Project Overview
Our client, a major online retailer specializing in specialty auto parts, processes hundreds of thousands of transactions yearly, driving a revenue stream well into the nine figures. As they looked to enhance both scalability and cost efficiency, the need to optimize infrastructure became urgent with Q4—an eCommerce peak—approaching. The challenge was clear: reduce costs, enhance resilience, and complete this transformation in time for Q4.
Challenge
After migrating their infrastructure to Google Cloud Platform (GCP), the client encountered significant costs from Windows licensing fees across their environments. To cut down expenses, a shift to Linux was critical. However, with no prior experience in containers, moving to Google Kubernetes Engine (GKE) posed a learning curve. Additionally, their high-throughput, three-tier application required custom handling of scalability and load balancing due to extended per-request processing times. Conventional metrics like CPU load and requests per second weren’t suitable indicators for scaling, making a tailored solution necessary.
Solution
We designed and implemented a cloud-native solution that leveraged containerization, spot instances, and a refined CI/CD process, allowing the client to transition seamlessly to a scalable, resilient infrastructure.
- Containerization and Automation with CI/CD: We containerized the application, migrated to a Linux-based GKE cluster, and revamped the CI/CD pipeline to automate container builds and deployments. This change allowed rapid adoption of containerization, with GKE’s managed environment simplifying maintenance and scaling.
- Efficient Use of Spot Instances with Smaller Nodes: GCP spot instances provided a considerable compute cost discount, ideal for cost-sensitive environments. We deployed smaller nodes, significantly reducing the likelihood of evictions (a risk with spot instances, as they leverage Google Cloud’s excess capacity and are evicted when that capacity is needed elsewhere). By using more, smaller nodes, the client could maintain stability without sharp spikes in error rates during node failures, improving end-user experience. Containers enabled rapid reallocation of workloads across nodes, ensuring minimal disruption during instance evictions.
- Custom Load Balancing and Scaling Metrics: To address the application’s specific needs, we developed custom metrics for load balancing based on real-time transaction demand rather than conventional CPU metrics. This fine-tuned scaling ensured that the infrastructure adjusted precisely to usage spikes without over-provisioning, enhancing both performance and cost efficiency.
Results
The project achieved impressive results that not only met but exceeded the client’s goals:
- Major Cost Reduction: The move from Windows to Linux and the use of GCP spot instances reduced compute expenses by nearly 90%, transforming the client’s cost structure while retaining high performance. Monthly costs for the infrastructure dropped dramatically, allowing for budget reallocation to other business areas.
- Enhanced Resilience and Lower Error Rates: By distributing workloads across multiple smaller nodes, the system experienced reduced error rates during instance failures. This setup provided a buffer against disruptions and minimized the impact of any single node’s downtime. GKE’s automated failover and load redistribution capabilities further strengthened application reliability, ensuring a seamless experience for end users.
- Scalability Ready for Peak Demand: With the new autoscaling infrastructure, the client was fully equipped to handle seasonal traffic surges without straining resources. The improved scaling approach allowed the client to meet demand in real time, setting them up for continued growth in peak sales periods.
Long-Term Impact
This transformation has positioned the client for sustained success, balancing cost efficiency with the flexibility to scale effortlessly. With GKE handling much of the underlying infrastructure, the client’s team now has the freedom to focus on application development and strategic growth initiatives, confident in the stability and resilience of their system. The client started off paying approximately $6,200/month to run their web application, excluding database costs. In the end, they saw a 59% reduction to $2,700/month to run the same application. This represents a 56% reduction in monthly expenses.