After an intensive 18-month development cycle, our team successfully deployed a revamped AI research assistant platform. This project transformed the client's service, making it more scalable, efficient, and user-focused. It now serves as a cornerstone tool in the global academic community, facilitating research and insights with unprecedented efficiency.

The strategy for overhauling the client's technological foundation and tackling the present challenges was comprehensive and well-planned. Our team meticulously crafted and executed solutions that spanned infrastructure improvements, database management enhancements, and optimizations in AI service efficiency, aligning with the established goals. The deployment approach was guided by principles of agility, scalability, and readiness for future developments, guaranteeing that the client could overcome existing hurdles while remaining adaptable and competitive in the changing technology landscape.

4.1 Decoupling the Monolithic Architecture

Objective: Transition to a scalable, manageable microservices architecture.

Implementation Strategy:

1. Microservices Architecture Definition: We commenced by dissecting the existing monolithic system to delineate distinct service boundaries, such as User Management, Document Processing, and AI Model Training. This segmentation allowed for modular development and deployment.
2. Adoption of Serverless Technologies: AWS Lambda was adopted to run the microservices, eliminating the need for traditional server management and enabling on-demand scaling that matched the system's operational demands. This transition to serverless computing was pivotal in enhancing system scalability and cost-efficiency.
3. Continuous Integration and Continuous Deployment (CI/CD): We leveraged GitHub Actions to automate the deployment pipeline, facilitating smoother and more rapid updates as well as ensuring consistencies across environments through Infrastructure as Code (IaC) practices.

4.2 Scalable and Managed Database Solution

Objective: Enhance database scalability, performance, and maintainability through migration to Amazon RDS for PostgreSQL.

Implementation Strategy:

1. Database Migration to Amazon RDS: The transition involved migrating the existing database from its colocated environment with the legacy API server to a cloud-native Amazon RDS for PostgreSQL instance. This move aimed at achieving higher performance, automatic scaling, and improved data integrity.
2. Implementation of Read Replicas and Backup Strategies: To ensure high availability and load balancing for read operations, we deployed multiple read replicas. Automated backups and manual snapshot features of Amazon RDS were leveraged to safeguard data against loss and ensure business continuity.
3. Connection Pooling with AWS Lambda: We introduced connection pooling mechanisms to optimize database connections established by AWS Lambda, significantly reducing overheads related to database connections and enhancing overall system performance.

4.3 Improving the AI Indexing Service

Objective: Significantly reduce the AI indexing service's document processing time while maintaining system scalability and responsiveness.

Implementation Strategy:

1. Integration of GROBID with Amazon ECS: GROBID was deployed within Amazon Elastic Container Service (ECS) to handle the extraction and analysis of bibliographic information from academic papers. This containerized deployment facilitated scalable and efficient document processing.
2. Event-driven Architecture with Amazon S3 and SQS: We adopted an event-driven architecture where document uploads to Amazon S3 triggered event notifications. These notifications were then processed by Amazon SQS to manage the workload efficiently, ensuring dynamic scaling of processing resources based on demand.
3. Data Indexing with Amazon Elasticsearch Service: Following the processing with GROBID, the extracted data was indexed using a managed search service. This facilitated advanced search and analysis functions, crucial for the AI models to deliver pertinent suggestions and insights.
4. Autoscaling and Robust Error Handling: Autoscaling policies for the ECS service housing GROBID ensured that resource allocation matched the incoming workload, optimizing both performance and cost. A Dead Letter Queue (DLQ) strategy was implemented to manage processing failures, allowing for the isolation and reprocessing of problematic documents.

Impact on Client’s Business

• Reduced AI Indexing Service Processing Time: The optimization of the AI indexing service, including the integration of GROBID and the adoption of an event-driven architecture, has led to a dramatic reduction in document processing time. From the initial seven minutes per paper, the processing time has been cut significantly, vastly improving the user experience and enabling researchers to receive timely assistance and insights for their academic work.
• Enhanced System Scalability: The shift to a microservices architecture and the adoption of serverless computing technologies have significantly enhanced scalability for the platform. It is now equipped to seamlessly manage growing volumes of user interactions and data processing activities, maintaining high performance levels. This advancement supports the client's ambitious plans for expansion and development.
• Cost Efficiency Improvements: By utilizing managed cloud services and enhancing resource efficiency, we've realized significant savings in operational expenses for the client. The embrace of serverless technologies and streamlined database management approaches has reduced the costs associated with scaling, ensuring the platform's economic feasibility and sustainability over time.
• High Availability and Performance: The introduction of Amazon RDS read replicas and the implementation of connection pooling have ensured high availability and boosted the performance of the database services. This enhancement has further solidified the platform's reliability, providing a seamless research experience to users even under peak load conditions.

Technology Integration

The adoption of an event-driven architecture necessitated a nuanced selection of technologies, each playing a crucial role in achieving the desired outcomes of scalability, performance, and user engagement.

• Backend Processing: Node.js was chosen for its non-blocking I/O model, making it exceptionally well-suited for building scalable network applications that can efficiently process a high volume of events and requests.

• Frontend Development: React was utilized for creating dynamic and responsive user interfaces, enhanced by Next.js for its server-side rendering capabilities. This combination ensures quick loading times and a seamless interactive experience for users navigating the platform.

• Event-Driven Infrastructure:

  • AWS Lambda: Central to our serverless computing strategy, Lambda functions are triggered by various AWS service events, allowing for highly scalable, event-driven processing without managing infrastructure.
  • Amazon S3 and CloudFront: S3's event notification feature serves as a source for Lambda triggers, enabling operations like processing uploaded documents in real-time. CloudFront’s global content delivery network ensures fast, secure delivery of web content.
  • Amazon SQS and SNS (Simple Notification Service): SQS queues and SNS topics facilitate decoupled components within the architecture, managing message queues and push notifications that trigger downstream processes in an orderly, scalable manner.
  • AWS Step Functions: Coordinates the multiple microservices and Lambda functions, orchestrating complicated workflows in response to events with ease and reliability.

• Monitoring and Autoscaling:

  • Amazon CloudWatch: Monitors the performance of AWS resources and applications in real time, enabling automated responses to system-wide performance changes based on predefined metrics and events.
  • AWS Auto Scaling Groups (ASG): Automatically adjusts necessary compute resources to maintain performance and minimize costs, dynamically responding to fluctuations in demand.

• Database and Caching Technologies:

  • PostgreSQL for structured data storage, alongside DynamoDB for scalable NoSQL storage options that cater to different data models and access patterns inherent in an event-driven architecture.
  • Redis: Provides high-performance caching to speed up dynamic data retrieval, crucial for maintaining fast response times in an event-driven system.

Before embarking on this project, the client and BigCircle had been in collaboration for over a year. To tackle the specific needs of scaling the streaming platform, most of the team was carefully pieced together by us. For this undertaking, we assembled a comprehensive team, comprising a Product Architect, 3 Developers, 1 DevOps Engineer, 1 QA Engineer, and a Project Manager.