The application is an AI-based education platform that delivers learning content, features guided practice, recommendations, and in-depth analytics. This Java-based application is available on the web and mobile (Android and iOS) and is deployed in a Kubernetes environment. With over 3 million monthly visitors on the app, the engagement is enormous.

A leading edtech platform powered by an AI engine was all set to provide digital education experiences to students and a wide audience of learning aspirants for professional skills and higher education. The portal connects over 2 million schools and leverages analytics to deliver personalized learning outcomes for end users. The app is envisioned to be scalable across education markets, devices, and data by delivering content to a diverse learning audience.

• Conduct a maturity assessment of the application and API requests to ensure reliability and performance.
• Define a workload model based on production volume transactions and research of similar products to determine a peak load of 100K concurrent users.
• Perform load testing using a suitable tool, considering its feasibility and event-driven testing capabilities for scaling users.
• Support a distributed and scalable number of users in a single process to conduct tool-based tests.
• Monitor API request and response times against user load, progressively adding users until the target concurrent load is achieved.
• Capture Locust test metrics via a CSV file and monitor server metrics using New Relic APM for overall performance assessment.
• Monitor individual pod servers in the kubernetes environment using Prometheus & Grafana tools.
• Verify API response stability within the Kubernetes environment deployment.