ganesh-sadanala / distributed-real-time-payment-processing-system Goto Github PK

Description: This project aims to develop a highly scalable and fault-tolerant real-time payment processing system that can handle a massive volume of transactions across multiple geographical regions. The system will leverage a microservices architecture, event-driven design, and distributed computing principles to ensure reliability, low latency, and high throughput.\

Key Features

• Microservices Architecture: Decompose the system into independent microservices, such as payment gateway, transaction processing, and settlement services, each responsible for a specific business capability.
• Event-Driven Architecture: Implement an event-driven architecture using a message broker Apache Kafka to enable asynchronous communication between microservices and decouple components.
• Distributed Transaction Processing: Develop a distributed transaction processing engine that can handle high volumes of concurrent transactions while ensuring data consistency and integrity across multiple databases (PostgreSQL and NoSQL).
• Concurrency and Scalability: Leverage JEE concurrency concepts, such as thread pools and asynchronous programming, to handle high concurrency and scale the system horizontally across multiple nodes.
• Fraud Detection and Risk Management: Implement advanced fraud detection algorithms and risk management strategies to identify and mitigate potential fraudulent activities in real-time.
• Load Balancing and Failover: Implement load balancing techniques to distribute incoming requests across multiple nodes and ensure high availability through failover mechanisms.
• Monitoring and Analytics: Develop monitoring and analytics tools to track system performance, identify bottlenecks, and optimize the system for better efficiency.
• Security and Compliance: Implement robust security measures, such as encryption, authentication, and authorization, to ensure the system's security and compliance with industry standards and regulations.\

Architectural Principles and Distributed Environment

• Microservices Architecture: Break down the system into smaller, independent services that can be developed, deployed, and scaled independently.
• Event-Driven Architecture: Implement an event-driven architecture to decouple components and enable asynchronous communication between services.
• Distributed Data Management: Implement distributed data management strategies, such as sharding, replication, and partitioning, to ensure data availability and consistency across multiple nodes.
• Containerization and Orchestration: Use containerization technologies (e.g., Docker) and container orchestration platforms (e.g., Kubernetes) to deploy and manage the distributed system across multiple nodes.
• Service Discovery and Load Balancing: Implement service discovery mechanisms (e.g., Consul, Zookeeper) and load balancing techniques to distribute incoming requests across multiple nodes.
• Circuit Breaker and Bulkhead Patterns: Implement circuit breaker and bulkhead patterns to prevent cascading failures and ensure system resilience.
• Caching and Content Delivery Networks (CDNs): Implement caching mechanisms and leverage CDNs to improve performance and reduce the load on the backend services.

Database design decision

• transaction-processing: MongoDB/NoSQL/DocumentDB
  • Reasons: Horizontal Scalability (Sharding), High Availability (Replica sets), Schema flexibility, Write Performance.
  • Anticipating that the current system is handling large volumes of diverse transaction data and require flexible, scalable storage, we are going with MongoDB.
• settlement: PostgreSQL
  • Reasons: Necessary transactional integrity, relationships, consistency, security features.