# ⚡ NovaLang Performance & Optimization Features

## Automatic Performance Optimization
```nova
@PerformanceOptimized
@Profile(enabled: true, detailed: true)
class DataProcessingService {
    
    @AutoOptimize(
        targetLatency: "100ms",
        targetThroughput: "1000 ops/sec",
        memoryLimit: "512MB"
    )
    @Benchmark
    async processLargeDataset(
        @Input dataset: List<DataRecord>
    ): ProcessedResult {
        
        // Compiler automatically optimizes this based on profiling data
        @ParallelProcessing(threads: "auto") // Auto-detect optimal thread count
        @MemoryOptimized(strategy: "streaming") // Use streaming for large datasets
        @CacheOptimized(strategy: "lru", size: "adaptive") // Adaptive cache sizing
        
        let result = dataset
            .parallelStream() // Automatic parallelization
            .filter(record -> record.isValid()) // Predicate pushdown optimization
            .map(record -> transformRecord(record)) // Vectorized operations
            .collect(Collectors.toOptimizedList()) // Memory-efficient collection
        
        return ProcessedResult.of(result)
    }
    
    @MemoryPool(size: "1GB", type: "off-heap")
    @ZeroCopy
    async processStreamingData(
        @Stream dataStream: Publisher<byte[]>
    ): Publisher<ProcessedData> {
        
        return dataStream
            .buffer(1024) // Optimal buffer size auto-calculated
            .map(buffer -> processBuffer(buffer)) // Zero-copy processing
            .onBackpressureBuffer(10000) // Adaptive backpressure
    }
    
    @JITOptimized // Just-in-time compilation optimization
    @InlineHint // Suggest method inlining
    private transformRecord(record: DataRecord): TransformedRecord {
        // Hot path - compiler will aggressively optimize
        return TransformedRecord.builder()
            .id(record.id)
            .value(record.value * 1.5) // Constant folding
            .timestamp(System.currentTimeMillis()) // CSE optimization
            .build()
    }
}
```

## Advanced Memory Management
```nova
@MemoryManagement(
    gcAlgorithm: "G1GC",
    heapSize: "adaptive",
    offHeapEnabled: true
)
class HighPerformanceCache {
    
    @OffHeapStorage(size: "2GB")
    private cache: OffHeapMap<string, CachedObject>
    
    @MemoryMapped(file: "cache.dat", size: "4GB")
    private persistentCache: MemoryMappedFile
    
    @Pooled(initialSize: 100, maxSize: 1000)
    private objectPool: ObjectPool<ExpensiveObject>
    
    @NoGC // Indicate this method should not trigger GC
    @LockFree // Use lock-free data structures
    async get(@Key key: string): Optional<CachedObject> {
        
        // Try L1 cache (CPU cache-friendly)
        @L1Cache
        let l1Result = l1Cache.get(key)
        if (l1Result.isPresent()) {
            return l1Result
        }
        
        // Try off-heap cache
        let offHeapResult = cache.get(key)
        if (offHeapResult != null) {
            l1Cache.put(key, offHeapResult) // Promote to L1
            return Optional.of(offHeapResult)
        }
        
        // Try persistent cache
        let persistent = persistentCache.get(key)
        if (persistent != null) {
            cache.put(key, persistent) // Promote to memory
            return Optional.of(persistent)
        }
        
        return Optional.empty()
    }
    
    @BulkOperation // Optimize for bulk operations
    @Vectorized // Use SIMD instructions when possible
    async putBatch(entries: Map<string, CachedObject>): void {
        
        // Batch write for better performance
        @TransactionalWrite
        @WriteCoalescing // Combine multiple writes
        entries.forEach((key, value) -> {
            cache.put(key, value)
            persistentCache.put(key, value)
        })
    }
    
    @MemoryPressureAware
    async evictIfNeeded(): void {
        let memoryUsage = MemoryMonitor.getUsagePercentage()
        
        if (memoryUsage > 0.8) { // 80% memory usage
            @LRUEviction(percentage: 0.2) // Evict 20% of LRU items
            cache.evictLRU(0.2)
        }
    }
}
```

## CPU Optimization & SIMD
```nova
@CPUOptimized
class MathOperations {
    
    @Vectorized(instruction: "AVX2") // Use AVX2 SIMD instructions
    @Unrolled(factor: 4) // Loop unrolling
    static dotProduct(a: float[], b: float[]): float {
        require(a.length == b.length, "Arrays must have same length")
        
        // Compiler generates optimized SIMD code
        let sum = 0.0f
        for (let i = 0; i < a.length; i += 8) { // Process 8 floats at once
            sum += simd.dotProduct8(a, b, i)
        }
        
        return sum
    }
    
    @ParallelFor(grainSize: 1000) // Parallel execution with optimal grain size
    @NUMA(aware: true) // NUMA-aware scheduling
    static matrixMultiply(
        @Aligned(32) a: float[][],
        @Aligned(32) b: float[][],
        @Output @Aligned(32) result: float[][]
    ): void {
        
        let n = a.length
        let m = b[0].length
        let p = b.length
        
        // Compiler generates cache-friendly, SIMD-optimized code
        parallel.forEach(0..<n) { i ->
            for (let j = 0; j < m; j += 8) { // Vectorized inner loop
                for (let k = 0; k < p; k++) {
                    simd.fmadd8(result[i], a[i][k], b[k], j)
                }
            }
        }
    }
    
    @BranchOptimized // Optimize branch prediction
    @PrefetchHint(strategy: "sequential") // Memory prefetching hints
    static quickSort(
        @InOut array: int[],
        low: int,
        high: int
    ): void {
        if (low < high) {
            let pivot = partition(array, low, high)
            
            // Compiler optimizes recursive calls
            @TailRecursion
            quickSort(array, low, pivot - 1)
            quickSort(array, pivot + 1, high)
        }
    }
}
```

## Database & Query Optimization
```nova
@DatabaseOptimized
@Repository
class OptimizedUserRepository {
    
    @Query("""
        SELECT u.id, u.name, u.email, p.bio, p.avatar
        FROM users u
        LEFT JOIN profiles p ON u.id = p.user_id
        WHERE u.status = :status
        AND u.created_at >= :since
    """)
    @QueryHint(hint: "USE_INDEX(users, idx_status_created)")
    @BatchSize(50) // Optimal batch size for this query
    @Cached(ttl: "5m", region: "user-queries")
    @ReadOnly // Optimization hint for read-only query
    async findActiveUsers(
        @Param("status") status: UserStatus,
        @Param("since") since: LocalDateTime
    ): List<UserWithProfile>
    
    @Query("SELECT * FROM users WHERE id IN :ids")
    @BatchLoad // Automatically batch multiple calls
    @DataLoader(maxBatchSize: 100) // Use DataLoader pattern
    async findUsersByIds(@Param("ids") ids: List<string>): Map<string, User>
    
    @Modifying
    @Query("""
        UPDATE users 
        SET last_login = :timestamp, login_count = login_count + 1
        WHERE id = :userId
    """)
    @BulkOperation // Optimize for bulk updates
    @WriteOptimized
    async updateLastLogin(
        @Param("userId") userId: string,
        @Param("timestamp") timestamp: LocalDateTime
    ): int
    
    @NativeQuery("""
        WITH RECURSIVE user_hierarchy AS (
            SELECT id, name, manager_id, 0 as level
            FROM users
            WHERE manager_id IS NULL
            
            UNION ALL
            
            SELECT u.id, u.name, u.manager_id, uh.level + 1
            FROM users u
            JOIN user_hierarchy uh ON u.manager_id = uh.id
        )
        SELECT * FROM user_hierarchy
        ORDER BY level, name
    """)
    @Streaming // Stream large result sets
    async getUserHierarchy(): Publisher<UserHierarchy>
}
```

## Network & I/O Optimization
```nova
@NetworkOptimized
class HighPerformanceHttpClient {
    
    @ConnectionPool(
        maxConnections: 200,
        keepAlive: true,
        tcpNoDelay: true
    )
    @HTTP2Enabled
    private httpClient: HttpClient
    
    @Circuit(
        breaker: true,
        timeout: "5s",
        retries: 3,
        backoff: "exponential"
    )
    @RequestMultiplexing // HTTP/2 multiplexing
    async makeRequest(
        @URL url: string,
        @Headers headers: Map<string, string>
    ): HttpResponse {
        
        // Automatic request optimization
        return await httpClient.send(
            HttpRequest.builder()
                .uri(url)
                .headers(headers)
                .version(HttpVersion.HTTP_2) // Prefer HTTP/2
                .build()
        )
    }
    
    @Streaming
    @BackpressureAware
    async downloadLargeFile(
        @URL url: string,
        @Output destination: Path
    ): Publisher<DownloadProgress> {
        
        return httpClient.streamDownload(url)
            .buffer(8192) // Optimal buffer size
            .writeToFile(destination)
            .progress()
    }
    
    @BatchRequests(maxBatch: 10, timeout: "100ms")
    @RequestCoalescing // Combine similar requests
    async batchApiCalls(
        requests: List<ApiRequest>
    ): List<ApiResponse> {
        
        // Automatically batch requests for efficiency
        return await httpClient.sendBatch(requests)
    }
}
```

## Compiler Optimizations
```nova
@CompilerOptimizations(
    inlining: "aggressive",
    loopOptimization: true,
    deadCodeElimination: true,
    constantFolding: true
)
class OptimizedAlgorithms {
    
    @CompileTimeConstant
    static final PI = 3.14159265359
    
    @Inline(always: true) // Force inlining
    @Pure // No side effects - enables more optimizations
    static square(x: double): double {
        return x * x // Will be inlined and optimized
    }
    
    @LoopOptimization(
        unroll: true,
        vectorize: true,
        prefetch: true
    )
    static sumSquares(numbers: double[]): double {
        let sum = 0.0
        
        // Compiler optimizations:
        // - Loop unrolling for better CPU utilization
        // - SIMD vectorization for parallel execution
        // - Memory prefetching for cache optimization
        for (let i = 0; i < numbers.length; i++) {
            sum += square(numbers[i]) // Inlined
        }
        
        return sum
    }
    
    @BranchPrediction(likely: true)
    static processConditional(value: int): int {
        @Likely // Branch prediction hint
        if (value > 0) {
            return value * 2 // Hot path
        } else {
            @Unlikely // Cold path
            return value * -1
        }
    }
    
    @ConstantPropagation
    @DeadCodeElimination
    static optimizedCalculation(): double {
        let a = 10.0 // Constant
        let b = 20.0 // Constant
        let c = a + b // Compile-time constant: 30.0
        
        // Dead code elimination removes unused variables
        let unused = 42.0
        
        return c * PI // Constant folding: 30.0 * 3.14159...
    }
}
```

## Profiling & Monitoring
```nova
@PerformanceMonitoring
class MonitoredService {
    
    @ProfiledMethod(
        captureStackTrace: true,
        measureMemory: true,
        sampleRate: 0.01 // Sample 1% of calls
    )
    @PerformanceCounter("service.process.time")
    async processData(@Input data: ProcessingRequest): ProcessingResult {
        
        @Timer("data.validation.time")
        let validation = await validateData(data)
        
        @MemoryProfiler("data.processing.memory")
        let result = await processValidatedData(validation)
        
        @Counter("data.processed.count")
        metricsRegistry.increment("processed_items", result.itemCount)
        
        return result
    }
    
    @CPUProfiler(interval: "1ms")
    @MemoryProfiler(heapDump: true, threshold: "1GB")
    async heavyProcessing(): void {
        // Automatic profiling and optimization suggestions
        // Generates flame graphs and memory usage reports
    }
    
    @PerformanceBaseline(
        expectedLatency: "50ms",
        expectedThroughput: "1000 ops/sec",
        alertThreshold: 1.5 // Alert if 50% slower
    )
    @AutoTuning(enabled: true) // Automatic performance tuning
    async criticalOperation(): OperationResult {
        // System automatically monitors and tunes this method
        // Adjusts thread pools, cache sizes, batch sizes, etc.
        
        return performCriticalWork()
    }
}
```
