In this article, we will explore the Top 10 Spring Data JPA mistakes and how to avoid them, with real-world examples.
1️⃣ Using EAGER Fetch Type Everywhere ⚠️
❌ Mistake: Fetching All Related Entities by Default
By default, @ManyToOne and @OneToOne relationships use EAGER fetching, which can result in N+1 query issues.
@Entity
public class Order {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
@ManyToOne(fetch = FetchType.EAGER) // ❌ Avoid EAGER fetching unless necessary
private Customer customer;
}
✔ Issue: Every time you fetch an Order, it automatically fetches the associated Customer, even when it's not needed.
✅ Solution: Use LAZY Fetching
@ManyToOne(fetch = FetchType.LAZY) // ✅ Fetch only when needed
private Customer customer;
✔ Best Practice: Use JPQL or JOIN FETCH queries when eager loading is required.
2️⃣ Ignoring Transactions (@Transactional) 🔄
❌ Mistake: Performing Multiple Database Calls Without a Transaction
public void updateUser(Long id, String email) {
User user = userRepository.findById(id).orElseThrow();
user.setEmail(email);
userRepository.save(user); // ❌ No transaction control
}
✔ Issue: If an error occurs after fetching the user, the update may never happen, leading to inconsistent data.
✅ Solution: Use @Transactional to Ensure Atomicity
@Transactional
public void updateUser(Long id, String email) {
User user = userRepository.findById(id).orElseThrow();
user.setEmail(email);
}
✔ Best Practice: Let JPA automatically commit changes within a transaction.
3️⃣ Not Using DTO for Read Operations 📥
❌ Mistake: Fetching Entire Entities When Only a Few Fields Are Needed
List<User> users = userRepository.findAll(); // ❌ Fetches all columns
✔ Issue: Fetching unnecessary fields increases memory usage and slows down queries.
✅ Solution: Use DTO Projection for Efficient Queries
public interface UserDto {
String getFirstName();
String getEmail();
}
@Query("SELECT u.firstName AS firstName, u.email AS email FROM User u")
List<UserDto> findUsers();
✔ Best Practice: Use DTOs for read operations and entities for writes.
4️⃣ Misusing CascadeType.ALL ❌
❌ Mistake: Unintentionally Deleting Related Entities
@OneToMany(mappedBy = "customer", cascade = CascadeType.ALL)
private List<Order> orders;
✔ Issue: Deleting a Customer will also delete all their Orders, which may not be intended.
✅ Solution: Use CascadeType.PERSIST or CascadeType.MERGE
@OneToMany(mappedBy = "customer", cascade = {CascadeType.PERSIST, CascadeType.MERGE})
private List<Order> orders;
✔ Best Practice: Use CascadeType.REMOVE only when required.
5️⃣ Not Using EntityGraph for Complex Queries 🚀
❌ Mistake: Multiple Queries Due to Lazy Loading
@Query("SELECT o FROM Order o WHERE o.customer.id = :customerId")
List<Order> findOrdersByCustomerId(Long customerId);
✔ Issue: Fetching an Order and its OrderItems results in N+1 queries.
✅ Solution: Use @EntityGraph to Optimize Queries
@EntityGraph(attributePaths = {"orderItems"})
@Query("SELECT o FROM Order o WHERE o.customer.id = :customerId")
List<Order> findOrdersByCustomerId(Long customerId);
✔ Best Practice: Use @EntityGraph for optimizing complex queries.
6️⃣ Using Native Queries Instead of JPQL 🔥
❌ Mistake: Writing Native SQL Queries for Simple Operations
@Query(value = "SELECT * FROM user WHERE email = ?1", nativeQuery = true)
User findByEmail(String email);
✔ Issue: Native queries tie your code to a specific database and lack flexibility.
✅ Solution: Use JPQL for Database Portability
@Query("SELECT u FROM User u WHERE u.email = :email")
User findByEmail(@Param("email") String email);
✔ Best Practice: Use native queries only for complex queries that JPQL cannot handle.
7️⃣ Not Using @Modifying for Update/Delete Queries 🛠️
❌ Mistake: Forgetting @Modifying in Update/Delete Queries
@Query("UPDATE User u SET u.status = 'ACTIVE' WHERE u.id = :id")
void activateUser(Long id); // ❌ Does not execute properly
✔ Issue: Without @Modifying, the query will not modify data.
✅ Solution: Use @Modifying for Update/Delete Queries
@Modifying
@Query("UPDATE User u SET u.status = 'ACTIVE' WHERE u.id = :id")
void activateUser(@Param("id") Long id);
✔ Best Practice: Always use @Modifying for update/delete queries.
8️⃣ Not Closing Database Connections 🛑
❌ Mistake: Leaking Database Connections
public void fetchUsers() {
List<User> users = userRepository.findAll(); // ❌ May keep connections open
}
✔ Issue: Unclosed connections can exhaust the database connection pool.
✅ Solution: Use try-with-resources or Connection Pooling
✔ Best Practice: Use HikariCP (Spring Boot default connection pool).
9️⃣ Using findAll() for Large Datasets 📊
❌ Mistake: Loading Millions of Rows Into Memory
List<User> users = userRepository.findAll(); // ❌ Memory overload
✔ Issue: Fetching large datasets at once can crash the application.
✅ Solution: Use Pagination
Page<User> users = userRepository.findAll(PageRequest.of(0, 10));
✔ Best Practice: Always use pagination for large datasets.
🔟 Ignoring Indexing & Performance Optimization ⚡
❌ Mistake: Not Adding Indexes to Frequently Queried Columns
@Column(nullable = false)
private String email;
✔ Issue: Queries on email are slow without an index.
✅ Solution: Add Indexing for Better Query Performance
@Column(nullable = false)
@Index(name = "idx_email", columnList = "email")
private String email;
✔ Best Practice: Index foreign keys, search columns, and large datasets.
🎯 Conclusion
Spring Data JPA is powerful, but common mistakes can cause performance bottlenecks, data inconsistency, and unnecessary complexity.
Quick Recap
✔ Use LAZY fetching for better performance
✔ Always use @Transactional for database consistency
✔ Use DTOs for reading queries to avoid unnecessary data loading
✔ Optimize queries using @EntityGraph and @Query
✔ Enable pagination for large datasets
You can build efficient, scalable, and high-performance Spring Boot applications by following these best practices.
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