Jul 31, 2024
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Ongoing
Managing 15x more traffic than expected. Performance Tuning for Distributed Systems.
$10,000+
2-3 months
Germany, Berlin
2-5
Service categories
Service Lines
Cloud Consulting
DevOps
Domain focus
Commerce
Programming language
JavaScript
Frameworks
Node.js
React.js
Subcategories
Cloud Consulting
Public Cloud
DevOps
DevOps as a Service
DevOps Automation
Kubernetes
Challenge
Our client launched a TV advertising campaign that caused a user traffic spike. The product infrastructure was not ready for this spike. The original setup overutilised Kubernetes tools, which, added unnecessary complexity and latency to the autoscaling process. Also, they did not have a proper monitoring system.
Our client launched a TV advertising campaign that caused a user traffic spike. The product infrastructure was not ready for this spike. The original setup overutilised Kubernetes tools, which, added unnecessary complexity and latency to the autoscaling process. Also, they did not have a proper monitoring system.
Solution
We simplified the use of Kubernetes, implemented a solid monitoring system-alarms, health checks, logging, and tracing to provide precise insights into system performance and health. We closely worked with developers to optimize the performance.
We simplified the use of Kubernetes, implemented a solid monitoring system-alarms, health checks, logging, and tracing to provide precise insights into system performance and health. We closely worked with developers to optimize the performance.
Results
The infrastructure could now handle traffic spikes without performance degradation. The new monitoring system provided real-time insights, allowing for proactive management of system health. Reduced complexity in Kubernetes utilization led to more efficient autoscaling and resource management.
The infrastructure could now handle traffic spikes without performance degradation. The new monitoring system provided real-time insights, allowing for proactive management of system health. Reduced complexity in Kubernetes utilization led to more efficient autoscaling and resource management.