Bulk Inserts with Spring Boot and JpaRepository

Bulk inserts are a common optimization technique for efficiently inserting multiple rows of data into a database. Spring Boot and its JDBC-based persistence layer, JpaRepository, provide support for performing bulk inserts.

Coding an Example of Bulk Inserts with Spring Boot


  • Java installed
  • Spring Boot project set up with necessary dependencies
  • Database configured and connected

Step 1: Configure Entity Class

First, create an entity class (e.g., User) annotated with @Entity that represents the table to perform bulk inserts.

public class User {
    // Entity fields, setters, and getters

Step 2: Configure Repository Interface

Create a repository interface for the User entity that extends JpaRepository.

public interface UserRepository extends JpaRepository<User, Long> {
    // Additional repository methods if needed

Step 3: Configure Batch Size in application.properties

Hibernate, the underlying ORM framework used by Spring Data JPA, can optimize bulk inserts by batching multiple rows together and sending them to the database as a single statement. To enable batching, you can set the spring.jpa.properties.hibernate.jdbc.batch_size property in your application.properties file. This property specifies the maximum number of entities to include in each batch. For example, setting the value to 50 will instruct Hibernate to group rows into batches of 50 entities before sending them to the database.


Step 4: Implement Bulk Inserts

To perform bulk inserts, you can utilize the saveAll() method provided by JpaRepository. This method accepts a list of entities to be saved to the database. When used in conjunction with the batch size setting, Hibernate will automatically group entities into batches and execute them efficiently.

public class UserService {

    private final UserRepository userRepository;

    public UserService(UserRepository userRepository) {
        this.userRepository = userRepository;

    public void saveUsersInBulk(List<User> userList) {

Step 5: Implement the Usage

Invoke the saveUsersInBulk() method from a controller or any other component by passing a list of entities to be inserted.

public class UserController {

    private final UserService userService;

    public UserController(UserService userService) {
        this.userService = userService;

    public ResponseEntity<String> insertUsersInBulk(@RequestBody List<User> userList) {
        return ResponseEntity.ok("Bulk insert completed successfully");

Benefits of Bulk Inserts

Bulk inserts offer several benefits compared to individual inserts:

  1. Improved Performance: Batch inserts can significantly reduce the number of network round trips and database transactions, leading to improved performance and reduced latency.
  2. Optimized Query Processing: Batching allows the database to optimize query processing and avoid unnecessary locking and locking contention.
  3. Reduced Network Traffic: By grouping rows into batches, bulk inserts reduce the overall network traffic between the application and the database.
  4. Enhanced Scalability: Bulk inserts can help improve the scalability of applications that handle large volumes of data.


While bulk inserts offer performance benefits, it’s important to consider the following factors:

  1. Memory Usage: Batching can increase memory usage, especially for large batches. Ensure the application has sufficient memory to accommodate the batch size.
  2. Database Limitations: Check the database’s maximum batch size and transaction limits to avoid exceeding its capabilities.
  3. Data Integrity: Ensure the batched data is valid and consistent to avoid potential data integrity issues.
  4. Concurrency and Conflicts: If multiple threads or processes are performing bulk inserts simultaneously, consider using optimistic locking or other concurrency control mechanisms to avoid conflicts.

In summary, bulk inserts can be a powerful tool for optimizing data insertion performance in Spring Boot applications. By setting the appropriate batch size and using the saveAll() method, you can significantly improve the efficiency of data ingestion and reduce the overall workload on the database.

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