17-year internet software developer specializing in AI applications, networking, architecture, and open source. Led the delivery of network services handling hundreds of millions of concurrent devices and tens of millions of QPS, and has three years of experience designing and building an agent platform. Follow to stay updated.
This article details the evolution from cold to hot backup in same-city disaster recovery, covering fault domain analysis, RPO/RTO definitions, data replication strategies, capacity planning, traffic switching challenges, control plane resilience, and phased implementation approaches for systems handling ten million QPS.
This article details how to build reliable automated rollback systems for high-throughput architectures by establishing change identity, causal evidence through experiment/control groups, tiered rollback actions, state-machine execution, data compatibility matrices, verification budgets, and guardrails—progressing from manual processes to closed-loop autonomy.
This article details how high-throughput systems shift data protection from reactive backups to real-time guardrails, covering write-path constraints, CDC-based anomaly detection, reversible change state machines, object-level rollback with downstream compensation, and a phased evolution path to automate damage control at ten million QPS.
At ten million QPS, a single slow dependency can cascade into full outage; this article details a systematic degradation framework: business capability grading (L0–L3), degradation contracts with metadata, budget-constrained action ladders, automated controllers with leases and hysteresis, multi-level recovery state machines, cross-service guardrails, and validation via chaos drills.
This article details a complete control-loop design for automatic fault isolation in 10M QPS systems, covering evidence-based detection, capacity-aware action planning, guardrails, phased rollout from shadow mode to autonomy, and evaluation metrics for speed, correctness, and safety.
This article details how to achieve second-level fault mitigation in 10M QPS systems by building a closed-loop control system with layered signals, tiered actions, independent control planes, pre-authorized automation, automated verification, blast radius management, and progressive drills, moving beyond manual response to automated risk convergence.
This article details a practical roadmap for building production-grade fault prediction systems at massive scale, covering target selection, data governance, model evolution, time-window design, and safe action loops—emphasizing that reliable prediction requires engineering rigor beyond just model training.
This article presents a six-dimensional fault classification framework for hyperscale systems, replacing experience-based labels with structured, evidence-driven dimensions—layer, failure mode, scope, temporal pattern, observability, and business impact—to enable automated routing, confident remediation, and continuous improvement at ten million QPS.
This article details how to build automated fault localization for 10M QPS systems by unifying entity identities, aligning timestamps, integrating change records, and applying a four-layer engine—anomaly normalization, temporal correlation, topological pruning, and causal scoring—to converge millions of anomalies into verifiable hypotheses while avoiding correlation-causation pitfalls through counterfactual evidence and phased rollout.
This article explains how to reduce fault detection latency from minutes to seconds in 10M QPS systems by implementing layered signals, combined evidence detection, distributed judgment, event normalization, and safe automation guardrails, rather than simply increasing sampling frequency.
