Netflix-Skunkworks/service-capacity-modeling
Apr 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentBug FixCapacity PlanningPerformance OptimizationSystem DesignTesting
ayushisingh29
PROFILE
Ayushisingh29
Worked on the Netflix-Skunkworks/service-capacity-modeling repository to enhance backend capacity planning features and improve system reliability. Developed zonal capacity planning and resource derivation, enabling accurate calculation of CPU, memory, network, and disk requirements per zone using Python and Cassandra. Refactored core logic for maintainability and added comprehensive tests to ensure correctness. Addressed critical bugs, including disk allocation for EBS-backed volumes and disk usage calculations for Cassandra zones, by refining data handling and incorporating cluster instance counts. Introduced memory preservation logic based on derived buffers, optimizing database resource management. Focused on performance optimization, system design, and robust testing throughout development.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
2
Lines of code
560
Activity Months2
Your Network
24 peopleShared Repositories
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Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025Delivered two capacity-modeling enhancements in Netflix-Skunkworks/service-capacity-modeling (June 2025): (1) Cassandra Disk Usage Capacity Modeling Accuracy Fix—corrected zone disk usage by including cluster instance count; (2) Cassandra Memory Preservation Logic Enhancement—added derived-buffer-based memory preservation logic with tests. These changes increase the accuracy of capacity forecasts, reduce provisioning risk, and improve modeling reliability.
2 Commits • 1 Features
Jun 1, 2025Delivered two capacity-modeling enhancements in Netflix-Skunkworks/service-capacity-modeling (June 2025): (1) Cassandra Disk Usage Capacity Modeling Accuracy Fix—corrected zone disk usage by including cluster instance count; (2) Cassandra Memory Preservation Logic Enhancement—added derived-buffer-based memory preservation logic with tests. These changes increase the accuracy of capacity forecasts, reduce provisioning risk, and improve modeling reliability.
June 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly performance summary for Netflix-Skunkworks/service-capacity-modeling focusing on delivering reliable, business-value-driven capacity planning capabilities.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly performance summary for Netflix-Skunkworks/service-capacity-modeling focusing on delivering reliable, business-value-driven capacity planning capabilities.
Activity
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Quality Metrics
Correctness87.6%
Maintainability90.0%
Architecture82.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentBug FixCapacity PlanningCloud ComputingDatabase OptimizationPerformance OptimizationSystem DesignTesting
Repositories Contributed To
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