The article explains why StringBuilder is not thread‑safe, analyzes its internal implementation and the non‑atomic count update that leads to incorrect lengths and occasional ArrayIndexOutOfBoundsException, and shows that using synchronized StringBuffer avoids these issues.
The article describes how to load‑test Dubbo queue‑add and queue‑delete methods by pre‑generating messages, storing them in a thread‑safe LinkedBlockingQueue, and driving concurrent requests with custom Java code while addressing data‑initialisation challenges during repeated runs.
This article explains why thread safety concerns memory rather than threads, compares stack‑local and heap‑shared data, introduces ThreadLocal for per‑thread isolation, and covers mutual‑exclusion locks, optimistic CAS, and their appropriate use cases in concurrent programming.
By introducing request merging and batch APIs, this article demonstrates how to improve high‑concurrency performance in Java backend services, detailing the design, data structures, scheduled execution, and code implementation while discussing trade‑offs and practical considerations.
This article explores the evolution of parallel computing in Java, explaining why single‑core performance stalls, how multi‑core CPUs and GPUs enable true concurrency, and demonstrating practical implementations using CountDownLatch, CompletableFuture, Fork/Join, parallel streams, and sharding, while highlighting performance considerations and pitfalls.
The article explains the concepts of pessimistic and optimistic locking, details how CAS (Compare‑And‑Swap) implements optimistic locks in Java, discusses their advantages, drawbacks such as the ABA problem and high spin costs, and compares CAS usage with synchronized blocks in concurrent programming.
This article explains how the Disruptor library implements lock‑free, bounded queues using a ring‑buffer, cache‑line padding and CAS operations, compares single‑ and multi‑producer sequencers, and provides practical Java code examples for building ultra‑low‑latency producer‑consumer pipelines.
This article explains the concept of false sharing in Java, demonstrates its dramatic impact on multithreaded performance, and shows how adding cache‑line padding variables can eliminate write‑contention, turning a 5‑to‑1 slowdown into a significant speedup.
This article explains the concept of false sharing, its roots in CPU cache line contention and the MESI protocol, demonstrates its impact with Java benchmark code, and presents practical padding and alignment techniques to mitigate the performance degradation caused by false sharing.
The article examines how distributed systems face mutual‑exclusion and idempotency challenges, explains traditional thread and process synchronization, then details distributed‑lock techniques (e.g., Zookeeper, Redis, Tair, Cerberus) and global‑ID‑based idempotent services, emphasizing the importance of external storage, fault‑tolerance, and proper lock granularity for reliable high‑throughput applications.
