Case Study
Building Performance Resilience into Cloud-Native Applications with Automated Testing
Download Case StudyIndustry: Financial Services / Lease-to-Own Solutions
Key Technologies / Platforms: Gatling 3.9.5, Scala, Maven, GitHub Actions, Jenkins, CircleCI, Octopus Deploy, Argo CD, AWS EKS, Docker, Kubernetes, AWS CloudWatch, Splunk, Grafana, Dynatrace
About the Client
The client offers lease-to-own purchase options online, through its app, and at retail locations. Consumers use its technology platform to receive an instant decision and choose flexible payment terms when buying furniture, appliances, electronics, and other products.
Business Requirements
- Validate application stability and throughput under peak loads.
- Introduce continuous performance testing in CI/CD.
- Build a reusable performance-as-code framework for microservices.
- Test AWS EKS auto-scaling under high concurrency.
- Connect performance results with application and infrastructure telemetry.
- Modernize legacy Jenkins pipelines and reduce tool dependencies.
- Adopt Git-based, Kubernetes-native delivery practices.
Business Challenges
- Performance at Peak Load: The newly migrated AWS EKS environment needed testing for stability, throughput, and response times under high concurrency.
- No Automated Load Testing: Without automated performance testing, performance regressions could remain unnoticed until after deployment.
- Disconnected Performance Data: Monitoring was spread across multiple tools, making it difficult to connect load-test results with application and infrastructure behavior.
- EKS Scalability Under Stress: The team needed to understand how EKS auto-scaling would respond to sudden traffic increases without affecting application performance.
- Limited Reusable Test Assets: Teams lacked a common set of performance tests they could reuse across microservices, so they often had to recreate similar scenarios.
Our Approach and Solutions
- Gatling Performance Testing Framework: Jade Global used Scala and Gatling performance testing for the CardPayment and ACH flows. Simulations, test data, requests, and user journeys were maintained separately, making it easier to use the same test components across different microservices.
- Continuous Performance Testing in CI/CD: GitHub Actions brought performance testing into the pipeline. Teams can adjust user load, ramp-up period, test duration, or environment based on what they need to test across QA, Staging, and Production.
- Performance Monitoring and Analysis: The team reviewed test runs alongside infrastructure data to trace slow responses and errors back to system behavior during the test. This gave the team deep visibility into the cloud-native environment's performance.
- CI/CD Modernization: The delivery setup evolved in two stages. FastPass 1.0 replaced the earlier Jenkins setup with CircleCI, Octopus Deploy, and Argo CD. FastPass 2.0 then brought more of the workflow into GitHub Actions, cutting down the number of tools involved in the release process.
Impact and Business Benefits
- Automated performance regression testing within GitHub Actions.
- Reusable performance scenarios for payment services and microservices
- Better correlation between test results and system health through integrated observability.
- Greater visibility into latency, throughput, and service errors.
- Reduced CI/CD tool dependencies and infrastructure overhead.
- Established a foundation for GitOps and Kubernetes-native delivery
- Reduced CI/CD Infrastructure Overhead: Cut pipeline tool dependencies and related compute costs by 35%–50% by consolidating performance testing directly within GitHub Actions.
- Accelerated Release Cycles: Shifted performance validation earlier into the development process, resolving load bottlenecks 3x faster before reaching production.
- Optimized Infrastructure Scalability: Validated AWS EKS clusters to successfully support high-concurrency loads exceeding 10,000+ peak requests per second (RPS) without degraded service.
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