Kubernetes Migration for High-Frequency SaaS API
Client Partner: Quantify Metrics Inc. · Build Timeline: 5 Months
Project Tech Stack
Allocated Engineering Pod
- DevOps Architectsx2
- Systems Engineersx2
- QA Engineersx1
"The migration was handled without downtime. Our APIs now handle market spikes with ease, and SLA refunds have dropped to zero."
Elena Rostov
VP of Engineering, Quantify Metrics
1. Context & Business Goal
The Core Challenge: Quantify's API server crashed during peak stock market hours due to sudden traffic spikes, resulting in client SLA penalties and lost accounts.
The Agreed Objective: Migrate legacy virtual machines to a modern auto-scaling Kubernetes cluster, ensuring sub-second response times and 99.99% uptime.
2. Technical Research & Discovery
Analyzed API patterns to find database lockups and configure caching rules. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: DevOps monitoring dashboard showing CPU limits, network speeds, and cluster load metrics.
Database Infrastructure Setup: AWS EKS cluster running Node.js microservices, using Redis for session caching and ArgoCD for automatic deployments.
4. Production Implementation Lifecycle
Infrastructure-as-Code Setup
Wrote modular Terraform files to define EKS networks and database links.
Dockerization of Services
Migrated Node.js app code into optimized Docker containers.
CI/CD Pipeline Configurations
Established ArgoCD workflows to sync cluster states with git commits.
5. Validated Business Outcomes
- ✓Eliminated system crashes during high-volume trading hours.
- ✓Scaled API capacity from 5,000 to over 80,000 requests per second dynamically.
- ✓Reduced monthly server bill by 35% using container bin-packing.