Project Name

Implemented On-Premises Microservices-OpenShift Architecture For Fault Tolerance & Security

Microservices, Java, SpringBoot, OpenShift, Apache Spark, Kubernetes, Apache Kafka, Cassandra, Data Grid


Our client belongs to the finance industry and works on managing sensitive information and all types of transactions. They use Red Hat OpenShift for running servers on-premises and have a secure Linux-based environment. Additionally, there were issues of fault tolerance and high concurrency arising in their system. This makes the client search for a highly efficient system that can handle thousands of concurrent users and process millions of transactions each day.



  • Their existing system was built on a monolithic architecture that causes performance issues, scalability issues, and technical debt issues.
  • It was quite difficult for them to manage several users and provide them with services simultaneously.
  • The client was unable to manage a system with multiple functionalities and dependencies for a smooth operation.

Our Solution

After understanding the client’s requirements and challenges, the Ksolves team provided a robust approach to the client that leveraged the use of modern microservices architectural patterns and technologies. The entire Ksolves solution is mentioned below, step-by-step:

  • First, the Ksolves team implemented a microservices architecture pattern for a direct transition from a monolithic architecture to facilitate scalability, manage fault isolation, and ease of management.
  • Our team then implemented the microservices framework, Saga Pattern, for distributed transaction management to maintain consistency across microservices, which involves multiple steps layers to complete the transaction end to end.
  • We then implemented Circuit Breaker, another pattern of microservices that makes sure to handle failures gracefully and ensure that failed transactions do not affect the entire system.
  • By leveraging the Service Registry, another Microservices pattern, the client can manage and direct thousands of applications efficiently for instant communication between multiple services and have complete directions for all applications running behind.
  • The Ksolves team implemented CQRS (Command Query Responsibility Segregation) to separate read and update operations for a data store.
  • One another, the database per service pattern of microservices was implemented to improve data isolation, scalability, and independence between services.
  • With the implementation of BFF (Backend for Frontend), it becomes possible for the client to develop dedicated services for front-end applications, allowing them to tailor the data for data manipulation and presentation.
  • Then, we implemented an event-driven architecture to facilitate instant communication and data flow between microservices that enables efficient handling of complex workflows.
  • At last, the deployment of the API gateway helped the client provide a single entry point for client applications, manage requests, and route them to appropriate services.

Data Flow Diagram



With the implementation of Modern Microservices Architectural Patterns and Red Hat OpenShift’s capabilities, the Ksolves team provided a robust approach to the client for handling high concurrency and ensuring fault tolerance. The successful transition to a microservice architecture was coupled with the adoption of multiple microservice patterns to manage the complex system while maintaining security and performance standards. These successful enhancements helped the client serve thousands of concurrent users to deliver an instant experience while safeguarding sensitive financial information.

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