Sharing over a decade of architecture experience from Baidu, Alibaba, and Tencent.
This guide explains how to identify and resolve sudden latency increases in running services by collecting key metrics, focusing on GC behavior, thread and lock analysis, CPU and system resources, and using diagnostic tools such as jstat, jstack, and distributed tracing.
This article compares three common distributed ID generation methods—database auto‑increment, UUID, and Snowflake—detailing their architectures, advantages, drawbacks, and ideal use‑cases to help engineers select the most suitable approach for their systems.
This article explains the fundamentals of Single Sign-On (SSO), detailing its centralized authentication principle, the roles of CAS Server, CAS Client, and browser, and walks through the complete login flow with diagrams and code snippets for distributed systems.
This guide explains how to systematically analyze and optimize Java garbage‑collection pauses—cutting typical 200 ms stalls down to around 20 ms—by enabling detailed logs, selecting the right collector, tuning heap and generation settings, minimizing allocation, handling large objects, and balancing GC threads with CPU resources.
When MySQL QPS reaches 10,000, common bottlenecks include CPU saturation, low buffer pool hit rate, connection limits, and lock contention, which can be mitigated through internal tuning, cache interception, read‑write splitting, and horizontal sharding.
This guide explains how to achieve up to ten‑fold performance gains in Nginx by aligning worker processes with CPU cores, raising file‑descriptor limits, extending keep‑alive settings, and enabling zero‑copy transmission features such as sendfile, tcp_nopush, and tcp_nodelay.
This guide explains why Kafka backlog occurs when production outpaces consumption and provides practical steps—such as increasing consumer instances, optimizing processing, expanding partitions, applying flow‑control, and managing message capacity—to eliminate the backlog and keep the cluster healthy.
This guide explains how to design a high‑performance Nginx architecture that can handle up to half a million concurrent requests, covering horizontal scaling, OS and hardware tuning, critical Nginx directives, and complementary optimization techniques.
This article explains how TPS (transactions per second), QPS (queries per second), and concurrency differ, shows how each metric is calculated with concrete examples, and clarifies their relationship for evaluating system throughput and resource usage in high‑load environments.
This guide explains how to dramatically increase Nginx's concurrent handling capacity by tuning worker processes, connections, keep‑alive settings, and high‑performance I/O options, providing concrete configuration examples and practical advice for high‑traffic deployments.
