Database - Optimization (Spring Boot)

 

7 Database Optimizations Every Spring Boot A

When it comes to Spring Boot performance, the database is usually the bottleneck. You can have the fastest APIs and the most optimized code, but if your database layer isn’t tuned, your app will feel slow under load.

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After working with multiple production systems, I’ve identified 7 essential database optimizations every Spring Boot developer should know.

1. Use Connection Pooling (HikariCP)

Spring Boot ships with HikariCP by default, but many developers don’t fine-tune it.

spring:
  datasource:
    hikari:
      maximum-pool-size: 50
      minimum-idle: 10

This reduces the overhead of creating new connections and ensures smoother performance under load.

2. Batch Inserts and Updates

Performing thousands of inserts one by one kills performance. Hibernate supports batching with just a few lines:

spring:
  jpa:
    properties:
      hibernate.jdbc.batch_size: 50
      hibernate.order_inserts: true
      hibernate.order_updates: true

This can reduce DB roundtrips by up to 90%.

3. Avoid N+1 Queries with fetch = FetchType.LAZY

One of the most common issues in Spring Boot + JPA apps is the N+1 query problem.

@Entity
class User {
    @OneToMany(fetch = FetchType.LAZY, mappedBy = "user")
    private List<Order> orders;
}

Combine lazy loading with JOIN FETCH queries when needed to fetch related entities in a single query.

4. Use Indexes Wisely

Indexes are the cheapest optimization you can add. For example, if you query users by email often:

CREATE INDEX idx_users_email ON users(email);

But beware: too many indexes slow down inserts/updates. Optimize based on query patterns.

5. Cache Frequently Accessed Data

Spring Boot makes it easy to integrate caching with just one annotation:

@Cacheable("products")
public Product getProductById(Long id) {
    return productRepository.findById(id).orElseThrow();
}

Combine with Redis or Caffeine for high-performance caching.

6. Limit Fetch Size for Large Queries

Fetching thousands of rows into memory will kill performance. Instead, stream data in chunks:

@QueryHints(value = {
   @QueryHint(name = org.hibernate.jpa.QueryHints.HINT_FETCH_SIZE, value = "50")
})
@Query("SELECT u FROM User u")
List<User> findAllUsers();

Prevents out-of-memory issues in large datasets.

7. Monitor with Actuator + Micrometer

You can’t optimize what you don’t measure. Enable metrics in your Spring Boot app:

management:
  endpoints:
    web:
      exposure:
        include: health,metrics

Integrate with Prometheus + Grafana to spot slow queries before they become production incidents.

Final Thoughts

Database optimizations are not optional — they’re mandatory if you want your Spring Boot app to scale. With these 7 techniques, you’ll:

• Reduce query times
• Handle higher traffic
• Avoid production slowdowns

Start small: enable batching, fix N+1 queries, and add the right indexes. The rest will follow naturally.