⚡ @ Operator Performance in Rust

TuskLang provides high-performance @ operators in Rust with zero-cost abstractions, efficient memory management, and comprehensive performance monitoring capabilities.

Performance Characteristics

// Zero-cost abstractions for @ operators
#[derive(Debug, Clone)]
struct PerformanceMetrics {
    pub execution_time: std::time::Duration,
    pub memory_usage: usize,
    pub cpu_usage: f64,
    pub cache_hits: u64,
    pub cache_misses: u64,
}
// Performance monitoring trait
trait PerformanceMonitor {
    fn start_timer(&mut self, operation: &str);
    fn end_timer(&mut self, operation: &str) -> std::time::Duration;
    fn record_memory_usage(&mut self, operation: &str, bytes: usize);
    fn record_cache_hit(&mut self, key: &str);
    fn record_cache_miss(&mut self, key: &str);
    fn get_metrics(&self) -> PerformanceMetrics;
}
// High-performance operator implementation
struct OptimizedOperator {
    cache: std::collections::HashMap<String, (Value, std::time::Instant)>,
    monitor: Box<dyn PerformanceMonitor>,
}
impl OptimizedOperator {
    fn new(monitor: Box<dyn PerformanceMonitor>) -> Self {
        Self {
            cache: std::collections::HashMap::new(),
            monitor,
        }
    }
    
    fn execute_with_caching<F>(&mut self, key: &str, ttl: std::time::Duration, f: F) -> Result<Value, Box<dyn std::error::Error>>
    where
        F: FnOnce() -> Result<Value, Box<dyn std::error::Error>>,
    {
        // Check cache first
        if let Some((value, timestamp)) = self.cache.get(key) {
            if timestamp.elapsed() < ttl {
                self.monitor.record_cache_hit(key);
                return Ok(value.clone());
            }
        }
        
        self.monitor.record_cache_miss(key);
        
        // Execute operation
        self.monitor.start_timer(key);
        let result = f()?;
        let execution_time = self.monitor.end_timer(key);
        
        // Cache result
        self.cache.insert(key.to_string(), (result.clone(), std::time::Instant::now()));
        
        // Record memory usage
        let memory_usage = std::mem::size_of_val(&result);
        self.monitor.record_memory_usage(key, memory_usage);
        
        Ok(result)
    }
}

Database Query Performance

// Optimized database queries
struct DatabasePerformanceOptimizer {
    connection_pool: sqlx::PgPool,
    query_cache: std::collections::HashMap<String, (Vec<Value>, std::time::Instant)>,
    prepared_statements: std::collections::HashMap<String, sqlx::PgStatement>,
}
impl DatabasePerformanceOptimizer {
    async fn optimized_query(&mut self, sql: &str, params: Vec<Value>) -> Result<Vec<Value>, Box<dyn std::error::Error>> {
        let start_time = std::time::Instant::now();
        
        // Use prepared statements for repeated queries
        let statement = if let Some(stmt) = self.prepared_statements.get(sql) {
            stmt
        } else {
            let stmt = self.connection_pool.prepare(sql).await?;
            self.prepared_statements.insert(sql.to_string(), stmt);
            self.prepared_statements.get(sql).unwrap()
        };
        
        // Execute query
        let result = statement.query_with(params).fetch_all(&self.connection_pool).await?;
        
        let execution_time = start_time.elapsed();
        
        // Record performance metrics
        @metrics.record("database_query_time", execution_time.as_millis() as f64);
        @metrics.record("database_query_count", 1.0);
        
        Ok(result.into_iter().map(|row| row.into()).collect())
    }
    
    // Batch operations for better performance
    async fn batch_insert(&self, table: &str, records: Vec<Value>) -> Result<(), Box<dyn std::error::Error>> {
        if records.is_empty() {
            return Ok(());
        }
        
        let batch_size = 1000;
        let start_time = std::time::Instant::now();
        
        for chunk in records.chunks(batch_size) {
            let sql = self.build_batch_insert_sql(table, chunk.len())?;
            let params = self.flatten_batch_params(chunk)?;
            
            self.connection_pool.execute(&sql, &params).await?;
        }
        
        let execution_time = start_time.elapsed();
        @metrics.record("batch_insert_time", execution_time.as_millis() as f64);
        @metrics.record("batch_insert_records", records.len() as f64);
        
        Ok(())
    }
}

Memory Management

// Efficient memory management for @ operators
struct MemoryOptimizedOperator {
    arena: typed_arena::Arena<Value>,
    string_pool: string_interner::StringInterner,
}
impl MemoryOptimizedOperator {
    fn new() -> Self {
        Self {
            arena: typed_arena::Arena::new(),
            string_pool: string_interner::StringInterner::new(),
        }
    }
    
    // Use arena allocation for temporary values
    fn process_data(&self, data: &[Value]) -> Result<Vec<&Value>, Box<dyn std::error::Error>> {
        let mut processed = Vec::new();
        
        for value in data {
            // Allocate in arena for better performance
            let processed_value = self.arena.alloc(self.transform_value(value)?);
            processed.push(processed_value);
        }
        
        Ok(processed)
    }
    
    // String interning for repeated strings
    fn intern_string(&mut self, s: &str) -> string_interner::Symbol {
        self.string_pool.get_or_intern(s)
    }
    
    // Memory-efficient data processing
    fn process_large_dataset(&self, data: &[Value]) -> Result<Value, Box<dyn std::error::Error>> {
        // Use iterators to avoid loading everything into memory
        let result = data
            .iter()
            .filter(|v| self.should_process(v))
            .map(|v| self.transform_value(v))
            .collect::<Result<Vec<_>, _>>()?;
        
        Ok(Value::Array(result))
    }
}

Caching Strategies

// Multi-level caching for optimal performance
struct MultiLevelCache {
    l1_cache: std::collections::HashMap<String, (Value, std::time::Instant)>, // In-memory
    l2_cache: redis::Client, // Redis
    l3_cache: Box<dyn CacheBackend>, // Database
}
impl MultiLevelCache {
    async fn get(&self, key: &str) -> Result<Option<Value>, Box<dyn std::error::Error>> {
        // L1 cache (fastest)
        if let Some((value, timestamp)) = self.l1_cache.get(key) {
            if timestamp.elapsed() < std::time::Duration::from_secs(60) {
                @metrics.increment("cache_l1_hits", 1);
                return Ok(Some(value.clone()));
            }
        }
        
        // L2 cache (Redis)
        if let Ok(Some(value)) = self.l2_cache.get::<String>(key).await {
            @metrics.increment("cache_l2_hits", 1);
            return Ok(Some(serde_json::from_str(&value)?));
        }
        
        // L3 cache (Database)
        if let Some(value) = self.l3_cache.get(key).await? {
            @metrics.increment("cache_l3_hits", 1);
            // Populate upper levels
            self.l2_cache.set_ex(key, serde_json::to_string(&value)?, 3600).await?;
            return Ok(Some(value));
        }
        
        @metrics.increment("cache_misses", 1);
        Ok(None)
    }
    
    async fn set(&self, key: &str, value: Value, ttl: std::time::Duration) -> Result<(), Box<dyn std::error::Error>> {
        // Set in all cache levels
        self.l1_cache.insert(key.to_string(), (value.clone(), std::time::Instant::now()));
        self.l2_cache.set_ex(key, serde_json::to_string(&value)?, ttl.as_secs() as usize).await?;
        self.l3_cache.set(key, value, ttl).await?;
        
        Ok(())
    }
}

Async Performance

// High-performance async @ operators
struct AsyncPerformanceOptimizer {
    runtime: tokio::runtime::Runtime,
    thread_pool: tokio::task::JoinSet<Result<Value, Box<dyn std::error::Error>>>,
}
impl AsyncPerformanceOptimizer {
    fn new() -> Self {
        Self {
            runtime: tokio::runtime::Runtime::new().unwrap(),
            thread_pool: tokio::task::JoinSet::new(),
        }
    }
    
    // Parallel processing for better performance
    async fn parallel_process(&mut self, data: Vec<Value>) -> Result<Vec<Value>, Box<dyn std::error::Error>> {
        let chunk_size = (data.len() + num_cpus::get() - 1) / num_cpus::get();
        
        for chunk in data.chunks(chunk_size) {
            let chunk = chunk.to_vec();
            self.thread_pool.spawn(async move {
                process_chunk(chunk).await
            });
        }
        
        let mut results = Vec::new();
        while let Some(result) = self.thread_pool.join_next().await {
            results.push(result??);
        }
        
        Ok(results.into_iter().flatten().collect())
    }
    
    // Async streaming for large datasets
    async fn stream_process(&self, data: Vec<Value>) -> tokio::sync::mpsc::Receiver<Value> {
        let (tx, rx) = tokio::sync::mpsc::channel(1000);
        
        tokio::spawn(async move {
            for value in data {
                let processed = process_value(value).await?;
                tx.send(processed).await?;
            }
            Ok::<(), Box<dyn std::error::Error>>(())
        });
        
        rx
    }
}
async fn process_chunk(chunk: Vec<Value>) -> Result<Vec<Value>, Box<dyn std::error::Error>> {
    let mut results = Vec::new();
    
    for value in chunk {
        let processed = process_value(value).await?;
        results.push(processed);
    }
    
    Ok(results)
}
async fn process_value(value: Value) -> Result<Value, Box<dyn std::error::Error>> {
    // Simulate async processing
    tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;
    
    match value {
        Value::String(s) => Ok(Value::String(s.to_uppercase())),
        Value::Number(n) => Ok(Value::Number(n)),
        _ => Ok(value),
    }
}

Performance Monitoring

// Comprehensive performance monitoring
struct PerformanceMonitor {
    metrics: std::collections::HashMap<String, f64>,
    histograms: std::collections::HashMap<String, histogram::Histogram>,
    traces: Vec<TraceSpan>,
}
#[derive(Debug)]
struct TraceSpan {
    operation: String,
    start_time: std::time::Instant,
    end_time: Option<std::time::Instant>,
    metadata: std::collections::HashMap<String, String>,
}
impl PerformanceMonitor {
    fn new() -> Self {
        Self {
            metrics: std::collections::HashMap::new(),
            histograms: std::collections::HashMap::new(),
            traces: Vec::new(),
        }
    }
    
    fn start_trace(&mut self, operation: &str) -> usize {
        let span = TraceSpan {
            operation: operation.to_string(),
            start_time: std::time::Instant::now(),
            end_time: None,
            metadata: std::collections::HashMap::new(),
        };
        
        self.traces.push(span);
        self.traces.len() - 1
    }
    
    fn end_trace(&mut self, span_id: usize) {
        if let Some(span) = self.traces.get_mut(span_id) {
            span.end_time = Some(std::time::Instant::now());
            
            if let Some(duration) = span.end_time.unwrap().checked_duration_since(span.start_time) {
                self.record_histogram(&format!("{}_duration", span.operation), duration.as_millis() as f64);
            }
        }
    }
    
    fn record_histogram(&mut self, name: &str, value: f64) {
        let histogram = self.histograms.entry(name.to_string()).or_insert_with(|| {
            histogram::Histogram::new(histogram::Config::new())
                .expect("Failed to create histogram")
        });
        
        histogram.record(value as u64).expect("Failed to record value");
    }
    
    fn get_performance_report(&self) -> PerformanceReport {
        let mut report = PerformanceReport::new();
        
        for (name, histogram) in &self.histograms {
            report.add_metric(name, histogram.percentile(50.0).unwrap_or(0) as f64);
            report.add_metric(&format!("{}_p95", name), histogram.percentile(95.0).unwrap_or(0) as f64);
            report.add_metric(&format!("{}_p99", name), histogram.percentile(99.0).unwrap_or(0) as f64);
        }
        
        report
    }
}
#[derive(Debug)]
struct PerformanceReport {
    metrics: std::collections::HashMap<String, f64>,
}
impl PerformanceReport {
    fn new() -> Self {
        Self {
            metrics: std::collections::HashMap::new(),
        }
    }
    
    fn add_metric(&mut self, name: &str, value: f64) {
        self.metrics.insert(name.to_string(), value);
    }
    
    fn print_summary(&self) {
        println!("Performance Report:");
        for (name, value) in &self.metrics {
            println!("  {}: {:.2}", name, value);
        }
    }
}

Optimization Techniques

// Performance optimization techniques
struct OptimizedOperatorExecutor {
    cache: MultiLevelCache,
    monitor: PerformanceMonitor,
    memory_pool: MemoryOptimizedOperator,
}
impl OptimizedOperatorExecutor {
    fn new() -> Self {
        Self {
            cache: MultiLevelCache::new(),
            monitor: PerformanceMonitor::new(),
            memory_pool: MemoryOptimizedOperator::new(),
        }
    }
    
    // Optimized @ operator execution
    async fn execute_optimized<F>(&mut self, operator: &str, args: Vec<Value>, f: F) -> Result<Value, Box<dyn std::error::Error>>
    where
        F: FnOnce(Vec<Value>) -> Result<Value, Box<dyn std::error::Error>> + Send + 'static,
    {
        let span_id = self.monitor.start_trace(operator);
        
        // Check cache first
        let cache_key = format!("{}:{}", operator, serde_json::to_string(&args)?);
        if let Some(cached_result) = self.cache.get(&cache_key).await? {
            self.monitor.end_trace(span_id);
            return Ok(cached_result);
        }
        
        // Execute operation
        let result = f(args)?;
        
        // Cache result
        self.cache.set(&cache_key, result.clone(), std::time::Duration::from_secs(3600)).await?;
        
        self.monitor.end_trace(span_id);
        Ok(result)
    }
    
    // Batch processing for multiple operations
    async fn batch_execute(&mut self, operations: Vec<(String, Vec<Value>)>) -> Result<Vec<Value>, Box<dyn std::error::Error>> {
        let mut results = Vec::new();
        let mut tasks = Vec::new();
        
        for (operator, args) in operations {
            let task = self.execute_optimized(&operator, args, |args| {
                // Default operation implementation
                Ok(Value::String(format!("Processed: {:?}", args)))
            });
            tasks.push(task);
        }
        
        // Execute all tasks concurrently
        for task in tasks {
            results.push(task.await?);
        }
        
        Ok(results)
    }
}

Benchmarking

// Performance benchmarking utilities
struct BenchmarkRunner {
    iterations: u32,
    warmup_iterations: u32,
}
impl BenchmarkRunner {
    fn new(iterations: u32, warmup_iterations: u32) -> Self {
        Self {
            iterations,
            warmup_iterations,
        }
    }
    
    fn benchmark<F>(&self, name: &str, f: F) -> BenchmarkResult
    where
        F: Fn() -> Result<Value, Box<dyn std::error::Error>>,
    {
        let mut times = Vec::new();
        
        // Warmup
        for _ in 0..self.warmup_iterations {
            let _ = f();
        }
        
        // Actual benchmark
        for _ in 0..self.iterations {
            let start = std::time::Instant::now();
            let _ = f();
            times.push(start.elapsed());
        }
        
        let avg_time = times.iter().sum::<std::time::Duration>() / times.len() as u32;
        let min_time = times.iter().min().unwrap();
        let max_time = times.iter().max().unwrap();
        
        BenchmarkResult {
            name: name.to_string(),
            avg_time,
            min_time: *min_time,
            max_time: *max_time,
            iterations: self.iterations,
        }
    }
}
#[derive(Debug)]
struct BenchmarkResult {
    name: String,
    avg_time: std::time::Duration,
    min_time: std::time::Duration,
    max_time: std::time::Duration,
    iterations: u32,
}
impl BenchmarkResult {
    fn print(&self) {
        println!("Benchmark: {}", self.name);
        println!("  Iterations: {}", self.iterations);
        println!("  Average: {:?}", self.avg_time);
        println!("  Min: {:?}", self.min_time);
        println!("  Max: {:?}", self.max_time);
    }
}
// Usage
fn run_performance_benchmarks() {
    let runner = BenchmarkRunner::new(1000, 100);
    
    let result = runner.benchmark("database_query", || {
        @query("SELECT * FROM users LIMIT 1", vec![])
    });
    
    result.print();
}

Best Practices

1. Use Appropriate Data Structures

// Use efficient data structures for different use cases
fn optimize_data_structures() {
    // For frequent lookups
    let mut user_cache: std::collections::HashMap<u32, User> = std::collections::HashMap::new();
    
    // For ordered data
    let mut sorted_data: std::collections::BTreeMap<String, Value> = std::collections::BTreeMap::new();
    
    // For unique values
    let mut unique_values: std::collections::HashSet<String> = std::collections::HashSet::new();
    
    // For priority queues
    let mut priority_queue: std::collections::BinaryHeap<i32> = std::collections::BinaryHeap::new();
}

2. Minimize Allocations

// Avoid unnecessary allocations
fn minimize_allocations() {
    // Use references instead of cloning
    let data = vec![1, 2, 3, 4, 5];
    let filtered: Vec<&i32> = data.iter().filter(|&&x| x > 2).collect();
    
    // Reuse buffers
    let mut buffer = Vec::with_capacity(1000);
    for i in 0..1000 {
        buffer.push(i);
        if buffer.len() >= 1000 {
            process_buffer(&buffer);
            buffer.clear(); // Reuse the same buffer
        }
    }
}

3. Use Async Appropriately

// Use async for I/O operations, sync for CPU-bound tasks
async fn optimized_processing() -> Result<Value, Box<dyn std::error::Error>> {
    // I/O operations - use async
    let data = @fetch_data().await?;
    
    // CPU-bound processing - use sync
    let processed = tokio::task::spawn_blocking(move || {
        cpu_intensive_processing(data)
    }).await?;
    
    // More I/O - use async
    @save_result(processed).await?;
    
    Ok(processed)
}

4. Profile and Monitor

// Continuous performance monitoring
fn setup_performance_monitoring() {
    // Set up metrics collection
    @metrics.gauge("memory_usage", @get_memory_usage());
    @metrics.gauge("cpu_usage", @get_cpu_usage());
    @metrics.counter("operations_per_second", @get_ops_per_second());
    
    // Set up alerts
    if @memory_usage > 80.0 {
        @alert.send("High memory usage detected");
    }
    
    if @cpu_usage > 90.0 {
        @alert.send("High CPU usage detected");
    }
}

The @ operator performance optimization in Rust provides high-performance execution with comprehensive monitoring and optimization techniques.