This article explains the purpose, classification, and implementation of MySQL locks—including global, table, page, row, intention, and gap/record/next-key locks—as well as optimistic and pessimistic locking strategies, providing code examples and usage scenarios for ensuring data consistency in concurrent transactions.
The article explains the difference between instruction reordering and ordering, analyzes how the double‑checked locking pattern can suffer from reordering at the bytecode level, and demonstrates how synchronized provides ordering while volatile only prevents visibility issues, using Java code examples and detailed bytecode analysis.
This article explains why traditional Unix fork/exec models are costly, introduces POSIX threads (Pthreads) as a lightweight alternative, and provides detailed guidance on thread data structures, creation, termination, synchronization primitives, and attribute configuration with practical code examples for Linux developers.
This article explains how to correctly declare, monitor, and configure Java thread pools, recommends naming conventions, discusses CPU vs I/O workload sizing, highlights common pitfalls such as deadlocks and ThreadLocal misuse, and introduces dynamic pool‑adjustment techniques used by large tech companies.
The article explains the distinction between instruction reordering and ordering in Java, analyzes how volatile and synchronized affect memory visibility and atomicity, and demonstrates with bytecode and multithreaded examples why double‑checked locking requires both volatile and synchronized to avoid partially constructed singleton instances.
This article explains the Java ThreadLocal class, demonstrates how it provides per‑thread variable isolation to avoid concurrency issues, and shares two real‑world scenarios—including a utility class and a Spring Boot case—showing how ThreadLocal can simplify thread‑local state management while warning about potential memory leaks.
This article investigates a production thread‑pool exhaustion issue caused by a JVM class‑initialization deadlock, explains the underlying class‑loading mechanics, presents detailed stack‑trace analysis, and offers a preventive solution with early bean initialization and a reproducible demo.
This article walks through an interview question about the maximum concurrent requests a SpringBoot project can handle, builds a minimal demo, measures Tomcat and Undertow thread‑pool limits, explains the underlying thread‑pool mechanics, and offers practical tips for answering such questions in interviews.
Under high concurrency, improper configuration of MyBatis parameterType and resultType can lead to thread BLOCKED states due to repeated TypeHandler cache misses, and this article analyzes the root causes, demonstrates debugging steps, and provides recommendations to avoid such performance bottlenecks.
This article explores Go's channel implementation—from its CSP‑inspired design and internal hchan structure to real‑world memory‑leak debugging, creation nuances, send/receive mechanics, and proper closing—providing developers with a comprehensive understanding of safe concurrent programming in Go.
This article explains the limitations of Java Future, introduces CompletableFuture's asynchronous and compositional capabilities, and demonstrates how to refactor a store detail page service using CompletableFuture, custom thread pools, and design patterns to achieve parallel execution, reduced latency, and better code maintainability.
This guide explains why distributed locks are needed, how to correctly acquire and release them, compares Redis and Zookeeper implementations—including single‑master, Redlock, and Zookeeper approaches—and offers practical recommendations for ensuring atomicity, preventing deadlocks, and protecting shared resources in production environments.
This article provides a comprehensive walkthrough of Google Guava's RateLimiter, covering its practical rate‑limiting scenarios, underlying token‑bucket algorithm, detailed source‑code structure, core classes such as RateLimiter, SmoothRateLimiter, SmoothBursty and SmoothWarmingUp, usage examples, and considerations for extending or adapting the component in distributed systems.
This article analyzes the common overselling problem in high‑traffic flash‑sale systems and presents seven concrete solutions—including improved locking, AOP locking, pessimistic and optimistic locks, as well as blocking‑queue and Disruptor‑based designs—complete with SpringBoot code samples and performance testing results.
This article summarizes the key pitfalls and recommended practices for creating, configuring, monitoring, and naming Java thread pools, including proper declaration, parameter tuning for CPU‑ and I/O‑bound workloads, avoiding OOM and deadlocks, and leveraging dynamic pool frameworks.
The article explains why regular ThreadLocal cannot share data from a parent thread to its child, introduces InheritableThreadLocal as a built‑in solution, shows its limitation with thread‑pool reuse, and demonstrates how Alibaba's TransmittableThreadLocal overcomes this issue with concrete code examples and output.
Meta is promoting PEP 703, which proposes adding a --disable-gil build option to CPython so Python can run without the Global Interpreter Lock, and has pledged three engineer‑years by 2025 to help implement the change, while also noting its Threads product already runs on a heavily modified CPython backend.
The article explains Java's ThreadLocal mechanism, detailing how each thread maintains its own ThreadLocalMap, provides practical code examples, examines the underlying source implementation—including set and get methods—and discusses potential memory‑leak pitfalls and ways to avoid them.
This extensive guide covers Python 2 vs 3 differences, conversion tools, essential and advanced libraries, concurrency patterns, design patterns, database and system fundamentals, along with numerous code snippets and practical examples for developers seeking a deep understanding of Python programming.
This article presents a detailed curriculum for backend development, covering fundamental concepts and practical topics such as database design and optimization, popular frameworks like Spring and Hibernate, microservice architecture, message middleware (Redis, RabbitMQ, Kafka), common data structures, concurrency, JVM internals, and related interview questions.
This article explains how to use Java's CyclicBarrier to coordinate three threads that alternately print the characters A, B, and C, providing a detailed analysis, example analogy, complete implementation code, execution result, and a brief summary of its relevance in multithreading interviews.
This article delves into the often‑overlooked locking mechanisms inside Java’s ThreadPoolExecutor, explaining the purpose of the mainLock ReentrantLock, the worker‑level lock, and how they prevent interrupt storms, ensure accurate statistics, and coordinate thread‑safe access to internal data structures.
This article explains how to locate Java deadlocks by using command‑line tools such as jps and jstack, the ThreadMXBean API’s findDeadlockedThreads method, and online utilities like FastThread and JStack Review, while also offering guidance on preventing and fixing deadlocks in production.
This article presents a detailed Java backend interview recap covering self‑introduction, thread‑pool choices, Snowflake ID usage, List implementations, List vs Set differences, common HTTP status codes, flow and congestion control, URL request flow, TCP three‑way handshake, HTTP vs HTTPS, database index types, B+‑tree advantages, transaction isolation problems, and practical interview preparation tips.
Herb Sutter reveals that the ISO C++ committee has approved the C++26 schedule, promising major concurrency and parallelism enhancements, new language features like wildcard support and dangerous pointers, while discussing competition from Rust, Google’s Carbon, and his experimental cppfront project.
This article explains cache fundamentals, common use cases, design constraints, and a Go implementation that combines hash‑maps with doubly‑linked lists, LRU eviction, TTL handling, and mutex‑based concurrency control, providing practical code examples and deployment tips.
This article explains why high‑performance concurrent and asynchronous PHP applications are needed, introduces ReactPHP and Amp as non‑blocking I/O libraries, and provides step‑by‑step code examples for creating a simple HTTP server with ReactPHP and performing parallel HTTP requests using both ReactPHP and Amp.
This extensive guide compiles Python 2 vs 3 differences, essential built‑in modules, common and advanced libraries, multiprocessing, async programming, testing techniques, networking fundamentals, database and cache concepts, Linux I/O models, design patterns, and algorithm implementations, providing a valuable reference for developers.
Go maps are not safe for concurrent access, so programs can panic when multiple goroutines read and write the same map; to prevent this you can use sync.Once for immutable data, protect maps with sync.RWMutex, employ sharded locks via concurrent‑map, or use the built‑in sync.Map, each offering different performance trade‑offs depending on read/write ratios and concurrency level.
This article explains why ThreadLocal can cause information loss, corruption, or OOM in Java applications and presents two practical approaches—Java agent bytecode manipulation and proxy‑based thread‑pool wrappers—along with concrete Spring Sleuth implementations and testing results to ensure safe propagation and cleanup.
This article explains service performance concepts and presents systematic code‑level and architectural optimizations for Java back‑ends, covering class preloading, thread‑pool usage, static variables, cache‑line alignment, false sharing mitigation, branch prediction, copy‑on‑write, method inlining, reflection caching, exception handling, logging practices, lock granularity, and pooling strategies, all illustrated with concrete code examples.
This article examines why data consistency between microservices is critical, defines key terminology, and presents two practical approaches—business‑side final consistency and platform‑side final consistency—detailing their core ideas, design principles, workflow diagrams, and real‑world implementation considerations such as idempotency, storage choices, latency tolerance, state‑machine design, concurrency control, and observability.
Linux introduced the read‑write semaphore (rwsem) as a sleep lock that lets multiple readers hold the lock concurrently, improving read‑heavy workload performance, and the article details its internal state representation, acquisition paths for reads and writes, optimistic spinning, handoff mechanisms, and trade‑offs, noting that mobile kernels may need further tuning.
This case study details the performance bottlenecks of the iQIYI Kids feed on low‑end devices and presents a series of engineering solutions—including async card rendering, preloading strategies, image pre‑decoding, and cache optimizations—that reduced scroll hitch time to 1.4 ms, dramatically improving user experience.
This article thoroughly explains the low‑level implementation of Java's volatile keyword by analysing CPU multi‑level cache design, the MESI cache‑coherency protocol, the Java Memory Model, memory barriers, the happens‑before principle, and the impact on singleton patterns and synchronized blocks.
This article explores comprehensive performance optimization techniques for Java services, covering code-level strategies such as preloading classes, cache line alignment, branch prediction, copy‑on‑write, inlining, and design approaches like caching, asynchronous processing, pooling, and pre‑handling, while highlighting trade‑offs and practical examples.
This article provides a comprehensive guide to RunnerGo's performance testing module, detailing plan management, various load test modes such as concurrency, step, error‑rate, response‑time, and requests‑per‑second, along with configuration options, execution workflow, and links to the open‑source repository.
The article explains Apache's three MPMs (prefork, worker, event), how Nginx's event‑driven architecture and epoll model improve concurrency handling, and provides performance comparisons showing Nginx handling tens of thousands of connections with far lower memory usage than Apache.
This article explains how improper use of AOP in Spring can cause object escape leading to concurrency safety problems, discusses issues such as asynchronous thread modifications, caching, and incorrect advice ordering with @Transaction, @Retryable, and dynamic datasource switching, and provides practical guidance to avoid these pitfalls.
The article explains how to properly combine Java's CompletableFuture with Stream APIs to achieve true parallel execution, highlights common pitfalls that prevent concurrency, and provides the correct pattern of splitting streams and applying terminal operations for effective asynchronous processing.
This article explains why Java's SimpleDateFormat is not thread‑safe, demonstrates the concurrency problem with a multithreaded demo, and presents three solutions—creating new instances, synchronizing access, and using ThreadLocal—highlighting the ThreadLocal approach with code examples and discussion of potential memory‑leak considerations.
This article deep‑dives into the LinkedBlockingQueue.dequeue implementation, revealing why the line h.next = h is essential for helping the garbage collector, how cross‑generation references affect GC performance, and the role of weakly‑consistent iterators in Java's concurrent queue.
This article explains how to use Java's CompletableFuture for parallel execution, demonstrates the limitations of simple timeout handling in JDK 8, and shows how JDK 9's built‑in orTimeout method and a custom utility class can provide precise asynchronous timeout control for backend services.
In high‑concurrency Java applications, creating threads directly with the Thread class leads to performance degradation, resource exhaustion, and lack of management, whereas using the Executor framework’s ThreadPoolExecutor—configured via corePoolSize, maximumPoolSize, keepAliveTime, workQueue, threadFactory, and rejectHandler—provides reusable threads, controlled concurrency, task scheduling, monitoring, and customizable rejection policies, dramatically improving efficiency.
This article systematically traces the evolution of Java multithreading from the early native Thread model through the introduction of java.util.concurrent and synchronized optimizations to the modern virtual thread era, highlighting key concepts, milestones, and performance impacts.
This article explains the importance and challenges of concurrent programming, details Java's memory model and synchronization primitives, analyzes a real‑world case where concurrent addAll on ArrayList caused missing UI elements, and demonstrates how using thread‑safe collections resolves the issue.
This article explains various ways to terminate a running Java thread, including using exit flags, the deprecated stop/suspend/resume methods, interrupt, handling InterruptedException, and the risks of forceful termination, accompanied by detailed code examples and best‑practice recommendations.
This article explains the concept, benefits, and inner workings of CAS optimistic locks in Java, illustrating how Compare‑And‑Swap enables lock‑free synchronization and providing a practical AtomicInteger counter example to demonstrate atomic increments without traditional locking.
The article explains why Go has entered the TIOBE top‑10, highlighting its built‑in concurrency, garbage collection, static typing, Google backing, and its role in projects like Docker and Kubernetes, while describing how the TIOBE index is calculated and how it can guide language selection decisions.
This article explains why Redis experiences key‑setting race conditions under high concurrency and presents two practical solutions—using a distributed lock with timestamps and serializing writes via a message queue—to ensure correct ordering and data consistency.
While Go offers concise syntax, non‑intrusive interfaces, lightweight goroutine concurrency, a simple three‑color garbage collector and a small native binary footprint, Java provides a mature object‑oriented model, extensive libraries, generational GC and robust tooling, making the optimal language choice depend on project performance, ecosystem and development speed requirements.
This article explains the core concepts and common pitfalls of Java's AbstractQueuedSynchronizer (AQS) and its derived utilities such as ReentrantLock, ReentrantReadWriteLock, CountDownLatch, Semaphore, as well as the design and behavior of blocking queues, thread‑pool parameters, CopyOnWriteArrayList, and ConcurrentHashMap, providing code examples and practical guidance.
This article explains why distributed locks are needed, describes the five essential characteristics of Redis locks, compares three common implementation methods, and provides detailed Java code examples with watchdog, re‑entrancy, and expiration handling to guide developers in building reliable distributed locking solutions.
The article explains why ThreadLocalRandom should not be stored in a static field, demonstrates how using ThreadLocalRandom.current().nextX(...) avoids predictable random numbers, compares behavior between JDK 11 and JDK 19, and provides the proper usage pattern with code examples.
This article explains the step‑by‑step evolution of Redis‑based distributed lock implementations—from a simple SETNX approach to a robust solution using UUIDs, expiration, atomic Lua scripts, and finally Redisson’s high‑level lock API—illustrating common pitfalls and their fixes.
This article presents a complete Netty‑based TCP demo for IoT projects, detailing the project background, architecture, module layout, business flow, and in‑depth Java code examples—including a local queue, multithreaded processing, client creation, Redis locking, and SpringBoot integration—along with testing instructions and source links.
This article explains why time‑consuming business operations need a distributed lock, walks through using Redis as a lock with Spring AOP, details the lock‑acquire, timeout, and renewal mechanisms, provides full code examples, testing steps, and best‑practice recommendations for reliable concurrency control in Java back‑end services.
The article explains Java's new Virtual Thread (coroutine) feature, compares the traditional thread‑per‑request model with asynchronous and coroutine approaches, discusses Little's Law for scalability, and outlines the benefits and pitfalls of using coroutines in server‑side Java applications.
This article explores Java multithreading concepts, JVM safety tools, thread creation methods, coordination mechanisms, and practical scenarios such as parallel data aggregation, for‑loop conversion, map‑based processing, and multi‑threaded transaction handling, providing code examples and performance tips for backend developers.
This article explains how to protect time‑consuming business operations using a Redis‑based distributed lock implemented with custom annotations, Spring AOP, and a scheduled thread pool that automatically extends lock expiration to avoid premature release.
This article explains Python's multiprocessing module, shows how to create and manage processes and process pools with clear code samples, and outlines common use cases, best practices, and pitfalls for efficiently handling CPU‑bound tasks.
This article explains how to protect time‑consuming business operations by using Redis as a distributed lock, designing a custom @RedisLock annotation, applying Spring AOP to intercept methods, handling lock acquisition, expiration, renewal via a ScheduledExecutorService, and releasing the lock safely after execution.
This article explains the four common Redis caching challenges—cache penetration, avalanche, breakdown, and data inconsistency—demonstrates their impact under high concurrency, and provides practical Java Spring Boot solutions including caching null objects, Bloom filters, distributed locks, random expiration, and delayed double‑delete strategies with full code examples.
Switching from single‑threaded synchronous code to multithreaded asynchronous execution can improve performance, but introduces nine major issues—including missing return values, data loss, ordering problems, thread‑safety, ThreadLocal anomalies, OOM risks, high CPU usage, transaction failures, and service crashes—each explained with Java examples and solutions.
This article explains why Tokio may discard asynchronous tasks when the thread pool is saturated and demonstrates how to use a bounded JoinSet to block task creation until resources become available, ensuring all tasks complete.
This article examines frequent mistakes when using Java thread pools—such as improper creation, unreasonable parameters, unreleased local pools, and unsafe concurrent operations—and provides detailed analysis, configuration guidelines, and best‑practice recommendations to prevent memory leaks, deadlocks, and performance issues.
This article walks through the step‑by‑step evolution of Redis‑based distributed lock implementations—from simple SETNX locking to atomic expiration, UUID‑based ownership, Lua‑scripted safe release, and finally using Redisson’s high‑level lock API—illustrating common pitfalls and robust solutions.
This article explains how the JVM optimizes locking through spin locks, adaptive spinning, lock elimination, and lock coarsening, and describes escape analysis techniques that enable stack allocation, scalar replacement, and synchronization removal, illustrated with Java code examples and diagrams.
This article explains how to protect time‑consuming business operations using a Redis‑based distributed lock in Spring Boot, covering annotation design, AOP pointcut definition, lock acquisition and release, automatic lock renewal with a scheduled executor, and practical testing results.
This article compiles a detailed interview guide covering TCP/UDP differences, three‑way handshake and four‑way termination, flow and congestion control, select/poll/epoll mechanisms, zero‑copy techniques, Redis advantages and persistence, MySQL MVCC and repeatable‑read semantics, as well as core C++ topics and common algorithm questions.
Linux kernel synchronization requires protecting shared mutable state from concurrent access using primitives such as spinlocks, mutexes, read‑write locks, or lock‑less techniques like RCU, which copies data and waits for a grace period, each offering distinct performance, latency, and complexity trade‑offs.
This article explains three Java singleton implementations—double‑checked locking with volatile, static inner‑class holder, and enum singleton—detailing their thread‑safety, lazy initialization, and how they prevent issues such as instruction reordering, serialization, and reflection attacks.
The article explains Apache’s three processing models (prefork, worker, event), compares their resource usage with Nginx, and demonstrates how Nginx’s event‑driven, single‑threaded architecture achieves far higher concurrent connection capacity and better performance for web services.
This article explains the seven ways to create Java thread pools, categorizes them by ThreadPoolExecutor and Executors, and provides detailed code examples for fixed-size, cached, scheduled, single‑thread, work‑stealing pools, as well as custom thread factories and task result handling.
The article explains how Spring's @Async annotation works, compares synchronous and asynchronous calls, reviews the built‑in executors such as SimpleAsyncTaskExecutor, discusses their drawbacks, and provides detailed guidance on creating custom thread pools via AsyncConfigurer, AsyncConfigurerSupport, or a TaskExecutor bean, including code examples.
This article explains the Java synchronized keyword, its three usage forms, the underlying JVM object header and monitor structures, the lock acquisition process, and the progression from no lock through biased, lightweight, and heavyweight locks, illustrating how synchronization ensures thread safety.
The article explains that the 65,535 TCP port limit does not bound a Linux server’s concurrent connections, detailing how TCP connections are identified, the theoretical maximum of 2⁴⁸ connections for a server, and the practical constraints imposed by memory and file‑descriptor limits.
This article explains how Spring Boot relies on Tomcat's thread and connection settings, demonstrates how to configure these parameters, runs a practical test to observe request handling limits, and discusses the resulting concurrency problems and their root causes.
This article explains how TCP connections are identified, the theoretical limits for client (65,535) and server (up to 2^48) connections, and why practical concurrency is governed by system resources, port reuse, and server architecture rather than the raw port count.
This comprehensive guide walks Go developers through performance tuning fundamentals, recommended profiling tools, code-level optimizations, and real-world case studies, offering actionable insights to measure, diagnose, and improve CPU, memory, and concurrency efficiency in high‑throughput Go services.
This article explains how RocketMQ creates and manages threads, covering single‑thread creation via Runnable and Thread inheritance, the ServiceThread abstract class, ThreadPoolExecutor configuration, custom thread factories, and the importance of descriptive thread names for debugging.
This article explains Java's CompletableFuture API introduced in JDK 8, covering how to create asynchronous tasks with supplyAsync and runAsync, use various callback methods such as thenApply, thenAccept, thenRun, whenComplete, handle, and combine multiple futures with thenCombine, allOf, anyOf, providing code examples and execution results.
This article explains various ways to terminate a running Java thread, including using exit flags, interrupt(), deprecated stop()/suspend()/resume() methods, handling InterruptedException, while providing code examples and discussing the risks of forceful termination.
This article explains how Go implements its own goroutine scheduler within the runtime, demonstrates it with sample code, and details the GMP (Goroutine‑M‑Processor) model, its scheduling strategies, and the evolution from the earlier GM model.
This article explains the definitions and differences of QPS and TPS, describes concurrency and throughput concepts, introduces related metrics such as PV, UV, DAU, MAU, and outlines key considerations for system performance testing and capacity planning.
This article explains how the JVM improves concurrency performance through lock optimization techniques such as spin locks, lock elimination, lock coarsening, and escape analysis, illustrating each concept with code examples and discussing related optimizations like stack allocation and scalar replacement.
This article explains the definitions and differences between QPS and TPS, explores related concepts such as concurrency, throughput, PV, UV, DAU, MAU, and outlines practical methods for evaluating system performance and capacity planning.
This article explains various ways to terminate a running Java thread, comparing safe flag‑based exit, the deprecated stop/suspend/resume methods, and the recommended interrupt approach, while demonstrating how to detect interruption status, handle exceptions, and avoid common pitfalls with code examples.
This article explains the JVM object memory layout—including object header, instance data, and alignment padding—details the structure of the Mark Word and Klass Word, describes how monitors and the synchronized keyword work at the bytecode level, and covers lock states, inflation, bias revocation, and spin optimizations with practical code examples.
This article presents the architecture, technology choices, and practical implementation details of FairPushy—a Dart‑driven backend and Flutter dynamic‑update platform—covering server development, concurrency handling, ORM integration, automated build pipelines, and deployment strategies for seamless hot‑reloading of mobile applications.
The article introduces Java's ForkJoinPool for dividing large, compute‑intensive tasks into smaller subtasks, explains its suitability for performance testing scenarios such as high‑throughput QPS/RT data collection, and provides a complete Groovy‑based demo that defines a RecursiveTask, implements the compute method, and runs a sum calculation using a thread pool.
This article walks through a hands‑on Go RPC demo, explains the underlying server and client mechanisms, explores registration, HTTP handling, request processing, object pooling, and the gob codec, and clarifies why Go includes RPC alongside HTTP.
This article explains what a deadlock is, demonstrates a simple Java deadlock example with code, outlines the four necessary conditions, and introduces four practical tools—jstack, jconsole, jvisualvm, and jmc—for diagnosing and resolving deadlocks in Java applications.
This article demonstrates how to simulate high‑concurrency flash‑sale scenarios using SpringBoot, MySQL, and JMeter, and compares seven approaches—including lock‑based, AOP, pessimistic and optimistic locking, and queue‑based solutions such as BlockingQueue and Disruptor—to prevent overselling and improve performance.
This article explains how Go’s fast compilation, lightweight runtime, built‑in concurrency, strong type safety, and cross‑platform compatibility make it a compelling choice for building scalable, high‑performance backend services, and provides practical code examples to get started quickly.
This article explains the differences and similarities between Java's ObjectMonitor and the AbstractQueuedSynchronizer (AQS) implementations of heavyweight locks, covering their internal data structures, waiting queues, lock acquisition order, and how synchronized interacts with wait/notify mechanisms.
This article demonstrates how to convert multiple asynchronous queries into a synchronous response using Java's CompletableFuture, providing step‑by‑step code examples in both Java and Groovy, explaining execution flow, timeout handling, and practical considerations for WebSocket‑like scenarios.
This article analyzes the overselling problem in high‑concurrency flash‑sale scenarios, demonstrates seven concrete solutions—including improved locks, AOP locks, pessimistic and optimistic database locks, and queue‑based approaches using BlockingQueue and Disruptor—provides full SpringBoot code, JMeter test results, and practical recommendations for reliable stock reduction.
This article explains why tasks submitted to a Java ThreadPool via submit do not print exceptions, how to retrieve them using Future.get(), and presents three practical solutions—including try‑catch, Thread.setDefaultUncaughtExceptionHandler, and overriding afterExecute—to reliably capture and process thread pool exceptions.
This article explores why simple @Transactional locking can still cause product overselling under high concurrency, then presents seven detailed Java/SpringBoot solutions—including improved locking, AOP, pessimistic and optimistic database locks, and queue‑based approaches—complete with code, performance tests and best‑practice recommendations.
