builder::new_multi_thread()
            .worker_threads(num_cpus::get())
            .enable_all()
            .build()
            .unwrap();
        
        Self {
            runtime,
            semaphore: std::sync::Arc::new(tokio::sync::Semaphore::new(max_concurrent)),
        }
    }
    
    pub async fn process_concurrent<T, F, Fut>(&self, items: Vec<T>, processor: F) -> Vec<Result<serde_json::Value, Box<dyn std::error::Error + Send + Sync>>>
    where
        F: Fn(T) -> Fut + Send + Sync + 'static,
        Fut: std::future::Future<Output = Result<serde_json::Value, Box<dyn std::error::Error + Send + Sync>>> + Send + 'static,
        T: Send + 'static,
    {
        let semaphore = self.semaphore.clone();
        
        let futures: Vec<_> = items.into_iter().map(|item| {
            let semaphore = semaphore.clone();
            async move {
                let _permit = semaphore.acquire().await.unwrap();
                processor(item).await
            }
        }).collect();
        
        futures::future::join_all(futures).await
    }
}
// Usage
let processor = ConcurrentProcessor::new(8);
let async_processor = AsyncProcessor::new(100);
// Process database queries concurrently
let queries = vec![
    "SELECT * FROM users WHERE id = 1",
    "SELECT * FROM products WHERE category = 'electronics'",
    "SELECT COUNT(*) FROM orders WHERE status = 'pending'",
];
let work_items: Vec<WorkItem> = queries.into_iter().enumerate().map(|(i, query)| {
    WorkItem {
        id: i as u64,
        operation: "database_query".to_string(),
        data: serde_json::json!({ "query": query }),
        priority: 1,
    }
}).collect();
let results = processor.process_batch(work_items);

Caching Strategies

// Multi-level caching system
pub struct MultiLevelCache {
    l1_cache: std::sync::Arc<tokio::sync::RwLock<lru::LruCache<String, CachedValue>>>,
    l2_cache: std::sync::Arc<redis::Client>,
    l3_cache: std::sync::Arc<Database>,
}
#[derive(Clone)]
pub struct CachedValue {
    pub data: serde_json::Value,
    pub timestamp: std::time::Instant,
    pub ttl: std::time::Duration,
}
impl MultiLevelCache {
    pub fn new(l1_size: usize, redis_url: &str, database: Database) -> Self {
        let l1_cache = std::sync::Arc::new(tokio::sync::RwLock::new(lru::LruCache::new(l1_size)));
        let l2_cache = std::sync::Arc::new(redis::Client::open(redis_url).unwrap());
        let l3_cache = std::sync::Arc::new(database);
        
        Self {
            l1_cache,
            l2_cache,
            l3_cache,
        }
    }
    
    pub async fn get(&self, key: &str) -> Result<Option<serde_json::Value>, Box<dyn std::error::Error + Send + Sync>> {
        // Check L1 cache first
        if let Some(cached) = self.l1_cache.read().await.get(key) {
            if cached.timestamp.elapsed() < cached.ttl {
                return Ok(Some(cached.data.clone()));
            }
        }
        
        // Check L2 cache (Redis)
        if let Ok(Some(data)) = self.get_from_redis(key).await {
            // Update L1 cache
            let cached_value = CachedValue {
                data: data.clone(),
                timestamp: std::time::Instant::now(),
                ttl: std::time::Duration::from_secs(300), // 5 minutes
            };
            self.l1_cache.write().await.put(key.to_string(), cached_value);
            return Ok(Some(data));
        }
        
        // Check L3 cache (Database)
        if let Ok(Some(data)) = self.get_from_database(key).await {
            // Update L2 and L1 caches
            self.set_in_redis(key, &data, 3600).await?; // 1 hour TTL
            
            let cached_value = CachedValue {
                data: data.clone(),
                timestamp: std::time::Instant::now(),
                ttl: std::time::Duration::from_secs(300),
            };
            self.l1_cache.write().await.put(key.to_string(), cached_value);
            
            return Ok(Some(data));
        }
        
        Ok(None)
    }
    
    pub async fn set(&self, key: &str, value: &serde_json::Value, ttl: u64) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        // Set in all cache levels
        let cached_value = CachedValue {
            data: value.clone(),
            timestamp: std::time::Instant::now(),
            ttl: std::time::Duration::from_secs(ttl),
        };
        
        self.l1_cache.write().await.put(key.to_string(), cached_value);
        self.set_in_redis(key, value, ttl).await?;
        self.set_in_database(key, value).await?;
        
        Ok(())
    }
    
    async fn get_from_redis(&self, key: &str) -> Result<Option<serde_json::Value>, Box<dyn std::error::Error + Send + Sync>> {
        let mut conn = self.l2_cache.get_async_connection().await?;
        let result: Option<String> = redis::cmd("GET").arg(key).query_async(&mut conn).await?;
        
        match result {
            Some(data) => Ok(Some(serde_json::from_str(&data)?)),
            None => Ok(None),
        }
    }
    
    async fn set_in_redis(&self, key: &str, value: &serde_json::Value, ttl: u64) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let mut conn = self.l2_cache.get_async_connection().await?;
        let data = serde_json::to_string(value)?;
        
        redis::cmd("SETEX")
            .arg(key)
            .arg(ttl)
            .arg(data)
            .query_async(&mut conn)
            .await?;
        
        Ok(())
    }
    
    async fn get_from_database(&self, key: &str) -> Result<Option<serde_json::Value>, Box<dyn std::error::Error + Send + Sync>> {
        let result = @db.table("cache")
            .where("cache_key", key)
            .where("expires_at", ">", @date.now())
            .first()
            .await?;
        
        Ok(result.map(|row| row["cache_value"].clone()))
    }
    
    async fn set_in_database(&self, key: &str, value: &serde_json::Value) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let expires_at = @date.add_hours(@date.now(), 24); // 24 hours TTL
        
        @db.table("cache")
            .upsert(&serde_json::json!({
                "cache_key": key,
                "cache_value": value,
                "expires_at": expires_at,
                "updated_at": @date.now()
            }))
            .await?;
        
        Ok(())
    }
}
// Usage with TuskLang
let cache = MultiLevelCache::new(1000, "redis://localhost:6379", database);
// Cache TuskLang configuration
let config = @config.load("app_config.tsk");
cache.set("app_config", &config, 3600).await?;
// Retrieve cached configuration
if let Some(cached_config) = cache.get("app_config").await? {
    // Use cached configuration
    let db_url = cached_config["database"]["url"].as_str().unwrap();
    let api_key = cached_config["api"]["key"].as_str().unwrap();
}

Database Optimization

// Optimized database operations with TuskLang
pub struct OptimizedDatabase {
    connection_pool: deadpool_postgres::Pool,
    query_cache: std::sync::Arc<tokio::sync::RwLock<lru::LruCache<String, PreparedStatement>>>,
    batch_size: usize,
}
impl OptimizedDatabase {
    pub fn new(pool: deadpool_postgres::Pool, batch_size: usize) -> Self {
        Self {
            connection_pool: pool,
            query_cache: std::sync::Arc::new(tokio::sync::RwLock::new(lru::LruCache::new(100))),
            batch_size,
        }
    }
    
    pub async fn execute_batch(&self, queries: Vec<String>) -> Result<Vec<serde_json::Value>, Box<dyn std::error::Error + Send + Sync>> {
        let mut results = Vec::new();
        let batches: Vec<_> = queries.chunks(self.batch_size).collect();
        
        for batch in batches {
            let batch_results = self.execute_batch_internal(batch).await?;
            results.extend(batch_results);
        }
        
        Ok(results)
    }
    
    async fn execute_batch_internal(&self, queries: &[String]) -> Result<Vec<serde_json::Value>, Box<dyn std::error::Error + Send + Sync>> {
        let conn = self.connection_pool.get().await?;
        let mut results = Vec::new();
        
        for query in queries {
            // Check query cache first
            if let Some(stmt) = self.query_cache.read().await.get(query) {
                let result = conn.execute(stmt, &[]).await?;
                results.push(serde_json::to_value(result)?);
            } else {
                // Prepare and cache the statement
                let stmt = conn.prepare(query).await?;
                self.query_cache.write().await.put(query.to_string(), stmt.clone());
                
                let result = conn.execute(&stmt, &[]).await?;
                results.push(serde_json::to_value(result)?);
            }
        }
        
        Ok(results)
    }
    
    pub async fn optimized_query(&self, query: &str, params: &[serde_json::Value]) -> Result<serde_json::Value, Box<dyn std::error::Error + Send + Sync>> {
        // Use TuskLang query optimization
        let optimized_query = @query.optimize(query)?;
        let execution_plan = @query.analyze(&optimized_query)?;
        
        @log.debug("Query execution plan", {
            original_query = query,
            optimized_query = &optimized_query,
            execution_plan = &execution_plan,
        });
        
        // Execute with optimized parameters
        let conn = self.connection_pool.get().await?;
        let stmt = conn.prepare(&optimized_query).await?;
        let result = conn.query(&stmt, &params).await?;
        
        Ok(serde_json::to_value(result)?)
    }
    
    pub async fn bulk_insert(&self, table: &str, data: Vec<serde_json::Value>) -> Result<u64, Box<dyn std::error::Error + Send + Sync>> {
        if data.is_empty() {
            return Ok(0);
        }
        
        let conn = self.connection_pool.get().await?;
        let columns: Vec<_> = data[0].as_object().unwrap().keys().cloned().collect();
        
        // Build bulk insert query
        let placeholders: Vec<_> = (0..data.len()).map(|i| {
            let row_placeholders: Vec<_> = (0..columns.len()).map(|j| format!("${}", i * columns.len() + j + 1)).collect();
            format!("({})", row_placeholders.join(", "))
        }).collect();
        
        let query = format!(
            "INSERT INTO {} ({}) VALUES {}",
            table,
            columns.join(", "),
            placeholders.join(", ")
        );
        
        // Flatten parameters
        let mut params = Vec::new();
        for row in &data {
            for column in &columns {
                params.push(row[column].clone());
            }
        }
        
        let stmt = conn.prepare(&query).await?;
        let result = conn.execute(&stmt, &params).await?;
        
        Ok(result)
    }
}
// Usage
let db = OptimizedDatabase::new(connection_pool, 1000);
// Optimized batch processing
let queries = vec![
    "SELECT * FROM users WHERE status = 'active'".to_string(),
    "SELECT COUNT(*) FROM orders WHERE created_at > NOW() - INTERVAL '1 day'".to_string(),
    "SELECT product_id, SUM(quantity) FROM order_items GROUP BY product_id".to_string(),
];
let results = db.execute_batch(queries).await?;
// Bulk insert with TuskLang data
let user_data = @db.users.get_all()?;
let serialized_data: Vec<serde_json::Value> = user_data.into_iter().map(|user| {
    serde_json::to_value(user).unwrap()
}).collect();
let inserted_count = db.bulk_insert("users_backup", serialized_data).await?;

Network Optimization

// Optimized HTTP client with connection pooling
pub struct OptimizedHttpClient {
    client: reqwest::Client,
    connection_pool: std::sync::Arc<tokio::sync::RwLock<lru::LruCache<String, reqwest::Client>>>,
    rate_limiter: std::sync::Arc<RateLimiter>,
}
impl OptimizedHttpClient {
    pub fn new() -> Self {
        let client = reqwest::Client::builder()
            .pool_max_idle_per_host(10)
            .pool_idle_timeout(std::time::Duration::from_secs(30))
            .timeout(std::time::Duration::from_secs(30))
            .build()
            .unwrap();
        
        Self {
            client,
            connection_pool: std::sync::Arc::new(tokio::sync::RwLock::new(lru::LruCache::new(50))),
            rate_limiter: std::sync::Arc::new(RateLimiter::new(100, 1.0)), // 100 requests per second
        }
    }
    
    pub async fn optimized_request(&self, method: &str, url: &str, body: Option<serde_json::Value>) 
        -> Result<reqwest::Response, Box<dyn std::error::Error + Send + Sync>> {
        
        // Apply rate limiting
        self.rate_limiter.acquire().await?;
        
        // Use TuskLang HTTP optimization
        let optimized_url = @http.optimize_url(url)?;
        let optimized_headers = @http.get_optimized_headers(method, url)?;
        
        let request = match method.to_uppercase().as_str() {
            "GET" => self.client.get(&optimized_url),
            "POST" => {
                let mut req = self.client.post(&optimized_url);
                if let Some(body_data) = body {
                    req = req.json(&body_data);
                }
                req
            }
            "PUT" => {
                let mut req = self.client.put(&optimized_url);
                if let Some(body_data) = body {
                    req = req.json(&body_data);
                }
                req
            }
            "DELETE" => self.client.delete(&optimized_url),
            _ => return Err("Unsupported HTTP method".into()),
        };
        
        // Add optimized headers
        let request = optimized_headers.iter().fold(request, |req, (key, value)| {
            req.header(key, value)
        });
        
        let response = request.send().await?;
        
        // Cache successful responses
        if response.status().is_success() {
            self.cache_response(&optimized_url, &response).await?;
        }
        
        Ok(response)
    }
    
    async fn cache_response(&self, url: &str, response: &reqwest::Response) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        if let Some(cache_control) = response.headers().get("cache-control") {
            if cache_control.to_str()?.contains("max-age=") {
                let body = response.bytes().await?;
                @cache.set(url, &body, 3600).await?; // Cache for 1 hour
            }
        }
        Ok(())
    }
    
    pub async fn batch_requests(&self, requests: Vec<HttpRequest>) -> Vec<Result<reqwest::Response, Box<dyn std::error::Error + Send + Sync>>> {
        let futures: Vec<_> = requests.into_iter().map(|req| {
            self.optimized_request(&req.method, &req.url, req.body)
        }).collect();
        
        futures::future::join_all(futures).await
    }
}
pub struct HttpRequest {
    pub method: String,
    pub url: String,
    pub body: Option<serde_json::Value>,
}
pub struct RateLimiter {
    tokens: std::sync::atomic::AtomicU32,
    max_tokens: u32,
    refill_rate: f64,
    last_refill: std::sync::Mutex<std::time::Instant>,
}
impl RateLimiter {
    pub fn new(max_tokens: u32, refill_rate: f64) -> Self {
        Self {
            tokens: std::sync::atomic::AtomicU32::new(max_tokens),
            max_tokens,
            refill_rate,
            last_refill: std::sync::Mutex::new(std::time::Instant::now()),
        }
    }
    
    pub async fn acquire(&self) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        loop {
            self.refill_tokens();
            
            let current_tokens = self.tokens.load(std::sync::atomic::Ordering::Relaxed);
            if current_tokens > 0 {
                if self.tokens.compare_exchange(
                    current_tokens,
                    current_tokens - 1,
                    std::sync::atomic::Ordering::Relaxed,
                    std::sync::atomic::Ordering::Relaxed,
                ).is_ok() {
                    return Ok(());
                }
            }
            
            tokio::time::sleep(std::time::Duration::from_millis(10)).await;
        }
    }
    
    fn refill_tokens(&self) {
        if let Ok(mut last_refill) = self.last_refill.lock() {
            let now = std::time::Instant::now();
            let elapsed = now.duration_since(*last_refill).as_secs_f64();
            let tokens_to_add = (elapsed * self.refill_rate) as u32;
            
            if tokens_to_add > 0 {
                let current_tokens = self.tokens.load(std::sync::atomic::Ordering::Relaxed);
                let new_tokens = std::cmp::min(current_tokens + tokens_to_add, self.max_tokens);
                self.tokens.store(new_tokens, std::sync::atomic::Ordering::Relaxed);
                *last_refill = now;
            }
        }
    }
}
// Usage
let http_client = OptimizedHttpClient::new();
// Batch API requests
let requests = vec![
    HttpRequest {
        method: "GET".to_string(),
        url: "https://api.example.com/users".to_string(),
        body: None,
    },
    HttpRequest {
        method: "POST".to_string(),
        url: "https://api.example.com/users".to_string(),
        body: Some(serde_json::json!({
            "name": "John Doe",
            "email": "john@example.com"
        })),
    },
];
let responses = http_client.batch_requests(requests).await;

Algorithm Optimization

// Optimized algorithms with TuskLang integration
pub struct OptimizedAlgorithms;
impl OptimizedAlgorithms {
    // Optimized sorting with custom comparison
    pub fn optimized_sort<T, F>(items: &mut [T], compare: F) -> Result<(), Box<dyn std::error::Error + Send + Sync>>
    where
        F: Fn(&T, &T) -> std::cmp::Ordering + Send + Sync,
    {
        // Use TuskLang to determine optimal sorting algorithm
        let algorithm = @algorithm.select_sorting(items.len(), &compare)?;
        
        match algorithm.as_str() {
            "quicksort" => Self::quicksort(items, &compare),
            "mergesort" => Self::mergesort(items, &compare),
            "heapsort" => Self::heapsort(items, &compare),
            "timsort" => Self::timsort(items, &compare),
            _ => items.sort_by(compare),
        }
        
        Ok(())
    }
    
    // Optimized search with indexing
    pub fn optimized_search<T, F>(items: &[T], target: &T, compare: F) -> Option<usize>
    where
        F: Fn(&T, &T) -> std::cmp::Ordering + Send + Sync,
        T: Clone,
    {
        // Use TuskLang to determine optimal search strategy
        let strategy = @algorithm.select_search(items.len(), target)?;
        
        match strategy.as_str() {
            "binary_search" => items.binary_search_by(|item| compare(item, target)).ok(),
            "linear_search" => items.iter().position(|item| compare(item, target) == std::cmp::Ordering::Equal),
            "hash_search" => {
                let hash_map: std::collections::HashMap<_, _> = items.iter().enumerate().collect();
                hash_map.get(target).copied()
            }
            _ => items.binary_search_by(|item| compare(item, target)).ok(),
        }
    }
    
    // Optimized data processing pipeline
    pub async fn optimized_pipeline<T, U, F>(items: Vec<T>, processor: F, batch_size: usize) 
        -> Result<Vec<U>, Box<dyn std::error::Error + Send + Sync>>
    where
        F: Fn(T) -> U + Send + Sync + 'static,
        T: Send + 'static,
        U: Send + 'static,
    {
        // Use TuskLang to optimize pipeline
        let pipeline_config = @pipeline.optimize(items.len(), batch_size)?;
        
        let chunks: Vec<_> = items.chunks(pipeline_config.batch_size).collect();
        let mut results = Vec::new();
        
        for chunk in chunks {
            let chunk_results: Vec<U> = chunk.iter().cloned().map(&processor).collect();
            results.extend(chunk_results);
        }
        
        Ok(results)
    }
    
    // Optimized caching algorithm
    pub fn optimized_cache<T>(&self, key: &str, generator: impl Fn() -> T, ttl: std::time::Duration) -> T
    where
        T: Clone + Send + Sync + 'static,
    {
        // Use TuskLang cache optimization
        let cache_key = @cache.optimize_key(key)?;
        let cache_strategy = @cache.select_strategy(&cache_key, ttl)?;
        
        match cache_strategy.as_str() {
            "lru" => self.lru_cache.get_or_insert_with(cache_key, generator),
            "lfu" => self.lfu_cache.get_or_insert_with(cache_key, generator),
            "ttl" => self.ttl_cache.get_or_insert_with(cache_key, generator, ttl),
            _ => generator(),
        }
    }
}
// Usage
let algorithms = OptimizedAlgorithms;
// Optimized sorting
let mut data = vec![5, 2, 8, 1, 9, 3];
algorithms.optimized_sort(&mut data, |a, b| a.cmp(b))?;
// Optimized search
let target = 8;
if let Some(index) = algorithms.optimized_search(&data, &target, |a, b| a.cmp(b)) {
    println!("Found {} at index {}", target, index);
}
// Optimized pipeline processing
let numbers = (1..1000).collect::<Vec<i32>>();
let processed = algorithms.optimized_pipeline(numbers, |n| n * 2, 100).await?;

This comprehensive guide covers Rust-specific performance optimization patterns, ensuring maximum performance while maintaining the power and flexibility of TuskLang's capabilities.