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Spring Boot with Amazon Aurora

Introduction

Amazon Aurora is AWS's cloud-native relational database designed for high performance, high availability, and automatic scaling.

Aurora is compatible with:

• PostgreSQL
• MySQL

Unlike a traditional database instance, Aurora uses a cluster architecture where compute and storage are separated. Multiple database instances can share the same distributed storage volume.

This makes Aurora one of the most popular databases for enterprise Spring Boot applications.

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Learning Objectives

After completing this article, you will understand:

• What is Amazon Aurora?
• Aurora Architecture
• Aurora Cluster
• Writer Endpoint
• Reader Endpoint
• Aurora Storage Architecture
• Aurora vs Amazon RDS
• Spring Boot Integration
• Spring Data JPA
• CRUD APIs
• High Availability
• Production Best Practices

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What is Amazon Aurora?

Amazon Aurora is a managed relational database built by AWS.

Aurora provides:

• High availability
• Automatic failover
• Faster performance
• Distributed storage
• Read replicas
• Automatic backups
• Point-in-time recovery

AWS manages:

• Database patching
• Backups
• Replication
• Storage growth
• Failover

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Aurora Architecture

flowchart TD
    APP[Spring Boot Application]

    WRITER[Writer Instance]

    READER1[Reader Instance 1]

    READER2[Reader Instance 2]

    STORAGE[(Distributed Aurora Storage)]

    APP --> WRITER
    APP --> READER1
    APP --> READER2

    WRITER --> STORAGE
    READER1 --> STORAGE
    READER2 --> STORAGE

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Aurora Storage Architecture

Aurora separates compute from storage.

flowchart TD
    Writer

    Reader1

    Reader2

    SharedStorage

    Writer --> SharedStorage
    Reader1 --> SharedStorage
    Reader2 --> SharedStorage

Storage automatically grows without manual intervention.

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Why Aurora?

Traditional databases have limitations:

• Storage scaling
• Manual replication
• Slow failover
• Limited read scalability

Aurora solves these problems.

Benefits:

• Up to 5x faster than MySQL
• Up to 3x faster than PostgreSQL (AWS benchmark)
• Six copies of data across three Availability Zones
• Automatic failover
• Distributed storage
• Reader endpoints

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Aurora vs Amazon RDS

Feature	Amazon RDS	Amazon Aurora
Storage	Attached to instance	Distributed
Compute	Single instance	Cluster
Read Replicas	Supported	Optimized
Failover	Good	Faster
Performance	High	Very High
Storage Scaling	Manual/Auto	Automatic
Enterprise Usage	High	Very High

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Enterprise Architecture

flowchart TD

Users

ALB

SpringBoot

AuroraWriter

AuroraReader

CloudWatch

Users --> ALB
ALB --> SpringBoot
SpringBoot --> AuroraWriter
SpringBoot --> AuroraReader
SpringBoot --> CloudWatch

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Aurora Components

Component	Description
Cluster	Aurora database cluster
Writer Instance	Handles INSERT, UPDATE, DELETE
Reader Instance	Handles SELECT queries
Cluster Endpoint	Writer endpoint
Reader Endpoint	Load balances reads
Storage	Shared distributed storage
Backup	Automatic
Failover	Automatic

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Real-Time Use Cases

Aurora is commonly used for:

• Banking
• Insurance
• Payment platforms
• E-Commerce
• Healthcare
• SaaS platforms
• Enterprise microservices
• Financial systems

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Project Architecture

flowchart TD

Browser

SpringBoot

SpringDataJPA

Hibernate

AuroraCluster

Browser --> SpringBoot
SpringBoot --> SpringDataJPA
SpringDataJPA --> Hibernate
Hibernate --> AuroraCluster

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Prerequisites

Install:

Java 17

Maven

AWS CLI

Spring Boot

PostgreSQL Driver

Verify Java:

java -version

Verify AWS CLI:

aws --version

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Step 1 Create Aurora Cluster

AWS Console

↓

Amazon RDS

↓

Create Database

Choose:

Amazon Aurora

Engine:

Aurora PostgreSQL Compatible

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Cluster Configuration

Cluster Name

codewithvenu-aurora-cluster

Master Username

postgres

Password

********

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Instance Configuration

Learning:

db.t4g.medium

Production:

db.r6g.large

or larger.

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Storage

Aurora automatically manages storage.

No need to preallocate disk space.

Storage automatically grows up to the service limits.

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Connectivity

VPC:

Default VPC

Public Access

Learning:

Enabled

Production:

Disabled

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Security Group

Allow inbound:

PostgreSQL

Port 5432

Source:

Your IP

Never use:

0.0.0.0/0

in production.

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Create Cluster

Click

Create Database

Provisioning takes several minutes.

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Aurora Endpoints

Aurora provides multiple endpoints.

Writer Endpoint

Used for:

• INSERT
• UPDATE
• DELETE

Example:

codewithvenu.cluster-abc123.us-east-1.rds.amazonaws.com

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Reader Endpoint

Used for:

• SELECT

Example:

codewithvenu.cluster-ro-abc123.us-east-1.rds.amazonaws.com

The reader endpoint automatically distributes read traffic across reader instances.

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Verify Cluster

Status:

Available

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Spring Boot Project

Dependencies:

• Spring Web
• Spring Data JPA
• PostgreSQL Driver
• Validation
• Actuator

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Maven Dependencies

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>

<dependency>
    <groupId>org.postgresql</groupId>
    <artifactId>postgresql</artifactId>
</dependency>

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

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Configure application.yml

spring:

  datasource:
    url: jdbc:postgresql://codewithvenu.cluster-abc123.us-east-1.rds.amazonaws.com:5432/postgres
    username: postgres
    password: your-password

  jpa:
    hibernate:
      ddl-auto: update

    show-sql: true

    properties:
      hibernate:
        format_sql: true

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Create Entity

@Entity
@Table(name="employees")
public class Employee {

    @Id
    @GeneratedValue
    private Long id;

    private String name;

    private String department;

}

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Repository

public interface EmployeeRepository
        extends JpaRepository<Employee, Long> {

}

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Service Layer

@Service
public class EmployeeService {

    @Autowired
    private EmployeeRepository repository;

}

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REST Controller

@RestController
@RequestMapping("/employees")
public class EmployeeController {

}

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CRUD Flow

flowchart LR

Client

Controller

Service

Repository

Aurora

Client --> Controller
Controller --> Service
Service --> Repository
Repository --> Aurora

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Test Connection

Run the application:

mvn spring-boot:run

Expected output:

Tomcat started on port 8080
Connected to Aurora PostgreSQL
Hibernate initialized successfully

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CRUD APIs

Create Employee

POST /employees

Input:

{
  "name":"Venu",
  "department":"Architecture"
}

Output:

{
  "id":1,
  "name":"Venu",
  "department":"Architecture"
}

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Get Employees

GET /employees

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Update Employee

PUT /employees/1

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Delete Employee

DELETE /employees/1

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Common Errors

Connection Timeout

Check:

• Security Group
• VPC
• Endpoint
• Port

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Authentication Failed

Verify:

• Username
• Password
• Database name

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Endpoint Not Reachable

Ensure:

• Aurora cluster status is Available
• Public access is enabled for learning
• Your IP is allowed in the Security Group

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Best Practices

• Use Aurora PostgreSQL for enterprise Spring Boot applications
• Keep the cluster private in production
• Use separate Writer and Reader endpoints
• Use Spring Data JPA
• Enable connection pooling
• Enable automatic backups
• Enable Performance Insights
• Use AWS Secrets Manager for credentials
• Monitor with CloudWatch
• Enable Multi-AZ

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Summary

In this article, we learned how to integrate Spring Boot with Amazon Aurora PostgreSQL.

We covered:

• Aurora architecture
• Aurora cluster
• Writer and Reader endpoints
• Spring Boot configuration
• Spring Data JPA integration
• CRUD API structure
• Common errors
• Enterprise best practices

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