🦀 TuskLang Rust Advanced Features

"We don't bow to any king" - Rust Edition

Unlock the full power of TuskLang Rust with advanced features: WebAssembly support, intelligent caching, machine learning integration, real-time monitoring, and performance optimization. Build applications that scale with zero compromises.

🌐 WebAssembly Support

Basic WASM Integration

use tusklang_rust::{parse, wasm::TuskWasm};
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub struct TuskConfig {
    inner: TuskWasm,
}
#[wasm_bindgen]
impl TuskConfig {
    #[wasm_bindgen(constructor)]
    pub fn new(tsk_content: &str) -> Result<TuskConfig, JsValue> {
        let inner = TuskWasm::parse(tsk_content)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(TuskConfig { inner })
    }
    
    pub fn get(&self, key: &str) -> Result<JsValue, JsValue> {
        let value = self.inner.get(key)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(serde_wasm_bindgen::to_value(&value)?)
    }
    
    pub fn to_json(&self) -> Result<String, JsValue> {
        let json = self.inner.to_json()
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(json)
    }
    
    pub fn execute_fujsen(&self, section: &str, function: &str, args: &JsValue) -> Result<JsValue, JsValue> {
        let args_vec: Vec<JsValue> = serde_wasm_bindgen::from_value(args.clone())?;
        let result = self.inner.execute_fujsen(section, function, &args_vec)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(serde_wasm_bindgen::to_value(&result)?)
    }
}

Advanced WASM Features

use tusklang_rust::{parse, wasm::TuskWasm};
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub struct TuskWasmAdvanced {
    inner: TuskWasm,
    cache: std::collections::HashMap<String, JsValue>,
}
#[wasm_bindgen]
impl TuskWasmAdvanced {
    #[wasm_bindgen(constructor)]
    pub fn new(tsk_content: &str) -> Result<TuskWasmAdvanced, JsValue> {
        let inner = TuskWasm::parse(tsk_content)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(TuskWasmAdvanced {
            inner,
            cache: std::collections::HashMap::new(),
        })
    }
    
    pub fn get_cached(&mut self, key: &str) -> Result<JsValue, JsValue> {
        if let Some(cached) = self.cache.get(key) {
            return Ok(cached.clone());
        }
        
        let value = self.inner.get(key)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        let js_value = serde_wasm_bindgen::to_value(&value)?;
        self.cache.insert(key.to_string(), js_value.clone());
        Ok(js_value)
    }
    
    pub fn clear_cache(&mut self) {
        self.cache.clear();
    }
    
    pub fn get_cache_size(&self) -> usize {
        self.cache.len()
    }
    
    pub fn validate_schema(&self, schema: &str) -> Result<bool, JsValue> {
        let is_valid = self.inner.validate_schema(schema)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(is_valid)
    }
}

WASM Performance Optimization

use tusklang_rust::{parse, wasm::TuskWasm};
use wasm_bindgen::prelude::*;
#[wasm_bindgen]
pub struct TuskWasmOptimized {
    inner: TuskWasm,
    compiled_functions: std::collections::HashMap<String, js_sys::Function>,
}
#[wasm_bindgen]
impl TuskWasmOptimized {
    #[wasm_bindgen(constructor)]
    pub fn new(tsk_content: &str) -> Result<TuskWasmOptimized, JsValue> {
        let inner = TuskWasm::parse(tsk_content)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(TuskWasmOptimized {
            inner,
            compiled_functions: std::collections::HashMap::new(),
        })
    }
    
    pub fn compile_fujsen(&mut self, section: &str, function: &str) -> Result<(), JsValue> {
        let fujsen_code = self.inner.get_fujsen_code(section, function)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        
        // Compile FUJSEN to JavaScript function
        let js_function = js_sys::eval(&format!("({})", fujsen_code))
            .map_err(|e| JsValue::from_str(&e.to_string()))?
            .dyn_into::<js_sys::Function>()
            .map_err(|_| JsValue::from_str("Failed to convert to function"))?;
        
        let key = format!("{}:{}", section, function);
        self.compiled_functions.insert(key, js_function);
        Ok(())
    }
    
    pub fn execute_compiled(&self, section: &str, function: &str, args: &JsValue) -> Result<JsValue, JsValue> {
        let key = format!("{}:{}", section, function);
        let js_function = self.compiled_functions.get(&key)
            .ok_or_else(|| JsValue::from_str("Function not compiled"))?;
        
        let result = js_function.call1(&JsValue::NULL, args)
            .map_err(|e| JsValue::from_str(&e.to_string()))?;
        Ok(result)
    }
}

🧠 Machine Learning Integration

Basic ML Features

use tusklang_rust::{parse, Parser, ml::{MLPredictor, MLTrainer}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let tsk_content = r#"
[ml_config]
model_type: "linear_regression"
training_data: "data/training.csv"
test_data: "data/test.csv"
features: ["feature1", "feature2", "feature3"]
target: "target"
[predictions]
user_behavior: @learn("user_behavior_model", @request.user_data)
recommendation: @learn("recommendation_model", @request.user_preferences)
anomaly_score: @learn("anomaly_detection", @request.system_metrics)
"#;
    
    // Setup ML predictors
    let ml_predictor = MLPredictor::new("models/").await?;
    parser.set_ml_predictor(ml_predictor);
    
    let data = parser.parse(tsk_content).await?;
    
    println!("ML Configuration: {:?}", data["ml_config"]);
    println!("Predictions: {:?}", data["predictions"]);
    
    Ok(())
}

Advanced ML Features

use tusklang_rust::{parse, Parser, ml::{MLPredictor, MLTrainer, MLModel}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let tsk_content = r#"
[ml_advanced]
Auto-optimization
optimal_setting: @optimize("performance_setting", {
    "cpu_cores": [1, 2, 4, 8],
    "memory_gb": [2, 4, 8, 16],
    "cache_size": [100, 500, 1000]
}, "response_time")
Reinforcement learning
action_reward: @reinforce("user_action", @request.action, @request.reward)
Neural network predictions
image_classification: @neural("image_model", @request.image_data)
text_sentiment: @neural("sentiment_model", @request.text_data)
"#;
    
    let ml_predictor = MLPredictor::new("models/").await?;
    parser.set_ml_predictor(ml_predictor);
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Optimal setting: {:?}", data["ml_advanced"]["optimal_setting"]);
    println!("Action reward: {:?}", data["ml_advanced"]["action_reward"]);
    println!("Image classification: {:?}", data["ml_advanced"]["image_classification"]);
    
    Ok(())
}

⚡ Intelligent Caching

Multi-Level Caching

use tusklang_rust::{parse, Parser, cache::{MemoryCache, RedisCache, TieredCache}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    // Setup tiered caching
    let memory_cache = MemoryCache::new();
    let redis_cache = RedisCache::new(RedisConfig {
        host: "localhost".to_string(),
        port: 6379,
        db: 0,
    }).await?;
    
    let tiered_cache = TieredCache::new()
        .add_tier(memory_cache, "5s")  // L1: Memory cache, 5 seconds
        .add_tier(redis_cache, "1h");  // L2: Redis cache, 1 hour
    
    parser.set_cache(tiered_cache);
    
    let tsk_content = r#"
[cached_data]
Multi-level caching
user_profile: @cache("1h", "user_profile", @request.user_id)
expensive_calculation: @cache("5m", "expensive_operation")
api_response: @cache("30s", "api_call", @request.endpoint)
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("User profile: {:?}", data["cached_data"]["user_profile"]);
    println!("Expensive calculation: {:?}", data["cached_data"]["expensive_calculation"]);
    println!("API response: {:?}", data["cached_data"]["api_response"]);
    
    Ok(())
}

Adaptive Caching

use tusklang_rust::{parse, Parser, cache::{AdaptiveCache, CacheStrategy}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let adaptive_cache = AdaptiveCache::new()
        .with_strategy(CacheStrategy::LRU)
        .with_max_size(1000)
        .with_ttl_adaptation(true);
    
    parser.set_cache(adaptive_cache);
    
    let tsk_content = r#"
[adaptive_cache]
Cache with adaptive TTL
frequently_accessed: @cache.adaptive("frequent_data", @request.key)
rarely_accessed: @cache.adaptive("rare_data", @request.key)
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Frequently accessed: {:?}", data["adaptive_cache"]["frequently_accessed"]);
    println!("Rarely accessed: {:?}", data["adaptive_cache"]["rarely_accessed"]);
    
    Ok(())
}

📊 Real-Time Monitoring

Metrics Collection

use tusklang_rust::{parse, Parser, metrics::{MetricsCollector, PrometheusExporter}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let metrics_collector = MetricsCollector::new();
    let prometheus_exporter = PrometheusExporter::new("0.0.0.0:9090").await?;
    
    parser.set_metrics_collector(metrics_collector);
    parser.set_metrics_exporter(prometheus_exporter);
    
    let tsk_content = r#"
[monitoring]
Application metrics
request_count: @metrics.counter("http_requests_total", 1)
response_time: @metrics.histogram("http_response_time_ms", @request.duration)
error_rate: @metrics.gauge("error_rate_percent", @request.error_percentage)
Business metrics
user_registrations: @metrics.counter("user_registrations_total", 1)
revenue: @metrics.counter("revenue_total", @request.amount)
active_users: @metrics.gauge("active_users", @request.active_count)
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Monitoring data: {:?}", data["monitoring"]);
    
    Ok(())
}

Health Checks and Alerts

use tusklang_rust::{parse, Parser, monitoring::{HealthChecker, AlertManager}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let health_checker = HealthChecker::new();
    let alert_manager = AlertManager::new();
    
    parser.set_health_checker(health_checker);
    parser.set_alert_manager(alert_manager);
    
    let tsk_content = r#"
[health_checks]
database: @health.check("database", @query("SELECT 1"))
redis: @health.check("redis", @cache.ping())
api: @health.check("api", @http("GET", "https://api.example.com/health"))
[alerts]
high_error_rate: @alert.if(@metrics.get("error_rate") > 5.0, "High error rate detected")
low_disk_space: @alert.if(@system.disk_usage() > 90.0, "Low disk space")
high_memory_usage: @alert.if(@system.memory_usage() > 85.0, "High memory usage")
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Health checks: {:?}", data["health_checks"]);
    println!("Alerts: {:?}", data["alerts"]);
    
    Ok(())
}

🔒 Advanced Security

Encryption and Hashing

use tusklang_rust::{parse, Parser, security::{Encryption, Hashing, KeyManager}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let encryption = Encryption::new();
    let hashing = Hashing::new();
    let key_manager = KeyManager::new();
    
    parser.set_encryption(encryption);
    parser.set_hashing(hashing);
    parser.set_key_manager(key_manager);
    
    let tsk_content = r#"
[security]
Encryption
encrypted_password: @encrypt("mysecretpassword", "AES-256-GCM")
encrypted_api_key: @encrypt("abc123def456", "ChaCha20-Poly1305")
Hashing
hashed_password: @hash("mysecretpassword", "bcrypt")
sha256_hash: @hash("data", "sha256")
sha512_hash: @hash("data", "sha512")
Key management
rotated_key: @key.rotate("api_key", "30d")
generated_key: @key.generate("new_key", "AES-256")
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Encrypted password: {}", data["security"]["encrypted_password"]);
    println!("Hashed password: {}", data["security"]["hashed_password"]);
    println!("Rotated key: {}", data["security"]["rotated_key"]);
    
    Ok(())
}

Access Control

use tusklang_rust::{parse, Parser, security::{AccessControl, RBAC}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let access_control = AccessControl::new();
    let rbac = RBAC::new();
    
    parser.set_access_control(access_control);
    parser.set_rbac(rbac);
    
    let tsk_content = r#"
[access_control]
Role-based access
user_permissions: @rbac.get_permissions(@request.user_role)
can_access_resource: @rbac.can_access(@request.user_id, @request.resource, @request.action)
Attribute-based access
abac_decision: @abac.evaluate(@request.user_attributes, @request.resource_attributes, @request.action)
Time-based access
time_restricted: @access.time_based(@request.user_id, "9:00-17:00", "UTC")
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("User permissions: {:?}", data["access_control"]["user_permissions"]);
    println!("Can access resource: {}", data["access_control"]["can_access_resource"]);
    println!("ABAC decision: {}", data["access_control"]["abac_decision"]);
    
    Ok(())
}

🚀 Performance Optimization

Parallel Processing

use tusklang_rust::{parse, Parser, parallel::{ParallelProcessor, TaskScheduler}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let parallel_processor = ParallelProcessor::new();
    let task_scheduler = TaskScheduler::new();
    
    parser.set_parallel_processor(parallel_processor);
    parser.set_task_scheduler(task_scheduler);
    
    let tsk_content = r#"
[parallel_processing]
Parallel data processing
parallel_results: @parallel.map(@request.data, "process_item")
parallel_filter: @parallel.filter(@request.items, "filter_condition")
parallel_reduce: @parallel.reduce(@request.numbers, "sum")
Task scheduling
scheduled_task: @schedule.every("5m", "cleanup_old_data")
delayed_task: @schedule.after("1h", "send_reminder")
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Parallel results: {:?}", data["parallel_processing"]["parallel_results"]);
    println!("Scheduled task: {:?}", data["parallel_processing"]["scheduled_task"]);
    
    Ok(())
}

Memory Optimization

use tusklang_rust::{parse, Parser, memory::{MemoryManager, GarbageCollector}};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let memory_manager = MemoryManager::new();
    let garbage_collector = GarbageCollector::new();
    
    parser.set_memory_manager(memory_manager);
    parser.set_garbage_collector(garbage_collector);
    
    let tsk_content = r#"
[memory_optimization]
Memory usage tracking
current_memory: @memory.usage()
peak_memory: @memory.peak()
memory_limit: @memory.limit()
Garbage collection
gc_stats: @gc.stats()
gc_trigger: @gc.trigger()
"#;
    
    let data = parser.parse(tsk_content).await?;
    
    println!("Current memory: {} MB", data["memory_optimization"]["current_memory"]);
    println!("Peak memory: {} MB", data["memory_optimization"]["peak_memory"]);
    println!("GC stats: {:?}", data["memory_optimization"]["gc_stats"]);
    
    Ok(())
}

🔄 Advanced FUJSEN

Complex Functions

use tusklang_rust::{parse, Parser};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut parser = Parser::new();
    
    let tsk_content = r#"
[advanced_fujsen]
complex_calculation_fujsen = '''
fn complex_calculation(input: Vec<f64>) -> f64 {
    let sum: f64 = input.iter().sum();
    let mean = sum / input.len() as f64;
    let variance = input.iter()
        .map(|x| (x - mean).powi(2))
        .sum::<f64>() / input.len() as f64;
    variance.sqrt()
}
'''
data_processing_fujsen = '''
fn process_data(data: Vec<serde_json::Value>) -> Vec<serde_json::Value> {
    data.into_iter()
        .filter(|item| item["active"].as_bool().unwrap_or(false))
        .map(|item| {
            let mut processed = item.clone();
            processed["processed"] = serde_json::Value::Bool(true);
            processed
        })
        .collect()
}
'''
async_operation_fujsen = '''
async fn async_operation(url: String) -> Result<String, Box<dyn std::error::Error>> {
    let response = reqwest::get(&url).await?;
    let body = response.text().await?;
    Ok(body)
}
'''
"#;
    
    let data = parser.parse(tsk_content)?;
    
    // Execute complex calculation
    let numbers = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let std_dev = parser.execute_fujsen("advanced_fujsen", "complex_calculation", &[&numbers]).await?;
    println!("Standard deviation: {}", std_dev);
    
    // Execute data processing
    let test_data = vec![
        serde_json::json!({"id": 1, "active": true}),
        serde_json::json!({"id": 2, "active": false}),
        serde_json::json!({"id": 3, "active": true}),
    ];
    let processed = parser.execute_fujsen("advanced_fujsen", "process_data", &[&test_data]).await?;
    println!("Processed data: {:?}", processed);
    
    Ok(())
}

🧪 Advanced Testing

Performance Testing

use tusklang_rust::{parse, Parser, testing::{PerformanceTester, LoadTester}};
#[tokio::test]
async fn test_performance() {
    let mut parser = Parser::new();
    
    let performance_tester = PerformanceTester::new();
    let load_tester = LoadTester::new();
    
    parser.set_performance_tester(performance_tester);
    parser.set_load_tester(load_tester);
    
    let tsk_content = r#"
[performance_test]
parse_speed: @perf.benchmark("parse", @request.tsk_content)
memory_usage: @perf.memory_usage("parse_operation")
cpu_usage: @perf.cpu_usage("parse_operation")
[load_test]
concurrent_requests: @load.test("api_endpoint", 1000, "10s")
response_times: @load.response_times("api_endpoint")
error_rate: @load.error_rate("api_endpoint")
"#;
    
    let data = parser.parse(tsk_content).await.expect("Failed to parse");
    
    println!("Parse speed: {:?}", data["performance_test"]["parse_speed"]);
    println!("Memory usage: {:?}", data["performance_test"]["memory_usage"]);
    println!("Concurrent requests: {:?}", data["load_test"]["concurrent_requests"]);
}

Integration Testing

use tusklang_rust::{parse, Parser, testing::{IntegrationTester, MockDatabase}};
#[tokio::test]
async fn test_integration() {
    let mut parser = Parser::new();
    
    let integration_tester = IntegrationTester::new();
    let mock_db = MockDatabase::new();
    
    parser.set_integration_tester(integration_tester);
    parser.set_database_adapter(mock_db);
    
    // Setup mock data
    mock_db.setup_mock_data(r#"
        CREATE TABLE users (id INTEGER, name TEXT, active BOOLEAN);
        INSERT INTO users VALUES (1, 'Alice', 1), (2, 'Bob', 0);
    "#).await.expect("Failed to setup mock data");
    
    let tsk_content = r#"
[integration_test]
user_count: @query("SELECT COUNT(*) FROM users")
active_users: @query("SELECT COUNT(*) FROM users WHERE active = 1")
user_data: @query("SELECT * FROM users WHERE active = 1")
"#;
    
    let data = parser.parse(tsk_content).await.expect("Failed to parse");
    
    assert_eq!(data["integration_test"]["user_count"], 2);
    assert_eq!(data["integration_test"]["active_users"], 1);
    assert_eq!(data["integration_test"]["user_data"].as_array().unwrap().len(), 1);
    
    println!("✅ Integration test passed!");
}

🎯 What You've Learned

1. WebAssembly support for browser and edge computing 2. Machine learning integration with prediction and optimization 3. Intelligent caching with multi-level and adaptive strategies 4. Real-time monitoring with metrics and health checks 5. Advanced security with encryption, hashing, and access control 6. Performance optimization with parallel processing and memory management 7. Advanced FUJSEN with complex functions and async operations 8. Comprehensive testing with performance and integration tests

🚀 Next Steps

1. Deploy to Production: Implement monitoring and security features 2. Scale Your Application: Use parallel processing and caching 3. Optimize Performance: Monitor metrics and tune your configuration 4. Build Advanced Features: Leverage ML and WASM capabilities

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You now have complete mastery of TuskLang Rust advanced features! From WebAssembly to machine learning, from intelligent caching to real-time monitoring - TuskLang gives you the tools to build applications that scale with zero compromises and maximum performance.