Netflix-Skunkworks/service-capacity-modeling
Feb 2025 – Feb 2025
1 Month active
Languages Used
Python
Technical Skills
Backend DevelopmentCapacity PlanningCloud ComputingData ModelingRefactoringSystem Design
sdabdoub
PROFILE
Sdabdoub
In February 2025, Samir Dabdoub delivered a comprehensive overhaul of the Capacity Planner in the Netflix-Skunkworks/service-capacity-modeling repository. He refactored the backend Python code to integrate real-time cluster utilization into capacity planning, removed network and CPU-specific filtering to allow the model to determine requirements dynamically, and introduced a disk utilization metric for more holistic modeling. By reordering calculation steps, he ensured that instance data is available before per-instance computations, improving data flow and planning accuracy. Leveraging skills in backend development, data modeling, and system design, Samir’s work enhanced the flexibility and data-driven decision-making capabilities of the capacity planning system.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
311
Activity Months1
Your Network
111 people
Work History
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a major overhaul of the Capacity Planner in Netflix-Skunkworks/service-capacity-modeling. Consolidated five commits into a cohesive upgrade that integrates current cluster utilization into capacity_desires, removes network filtering, reorders calculation steps to ensure data availability before per-instance calculations, removes CPU-specific filtering so the model determines CPU requirements, and adds a disk utilization metric for more holistic capacity modeling. No major bugs fixed this month; changes improve planning accuracy, reduce hard-coded behavior, and enable data-driven decisions across clusters.
5 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a major overhaul of the Capacity Planner in Netflix-Skunkworks/service-capacity-modeling. Consolidated five commits into a cohesive upgrade that integrates current cluster utilization into capacity_desires, removes network filtering, reorders calculation steps to ensure data availability before per-instance calculations, removes CPU-specific filtering so the model determines CPU requirements, and adds a disk utilization metric for more holistic capacity modeling. No major bugs fixed this month; changes improve planning accuracy, reduce hard-coded behavior, and enable data-driven decisions across clusters.
February 2025
Activity
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Quality Metrics
Correctness88.0%
Maintainability88.0%
Architecture84.0%
Performance76.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentCapacity PlanningCloud ComputingData ModelingRefactoringSystem Design
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