The article explains a three‑layer strategy—pre‑intercept via Redis locks or temporary tokens, mid‑process control using order status and optimistic locking, and post‑fallback with delayed MQ messages and nightly reconciliation—to guarantee that multiple user clicks result in only one successful payment.
The article examines a payroll bug caused by exposing internal collections, then explains three immutable‑focused rules for aggregate roots—expose only through the root, keep each transaction to a single aggregate, and reference other aggregates by ID only—showing how proper design prevents consistency errors, performance loss, and concurrency conflicts in Spring Boot applications.
This article presents twelve practical MyBatis‑Plus techniques—including avoiding isNull checks, selecting specific fields, batch operations, EXISTS subqueries, safe ordering, LambdaQuery type safety, between clauses, index‑aware sorting, pagination limits, null‑handling, performance tracking, enum mapping, logical deletion, optimistic locking, and increment/decrement methods—to help developers write cleaner, faster, and more maintainable Java code.
The article explains that idempotency prevents the same operation from being executed twice, illustrates a real double‑charge bug, evaluates five concrete approaches—including Redis check‑then‑set, DB unique index, optimistic lock, Redis distributed lock, and a message deduplication table—details their failure conditions and suitable scenarios, and recommends combining Redis lock with a database unique index for the most robust protection.
High‑concurrency systems must go beyond simple locking; the article breaks down four progressive strategies—optimistic locking, database pessimistic locking, Redis distributed locks, and finally message‑queue serialization—explaining their trade‑offs, implementation details, pitfalls, and how to combine them into a layered architecture that sustains million‑QPS workloads with consistency, throughput, and recoverability.
The article explains why RabbitMQ may deliver the same message multiple times, emphasizes that business‑level idempotency is required, and compares five concrete deduplication approaches—unique message ID with a dedup table, database unique constraints, Redis SETNX, optimistic locking, and state‑machine design—detailing their implementation, suitable scenarios, and trade‑offs.
The article analyzes why duplicate consumption is inevitable in MQ systems, defines message idempotency, and presents four practical solutions—including Redis SETNX, database unique indexes, state‑machine with optimistic locking, and global unique constraints—along with their pros, cons, and best‑practice guidelines for SpringBoot applications.
The article walks through the pitfalls of a seemingly simple 15‑minute unpaid‑order cancellation requirement, evaluates five implementation options—from a basic @Scheduled poll to Redis ZSet, DelayQueue, and distributed Redisson solutions—culminating in a production‑grade Redisson scheduler with optimistic‑lock safeguards and detailed best‑practice guidelines.
This article presents a comprehensive state‑machine‑based approach for managing the lifecycle of business entities such as orders and work orders, detailing core pain points, essential questions a state machine must answer, a comparative analysis of four implementation options, and a recommended solution that combines a database transition table, domain services, and optimistic‑lock concurrency control, along with architecture diagrams, code snippets, and operational guidelines.
This article analyzes ten frequent pitfalls when using MyBatis-Plus—such as incorrect pagination totals, pagination plugin misconfiguration, logical delete failures, auto‑fill issues, optimistic‑lock mismatches, null handling in QueryWrapper, batch‑insert performance, enum mapping errors, wrapper condition overrides, and type‑handler problems—provides root‑cause explanations, concrete code examples, and practical solutions to help developers write more robust and efficient Java backend code.
This article examines ten frequent pitfalls when using MyBatis-Plus—such as incorrect pagination counts, disabled pagination, logical‑delete mishandling, auto‑fill failures, optimistic‑lock issues, batch‑insert slowness, enum mapping errors, JSON type‑handler problems, and query‑wrapper quirks—explains their causes, and provides concrete code‑level solutions and best‑practice recommendations.
The article enumerates ten typical traps when using MyBatis-Plus—such as incorrect pagination counts, ineffective pagination plugins, logical delete mishandling, auto‑fill failures, optimistic‑lock issues, null‑value conditions, batch‑insert slowness, enum mapping errors, wrapper overwrites, and type‑handler problems—explains why they occur, and provides concrete code‑level solutions and best‑practice recommendations.
The article shows why a simple @Transactional approach fails under 300,000 concurrent users, explains MVCC behavior, demonstrates how to use row‑level pessimistic locks, sorting, FOR NO KEY UPDATE, short transactions, optimistic locking, and proper isolation levels, and outlines a production‑ready architecture with read/write splitting, Redis rate limiting and connection‑pool tuning.
The author recounts five critical production incidents that crippleed an education mini‑program—Redis connection‑pool exhaustion, duplicate bookings, double refunds, mis‑firing no‑show jobs, and inventory oversell—detailing root causes, concrete fixes, and hard‑won lessons for building resilient backend services.
The article explains how Redis guarantees atomic operations through single‑command atomicity, MULTI/EXEC transactions, and the WATCH optimistic‑lock mechanism, providing Java/Jedis code examples and discussing their role in ensuring data consistency in distributed systems.
In high‑concurrency Java services, a Redis lock can appear to expire before its TTL because JVM Stop‑The‑World pauses freeze the application clock while Redis continues counting down, leading to lock loss and data races unless mitigated with watchdogs or optimistic locking at the database level.
In high‑concurrency Java services, a Redis lock that appears to have a valid TTL can still be taken by another thread when a long Stop‑The‑World pause freezes the JVM clock, causing the lock to expire on Redis while the original holder remains paused, leading to data races.
When half a million users simultaneously try to buy 5,000 concert tickets, naive ordering logic leads to race conditions and double bookings, so the article walks through the root causes, compares pessimistic, optimistic, and Redis distributed locks, and presents an industrial‑grade microservice design with a three‑stage state machine to ensure strong consistency and high availability.
This article walks through the fundamentals and advanced techniques for guaranteeing cache consistency, covering the Double‑Check pattern, root causes of inconsistency, interview‑ready questions, practical solutions like message queues, optimistic locking, multi‑level caching, and cutting‑edge schemes such as consistent hashing with singleflight.
This article examines strategies for low‑latency, consistent hot‑account updates under high concurrency, comparing optimistic DB locking, Redis‑based deduction with asynchronous merging, data sharding, and MQ‑driven batch processing, and also addresses read‑side solutions.
The article explains three practical methods for achieving API idempotency—leveraging unique database primary keys (with distributed IDs), applying optimistic locking via version fields, and using a global anti‑repeat token stored in Redis—detailing their applicability, requirements, and operational flow.
This article walks through the design and implementation of a complete seckill (flash‑sale) system built on SpringBoot, MyBatis, MySQL, RabbitMQ and Redis, covering double MD5 password hashing, distributed sessions, unified exception handling, caching strategies, memory flags, pre‑decrement inventory, asynchronous order processing, oversell prevention, and rate limiting, with code snippets and UI screenshots.
This article explains the fundamental ideas behind pessimistic, optimistic, and distributed locking, compares their strengths and weaknesses, outlines typical application scenarios such as inventory deduction, banking transfers, and cache protection, and provides concrete implementation examples using SQL row locks, version fields, CAS, Redis WATCH, and Lua scripts.
This article explains MySQL's optimistic locking strategy, detailing how a version column enables conflict detection during updates, provides step‑by‑step SQL examples, compares it with pessimistic locking, and shows why it improves performance in read‑heavy, low‑conflict environments.
This article explains common problems of concurrent database updates in Spring Boot 3 and demonstrates five practical solutions—including database locks, optimistic and pessimistic locking, transaction isolation levels, and application‑level locks—accompanied by complete code examples and test results.
Learn how Spring Boot 3 handles database deadlocks by exploring transaction isolation levels, optimistic and pessimistic locking, and practical code examples that show prevention, detection, and retry strategies to keep your applications reliable under high concurrency.
This article explores five practical lock-based approaches—including database primary keys, pessimistic and optimistic locks, state machines, and distributed locks—to achieve reliable API idempotency in distributed systems, highlighting implementation details, trade‑offs, and code examples.
The article examines a seemingly harmless Java update snippet that hides a concurrency race condition, discusses why a team might decide not to change it in low‑frequency scenarios, and then presents safer alternatives such as selective updates, optimistic and pessimistic locks, and strict guidelines for financial‑critical code.
To prevent overselling during flash sales, the article explains how non‑atomic database updates cause negative stock and presents solutions such as optimistic DB locking, Redis Lua atomic deductions, Redisson distributed locks, transactional message queues, and pre‑deduction with rate limiting, recommending a combined approach that achieved 120 000 QPS with zero oversell.
This article explains MySQL's three concurrency types—read‑read, read‑write, and write‑write—describes how InnoDB uses MVCC, shared and exclusive locks, and provides practical examples and code for handling update‑loss and write‑write conflicts with optimistic and pessimistic locking.
This article explains how MySQL transactions and locking mechanisms ensure data consistency and handle high concurrency, covering basic transaction commands, isolation levels, practical lock implementations, deadlock simulation, and best‑practice recommendations for avoiding common concurrency problems.
This article explains the concept of MySQL optimistic locking, its underlying version‑or‑timestamp mechanism, and provides step‑by‑step SQL examples for adding a version column, reading data, and safely updating records while handling concurrent modifications.
This article explains the concept of idempotency, outlines scenarios where it is essential, analyzes common causes of idempotency problems, and presents a comprehensive set of solutions—including unique constraints, optimistic and pessimistic locks, distributed locks, token mechanisms, state machines, deduplication tables, and global request IDs—accompanied by practical code examples and database design guidelines.
This article explains the principles of optimistic and pessimistic locking in Java, shows how atomic classes and synchronized constructs implement them, provides runnable code examples, and compares their strategies, scenarios, performance, and typical implementations.
This article explains the ABA concurrency issue in distributed systems and presents three practical solutions—using Redis locks, implementing optimistic locking with version fields, and leveraging ordered message queues—to ensure data consistency under concurrent updates.
Idempotency ensures that repeated API calls produce the same result without side effects, and this guide explains its principles, common scenarios like payments and messaging, root causes of idempotency failures, and multiple implementation strategies—including unique constraints, optimistic and pessimistic locks, distributed locks, token mechanisms, state machines, and deduplication tables—with practical code examples.
Bilibili tackled race‑condition vulnerabilities in its massive video content pipeline by replacing simple timestamp checks with optimistic locking (CAS) and rate‑limiting locks, adding version verification and observation tools that now eliminate missed reviews and improve security, scalability, and real‑time editing reliability.
This article explains the concept of idempotency, outlines scenarios where it is essential, analyzes common causes of idempotency issues, and presents multiple practical solutions—including unique constraints, optimistic and pessimistic locks, distributed locks, token mechanisms, state machines, deduplication tables, and global request IDs—accompanied by concrete code examples.
The article explains the concept of idempotence in computing, illustrates typical duplicate‑request scenarios, and presents three practical backend solutions—unique database indexes, optimistic locking with version control, and a Redis‑based token mechanism—along with their advantages, trade‑offs, and implementation details.
The article outlines the three‑step inventory deduction workflow—select, verify, update—and explains how concurrent requests can cause overselling, then compares mitigation strategies such as pessimistic SELECT FOR UPDATE locks, optimistic CAS version retries, atomic decrement SQL statements, and Redis transactions, highlighting their trade‑offs in consistency and throughput.
This article explains the purpose of locks in concurrent environments, compares optimistic (CAS) and pessimistic locking mechanisms, outlines their advantages, disadvantages, suitable scenarios, and provides PHP/PDO code examples demonstrating implementation and performance testing under high concurrency.
This article explains what optimistic locking is, why it matters for concurrent Java applications, and details four implementation methods—version numbers, timestamps, CAS, and sequence numbers—along with their principles, usage scenarios, and practical considerations for high‑performance, distributed systems.
The article analyzes the inventory oversell problem caused by concurrent purchase requests and presents four backend solutions—including a Redis distributed lock, MySQL row lock, optimistic locking with version fields, and conditional SQL updates—illustrated with Java code and SQL examples to ensure data consistency.
This article introduces MyBatis-Plus, an enhancement layer for MyBatis, and walks through its core features—non‑intrusive CRUD, Lambda queries, primary‑key strategies, automatic field filling, optimistic locking, pagination, logical delete, performance analysis, condition builders, and a code generator—providing complete code examples and configuration steps for Spring Boot integration.
This article explains the concepts of pessimistic and optimistic locking, compares their use cases, demonstrates version‑based and CAS implementations, and shows a complete Spring Boot example with retry‑enabled optimistic lock handling using AOP and custom annotations.
This guide explains MySQL's lock mechanisms—including shared and exclusive locks, row‑level locking requirements, pessimistic versus optimistic strategies, and practical implementations with InnoDB, transaction handling, and JMeter concurrency testing—to prevent overselling in high‑traffic scenarios.
The article explains the concept of idempotency, outlines common duplicate‑submission scenarios such as form resubmission, malicious voting, client retries, and message re‑consumption, and details three practical implementation methods—database constraints, token‑based middleware, and state‑machine logic—complete with code examples and trade‑off considerations.
The article examines multiple techniques—including direct SQL updates, compare‑and‑swap, optimistic locking, Redis‑based distributed locks, and MySQL shared locks—to ensure correct and consistent account balance modifications and order status changes in high‑concurrency scenarios.
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.
This article explains the need for distributed locks in multi‑node systems, describes their core characteristics, and compares popular implementation approaches such as database‑based optimistic and pessimistic locks, Redis atomic locks, and ZooKeeper sequential node locks, highlighting their principles and trade‑offs.
This article explains the concepts of optimistic and pessimistic locking, the CAS mechanism and its ABA issue, and demonstrates Java implementations such as AtomicInteger, AtomicStampedReference, ReentrantReadWriteLock, and various lock types with practical code examples.
This article explains various lock strategies—including optimistic and pessimistic locks, read‑write locks, heavyweight vs. lightweight locks, spin locks, fair vs. unfair locks, and reentrant locks—detailing their definitions, use‑cases, underlying mechanisms, and illustrative examples for concurrent programming in Java and other languages.
This article explains why MySQL needs locking, details every InnoDB lock type—including shared, exclusive, intention, record, gap, next‑key, insert‑intention and auto‑increment locks—shows how they interact, and provides practical guidance for avoiding deadlocks and safely updating rows.
Optimistic and pessimistic locks are conceptual concurrency control strategies used across databases and caching systems; the article explains their principles, MySQL implementations with SELECT ... FOR UPDATE and version checks, compares their trade‑offs, and advises preferring optimistic locking in high‑concurrency scenarios.
This article explains the concept of API idempotency, why duplicate requests can cause critical issues such as double payments, and presents a comprehensive set of client‑side and server‑side strategies—including token mechanisms, PRG pattern, unique indexes, optimistic locking, distributed locks, and more—to guarantee safe, repeatable operations.
This article analyzes the business scenario of flash‑sale (秒杀) systems, outlines their technical characteristics, dissects the request chain, compares optimistic and pessimistic locking mechanisms with code examples, and presents a comparative table of concurrency‑control outcomes.
This article examines flash‑sale business scenarios, outlines their high‑concurrency characteristics, dissects the request‑flow architecture from client to database, and compares optimistic, retry‑optimistic, and pessimistic locking techniques with practical examples and performance considerations.
This article examines flash‑sale systems by outlining typical business scenarios, analyzing read‑heavy and high‑concurrency technical traits, detailing the end‑to‑end request flow, and comparing optimistic, retry‑optimistic, and pessimistic locking mechanisms with practical SQL examples and performance tables.
This article explains the concept of API idempotency, why duplicate requests occur in distributed systems, and presents a comprehensive set of server‑side and client‑side techniques—including token mechanisms, PRG pattern, unique indexes, optimistic locking, distributed locks, and idempotent state machines—to guarantee that repeated calls produce only a single effect.
The article explains how concurrent updates can cause data loss in high‑traffic scenarios and demonstrates using optimistic and pessimistic locking in PostgreSQL with Python, including EFCore‑style token checks and a retry mechanism via the tenacity library.
The article compares Java's pessimistic locking mechanisms such as synchronized blocks and ReentrantLock with optimistic approaches like version checks, CAS‑based AtomicInteger and custom spin locks, illustrating each method through counter examples and discussing their performance trade‑offs and limitations.
The article explains the concept of idempotency, illustrates everyday examples of its importance, defines it mathematically and in software, and presents a comprehensive set of practical solutions—including database constraints, frontend controls, locking mechanisms, token‑based forms, message queues, and RPC safeguards—to achieve reliable idempotent behavior in distributed backend systems.
The article analyzes flash‑sale business flow, outlines the technical challenges of massive concurrent requests, and presents a comprehensive backend architecture—including request isolation, queueing, caching, sharding, replication, optimistic locking, and overload protection—to ensure correctness, scalability, and availability of seckill services.
This article analyses the business flow, unique characteristics, technical challenges, architectural principles, layer‑wise design, database strategies, high‑concurrency issues, cheating defenses, and data‑safety mechanisms required to build a reliable flash‑sale system capable of handling tens of thousands of simultaneous requests.
This article examines how to ensure reliable message delivery in RabbitMQ by using durable queues, the confirm mechanism, and supplemental strategies such as persisting messages to Redis, implementing idempotent processing with optimistic locking or unique‑ID fingerprints, and employing compensation tasks to achieve near‑zero message loss in high‑concurrency systems.
This article examines common pitfalls in RabbitMQ message delivery, explains persistence and confirm mechanisms, and proposes a robust solution combining pre‑storage in Redis, confirm callbacks, scheduled retries, and idempotent processing to ensure virtually zero message loss in high‑concurrency systems.
This article explains the various Java lock mechanisms—including optimistic and pessimistic locks, exclusive and shared locks, reentrant, read‑write, fair/unfair, spin, segment locks, and lock‑escalation techniques—detailing their principles, Java implementations, usage scenarios, and optimization strategies.
This article explains the concepts, mechanisms, and practical code examples of optimistic and pessimistic locking in MySQL/InnoDB, including version‑based optimistic locks, shared (read) locks, exclusive (write) locks, and how to apply them with Laravel's query builder.
This article explains the concepts, usage scenarios, and differences of various database locks—including shared (S) lock, exclusive (X) lock, mutex, pessimistic and optimistic locks—as well as row‑level, table‑level, and page‑level locks, and it discusses common concurrency problems such as lost updates, non‑repeatable reads, dirty reads, and deadlocks.
This article walks through the design of a flash‑sale system, starting with a simple SpringBoot‑MyBatis implementation, then addressing overselling with pessimistic and optimistic locks, applying rate‑limiting algorithms, time‑window controls, interface hiding, frequency limits, and a suite of production‑grade optimizations such as CDN, Nginx load balancing, Redis caching, message queues, and short‑URL handling.
This article explains how to implement optimistic locking in Go using GORM by adding a version field, writing raw SQL updates, creating a reusable wrapper function with retry logic, and leveraging interfaces and type assertions to handle concurrency safely.
This comprehensive guide walks you through MyBatis-Plus fundamentals, non‑intrusive features, quick project setup, CRUD operations, automatic field filling, optimistic and logical locking, powerful wrapper queries, pagination, and the built‑in code generator, all illustrated with runnable examples and code snippets.
The article explains the concept of idempotency, distinguishes it from duplicate submissions, outlines typical use cases such as network retries and form resubmissions, and presents various backend solutions—including unique indexes, deduplication tables, pessimistic and optimistic locks, distributed locks, and token‑based approaches—complete with SQL examples.
This article explains how Redis implements transactions, highlighting the lack of atomicity and isolation, the queuing mechanism, error handling, cancellation methods, and the use of WATCH for optimistic locking, accompanied by step‑by‑step command‑line examples.
This guide walks through setting up a MySQL database, configuring a Spring Boot project with MyBatis‑Plus, defining entities and mappers, and demonstrates CRUD operations, pagination, logical and physical deletes, optimistic locking, performance monitoring, and code generation, providing complete code snippets and configuration details.
This article explains the different lock types—pessimistic, optimistic, and distributed—covers their use cases, compares their advantages and drawbacks, and provides concrete code examples and four practical solutions for handling high‑concurrency deduction scenarios such as inventory or balance reduction.
This article explains the concept of idempotency and presents practical techniques—such as unique indexes, token validation, pessimistic and optimistic locking, distributed locks, and API design—to guarantee that repeated requests in a high‑traffic e‑commerce system produce a single consistent outcome.
This article explores various techniques to achieve API idempotency in high‑concurrency environments, including token mechanisms, deduplication tables, Redis SETNX keys, state‑machine checks, and both optimistic and pessimistic locking, highlighting their workflows, advantages, and trade‑offs.
The article explains why idempotent operations are essential in backend and financial systems, describes the mathematical concept, and presents practical techniques such as unique indexes, token validation, pessimistic and optimistic locking, distributed locks, state‑machine design, and API patterns with concrete SQL examples.
This article analyzes the business flow of flash‑sale (seckill) systems, identifies ten technical challenges such as high concurrency, bandwidth, stock‑deduction and cheating, and presents architecture principles, design patterns, code examples, and safety mechanisms to build a reliable, scalable backend for flash‑sale scenarios.
This article examines the end‑to‑end workflow of a flash‑sale system, outlines its unique characteristics and technical challenges, and presents a layered architecture—including frontend, site, service, and database designs—along with concurrency control, anti‑cheating measures, and data‑safety strategies for handling massive traffic.
This article explores the end‑to‑end design of a flash‑sale (秒杀) system, detailing business flow, technical challenges, architectural principles, front‑end and back‑end design, concurrency handling, anti‑cheating measures, data safety, and practical solutions for high‑traffic e‑commerce scenarios.
The article examines the technical difficulties of handling tens of thousands of requests per second in flash‑sale systems, analyzes root causes such as API design, overload, cheating, and data‑safety issues, and proposes practical backend strategies—including Redis caching, load‑balancing, rate‑limiting, and optimistic locking—to maintain performance and reliability.
This article explains MySQL's lock mechanisms—including row‑level, page‑level and table‑level locks, their shared and exclusive modes, the differences between optimistic and pessimistic concurrency control, and practical tips for avoiding deadlocks in InnoDB.
This article explains the concepts, advantages, and drawbacks of MySQL pessimistic and optimistic locks, shows how to implement each with SQL and PHP code, compares their suitability for different traffic patterns, and demonstrates a Redis‑based optimistic lock for high‑concurrency flash‑sale scenarios.
This article explains database concurrency problems, describes how pessimistic and optimistic locking work, provides practical MySQL examples with transaction control, and compares their suitability for different read‑write scenarios to help developers prevent dirty data and improve throughput.
This article explains how to implement token‑bucket rate limiting with Guava's RateLimiter, compare it to leaky‑bucket algorithms, and combine it with optimistic and pessimistic locking techniques to prevent overselling in high‑concurrency flash‑sale applications.
The article explains why idempotent operations are essential in high‑traffic systems, defines idempotency, and presents a comprehensive set of techniques—including unique indexes, token validation, pessimistic and optimistic locking, distributed locks, select‑plus‑insert patterns, state‑machine design, and API safeguards—to guarantee consistent outcomes even under repeated requests.
This article explains the Redis concurrent key competition problem, illustrates typical scenarios where multiple clients modify the same key, and presents four practical solutions—optimistic locking with WATCH/EXEC, distributed locks, timestamp ordering, and message‑queue serialization—to ensure data correctness under high concurrency.
This tutorial explains how to enable and configure transaction locks—including pessimistic and optimistic modes—in Spring Data JPA repositories, demonstrates the use of @Lock and @QueryHints annotations, and shows how to set lock timeouts to ensure data integrity under high concurrency.
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.
This article analyzes the business flow of flash‑sale (seckill) activities, identifies the high‑concurrency technical challenges, and presents a comprehensive backend architecture—including frontend static page handling, request throttling, service‑layer queuing, database sharding, caching, optimistic locking, and anti‑cheating measures—to ensure reliability, scalability, and data consistency.
This article explains the concepts of concurrency control, detailing pessimistic and optimistic locking mechanisms, their implementation in MySQL, practical SQL examples, advantages, drawbacks such as deadlocks and ABA problems, and guidance on selecting the appropriate lock strategy for high‑concurrency applications.
The article explains the concepts, advantages, and drawbacks of optimistic and pessimistic locking, provides real‑world examples such as lost updates and inconsistent reads, and offers practical guidelines and implementation techniques for selecting the appropriate concurrency control strategy in database‑driven applications.
This article explains why duplicate requests occur, defines idempotency, and presents common backend strategies—such as unique indexes, optimistic locks, anti‑repeat tokens, anti‑repeat tables, and distributed locks—illustrated with an order‑processing workflow.
This article examines the technical challenges of handling massive concurrent traffic during flash‑sale events, explains why backend APIs become bottlenecks, and presents practical strategies—including request design, overload protection, anti‑abuse measures, and data‑safety techniques such as pessimistic, FIFO, and optimistic locking—to keep web systems stable and performant.
This article explains the mathematical and programming concept of idempotency, provides real‑world examples such as duplicate form submissions and payment requests, and details practical techniques—including query/select operations, unique indexes, token mechanisms, pessimistic and optimistic locks, distributed locks, and API design—to ensure idempotent behavior in backend systems.
This article explains how to use pessimistic (FOR UPDATE) and optimistic (version field) database locks in a Java SSM red‑packet application to prevent over‑issuance, details implementation steps, code changes, performance testing, and the trade‑offs between lock types.
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 MySQL's lock mechanisms, covering table‑level, row‑level, and page locks, the differences between pessimistic and optimistic locking, shared locks, and provides guidance on when to choose table locks versus row locks in various application scenarios.
