Over 10 years as a senior tech expert at top-tier companies, seasoned interview officer, currently at leading firms like Alibaba.
The article explains how Spring Cloud Gateway achieves ultra‑high QPS by leveraging Netty's non‑blocking I/O, Project Reactor's event‑driven streams with backpressure, and lightweight route predicates combined with load‑balancing to maximize thread utilization and system throughput.
This article explains NVIDIA's CUDA platform, its CPU‑GPU division of labor, how massive parallelism accelerates AI large‑model training, and walks through a typical CUDA program workflow with concrete code examples and diagrams.
The article explains that “high concurrency” for Nginx is relative, depends on hardware, request type, response size, and overall architecture, and provides typical connection and QPS ranges for ordinary, medium, high‑traffic, and ultra‑high‑traffic systems.
This article surveys the most prominent AI large models, ranking global leaders like GPT‑5.5, Claude 4.x, and Gemini 2.5, outlining China’s top contenders such as DeepSeek V3 and Qwen 3, and summarizing open‑source options with their distinctive capabilities and open‑source status.
The article defines TPS (transactions per second), lists typical payment‑related operations that constitute a transaction, shows a concrete 1000‑TPS example, and presents an industry‑wide TPS classification table that helps determine when a payment system can be considered high‑concurrency, with practical thresholds for business, core, and large‑scale platforms.
The article examines four key distributed storage solutions—HDFS, Ceph, GlusterFS, and FastDFS—detailing their design principles, typical use cases, strengths such as scalability or simplicity, and limitations like handling small files or low‑latency random access.
The article explains Alibaba’s city‑level active‑active architecture, where two independent data centers in the same city simultaneously serve traffic, automatically fail over on outage, and are organized into traffic, application, and data layers with detailed design patterns.
The article explains that the Transformer, introduced by Google in the 2017 "Attention Is All You Need" paper, replaces RNN/CNN with self‑attention, enabling full parallel computation, solving sequential bottlenecks and long‑range dependency issues that power today’s large AI models.
This article explains how modern AI large models—such as GPT‑4, Claude 3.5, Gemini, Llama, and DeepSeek—rely on the Transformer architecture, detailing token embeddings, self‑attention, positional encoding, and the three‑stage training pipeline of pre‑training, instruction fine‑tuning, and alignment optimization.
This article reviews four popular high‑concurrency load‑testing tools—Apache JMeter, LoadRunner, Gatling, and wrk—detailing each tool’s supported protocols, performance characteristics, graphical or code‑based interfaces, typical use cases, and limitations such as resource consumption, cost, learning curve, or protocol scope.
