📦 Transaction Batch Process

Spring Boot application that automatically processes transaction files from AWS S3 and loads them into Oracle Database

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🎯 Overview

This is a production-ready, event-driven batch processing system that:

1. 📥 Listens to AWS SQS for S3 file upload notifications
2. 📁 Streams transaction files directly from S3 (memory efficient)
3. ⚙️ Processes records in configurable chunks using Spring Batch
4. 💾 Loads data into Oracle Database with full transaction management
5. 🔄 Idempotent - DB-backed file log prevents duplicate processing
6. 📊 Monitors job execution with comprehensive logging

Key Benefits

• ✅ Zero manual intervention - fully automated processing
• ✅ Scalable - handles files of any size with streaming
• ✅ Resilient - late-ack SQS strategy, skip policy, visibility heartbeat, and retry with backoff
• ✅ Idempotent - transaction_file_log table prevents duplicate file processing
• ✅ Production-ready - rolling log files, comprehensive monitoring

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🏗️ Architecture

AWS S3 Upload → S3 Event → SQS Message → Spring App → Stream File → Process Batch → Oracle DB

Detailed Flow:

┌─────────────┐
│ File Upload │ User uploads transaction file
└──────┬──────┘
       ▼
┌─────────────┐
│  S3 Bucket  │ ObjectCreated event triggered
└──────┬──────┘
       ▼
┌─────────────┐
│  SQS Queue  │ Event notification queued
└──────┬──────┘
       ▼
┌─────────────┐
│ Spring Boot │ @SqsListener receives message
│ Application │
└──────┬──────┘
       ▼
┌─────────────┐
│  S3 Stream  │ Download as InputStream (no temp files)
└──────┬──────┘
       ▼
┌─────────────┐
│Spring Batch │ Reader → Processor → Writer (chunks)
└──────┬──────┘
       ▼
┌─────────────┐
│  Oracle DB  │ Persisted with transaction management
└─────────────┘

👉 See ARCHITECTURE.md for detailed technical design

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📋 Prerequisites

Component	Version	Required
Java JDK	21+	✅
Maven	3.6+	✅
Oracle Database	12c+	✅
AWS Account	-	✅
AWS CLI (optional)	2.x	⭕

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🚀 Quick Start

1️⃣ Clone or Navigate to Project

cd d:\Java_Projects\transaction-batch-process

2️⃣ Configure Application

Edit src/main/resources/application.yml with your Oracle and AWS details.

AWS credentials are resolved via the AWS Default Credential Provider Chain — configure ~/.aws/credentials, environment variables (AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY), or an IAM role. No credential properties are read from application.yml.

3️⃣ Build

mvn clean package

4️⃣ Run

java -jar target/transaction-batch-process-1.0.0.jar

5️⃣ Test

# Upload sample file
aws s3 cp sample-data/transactions.txt s3://your-bucket/test.txt

Watch the logs for automatic processing! 🎉

👉 Need help? See QUICKSTART.md for 10-minute setup guide

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📁 File Format

The application processes # delimited flat files:

673440#245894#MGEIQ3RW9Q#2021-12-23#4459.12#3011.44#CREDIT#70945.54
673441#245895#MGEIQ3RW9R#2021-12-24#0.00#5000.00#DEPOSIT#75945.54

No header row — data starts at line 1.

Field Descriptions

Field	Type	Description
customerId	String	Customer identifier
trans_num	String	Transaction number
transaction_id	String	Unique transaction ID
transaction_date	Date	Format: YYYY-MM-DD
withdrawal_amount	Decimal	Withdrawal amount (use 0.00 if none)
deposit_amount	Decimal	Deposit amount (use 0.00 if none)
transaction_type	String	CREDIT/DEBIT/WITHDRAWAL/DEPOSIT
balance	Decimal	Account balance after transaction

---

⚙️ Configuration

Application Properties (application.yml)

# Database Configuration
spring:
  datasource:
    url: jdbc:oracle:thin:@localhost:1521/XEPDB1   # service name format, not SID
    username: your_oracle_user
    password: your_oracle_password

# AWS Configuration (credentials via Default Credential Provider Chain, not here)
aws:
  region: ap-south-1
  s3:
    bucket-name: your-transaction-bucket
  sqs:
    queue-url: https://sqs.ap-south-1.amazonaws.com/account-id/your-queue

# Batch Configuration
batch:
  chunk-size: 1000                    # Records per chunk
  skip-limit: 2147483647              # Integer.MAX_VALUE — individual failures never abort the job
  file-delimiter: "#"
  enable-dml-error-logging: true
  fallback-initial-delay-ms: 500      # Delay before individual fallback inserts
  lock-retry-max-attempts: 3          # Max retries for transient lock errors
  lock-retry-base-delay-ms: 200       # Exponential backoff base (ms)
  lock-retry-max-delay-ms: 2000       # Exponential backoff ceiling (ms)

---

🗄️ Database Schema

Table: transaction_process

CREATE TABLE transaction_process (
    id NUMBER(19) PRIMARY KEY,
    customer_id VARCHAR2(50) NOT NULL,
    trans_num VARCHAR2(50) NOT NULL,
    transaction_id VARCHAR2(50) UNIQUE NOT NULL,
    transaction_date DATE NOT NULL,
    withdrawal_amount NUMBER(15, 2),
    deposit_amount NUMBER(15, 2),
    transaction_type VARCHAR2(20) NOT NULL,
    balance NUMBER(15, 2) NOT NULL,
    file_name VARCHAR2(255),
    processed_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

The DDL is in src/main/resources/schema.sql and must be applied manually before first run — initialize-schema: never is set intentionally.

---

🎯 Features

✅ Automated Processing

• Event-driven architecture with SQS
• No manual job triggering required
• Processes files immediately after upload

✅ Memory Efficient

• Streaming from S3 (no temp files)
• Chunk-based processing
• Minimal memory footprint

✅ Idempotent Processing

• DB-backed file log (transaction_file_log) prevents duplicate processing
• UNIQUE constraint on object key with status machine (IN_PROGRESS → COMPLETED | FAILED)
• Failed files can be retried automatically on SQS redelivery

✅ Error Handling

• Late SQS acknowledgment — only on success; failures redeliver automatically
• Visibility heartbeat keeps messages invisible during long-running jobs
• Configurable skip policy (default: unlimited)
• DML error logging scoped per file via ORA_ERR_TAG$
• Fallback individual inserts with exponential backoff on lock errors
• Job status verified before marking complete

✅ Monitoring

• Job execution statistics via TransactionJobListener
• Read/Write/Skip counters
• Processing time tracking
• Rolling log files (50MB/file, 30-day history, 1GB cap)
• AWS SDK log noise suppressed (WARN level)

✅ Scalability

• Horizontal scaling (multiple instances with DB-level idempotency)
• Configurable chunk size
• Java 21 Virtual Threads enabled globally
• Database connection pooling

---

📊 Performance

Metric	Value
Throughput	10,000-50,000 records/min
Memory Usage	~50-100MB base + chunks
Chunk Size	1000 (configurable)
Skip Limit	Integer.MAX_VALUE (configurable)

---

🔧 AWS Setup

Required AWS Resources

1. S3 Bucket - for transaction file storage
2. SQS Queue - for event notifications
3. IAM User/Role - with S3 read and SQS permissions

Quick Setup Commands

# Create S3 bucket
aws s3 mb s3://your-transaction-bucket --region ap-south-1

# Create SQS queue
aws sqs create-queue --queue-name transaction-file-queue

# Configure S3 → SQS notification
# (See detailed steps in AWS_SETUP_GUIDE.md)

👉 Complete guide: See AWS_SETUP_GUIDE.md

---

📚 Documentation

Document	Description
INDEX.md	📖 Complete project overview and index
QUICKSTART.md	⚡ Get running in 10 minutes
SETUP.md	🛠️ Detailed setup and deployment guide
AWS_SETUP_GUIDE.md	☁️ Step-by-step AWS configuration
ARCHITECTURE.md	🏗️ Technical architecture and design
PROJECT_SUMMARY.md	📋 Complete feature list and specs
JDBCTEMPLATE_IMPLEMENTATION.md	🔧 JdbcTemplate batch operations guide

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🧪 Testing

Run Unit Tests

mvn test

Manual Testing

# 1. Upload sample file
aws s3 cp sample-data/transactions.txt s3://your-bucket/

# 2. Watch logs for processing
Get-Content logs/transaction-batch.log -Tail 50 -Wait

# 3. Verify in database
sqlplus user/pass@host:1521/ORCL
SELECT COUNT(*) FROM transaction_process;

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🐛 Troubleshooting

Issue: Connection to Oracle fails

# Test connection
sqlplus username/password@host:1521/ORCL

Issue: No messages from SQS

• Verify S3 event notification is configured
• Check SQS queue policy allows S3
• Test manually: aws sqs receive-message --queue-url YOUR_URL

Issue: Access Denied on S3

• Verify IAM credentials have S3 read permissions
• Test: aws s3 ls s3://your-bucket

👉 More solutions in SETUP.md troubleshooting section

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📈 Monitoring

Application Logs

INFO - Received SQS message
INFO - Processing S3 event: bucket=my-bucket, key=transactions.txt
INFO - === Starting Transaction Processing Job ===
INFO - Batch write completed: 1000 successful, 0 skipped
INFO - === Transaction Processing Job Completed ===
INFO - Job Status: COMPLETED
INFO - Records Read: 10000
INFO - Records Written: 10000
INFO - Duration: 45 seconds

Key Metrics to Monitor

• Job execution time
• Records processed per minute
• Skip/error rate
• SQS message processing time
• Database connection pool usage

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🔐 Security

Best Practices Implemented

✅ No hardcoded credentials ✅ Environment variable support ✅ .gitignore for sensitive files ✅ Connection pooling with timeouts

Recommended for Production

• Use AWS IAM roles (instead of access keys)
• Store credentials in AWS Secrets Manager
• Enable SSL/TLS for Oracle connections
• Regular credential rotation
• Enable CloudTrail for audit logging

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📦 Project Structure

transaction-batch-process/
├── src/main/java/com/transaction/batch/
│   ├── TransactionBatchProcessApplication.java    # Main app
│   ├── config/           # Batch, AWS, app config + reader/processor/writer/listeners
│   ├── model/            # Java records (TransactionRecord, TransactionProcess, S3EventNotification, ProcessingOutcome)
│   └── service/          # SqsListenerService, S3Service, BatchJobService, FileProcessingLogService
├── src/main/resources/
│   ├── application.yml   # Configuration
│   ├── schema.sql        # Database DDL (applied manually)
│   └── logback-spring.xml # Rolling file appender + log levels
├── sample-data/
│   └── transactions.txt  # Sample file
└── docs/                 # Documentation files

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🛠️ Technology Stack

• Spring Boot 3.4.0 - Application framework
• Spring Batch - Batch processing
• JdbcTemplate - Database access (no JPA/Spring Data)
• AWS SDK 2.29.0 - S3 and SQS integration
• Oracle JDBC 23.5.0 - Database driver
• HikariCP - Connection pooling
• Lombok - Code simplification
• Jackson - JSON processing

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🚀 Deployment

Local Development

mvn spring-boot:run

Production JAR

mvn clean package -DskipTests
java -jar target/transaction-batch-process-1.0.0.jar

Docker (Optional)

docker build -t transaction-batch-process .
docker run -p 8080:8080 transaction-batch-process

Windows Service

See SETUP.md for systemd/Windows Service configuration

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🎓 Next Steps

For Development

1. ✅ Review code structure
2. ✅ Test with sample data
3. ⬜ Customize processor for business rules
4. ⬜ Add additional validation
5. ⬜ Implement custom monitoring

For Production

1. ⬜ Configure production database
2. ⬜ Set up production AWS resources
3. ⬜ Use IAM roles for authentication
4. ⬜ Configure CloudWatch monitoring
5. ⬜ Set up log aggregation
6. ⬜ Deploy as system service

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📞 Support

For issues or questions:

1. Check application logs
2. Review documentation files
3. Verify AWS and database connectivity
4. Check docs/SETUP.md for troubleshooting

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📄 License

[Your License Here]

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🙏 Acknowledgments

Built with:

• Spring Framework
• AWS SDK for Java
• Oracle Database
• Maven

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⭐ Quick Links

• 📖 Complete Index
• ⚡ Quick Start Guide
• 🛠️ Setup Instructions
• ☁️ AWS Configuration
• 🏗️ Architecture Details
• 🔧 JdbcTemplate Implementation

Ready to process transactions at scale! 🚀