Spring Data JDBC

Spring Data JDBC is a lightweight data-access framework from Spring that helps you work with relational databases in a simple, predictable, and SQL-friendly way.

It is designed for developers who want control and clarity, not ORM magic.

What Is Spring Data JDBC?

Spring Data JDBC is part of the Spring Data family that allows you to:

• Map Java objects directly to database tables
• Perform CRUD operations easily
• Work with databases using JDBC principles
• Avoid complex ORM behaviors

👉 In simple words:

Spring Data JDBC = JDBC + Repository abstraction (No ORM)

Why Spring Data JDBC?

Spring Data JDBC was introduced to solve issues caused by heavy ORM frameworks like JPA/Hibernate.

Problems with Heavy ORM

• Hidden SQL queries
• Lazy loading confusion
• Unexpected performance issues
• Complex entity lifecycle
• Hard debugging

How Spring Data JDBC Helps

• Explicit SQL behavior
• Predictable execution
• Easy to debug
• No hidden magic

Core Philosophy (DDD-Based)

Spring Data JDBC follows Domain-Driven Design (DDD) principles.

Key Rules

• One Aggregate = One Root Entity
• No Lazy Loading
• No Automatic Joins
• One Table = One Entity
• Relationships must be explicit

Key Features of Spring Data JDBC

Spring Data JDBC is designed to be simple, explicit, and predictable.

Below is a breakdown of its key features and what they actually mean in practice.

Basic Architecture

Controller
   ↓
Service
   ↓
Repository (SpringData JDBC)
   ↓
Database

Entity Mapping (Basics) in Spring Data JDBC

Spring Data JDBC maps Java objects directly to database tables in a simple and predictable way.

Example Entity

import org.springframework.data.annotation.Id;
import org.springframework.data.relational.core.mapping.Table;

@Table("users")// Maps this class to the "users" table
publicclassUser {

@Id// Primary key
private Long id;

private String name;
private String email;

// Getters and setters
public LonggetId() {return id; }
publicvoidsetId(Long id) {this.id = id; }

public StringgetName() {return name; }
publicvoidsetName(String name) {this.name = name; }

public StringgetEmail() {return email; }
publicvoidsetEmail(String email) {this.email = email; }
}

Important Annotations

Key Points

• Mapping is direct and explicit; no hidden behavior.
• No proxies or lazy loading.
• Simple, predictable, and easy to debug.

Repository Basics

Spring Data JDBC provides repository interfaces to simplify data access. The most common one is CrudRepository, which provides basic CRUD operations out-of-the-box.

Example Repository

import org.springframework.data.repository.CrudRepository;

publicinterfaceUserRepositoryextendsCrudRepository<User, Long> {
// No implementation needed
}

• User → Entity type
• Long → Type of primary key (@Id field)

Common Methods Provided by CrudRepository

Key Points

• No implementation required: Spring Data JDBC auto-generates the implementation at runtime.
• Provides clean and predictable CRUD operations.
• Works seamlessly with Spring’s DI and transactional support.

Saving Data

Useruser=newUser();
user.setName("Amit");
user.setEmail("[email protected]");

// Save entity
userRepository.save(user);

Key Points

• Automatically performs INSERT (new entity) or UPDATE (existing entity based on @Id).
• No persistence context or lazy-loading issues — behavior is explicit and predictable.
• Works seamlessly with Spring’s transaction management.

Fetching Data

// Fetch a single user by ID
Optional<User> user = userRepository.findById(1L);

// Fetch all users
Iterable<User> users = userRepository.findAll();

Key Points

• Data is fetched immediately — no lazy loading.
• Returns Optional for single entity to handle null-safety.
• findAll() retrieves all records without proxies or hidden queries.

Updating Data

// Fetch user by ID and update
Useruser= userRepository.findById(1L).get();
user.setName("Rahul");
userRepository.save(user);

Key Points

• Performs a full row update.
• No dirty checking or hidden persistence context.
• Explicit and predictable behavior.

Deleting Data

// Delete user by ID
userRepository.deleteById(1L);

Key Points

• Simple and explicit deletion.
• No cascading or automatic entity state tracking.

Transactions Flow

Explains how transactions work in Spring Data JDBC without persistence context confusion.

Spring Data JDBC integrates seamlessly with Spring’s @Transactional:

@Transactional
publicvoidcreateUser(User user) {
    userRepository.save(user);
}

Key Points

• Straightforward transaction handling.
• No persistence context confusion.
• Works for multiple repository operations within a transaction.

Limitations of Spring Data JDBC

Spring Data JDBC does not provide the following features:

• Lazy loading of associations
• Automatic joins between tables
• Entity state tracking (no automatic dirty checking)
• Complex ORM behavior

Developers must explicitly manage queries, relationships, and updates.

When to Use Spring Data JDBC

Spring Data JDBC is ideal for scenarios that require simplicity, predictability, and control:

• Microservices and lightweight applications
• Simple CRUD operations
• Performance-critical systems
• Need for full SQL visibility
• Applications that favor explicit, predictable behavior

When Not to Use Spring Data JDBC

Avoid using it when your application requires:

• Complex domain models with deep object graphs
• Heavy relationships between entities
• Advanced ORM features such as caching or lazy loading

Common Beginner Mistakes

Some frequent pitfalls when starting with Spring Data JDBC:

• Expecting lazy loading like in JPA/Hibernate
• Treating it like a full-fledged JPA implementation
• Designing overly complex entity relationships
• Overusing aggregates beyond what is needed for simplicity

Differences Between Spring Data JDBC vs JPA

Conclusion:

Spring Data JDBC is a lightweight, straightforward framework for working with relational databases. It emphasizes simplicity, explicit SQL control, and predictable behavior, making it ideal for microservices and performance-critical applications. Unlike JPA, it avoids hidden complexity like lazy loading, automatic joins, or entity state tracking, allowing developers to write clean, maintainable, and easy-to-debug data access code.