Performance and Scalability for a SaaS-Based Identity Security Product

Case Overview

A SaaS-based Identity Security product company, serving customers since 2019 across USA,
EU, and ANZ, faced challenges in ensuring optimal performance of their web, application, and database components. The company utilized a technology stack comprising Node.js, Angular.js, MongoDB, and Python, with performance engineering tools such as Locust, Datadog, an in-house Performance Suite, and Robometrics.

ABOUT THE CLIENT

Our client is a renowned leader in workflow automation. Their mission is to unlock the full potential of every employee through innovative software solutions. They are committed to continuously improving their product to meet new and upcoming requirements.

BUSINESS CHALLENGE

The organization required a robust Performance Engineering Framework to evaluate and certify the performance of its microservices-based cloud application, addressing key concerns such as performance degradation under load conditions, scalability and availability issues in the containerized cloud environment, identification and mitigation of response time SLA breaches for critical microservices, and automation of performance regression testing to enhance efficiency and minimize manual effort.

Solution Overview

To address these challenges, the company established a comprehensive Performance Engineering Framework. This included conducting load and concurrency tests using Locust with 200 concurrent users to benchmark system performance. The team identified and resolved performance bottlenecks such as connection timeouts and backend server issues. System configurations were optimized to enhance response times, scalability, and application availability. Additionally, the company developed an in-house performance regression suite covering the top 51 most used APIs. By automating performance tests with reusable components, the company saved 200 hours per person across 80+ builds.

IMPACT

To address these challenges, the company established a comprehensive Performance Engineering Framework. It involved conducting load and concurrency tests using Locust with 200 concurrent users to benchmark system performance. The team identified and resolved performance bottlenecks, including connection timeouts and backend server issues. System configurations were optimized to enhance response times, scalability, and application availability. Additionally, an in-house performance regression suite was developed, covering the top 51 most used APIs. Performance tests were automated using reusable components, resulting in a savings of 200 hours per person across 80+ builds.

AT A GLANCE

CHALLENGE

Ensure optimal performance and scalability of a microservices-based cloud application. This required load testing, performance bottleneck resolution, system optimization, and automated regression testing to enhance efficiency and maintain SLA compliance.

IMPACT

Our Performance Engineering Framework optimized system efficiency, ensuring scalability and reliability under load. By automating performance tests and streamlining bottleneck resolution, we enhanced response times, reduced manual effort, and improved overall application stability, leading to faster and more reliable deployments.