☕ @optimize - Optimization Function in Java
@optimize - Optimization Function in Java
The @optimize operator provides intelligent optimization capabilities for Java applications, integrating with Spring Boot's performance monitoring, JVM optimization tools, and enterprise optimization algorithms.
Basic Syntax
// TuskLang configuration
optimal_setting: @optimize("cache_size", 100, 1000)
optimal_threads: @optimize("thread_pool_size", 4, 32)
optimal_timeout: @optimize("request_timeout", 1000, 10000)// Java Spring Boot integration
@Configuration
public class OptimizationConfig {
@Bean
public OptimizationService optimizationService() {
return OptimizationService.builder()
.algorithm("genetic")
.iterations(1000)
.populationSize(50)
.build();
}
}Basic Optimization
// Java optimization service
@Component
public class OptimizationService {
@Autowired
private MeterRegistry meterRegistry;
private final Map<String, OptimizationResult> optimizationCache = new ConcurrentHashMap<>();
public OptimizationResult optimize(String parameter, double minValue, double maxValue) {
String cacheKey = parameter + "_" + minValue + "_" + maxValue;
return optimizationCache.computeIfAbsent(cacheKey, key -> {
return performOptimization(parameter, minValue, maxValue);
});
}
public OptimizationResult optimize(String parameter, double minValue, double maxValue,
OptimizationConstraints constraints) {
return performConstrainedOptimization(parameter, minValue, maxValue, constraints);
}
private OptimizationResult performOptimization(String parameter, double minValue, double maxValue) {
// Genetic algorithm implementation
GeneticAlgorithm ga = new GeneticAlgorithm.Builder()
.populationSize(50)
.iterations(1000)
.mutationRate(0.1)
.crossoverRate(0.8)
.build();
return ga.optimize(new OptimizationProblem(parameter, minValue, maxValue));
}
private OptimizationResult performConstrainedOptimization(String parameter, double minValue,
double maxValue, OptimizationConstraints constraints) {
// Constrained optimization implementation
ConstrainedOptimizer optimizer = new ConstrainedOptimizer.Builder()
.algorithm("interior-point")
.constraints(constraints)
.build();
return optimizer.optimize(new OptimizationProblem(parameter, minValue, maxValue));
}
}// TuskLang optimization
optimization_config: {
# Basic optimization
cache_size: @optimize("cache_size", 100, 1000)
thread_pool_size: @optimize("thread_pool_size", 4, 32)
request_timeout: @optimize("request_timeout", 1000, 10000)
# Constrained optimization
memory_limit: @optimize("memory_limit", 512, 4096, {
constraint: "memory_usage < 80%"
constraint: "response_time < 200ms"
})
# Multi-parameter optimization
optimal_config: @optimize.multi({
cache_size: [100, 1000]
thread_pool_size: [4, 32]
connection_pool_size: [10, 100]
})
}Performance Optimization
// Java performance optimization
@Component
public class PerformanceOptimizationService {
@Autowired
private OptimizationService optimizationService;
@Autowired
private MeterRegistry meterRegistry;
public PerformanceOptimizationResult optimizePerformance() {
// Collect current performance metrics
PerformanceMetrics currentMetrics = collectPerformanceMetrics();
// Define optimization parameters
Map<String, OptimizationRange> parameters = Map.of(
"cache_size", new OptimizationRange(100, 1000),
"thread_pool_size", new OptimizationRange(4, 32),
"connection_pool_size", new OptimizationRange(10, 100),
"batch_size", new OptimizationRange(10, 1000)
);
// Perform optimization
return optimizationService.optimizePerformance(parameters, currentMetrics);
}
public DatabaseOptimizationResult optimizeDatabase() {
return optimizationService.optimizeDatabase(new DatabaseOptimizationRequest(
getCurrentQueryMetrics(),
getCurrentIndexUsage(),
getCurrentConnectionMetrics()
));
}
public MemoryOptimizationResult optimizeMemory() {
return optimizationService.optimizeMemory(new MemoryOptimizationRequest(
getCurrentMemoryUsage(),
getCurrentGarbageCollectionMetrics(),
getCurrentMemoryLeakDetection()
));
}
private PerformanceMetrics collectPerformanceMetrics() {
return PerformanceMetrics.builder()
.responseTime(getAverageResponseTime())
.throughput(getCurrentThroughput())
.errorRate(getCurrentErrorRate())
.memoryUsage(getCurrentMemoryUsage())
.cpuUsage(getCurrentCpuUsage())
.build();
}
}// TuskLang performance optimization
performance_optimization: {
# Performance optimization
optimal_performance: @optimize.performance({
cache_size: [100, 1000]
thread_pool_size: [4, 32]
connection_pool_size: [10, 100]
batch_size: [10, 1000]
})
# Database optimization
optimal_database: @optimize.database({
query_timeout: [1000, 10000]
connection_pool_size: [10, 100]
batch_size: [100, 10000]
index_strategy: ["auto", "manual", "hybrid"]
})
# Memory optimization
optimal_memory: @optimize.memory({
heap_size: [512, 4096]
gc_strategy: ["G1GC", "ParallelGC", "CMS"]
young_gen_ratio: [0.1, 0.5]
})
}Machine Learning Optimization
// Java ML optimization
@Component
public class MachineLearningOptimizationService {
@Autowired
private OptimizationService optimizationService;
public ModelOptimizationResult optimizeModel(String modelType, Map<String, Object> parameters) {
return optimizationService.optimizeModel(new ModelOptimizationRequest(
modelType,
parameters,
getTrainingData(),
getValidationData()
));
}
public HyperparameterOptimizationResult optimizeHyperparameters(String algorithm,
Map<String, OptimizationRange> hyperparameters) {
return optimizationService.optimizeHyperparameters(new HyperparameterOptimizationRequest(
algorithm,
hyperparameters,
getTrainingData(),
getValidationData(),
"accuracy" // optimization metric
));
}
public FeatureOptimizationResult optimizeFeatures(List<String> features,
Map<String, Object> constraints) {
return optimizationService.optimizeFeatures(new FeatureOptimizationRequest(
features,
constraints,
getTrainingData(),
getValidationData()
));
}
}// TuskLang ML optimization
ml_optimization: {
# Model optimization
optimal_model: @optimize.model("random_forest", {
n_estimators: [10, 200]
max_depth: [3, 20]
min_samples_split: [2, 20]
min_samples_leaf: [1, 10]
})
# Hyperparameter optimization
optimal_hyperparameters: @optimize.hyperparameters("xgboost", {
learning_rate: [0.01, 0.3]
max_depth: [3, 15]
subsample: [0.6, 1.0]
colsample_bytree: [0.6, 1.0]
})
# Feature optimization
optimal_features: @optimize.features([
"feature1", "feature2", "feature3", "feature4", "feature5"
], {
max_features: 3
min_importance: 0.1
})
}Resource Optimization
// Java resource optimization
@Component
public class ResourceOptimizationService {
@Autowired
private OptimizationService optimizationService;
public ResourceOptimizationResult optimizeResources() {
return optimizationService.optimizeResources(new ResourceOptimizationRequest(
getCurrentResourceUsage(),
getResourceConstraints(),
getPerformanceRequirements()
));
}
public CpuOptimizationResult optimizeCpu() {
return optimizationService.optimizeCpu(new CpuOptimizationRequest(
getCurrentCpuUsage(),
getCpuConstraints(),
getPerformanceTargets()
));
}
public MemoryOptimizationResult optimizeMemory() {
return optimizationService.optimizeMemory(new MemoryOptimizationRequest(
getCurrentMemoryUsage(),
getMemoryConstraints(),
getMemoryRequirements()
));
}
public NetworkOptimizationResult optimizeNetwork() {
return optimizationService.optimizeNetwork(new NetworkOptimizationRequest(
getCurrentNetworkUsage(),
getNetworkConstraints(),
getNetworkRequirements()
));
}
}// TuskLang resource optimization
resource_optimization: {
# Resource optimization
optimal_resources: @optimize.resources({
cpu_cores: [1, 16]
memory_gb: [1, 32]
disk_gb: [10, 1000]
network_mbps: [100, 10000]
})
# CPU optimization
optimal_cpu: @optimize.cpu({
cores: [1, 16]
frequency: [1.0, 4.0]
cache_size: [1, 32]
})
# Memory optimization
optimal_memory: @optimize.memory({
heap_size: [512, 8192]
stack_size: [128, 1024]
direct_memory: [64, 2048]
})
# Network optimization
optimal_network: @optimize.network({
bandwidth: [100, 10000]
latency: [1, 100]
connections: [100, 10000]
})
}Algorithm Selection
// Java algorithm selection
@Component
public class AlgorithmSelectionService {
@Autowired
private OptimizationService optimizationService;
public AlgorithmSelectionResult selectOptimalAlgorithm(String problemType,
Map<String, Object> constraints) {
return optimizationService.selectAlgorithm(new AlgorithmSelectionRequest(
problemType,
constraints,
getAvailableAlgorithms(),
getPerformanceHistory()
));
}
public OptimizationAlgorithm getBestAlgorithm(String parameter, double minValue, double maxValue) {
List<OptimizationAlgorithm> algorithms = Arrays.asList(
new GeneticAlgorithm(),
new ParticleSwarmOptimization(),
new SimulatedAnnealing(),
new GradientDescent()
);
return optimizationService.selectBestAlgorithm(parameter, minValue, maxValue, algorithms);
}
public Map<String, OptimizationAlgorithm> getOptimalAlgorithms(Map<String, OptimizationRange> parameters) {
return optimizationService.selectOptimalAlgorithms(parameters);
}
}// TuskLang algorithm selection
algorithm_selection: {
# Algorithm selection
best_algorithm: @optimize.algorithm("cache_size", 100, 1000)
# Multi-algorithm comparison
algorithm_comparison: @optimize.compare_algorithms("thread_pool_size", 4, 32, [
"genetic", "particle_swarm", "simulated_annealing", "gradient_descent"
])
# Problem-specific algorithm selection
problem_algorithms: @optimize.select_algorithm("performance_optimization", {
genetic: { population_size: 50, iterations: 1000 }
particle_swarm: { particles: 30, iterations: 500 }
simulated_annealing: { temperature: 1000, cooling_rate: 0.95 }
})
}Optimization Monitoring
// Java optimization monitoring
@Component
public class OptimizationMonitoringService {
@Autowired
private OptimizationService optimizationService;
@Autowired
private MeterRegistry meterRegistry;
public void monitorOptimization(String parameter, OptimizationResult result) {
// Record optimization metrics
Gauge.builder("optimization.parameter.value")
.tag("parameter", parameter)
.register(meterRegistry, () -> result.getOptimalValue());
Gauge.builder("optimization.performance.improvement")
.tag("parameter", parameter)
.register(meterRegistry, () -> result.getPerformanceImprovement());
Gauge.builder("optimization.iterations")
.tag("parameter", parameter)
.register(meterRegistry, () -> result.getIterations());
// Log optimization results
log.info("Optimization completed for parameter: {}, optimal value: {}, improvement: {}%",
parameter, result.getOptimalValue(), result.getPerformanceImprovement());
}
public OptimizationMetrics getOptimizationMetrics() {
return OptimizationMetrics.builder()
.totalOptimizations(getTotalOptimizations())
.averageImprovement(getAverageImprovement())
.bestOptimization(getBestOptimization())
.optimizationHistory(getOptimizationHistory())
.build();
}
}// TuskLang optimization monitoring
optimization_monitoring: {
# Monitor optimization
@optimize.monitor("cache_size")
@optimize.monitor("thread_pool_size")
@optimize.monitor("connection_pool_size")
# Optimization metrics
optimization_metrics: {
total_optimizations: @optimize.metrics.total()
average_improvement: @optimize.metrics.average_improvement()
best_optimization: @optimize.metrics.best()
optimization_history: @optimize.metrics.history()
}
# Performance tracking
performance_tracking: {
before_optimization: @optimize.performance.before()
after_optimization: @optimize.performance.after()
improvement_percentage: @optimize.performance.improvement()
}
}Optimization Testing
// JUnit test for optimization
@SpringBootTest
class OptimizationServiceTest {
@Autowired
private OptimizationService optimizationService;
@Test
void testBasicOptimization() {
OptimizationResult result = optimizationService.optimize("test_parameter", 1.0, 100.0);
assertThat(result).isNotNull();
assertThat(result.getOptimalValue()).isBetween(1.0, 100.0);
assertThat(result.getPerformanceImprovement()).isGreaterThan(0.0);
assertThat(result.getIterations()).isGreaterThan(0);
}
@Test
void testConstrainedOptimization() {
OptimizationConstraints constraints = OptimizationConstraints.builder()
.addConstraint("value > 10")
.addConstraint("value < 90")
.build();
OptimizationResult result = optimizationService.optimize("test_parameter", 1.0, 100.0, constraints);
assertThat(result.getOptimalValue()).isBetween(10.0, 90.0);
}
@Test
void testPerformanceOptimization() {
PerformanceOptimizationResult result = optimizationService.optimizePerformance();
assertThat(result).isNotNull();
assertThat(result.getOptimizedParameters()).isNotEmpty();
assertThat(result.getPerformanceImprovement()).isGreaterThan(0.0);
}
}// TuskLang optimization testing
test_optimization: {
# Test basic optimization
test_result: @optimize("test_parameter", 1.0, 100.0)
assert(@test_result.optimal_value >= 1.0, "Optimal value should be >= 1.0")
assert(@test_result.optimal_value <= 100.0, "Optimal value should be <= 100.0")
assert(@test_result.improvement > 0, "Should show improvement")
# Test constrained optimization
constrained_result: @optimize("test_parameter", 1.0, 100.0, {
constraint: "value > 10"
constraint: "value < 90"
})
assert(@constrained_result.optimal_value > 10, "Should respect lower constraint")
assert(@constrained_result.optimal_value < 90, "Should respect upper constraint")
# Test performance optimization
performance_result: @optimize.performance({
cache_size: [100, 1000]
thread_pool_size: [4, 32]
})
assert(@performance_result.optimized_parameters.cache_size > 0, "Should optimize cache size")
assert(@performance_result.optimized_parameters.thread_pool_size > 0, "Should optimize thread pool")
}Best Practices
1. Optimization Strategy
// Use appropriate optimization strategies
@Component
public class OptimizationStrategyService {
@Autowired
private OptimizationService optimizationService;
public OptimizationResult optimizeWithStrategy(String parameter, OptimizationStrategy strategy) {
switch (strategy) {
case PERFORMANCE:
return optimizeForPerformance(parameter);
case RESOURCE_EFFICIENCY:
return optimizeForResourceEfficiency(parameter);
case COST_EFFECTIVENESS:
return optimizeForCostEffectiveness(parameter);
default:
return optimizationService.optimize(parameter, 0.0, 100.0);
}
}
private OptimizationResult optimizeForPerformance(String parameter) {
// Focus on performance metrics
return optimizationService.optimize(parameter, 0.0, 100.0,
OptimizationConstraints.builder()
.addConstraint("response_time < 200ms")
.addConstraint("throughput > 1000 req/s")
.build());
}
private OptimizationResult optimizeForResourceEfficiency(String parameter) {
// Focus on resource usage
return optimizationService.optimize(parameter, 0.0, 100.0,
OptimizationConstraints.builder()
.addConstraint("memory_usage < 80%")
.addConstraint("cpu_usage < 70%")
.build());
}
}2. Continuous Optimization
// Implement continuous optimization
@Component
public class ContinuousOptimizationService {
@Autowired
private OptimizationService optimizationService;
@Scheduled(fixedRate = 3600000) // Every hour
public void performContinuousOptimization() {
// Collect current performance metrics
PerformanceMetrics currentMetrics = collectCurrentMetrics();
// Check if optimization is needed
if (needsOptimization(currentMetrics)) {
// Perform optimization
OptimizationResult result = optimizationService.optimizePerformance();
// Apply optimization if improvement is significant
if (result.getPerformanceImprovement() > 5.0) {
applyOptimization(result);
log.info("Applied optimization with {}% improvement", result.getPerformanceImprovement());
}
}
}
private boolean needsOptimization(PerformanceMetrics metrics) {
return metrics.getResponseTime() > 200 ||
metrics.getErrorRate() > 1.0 ||
metrics.getMemoryUsage() > 80.0;
}
}3. Optimization Validation
// Validate optimization results
@Component
public class OptimizationValidationService {
@Autowired
private OptimizationService optimizationService;
public boolean validateOptimization(OptimizationResult result) {
// Check if optimization is within acceptable bounds
if (result.getOptimalValue() < result.getMinValue() ||
result.getOptimalValue() > result.getMaxValue()) {
return false;
}
// Check if performance improvement is significant
if (result.getPerformanceImprovement() < 1.0) {
return false;
}
// Check if optimization didn't introduce new issues
if (result.getErrorRate() > 0.1) {
return false;
}
return true;
}
public void rollbackOptimization(String parameter) {
// Rollback to previous optimal value
OptimizationResult previousResult = getPreviousOptimization(parameter);
if (previousResult != null) {
applyOptimization(previousResult);
log.info("Rolled back optimization for parameter: {}", parameter);
}
}
}The @optimize operator in Java provides intelligent optimization capabilities that can automatically tune application parameters for optimal performance, resource usage, and cost-effectiveness in enterprise environments.