What are ways to Optimize the backend endpoints in Spring Boot

What are ways to Optimize the backend endpoints in Spring Boot

Posted on by Toma Velev

To Optimize backend endpoints in a Spring Boot app with Java 17 can significantly improve performance and scalability. Here’s a structured list of strategies, starting from database-level optimizations and moving to application and infrastructure layers:

Database/SQL Optimization ✅

  1. Optimize SQL Queries
    • Use EXPLAIN plans to analyze slow queries.
    • Use proper indexing on columns used in WHERE, JOIN, ORDER BY.
    • Avoid SELECT *; only select required columns.
    • Use pagination (LIMIT, OFFSET) for large result sets.
  2. Use Prepared Statements
    • Prevent SQL injection.
    • Improve performance via statement caching.
  3. Batch Inserts/Updates
    • Use JdbcTemplate.batchUpdate() or saveAll() in JPA with batching enabled.
  4. Avoid N+1 Query Problem
    • Use @EntityGraph or JOIN FETCH in JPQL.
    • Consider DTO projections for read-only operations.

✅ Spring Boot / JPA Optimization

  1. Enable JPA Query Caching (Second-Level Cache)
    • Use providers like EhCache, Caffeine, Redis.
    • Cache frequently accessed entities or queries.
  2. Use DTO Projections
    • Avoid loading entire entities if only partial data is needed.
    • Use JPQL constructor expressions or Spring Data Projections.
  3. Tune JPA Settings
    • Set hibernate.jdbc.batch_size, hibernate.order_inserts, etc.
    • Avoid lazy loading outside transaction boundaries.
  4. Connection Pooling
    • Use HikariCP (default in Spring Boot).
    • Tune connection pool size according to your DB and traffic.

✅ Java 17 / JVM-Level Optimization

  1. Use Records for Immutable DTOs
    • Lightweight, memory-efficient data holders.
    • Improves readability and serialization.
  2. Leverage Virtual Threads (if using Java 21+)
    • For highly concurrent workloads, consider using Executors.newVirtualThreadPerTaskExecutor().
  3. Tune Garbage Collection (GC)
    • Use G1GC or ZGC for low pause times.
    • Tune heap sizes and GC logs.
  4. Avoid Reflection When Possible
    • Use direct access over reflection for critical paths.

Spring Boot App-Level Optimization✅

  1. Use Response Caching (e.g. @Cacheable)
    • Cache expensive read endpoints.
    • Use Caffeine or Redis.
  2. Asynchronous Processing
    • Use @Async or CompletableFuture for non-blocking tasks.
  3. Enable GZIP Compression
    • Reduces payload size for REST responses (server.compression.enabled=true).
  4. Avoid Unnecessary Beans / Auto-configurations
    • Use @ConditionalOnProperty to load beans only when needed.

✅ API Endpoint Optimization

  1. Limit Payloads
    • Use pagination, filtering, and partial responses (e.g. GraphQL or JSON Views).
  2. Rate Limiting & Throttling
    • Prevent abuse using tools like Bucket4J or Spring Cloud Gateway filters.
  3. Enable HTTP/2
    • Reduces latency, especially with multiple requests.

✅ Observability & Diagnostics

  1. Use Actuator
    • Enable /actuator/metrics, /actuator/health for monitoring.
  2. Profiling Tools
    • Use tools like JFR (Java Flight Recorder), YourKit, or VisualVM.
  3. APM Tools
    • Integrate with Prometheus + Grafana, New Relic, or Datadog for insights.

Infrastructure-Level Optimizations✅

  1. Load Balancing & Clustering
    • Scale horizontally via Kubernetes or load balancers.
  2. Use CDN / Edge Caching for Static Assets
    • Offload traffic for faster response.
  3. Database Optimization
    • Use read replicas, partitioning, or materialized views for large datasets.