The Client’s vote tabulation platform relied on a complex architecture with cloud services like MongoDB and Lambda functions.
Simulating real-time voting traffic and generating data in multiple formats risked downtime during the mission-critical US election.
We developed a ‘fully configurable’ voting data simulation engine in three weeks.
The engine produced high volumes of voting data in formats like PDF, JSON, XML, and CSV, tailored to meet ‘county-specific’ requirements and real-world scenarios.
Achieved 12,000 web scrapes per minute, 50% more than the target of 8,000.
Identified critical throttling issues in MongoDB and Lambda functions, enabling the Client to optimize its architecture.
The Client is a not-for-profit news agency that provides a real-time vote tabulation platform to other media outlets. Their platform aggregates voting results from county websites, delivering critical election updates.
To ensure flawless performance during the high-pressure election period, the Client approached Qualitest just four weeks prior to the election date.
The Client’s vote tabulation platform was built on cloud services like AWS services such as Lambda functions, Kinesis stream, and MongoDB. The platform required real-time voting traffic in multiple formats (PDF, JSON, XML, HTML, CSV) to validate its functional and non-functional aspects before the US election. This is to identify any system vulnerabilities, performance issues, and functional defects etc.
Qualitest designed a configurable voting data simulation engine which produced comprehensive datasets customized to county-specific requirements. It delivered data in various formats, including PDF, JSON, XML, HTML and CSV, ensuring seamless compatibility with real-world scenarios. It managed to accurately reflect diverse voting patterns.
The simulation engine seamlessly integrated with AWS S3, enabling efficient and reliable data upload and processing for large-scale operations. This integration streamlined workflows, reduced manual intervention, and ensured fast and accurate handling of simulated voting data.
The engine featured adjustable settings such as thread count for optimizing data generation speed, update cycles for incremental data simulations, and flexible format options to cater to diverse county-specific requirements. These configurable features enhanced both flexibility and scalability, allowing the engine to adapt easily to varying workloads.
The engine delivered an impressive performance by achieving approximately 12,000 scrapes per minute, significantly exceeding the target of 8,000 scrapes per minute. This exceptional output demonstrated the system’s ability to handle huge workloads under real-world conditions. During testing, the engine exposed critical throttling issues within MongoDB and Lambda functions, which were causing bottlenecks in data processing.
By identifying these problems early, Qualitest enabled the Client to implement targeted optimizations, enhancing the overall system architecture. These improvements ensured stable and efficient performance while ensuring reliability and speed during peak election periods.
