Microservices-Based Retail Solution

We designed and implemented a microservices-based architecture that enabled the client to take control of their e-commerce platform. We helped transition from third-party systems to an in-house solution, moving critical operations such as account management, catalog, and order management to microservices that could scale seamlessly across multiple countries.

Microservices Architecture

We built a robust microservices architecture to replace the disparate systems. Key microservices included:

• Account Microservice: Centralized account management for multiple countries.
• Catalog Microservice: A critical API for retrieving product data and inventory information.
• OMS Microservice: Handled orders and integrated with the client’s inventory and warehouse management systems.

These microservices were designed to be modular, scalable, and easily integrated with both internal and third-party systems.

Data migration

• Account Data Migration: We migrated account data from third-party systems using a streamlined process:
• Data was exported in XML format, compressed into .gz files, and stored in an AWS S3 bucket.
• The migration process involved converting the XML files to a database format and performing integrity checks before moving the data to the new account system.
• Order Data Migration: We implemented a two-tier data migration process for order history:
• Recent data was migrated to a PostgreSQL database (hot storage) for quick access, while older data remained in a legacy datastore (cold storage).
• A Kafka migrator continuously transferred legacy data to the PostgreSQL database, ensuring real-time access to historical order data.

OMS (Order Management System) Integration

The OMS was integrated with the client’s inventory management (IMS) and warehouse management systems (WMS) using APIs and ETL (Extract, Transform, Load) processes for real-time data synchronization. We also implemented asynchronous processing for OMS batch jobs to enhance performance.

Catalog Performance Enhancements

• In-Memory Caching
• API Caching
• Concurrent Queries
• Denormalization

Order Data Access Optimization

We implemented a streamlined process for retrieving user order histories:

• When a user requests order history, the system first queries the PostgreSQL database for recent records.
• If data is insufficient, it queries the legacy datastore and merges the results.
• A Kafka pipeline continuously migrates older order history into PostgreSQL, progressively improving performance over time.

Performance Enhancements in OMS

• In-Memory Basket Data Store
• ElasticSearch Integration
• Database Query Optimization
• Database Sharding & Partitioning
• Asynchronous Logging

Frontend Optimization

• Server-Side Rendering (SSR)
• Lazy Loading
• Resource Compression

The client relied on different e-commerce solutions in each country. This led to inconsistent customer experiences, increased maintenance efforts, and operational challenges.

• Limited Vendor Control: The client lacked control over third-party vendors managing the applications, limiting flexibility and responsiveness to business needs.
• High License and Change Costs: The cost of maintaining third-party platforms was significant. Even small change requests (CRs) were expensive and time-consuming.
• Delayed Rollouts: New features and bug fixes were not rolled out on time, leading to lost revenue opportunities.
• Data Ownership: The client did not have full ownership of their e-commerce data, particularly customer and order data, which was managed by third-party vendors.
• Performance and Scalability: The existing systems struggled to handle peak traffic during major events, causing frequent downtimes and poor customer experiences.

• Reduced licensing costs and eliminated expensive change request fees by transitioning to an in-house platform, providing significant cost savings and minimizing vendor dependency.
• Gained full control over the e-commerce platform, enabling faster decision-making and real-time access to critical business data, improving operational efficiency and responsiveness.
• Achieved flexibility to quickly introduce new features and improvements, reducing delays in rolling out updates and capturing revenue opportunities more effectively.
• Delivered a consistent and superior shopping experience across all countries through enhanced platform optimizations like server-side rendering (SSR), lazy loading, and resource compression, resulting in improved load time and smoother user experiences.
• Optimized system architecture resulted in faster response time, better handling during peak traffic, and a reduction in downtime, ensuring seamless scalability and improved performance during high-demand events.

• Java
• Spring Boot
• PostgreSQL
• ElasticSearch
• Redis
• Kafka
• DynamoDB
• AWS S3
• MapStruct
• React.js
• SSR
• APIs
• ETL
• AWS
• Docker
• Kubernetes
• Jenkins