
During March 2026, Aiodev enhanced the pmndrs/jotai repository by expanding its benchmark suite for store operations performance evaluation. They replaced three basic benchmarks with eight scenario-driven tests, covering atom creation, batch read/write, derived-chain propagation, wide-fan-out, diamond dependencies, subscription churn, computed reads, and partial selection. Using TypeScript, they implemented a run-all script and integrated a bench command into package.json, enabling seamless execution via pnpm. This work improved benchmarking scalability from 10² to 10⁶ store sizes, providing more reliable and reproducible performance data. Their contributions deepened performance testing, supporting more accurate optimization and cross-team capacity planning.
March 2026 monthly summary for pmndrs/jotai: Delivered a significant enhancement to the benchmark suite for store operations performance evaluation. Replaced three simple benchmarks with eight comprehensive scenarios to broaden coverage, including atom-creation throughput, batch read/write on mounted atoms, deep derived-chain propagation, 1→N recompute performance, diamond dependency updates, subscribe/unsubscribe churn plus subscribe-write scaling, cached derived reads, and partial atom selection. Implemented a run-all script and a bench command to execute all benchmarks end-to-end, and added a bench script to package.json for convenient usage via pnpm bench. This work provides richer, scalable performance signals from 10^2 to 10^6 store size, enabling more accurate optimizations and capacity planning. The changes improve benchmarking reliability, reproducibility, and cross-team value for performance-focused decision making.
March 2026 monthly summary for pmndrs/jotai: Delivered a significant enhancement to the benchmark suite for store operations performance evaluation. Replaced three simple benchmarks with eight comprehensive scenarios to broaden coverage, including atom-creation throughput, batch read/write on mounted atoms, deep derived-chain propagation, 1→N recompute performance, diamond dependency updates, subscribe/unsubscribe churn plus subscribe-write scaling, cached derived reads, and partial atom selection. Implemented a run-all script and a bench command to execute all benchmarks end-to-end, and added a bench script to package.json for convenient usage via pnpm bench. This work provides richer, scalable performance signals from 10^2 to 10^6 store size, enabling more accurate optimizations and capacity planning. The changes improve benchmarking reliability, reproducibility, and cross-team value for performance-focused decision making.

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