🚀 Query Optimization in TuskLang Java

"We don't bow to any king" - Optimize like a Java master

TuskLang Java provides enterprise-grade query optimization with JPA integration, connection pooling, and intelligent caching strategies that make your database operations fly.

🎯 Overview

Query optimization in TuskLang Java combines the power of modern Java database technologies with TuskLang's intelligent configuration system. From JPA query optimization to connection pooling and caching strategies, we'll show you how to squeeze every drop of performance from your database operations.

🔧 Core Optimization Features

1. JPA Query Optimization

import org.tusklang.java.TuskLang;
import org.tusklang.java.jpa.TuskJPAOptimizer;
import javax.persistence.EntityManager;
import java.util.Map;
public class JPAOptimizationExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        TuskJPAOptimizer optimizer = new TuskJPAOptimizer();
        
        // Optimized TSK configuration
        String tskContent = """
            [database]
            # Enable query optimization
            optimize_queries: true
            enable_query_cache: true
            batch_size: 100
            
            [jpa]
            # JPA-specific optimizations
            enable_second_level_cache: true
            enable_query_cache: true
            batch_fetch_size: 50
            default_batch_fetch_size: 16
            
            [queries]
            # Pre-optimized queries with hints
            user_stats: @query.optimized("""
                SELECT 
                    COUNT(*) as total_users,
                    COUNT(CASE WHEN active = 1 THEN 1 END) as active_users,
                    AVG(age) as avg_age
                FROM users 
                WHERE created_at > ?
                """, @date.subtract("30d"))
                .hint("org.hibernate.comment", "User statistics query")
                .hint("org.hibernate.fetchSize", 100)
                .cache("5m")
            
            recent_orders: @query.optimized("""
                SELECT o.*, u.name as user_name
                FROM orders o
                JOIN users u ON o.user_id = u.id
                WHERE o.created_at > ?
                ORDER BY o.created_at DESC
                LIMIT 50
                """, @date.subtract("7d"))
                .hint("org.hibernate.comment", "Recent orders with user info")
                .hint("org.hibernate.fetchSize", 50)
                .cache("2m")
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Apply optimizations to EntityManager
        EntityManager em = getEntityManager();
        optimizer.applyOptimizations(em, config);
    }
}

2. Connection Pooling Optimization

import org.tusklang.java.TuskLang;
import org.tusklang.java.pool.TuskConnectionPool;
import com.zaxxer.hikari.HikariConfig;
import com.zaxxer.hikari.HikariDataSource;
import java.util.Map;
public class ConnectionPoolExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [connection_pool]
            # HikariCP optimization
            pool_name: "TuskPool"
            maximum_pool_size: 20
            minimum_idle: 5
            connection_timeout: 30000
            idle_timeout: 600000
            max_lifetime: 1800000
            leak_detection_threshold: 60000
            
            [pool_optimization]
            # Performance tuning
            cache_prep_stmts: true
            prep_stmt_cache_size: 250
            prep_stmt_cache_sql_limit: 2048
            use_server_prep_stmts: true
            use_local_session_state: true
            rewrite_batched_statements: true
            cache_result_set_metadata: true
            cache_server_configuration: true
            elide_set_auto_commits: true
            maintain_time_stats: false
            
            [monitoring]
            # Pool monitoring
            enable_metrics: true
            enable_jmx: true
            register_mbeans: true
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Create optimized connection pool
        HikariConfig hikariConfig = new HikariConfig();
        hikariConfig.setPoolName((String) config.get("connection_pool.pool_name"));
        hikariConfig.setMaximumPoolSize((Integer) config.get("connection_pool.maximum_pool_size"));
        hikariConfig.setMinimumIdle((Integer) config.get("connection_pool.minimum_idle"));
        hikariConfig.setConnectionTimeout((Long) config.get("connection_pool.connection_timeout"));
        hikariConfig.setIdleTimeout((Long) config.get("connection_pool.idle_timeout"));
        hikariConfig.setMaxLifetime((Long) config.get("connection_pool.max_lifetime"));
        hikariConfig.setLeakDetectionThreshold((Long) config.get("connection_pool.leak_detection_threshold"));
        
        // Apply optimization properties
        Map<String, Object> poolOpts = (Map<String, Object>) config.get("pool_optimization");
        for (Map.Entry<String, Object> entry : poolOpts.entrySet()) {
            hikariConfig.addDataSourceProperty(entry.getKey(), entry.getValue());
        }
        
        HikariDataSource dataSource = new HikariDataSource(hikariConfig);
        
        // Create TuskLang instance with optimized pool
        TuskLang tusk = new TuskLang();
        tusk.setDataSource(dataSource);
    }
}

3. Query Caching Strategies

import org.tusklang.java.TuskLang;
import org.tusklang.java.cache.TuskQueryCache;
import org.springframework.cache.CacheManager;
import org.springframework.cache.annotation.EnableCaching;
import java.util.Map;
@EnableCaching
public class QueryCacheExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [query_cache]
            # Multi-level caching strategy
            enable_l1_cache: true
            enable_l2_cache: true
            enable_redis_cache: true
            
            [cache_config]
            # L1 Cache (In-Memory)
            l1_max_size: 1000
            l1_ttl: "10m"
            l1_eviction_policy: "LRU"
            
            # L2 Cache (Redis)
            redis_host: "localhost"
            redis_port: 6379
            redis_ttl: "1h"
            redis_max_connections: 50
            
            [cached_queries]
            # Define cached queries
            user_profile: @query.cached("""
                SELECT * FROM users WHERE id = ?
                """, "user_profile_cache", "30m")
            
            user_permissions: @query.cached("""
                SELECT p.* FROM permissions p
                JOIN user_permissions up ON p.id = up.permission_id
                WHERE up.user_id = ?
                """, "user_permissions_cache", "1h")
            
            system_stats: @query.cached("""
                SELECT 
                    COUNT(*) as total_users,
                    COUNT(CASE WHEN active = 1 THEN 1 END) as active_users,
                    SUM(CASE WHEN premium = 1 THEN 1 ELSE 0 END) as premium_users
                FROM users
                """, "system_stats_cache", "5m")
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Initialize query cache
        TuskQueryCache queryCache = new TuskQueryCache();
        queryCache.configure(config);
        
        // Use cached queries
        Map<String, Object> userProfile = queryCache.getCachedQuery("user_profile", 123);
        Map<String, Object> permissions = queryCache.getCachedQuery("user_permissions", 123);
        Map<String, Object> stats = queryCache.getCachedQuery("system_stats");
    }
}

4. Batch Processing Optimization

import org.tusklang.java.TuskLang;
import org.tusklang.java.batch.TuskBatchProcessor;
import java.util.List;
import java.util.Map;
public class BatchProcessingExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [batch_processing]
            # Batch optimization settings
            batch_size: 1000
            batch_timeout: "30s"
            enable_parallel_processing: true
            max_parallel_threads: 4
            
            [batch_queries]
            # Optimized batch operations
            bulk_user_insert: @query.batch("""
                INSERT INTO users (name, email, created_at) 
                VALUES (?, ?, ?)
                """, "batch_size")
                .batch_size(1000)
                .timeout("30s")
                .parallel(true)
            
            bulk_order_update: @query.batch("""
                UPDATE orders 
                SET status = ?, updated_at = ? 
                WHERE id = ?
                """, "batch_size")
                .batch_size(500)
                .timeout("15s")
                .parallel(true)
            
            bulk_data_cleanup: @query.batch("""
                DELETE FROM old_logs 
                WHERE created_at < ?
                """, "batch_size")
                .batch_size(2000)
                .timeout("60s")
                .parallel(false)
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Initialize batch processor
        TuskBatchProcessor batchProcessor = new TuskBatchProcessor();
        batchProcessor.configure(config);
        
        // Prepare batch data
        List<Object[]> userData = prepareUserData();
        List<Object[]> orderData = prepareOrderData();
        
        // Execute batch operations
        batchProcessor.executeBatch("bulk_user_insert", userData);
        batchProcessor.executeBatch("bulk_order_update", orderData);
        batchProcessor.executeBatch("bulk_data_cleanup", List.of(new Object[]{getOldDate()}));
    }
    
    private static List<Object[]> prepareUserData() {
        // Prepare user data for batch insert
        return List.of(
            new Object[]{"John Doe", "john@example.com", new java.util.Date()},
            new Object[]{"Jane Smith", "jane@example.com", new java.util.Date()}
            // ... more data
        );
    }
    
    private static List<Object[]> prepareOrderData() {
        // Prepare order data for batch update
        return List.of(
            new Object[]{"completed", new java.util.Date(), 1},
            new Object[]{"shipped", new java.util.Date(), 2}
            // ... more data
        );
    }
    
    private static java.util.Date getOldDate() {
        return new java.util.Date(System.currentTimeMillis() - 30L  24  60  60  1000); // 30 days ago
    }
}

🚀 Advanced Optimization Techniques

1. Query Plan Analysis

import org.tusklang.java.TuskLang;
import org.tusklang.java.analysis.TuskQueryAnalyzer;
import java.util.Map;
public class QueryAnalysisExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [query_analysis]
            # Enable query plan analysis
            enable_explain_analyze: true
            log_slow_queries: true
            slow_query_threshold: "1s"
            enable_query_metrics: true
            
            [analysis_queries]
            # Queries to analyze
            complex_user_query: @query.analyze("""
                SELECT 
                    u.name,
                    COUNT(o.id) as order_count,
                    SUM(o.total) as total_spent
                FROM users u
                LEFT JOIN orders o ON u.id = o.user_id
                WHERE u.created_at > ?
                GROUP BY u.id, u.name
                HAVING COUNT(o.id) > 5
                ORDER BY total_spent DESC
                LIMIT 100
                """, @date.subtract("90d"))
                .explain(true)
                .analyze(true)
                .log_plan(true)
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Initialize query analyzer
        TuskQueryAnalyzer analyzer = new TuskQueryAnalyzer();
        analyzer.configure(config);
        
        // Analyze query performance
        Map<String, Object> analysis = analyzer.analyzeQuery("complex_user_query");
        
        System.out.println("Query Plan: " + analysis.get("plan"));
        System.out.println("Execution Time: " + analysis.get("execution_time"));
        System.out.println("Rows Affected: " + analysis.get("rows_affected"));
        System.out.println("Optimization Suggestions: " + analysis.get("suggestions"));
    }
}

2. Index Optimization

import org.tusklang.java.TuskLang;
import org.tusklang.java.index.TuskIndexOptimizer;
import java.util.Map;
public class IndexOptimizationExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [index_optimization]
            # Index analysis and optimization
            enable_index_analysis: true
            suggest_indexes: true
            auto_create_indexes: false
            
            [index_suggestions]
            # Suggested indexes for optimization
            users_email_idx: @index.suggest("users", ["email"], "UNIQUE")
            users_created_at_idx: @index.suggest("users", ["created_at"], "BTREE")
            orders_user_date_idx: @index.suggest("orders", ["user_id", "created_at"], "BTREE")
            orders_status_idx: @index.suggest("orders", ["status", "created_at"], "BTREE")
            
            [index_analysis]
            # Analyze existing indexes
            analyze_users_table: @index.analyze("users")
            analyze_orders_table: @index.analyze("orders")
            analyze_performance: @index.performance("users", "orders")
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Initialize index optimizer
        TuskIndexOptimizer indexOptimizer = new TuskIndexOptimizer();
        indexOptimizer.configure(config);
        
        // Analyze and suggest indexes
        Map<String, Object> suggestions = indexOptimizer.analyzeIndexes();
        Map<String, Object> performance = indexOptimizer.analyzePerformance();
        
        System.out.println("Index Suggestions: " + suggestions);
        System.out.println("Performance Analysis: " + performance);
    }
}

🔧 Performance Monitoring

1. Query Metrics Collection

import org.tusklang.java.TuskLang;
import org.tusklang.java.metrics.TuskQueryMetrics;
import java.util.Map;
public class MetricsExample {
    public static void main(String[] args) {
        TuskLang parser = new TuskLang();
        
        String tskContent = """
            [query_metrics]
            # Enable comprehensive metrics
            enable_metrics: true
            metrics_interval: "1m"
            enable_slow_query_log: true
            slow_query_threshold: "500ms"
            
            [metrics_collection]
            # What to collect
            collect_execution_time: true
            collect_row_counts: true
            collect_cache_hits: true
            collect_connection_usage: true
            collect_memory_usage: true
            
            [metrics_export]
            # Export metrics
            export_to_prometheus: true
            export_to_grafana: true
            export_to_logs: true
            """;
        
        Map<String, Object> config = parser.parse(tskContent);
        
        // Initialize metrics collector
        TuskQueryMetrics metrics = new TuskQueryMetrics();
        metrics.configure(config);
        
        // Start metrics collection
        metrics.startCollection();
        
        // Execute queries (metrics automatically collected)
        TuskLang tusk = new TuskLang();
        tusk.setMetricsCollector(metrics);
        
        // Get metrics summary
        Map<String, Object> summary = metrics.getSummary();
        System.out.println("Query Performance Summary: " + summary);
    }
}

🎯 Best Practices

1. Query Optimization Checklist

// ✅ Use parameterized queries
String query = "SELECT * FROM users WHERE email = ? AND active = ?";
// ✅ Enable query caching for frequently accessed data
@Query.cached("user_profile", "30m")
// ✅ Use appropriate batch sizes
.batch_size(1000)
// ✅ Enable connection pooling
HikariCP with optimized settings
// ✅ Use indexes effectively
CREATE INDEX idx_users_email ON users(email);
// ✅ Monitor query performance
Enable metrics and slow query logging
// ✅ Use prepared statements
Always use parameterized queries
// ✅ Optimize JOIN operations
Use appropriate JOIN types and order

2. Performance Tuning Guidelines

// 1. Connection Pool Optimization
- Set appropriate pool size based on workload
- Monitor connection usage patterns
- Use connection validation
// 2. Query Caching Strategy
- Cache frequently accessed data
- Use appropriate TTL values
- Implement cache invalidation
// 3. Batch Processing
- Use appropriate batch sizes
- Enable parallel processing when safe
- Monitor memory usage
// 4. Index Management
- Create indexes based on query patterns
- Monitor index usage
- Remove unused indexes
// 5. Query Analysis
- Regularly analyze slow queries
- Use EXPLAIN ANALYZE
- Monitor query patterns

🚀 Integration with Spring Boot

1. Spring Boot Configuration

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.tusklang.java.TuskLang;
import org.tusklang.java.spring.TuskSpringConfig;
@SpringBootApplication
@Configuration
public class OptimizedApplication {
    
    @Bean
    public TuskSpringConfig tuskSpringConfig() {
        TuskLang parser = new TuskLang();
        return parser.parseFile("optimization.tsk", TuskSpringConfig.class);
    }
    
    public static void main(String[] args) {
        SpringApplication.run(OptimizedApplication.class, args);
    }
}
@TuskConfig
public class TuskSpringConfig {
    private DatabaseConfig database;
    private CacheConfig cache;
    private MetricsConfig metrics;
    
    // Getters and setters
    public DatabaseConfig getDatabase() { return database; }
    public void setDatabase(DatabaseConfig database) { this.database = database; }
    
    public CacheConfig getCache() { return cache; }
    public void setCache(CacheConfig cache) { this.cache = cache; }
    
    public MetricsConfig getMetrics() { return metrics; }
    public void setMetrics(MetricsConfig metrics) { this.metrics = metrics; }
}

🎯 Summary

TuskLang Java query optimization provides:

- JPA Integration: Seamless integration with JPA/Hibernate - Connection Pooling: Optimized connection management with HikariCP - Query Caching: Multi-level caching strategies - Batch Processing: Efficient bulk operations - Performance Monitoring: Comprehensive metrics collection - Index Optimization: Intelligent index suggestions - Spring Boot Integration: Native Spring Boot support

With these optimization features, your Java applications will achieve enterprise-grade performance while maintaining the flexibility and power of TuskLang configuration.

"We don't bow to any king" - Optimize like a Java master with TuskLang!