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JD Tech Talk
JD Tech Talk
Apr 30, 2025 · Artificial Intelligence

Adaptive Degradation and Recovery for JD Alliance Recommendation System under High‑Frequency Traffic Spikes

The article presents a comprehensive adaptive degradation and automatic recovery framework for JD Alliance's recommendation system, designed to handle high‑frequency, instantaneous traffic surges during large promotions by combining real‑time monitoring, Wilson‑interval‑based timeout correction, scenario‑aware control, traffic slicing, linear‑programming‑driven chain optimization, and low‑cost business‑agnostic APIs, achieving over 90% reduction in traffic loss and zero incidents.

JD.comLinear ProgrammingReal-time Monitoring
0 likes · 11 min read
Adaptive Degradation and Recovery for JD Alliance Recommendation System under High‑Frequency Traffic Spikes
IT Services Circle
IT Services Circle
Apr 23, 2025 · Backend Development

Handling Sudden Traffic Spikes in Backend Systems

The article outlines a comprehensive approach for backend engineers to manage a sudden 100‑fold increase in traffic, covering emergency response, traffic analysis, robust system design, rate limiting, circuit breaking, scaling, sharding, pooling, caching, asynchronous processing, and stress testing to ensure system stability and performance.

Rate LimitingScalingasynchronous processing
0 likes · 13 min read
Handling Sudden Traffic Spikes in Backend Systems
JD Retail Technology
JD Retail Technology
Aug 14, 2024 · Artificial Intelligence

Adaptive Degradation and Recovery for JD Alliance Recommendation System During High‑Volume Promotions

This article describes how JD Alliance built an adaptive degradation and automatic recovery framework for its recommendation system to handle sudden, large‑scale traffic spikes during major sales events, ensuring stability while minimizing recommendation loss through real‑time monitoring, scenario‑aware control, and linear‑programming‑based pipeline orchestration.

JD.comLinear ProgrammingReal-time Monitoring
0 likes · 9 min read
Adaptive Degradation and Recovery for JD Alliance Recommendation System During High‑Volume Promotions