February 2026: Key feature delivery and reliability improvements for Netflix/hollow. Implemented a Maven-based protoc download with OS/arch detection, removing dependency on a system-installed protoc. This enhances cross-platform builds, simplifies contributor onboarding, and stabilizes CI workflows. No major bugs reported this month; all changes focused on build reliability and reproducibility. Technologies demonstrated: Maven, protoc, OS/arch detection, dependency management, and build pipeline automation. Business impact: faster onboarding, fewer build failures, and more reliable proto compilation leading to smoother feature delivery.

Month: 2026-01 — Delivered two key features for service-capacity-modeling: (1) Postgres/Aurora Data Model Migration and RDS Removal, migrating Entity and Control models to a Postgres/Aurora composition and removing the RDS model to simplify architecture; this aligns capacity modeling with new data sources and improves performance and maintainability. Commits: 9382186dfaebd4158795ca6aa8524fd77268c423; 4aff0112dad67227cfd70cb96485653438bc9705. (2) RDS Proxy Capacity Planning and Merged Plans, adding capacity planning for RDS proxy integration with Entity/Control models and enabling merged capacity plans across RDS and Aurora; Commit: 68b4a06a5fdbb3e9e7a5e0f7f3d4c81d1017e112.

Month 2025-12 highlights: Delivered Protocol Buffers integration for Hollow via a new adapter with automatic schema inference, type mapping for all proto scalars, nested messages, and collections; memory optimizations, lazy schema creation for well-known types, and thread-safety improvements. Updated hollow_primary_key to a message-type array syntax for clearer configuration. Added UINT32/UINT64 annotations with validation and test coverage to prevent silent data corruption. Impact: stronger data integrity, safer proto-based integrations, and improved performance and maintainability.

November 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling: Delivered three core capabilities accelerating capacity planning reliability and scalability. Implemented property-based testing with Hypothesis to replace uncertain planning across all capacity models, resulting in faster test execution, improved coverage, and clearer error reporting. Introduced a control app model that combines an in-memory cache with Aurora to support atomic Changes, optimizing read/write QPS and memory usage for scalable capacity planning. Updated Cassandra deployment sizing by raising the minimum RAM to 16 GiB and expanding node-density tests from 100–300 to 300–400, improving performance and reliability. All models now default to Hypothesis-based testing, reducing debugging time and accelerating release cycles. These changes collectively enhance test feedback speed, predictability of capacity planning, and overall system scalability.

Month 2025-10 — Netflix-Skunkworks/service-capacity-modeling: Code Quality and Platform Compatibility Improvements. This work focused on enhancing type safety, updating language and tooling, and aligning CI/CD with newer Python releases to reduce risk and improve developer velocity. Delivered concrete changes to enable stricter type checking and modernize Python support across the codebase.

March 2025 performance summary for Netflix-Skunkworks/service-capacity-modeling: Focused on accuracy and reliability of capacity planning in the RDS modeling workflow. Delivered a critical bug fix that eliminates float-based inaccuracies in disk-space estimation, thereby improving resource provisioning decisions and reducing risk of over- or under-provisioning. Key technical approach: applied math.ceil to the computed disk space (size_gib), using the formula x * 1.2, ensuring the required disk space is an integer. Commit reference: cdec66ce723fe2fd2d4695e439668a39753caa22. This work strengthens capacity planning and reliability across deployments.

February 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling. Focused on enhancing interval representation flexibility in the capacity modeling system while preserving behavior and API stability. Delivered a targeted refactor of typing to support multiple interval representations and laid groundwork for future extensibility.

January 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling: Focused on code quality, Pydantic v2 compatibility, and accurate disk sizing calculations to improve reliability of capacity projections and maintainability.

November 2024 monthly summary for Netflix-Skunkworks/service-capacity-modeling focused on delivering a key framework update and maintaining forward compatibility with dependencies.

Month: 2024-10. Focused on maintaining and stabilizing the Netflix-Skunkworks/service-capacity-modeling module. Achieved a Pydantic v2 compatibility upgrade by migrating configuration from inner Config to model_config and introducing a default for Instance.drive, reducing runtime errors and aligning with newer library versions. This work improves cross-version stability, simplifies future upgrades, and enhances reliability of capacity modeling workflows for downstream services.