This article explains how to achieve idempotent order creation and update by using unique order IDs, database primary‑key constraints, Redis flags, and a version‑column mechanism to detect and prevent duplicate submissions and ABA anomalies in distributed systems.
This article outlines the business background, detailed payment workflow, associated product and coupon management, and practical implementation tips for designing a robust payment system, covering process decomposition, sequence diagrams, data structures, transaction handling, and key technical considerations such as transaction management, locking, and settlement accuracy.
This article outlines comprehensive backend API design guidelines, covering parameter validation, extensibility, idempotency, logging, thread‑pool isolation, third‑party error handling, asynchronous processing, parallel queries, rate limiting, security, lock granularity, and strategies to avoid long‑running transactions.
This article explains how InnoDB resolves a deadlock by ordering the involved transactions, selecting a victim based on priority, rollback cost and table modifications, updating transaction weights, optionally logging the deadlock details, and finally waking the victim transaction to roll back.
This article analyzes the complexities of payment system design, covering business background, modular process breakdown, sequence diagrams for pre‑payment, payment integration, post‑payment stages, and detailed structural design, while also highlighting related business components and practical implementation tips.
This article explores the subtle timing of Spring transaction start and commit relative to lock/unlock, demonstrates how improper ordering can cause over‑selling in high‑concurrency scenarios, and provides debugging techniques and practical solutions to ensure atomicity and correct rollback behavior.
This article objectively compares NewSQL distributed databases with traditional middleware‑based sharding solutions, examining their architectures, transaction models, scalability, high‑availability mechanisms, storage engines, maturity, and offering a decision framework to help engineers choose the most suitable approach for their workloads.
This article explains how InnoDB simulates deadlocks, how the ib_srv_lock_to background thread checks for deadlocks, how it builds lock‑wait snapshots, constructs lock‑wait graphs, calculates transaction weights, and finally resolves deadlocks, illustrated with detailed SQL examples and code snippets.
The article outlines the challenges of payment systems, detailing business background, process decomposition, flow sequencing, structural design, related product and coupon management, and summarizes key technical considerations such as transaction management, locking, retry mechanisms, and fund settlement for robust backend development.
This article demonstrates how to leverage Spring's TransactionSynchronizationManager to detect active transactions and register synchronization callbacks that asynchronously push payment‑ledger messages to Kafka only after the transaction successfully commits, ensuring data consistency and minimal impact on the main business flow.
This article explains how InnoDB handles lock waiting by allocating lock structures, registering waiting slots, recording start times, and notifying transactions, outlining each step of the queue‑registration‑notification workflow in MySQL 8.0.32.
This article examines the trade‑offs of sending messages before, during, or after database persistence in order‑creation workflows, explores transaction‑message patterns, half‑message checks, and message‑table strategies, and offers practical guidance for building reliable backend messaging systems.
This article explains how to leverage Spring's TransactionSynchronizationManager to detect active transactions and register synchronization callbacks so that Kafka messages are sent asynchronously only after a transaction successfully commits, illustrated with a Spring Boot starter example.
This article explains how to use Spring's TransactionSynchronizationManager to detect active transactions and register callbacks that asynchronously send Kafka messages after a transaction successfully commits, ensuring data consistency in payment‑system archiving scenarios.
The article explains how to analyze, decompose, and design a complex payment workflow by breaking it into modules, defining pre‑payment, payment‑gateway, and post‑payment sequences, and outlining the essential data structures such as account management, transaction records, and order handling, while also highlighting practical implementation considerations.
This article objectively compares NewSQL databases with middleware‑based sharding solutions, examining their architectural differences, transaction handling, scalability, high‑availability mechanisms, storage engines, and maturity to help readers decide which approach best fits their specific workload and business requirements.
This article explains the timing of Spring's @Transactional commit relative to lock release, analyzes why committing after unlock can cause overselling, shows how to trace the transaction lifecycle in the source code, and offers practical solutions to ensure atomicity in high‑concurrency scenarios.
The article explains how to respond to accidental MySQL data deletion by analyzing loss causes, choosing recovery methods such as mysqldump backups, binary‑log point‑in‑time restores, or Percona’s InnoDB tool, provides step‑by‑step commands, and recommends preventive practices like regular backups, transactions, and strict permissions.
This article explains how to use Spring's TransactionSynchronizationManager to detect active transactions and register callbacks that asynchronously send Kafka messages only after a transaction successfully commits, illustrated with a payment‑system case study and complete code examples.
The article objectively compares NewSQL distributed databases with traditional middleware‑based sharding solutions, examining their architectural differences, distributed transaction support, performance, scalability, high‑availability mechanisms, storage engines, and practical suitability for various application scenarios.
This article explains how to use Spring's TransactionSynchronizationManager to detect active transactions and register synchronization callbacks so that Kafka messages are sent asynchronously after transaction commit, illustrated with a payment‑system logging scenario and complete Java code examples.
This article explains what SQL Server deadlocks are, why they occur, demonstrates a reproducible deadlock scenario with T‑SQL code, and provides practical strategies such as proper schema design, indexing, query optimization, shorter transactions, appropriate isolation levels, and monitoring to minimize deadlock occurrences.
This article explains how to dynamically manage multiple data sources and ensure transaction consistency across a master and several application databases in Spring by extending AbstractRoutingDataSource, using configuration‑file and database‑table solutions, and implementing a custom multi‑database transaction manager.
This article breaks down the essential "kernels" of payment, transaction, settlement, accounting, and related financial processes, illustrating each layer with diagrams and explanations that reveal how businesses, platforms, and institutions collaborate to move money securely and efficiently.
This comprehensive guide explains why MySQL needs InnoDB locking, details the seven lock types, shows how a single SQL statement acquires locks under different isolation levels, demonstrates how to view lock information, and walks through practical deadlock analysis examples.
This article provides a comprehensive overview of essential MySQL interview topics, covering join types, differences among DROP, DELETE, TRUNCATE, UNION vs UNION ALL, CHAR vs VARCHAR, transaction properties and isolation levels, database normalization, index types and usage, query execution flow, storage engine differences, and practical SQL optimization techniques.
This article explains MySQL 8.0.32 InnoDB's two‑phase commit process, detailing the flush, sync, and commit sub‑stages, the concept of group commit, and how queues and mutexes coordinate transaction logging to reduce redundant disk writes.
The article explains how to execute parallel tasks with CompletableFuture, why @Async cannot guarantee transactional integrity in a multi‑threaded Spring environment, and presents a programmatic transaction approach—including copying transaction resources between threads—to achieve consistent commit or rollback across concurrent operations.
This article explains how MySQL 8.0 implements the two‑phase commit protocol, detailing when it is used, the responsibilities of the prepare stage for both the binlog and InnoDB storage engines, and the five key actions InnoDB performs during preparation.
This article explains the seven Spring transaction propagation levels, shows how nested @Transactional methods interact under each level, and provides concrete code‑level examples that illustrate when transactions are merged, suspended, started anew, or rolled back, helping developers choose the right strategy for reliable enterprise applications.
This article explains Spring's seven transaction propagation behaviors—REQUIRED, SUPPORTS, MANDATORY, REQUIRES_NEW, NOT_SUPPORTED, NEVER, and NESTED—detailing how nested transactional methods interact, when new transactions are created or suspended, and how exceptions influence commit and rollback across different propagation settings.
This article explains why the @Transactional annotation fails in Spring when using multithreading, demonstrates a failing example, and provides a solution using manual SqlSession management to guarantee that all database changes are rolled back if any thread encounters an exception.
This tutorial explains how Redis optimistic locking works, demonstrates the WATCH, MULTI, EXEC, and DISCARD commands with live examples, and shows how to integrate the mechanism into a Spring Boot application using retry logic to handle concurrent updates safely.
This article explains how to guarantee transactional integrity when importing a 100,000‑row Excel file with asynchronous threads in Spring, covering double‑async optimization, Future and CompletableFuture handling, @Transactional pitfalls, manual transaction control, and a complete solution for cross‑thread transaction management.
This article explains Spring's seven transaction propagation modes, how they affect nested @Transactional method calls, and the resulting commit or rollback behavior in various scenarios, providing clear examples and diagrams for each propagation type.
This tutorial explains how to use PHP and PDO to quickly insert ten million rows into a remote MySQL database by preparing the connection, employing batch inserts, managing transactions, tuning MySQL settings, and optionally using the LOAD DATA statement for optimal performance.
Spring’s transaction propagation defines seven behaviors—REQUIRED, SUPPORTS, MANDATORY, REQUIRES_NEW, NOT_SUPPORTED, NEVER, and NESTED—detailing how methods join, create, suspend, or nest transactions, and the article explains each type, practical scenarios, and how proper selection ensures atomicity, consistency, and correct rollback handling.
This article explains how to execute two dependent tasks in parallel, guarantee their successful completion before a final step, and maintain transaction consistency across multiple threads in Spring by using CompletableFuture, programmatic transaction management, and a custom transaction‑resource copying mechanism.
This article explains the complete execution process of a MySQL UPDATE statement, covering InnoDB page structures, tablespaces, buffer pool caching, redo/undo logs, binlog, and the two‑phase commit mechanism, helping readers understand how MySQL ensures data consistency and durability.
This comprehensive guide explores MySQL transaction fundamentals, the ACID properties, various isolation levels, concurrency issues like dirty, non‑repeatable, and phantom reads, and dives deep into MVCC implementation, hidden fields, undo logs, and read views to help developers write reliable, consistent database code.
This article introduces the open‑source distributed database MatrixOne, tracing its early NewSQL/MPP architecture, the challenges that prompted a major redesign, and the resulting HTAP‑oriented three‑layer architecture with decoupled compute, transaction, and storage layers, highlighting performance, cost, and scalability improvements.
This article explains the various lock types in MySQL—including global, table, row, metadata, and intent locks—how they work under InnoDB, demonstrates lock acquisition with SQL examples, and provides practical guidance for detecting and resolving deadlocks in production environments.
The article explains why large database transactions degrade backend API performance, outlines issues such as data inconsistency, lock contention, and log overhead, and presents practical optimization techniques including avoiding RPC inside transactions, using programmatic transactions, batch processing, splitting into smaller transactions, and asynchronous parallel execution with CompletableFuture.
This article explains MySQL's explicit and implicit locking mechanisms, illustrates how transaction isolation levels affect lock scope, provides practical SQL examples for various scenarios, and offers guidelines on when to apply pessimistic locks versus optimistic approaches to maintain database performance.
This article examines seven typical messaging scenarios—ordinary, ordered, delayed, transactional, trace, dead‑letter, and priority messages—explaining the problem each solves, the trade‑offs, and concrete implementations in RocketMQ, Pulsar, RabbitMQ and Kafka with code samples.
This article explains the differences between Read Committed (RC) and Repeatable Read (RR) isolation levels in MySQL, detailing how snapshot reads work, why other levels avoid them, and illustrating their behavior with concrete transaction examples.
The article explains how bundling many business‑logic steps into a single large transaction harms API performance, outlines the resulting concurrency, locking, undo‑log, and DB‑pressure issues, and then presents practical techniques—programmatic transactions, batch processing, transaction splitting, and asynchronous parallelism—to optimize and speed up backend services.
This article explains the principle of deadlocks, the four necessary conditions, how they manifest in SQL Server resources, methods for investigating deadlocks with system procedures and Profiler, and practical techniques—including isolation level changes, lock timeouts, and bound sessions—to avoid and resolve them.
This article introduces a generic asynchronous processing SDK for Java Spring applications, covering its purpose, advantages, underlying principles, component choices such as Kafka, XXL‑Job, MySQL, design patterns, database schema, configuration via Apollo, usage instructions, safety considerations, and provides code examples and a GitHub repository link.
This article demonstrates why @Transactional fails in multithreaded Spring services, presents a thread‑pool based solution using manual SqlSession control to achieve full rollback across child threads, and provides complete code examples and test results illustrating both failure and successful transaction handling.
This article examines MySQL's various lock types—including shared, exclusive, gap, next‑key, and insert‑intention locks—illustrates how they interact during concurrent transactions, analyzes a real‑world deadlock incident, and provides recommendations for preventing similar concurrency issues in high‑traffic systems.
This article explains how to enable and configure Spring Boot's @Async annotation, demonstrates proper thread‑pool setup, highlights common mistakes such as calling async methods within the same class or mixing with @Transactional, and provides guidance on handling blocking tasks and exceptions for robust asynchronous execution.
This article explains what a MySQL deadlock is, shows a concrete example using two conflicting transactions on a simple test table, demonstrates how to detect the deadlock with InnoDB status output, and provides practical tips for preventing deadlocks in production systems.
This article explains how MySQL's SELECT ... FOR UPDATE statement applies different lock types—row, table, or none—depending on whether the WHERE clause uses primary keys, unique indexes, regular indexes, range queries, non‑indexed fields, or returns no rows, illustrated with practical transaction examples.
The article analyzes how to improve inventory‑update transactions during massive sales periods by comparing two SQL transaction patterns, calculating their TPS under defined latency assumptions, and suggesting that placing the row lock closer to the commit yields significantly higher throughput.
This article explains MySQL deadlocks, describing common scenarios such as multi‑table updates, nested transactions, inconsistent index order, and concurrent index updates, provides a concrete banking transfer example, and outlines practical steps for monitoring, terminating, retrying, analyzing, and preventing deadlocks in production.
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.
This article walks through how a Java application interacts with MySQL, covering the driver, connection pooling, thread handling, query parsing, the optimizer, storage engine, buffer pool, undo/redo logs, binlog, and transaction commit, providing a complete end‑to‑end view of MySQL’s internal processing.
This article explains the fundamentals of database transactions, introduces Spring's @Transactional annotation, and enumerates nine typical situations—such as incorrect method visibility, final methods, self‑invocation, unmanaged beans, multithreading, wrong propagation, swallowed exceptions, unsupported exception types, and non‑transactional storage engines—where transactions may not work as expected, offering guidance to prevent these pitfalls.
This article explains how Redis transactions and pipelines can guarantee data consistency during high‑concurrency order processing while dramatically improving throughput, and provides Java code examples, feature comparisons, usage guidelines, and practical case studies for backend developers.
This article breaks down MySQL's complex locking mechanisms—global, table, and row locks—explaining how different SELECT statements, transaction isolation levels, and index types affect lock acquisition, and provides practical test scripts and visual examples to help developers master lock behavior.
The article explains Vitess's approach to generating globally unique identifiers by using dedicated sequence tables, details the underlying MySQL schema and Go implementation, and compares it with VtDriver's CAS‑based method, highlighting trade‑offs between transaction locks and lock‑free updates.
The article explains the concept of idempotency, its significance in preventing duplicate operations such as double payments, and outlines various backend techniques—including MVCC, deduplication tables, token mechanisms, and distributed locks—to ensure consistent system behavior across retries and network failures.
This article examines why PostgreSQL appears to outrank MySQL in recent popularity surveys, explains the DB‑Engines ranking methodology, compares architectural choices such as MVCC, multi‑process vs multi‑thread models, transaction rollback behavior, licensing, and real‑world performance benchmarks like TPC‑C, ultimately revealing that MySQL still dominates many high‑throughput OLTP scenarios.
The article evaluates database success by comparing PostgreSQL and MySQL across popularity, features, licensing, transaction behavior, MVCC, concurrency models, and use cases, showing PostgreSQL leads in complex queries and feature richness while MySQL remains the most widely used OLTP system, especially in China.
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—covers lock compatibility, deadlock causes and prevention, compares optimistic and pessimistic locking, and shows how SELECT FOR UPDATE behaves under different isolation levels.
This article explains how to parallelize business steps with CompletableFuture, why Spring's @Async and @Transactional annotations cannot guarantee transaction consistency across threads, and presents a programmatic transaction approach—including copying Spring's transaction resources between threads—to achieve reliable multi‑threaded commits or rollbacks.
This article explains how to manage multithreaded database insert operations in Spring Boot, demonstrates why the @Transactional annotation fails in a multithreaded context, and provides a solution using manual SqlSession transaction control with code examples and test results.
This article analyzes a production deadlock caused by MyBatis‑Plus’s saveOrUpdate method, explains how MySQL gap locks lead to the issue, demonstrates reproducible tests, and offers practical solutions such as disabling gap locks or customizing the saveOrUpdate implementation.
This article walks you through building a minimal Spring Boot demo, setting breakpoints, analyzing call stacks and logs, and tracing the @Transactional rollback logic in the framework source code to understand why certain methods roll back or commit.
The article investigates why an UPDATE statement on a MySQL 5.7 InnoDB table with RR isolation experiences high lock time, demonstrates how missing indexes cause full‑table scans and blocking, and shows how adding an index and using optimizer trace can diagnose and resolve the issue.
This article explains why @Transactional fails in multithreaded Spring Boot scenarios, then demonstrates how to combine @Async, a custom thread‑pool, @MainTransaction and @SonTransaction annotations with AOP logic to achieve reliable multi‑threaded transaction control, complete with code examples and usage guidelines.
This article investigates how the MyBatis‑Plus saveOrUpdate method can trigger gap‑lock deadlocks in MySQL, explains the underlying concepts of deadlocks and gap locks, reproduces the issue with concrete SQL scripts, and proposes both risky and recommended mitigation strategies.
This article reproduces a MySQL 8.0.32 InnoDB scenario where inserting a row that violates a unique index causes a next‑key exclusive lock on the primary‑key supremum record, explains the underlying implicit‑lock mechanism, walks through the relevant source‑code stack, and details the lock‑conversion and rollback steps under REPEATABLE‑READ isolation.
The article explains how MySQL auto‑increment primary keys, InnoDB invisible row_id, transaction Xid and trx_id, and thread identifiers behave when they reach their numeric limits, showing the resulting errors, data overwrites, and potential visibility bugs in long‑running instances.
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.
This article explains the fundamentals of database transactions, the ACID properties, and how InnoDB uses redo and undo logs together with MVCC to ensure atomicity, consistency, isolation, durability, and reliable crash recovery.
The article explains how to achieve idempotent order creation and update in distributed systems by using database transactions, unique order identifiers, Redis locks, and version columns to prevent duplicate submissions and resolve the ABA anomaly, ensuring data consistency and reliable user experience.
This article explains how to ensure idempotency in order services by using database transactions, unique request identifiers, and version control to prevent duplicate orders and solve the ABA problem, providing practical code examples and best practices for backend developers.
This article details the design principles, architecture, multi‑tenant sharding strategy, state‑machine workflow, distributed transaction handling, and high‑availability measures of Vivo's transaction platform, illustrating how a micro‑service‑based e‑commerce system achieves low cost, extensibility, and robust performance.
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 dynamically manage multiple Spring data sources, switch between them at runtime, and implement custom multi‑database transaction handling, covering both configuration‑file and database‑table approaches, with code examples and AOP techniques for seamless integration.
This article explains how to handle large‑scale data inserts with multithreading in Spring Boot using MyBatis, why the @Transactional annotation fails across threads, and how to achieve reliable all‑or‑nothing rollbacks by manually managing transactions with SqlSession and thread pools.
This article compiles a technical interview recap covering MySQL MVCC, atomicity and persistence mechanisms, operating‑system page cache and deadlock concepts, TCP reliability and flow‑control, Redis persistence and clustering, as well as distributed transaction and consensus fundamentals.
The article explains how to process a batch of overdue unpaid orders in a Spring scheduled task by wrapping each order's business logic in a separate REQUIRES_NEW transaction, catching exceptions, and manually rolling back only the failing transaction so that other orders remain unaffected.
This article explains Spring's unified transaction model, the @Transactional annotation, and the various propagation and isolation settings, then details ten typical scenarios—such as missing Spring management, final or private methods, internal calls, wrong propagation, multithreading, and unsupported databases—that cause transactions to fail, providing code examples and solutions to avoid each issue.
This article examines how common business operations using SELECT ... FOR UPDATE and INSERT can cause Next‑Key lock deadlocks in MySQL under repeatable‑read isolation, demonstrates the locking behavior with practical SQL examples, and proposes strategies such as post‑locking and primary‑key updates to prevent such deadlocks.
The article explains why an UPDATE on the same row in MySQL can block in one transaction but not in another, by analyzing the types of row‑level locks (record, next‑key, gap) acquired during SELECT … FOR UPDATE, normal updates, and updates that modify primary‑key values, illustrating the lock behavior with B+‑tree examples.
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.
This article presents a series of MySQL 5.7.26 experiments that reveal how record locks on non‑indexed columns are applied to the primary (clustered) index under different isolation levels, how full‑table scans affect locking behavior, and why updates and inserts may or may not be blocked.
The article explains why transaction isolation is needed, describes common concurrency problems such as lost updates, dirty reads, non‑repeatable reads and phantom reads, outlines the ACID properties of a transaction, and details MySQL's four isolation levels and their impact on data consistency.
The article explains MySQL InnoDB’s internal storage hierarchy—from tablespaces, segments, and extents to pages and rows—its physical components such as buffer pool, change buffer, adaptive hash index, redo and binary logs, persistence techniques like double‑write buffering and log‑flushing policies, and how two‑phase commit and SQL execution interact, helping developers write correct, high‑performance applications.
This article explores MySQL’s write‑ahead log, two‑phase commit, engine plugins, memory management, lock strategies, and state‑machine design, showing how these internal mechanisms can inspire more reliable, scalable, and maintainable business system architectures.
This article explains how MySQL's InnoDB engine guarantees ACID properties by describing the four components of ACID, the four isolation levels, lock granularity and types, the MVCC mechanism with version chains and read views, and the roles of undo, redo, and bin logs in ensuring atomicity, consistency, isolation, and durability.
The article explains how Paxos can be generalized by defining its round number (rnd) over any partially ordered set, enabling both forced and non‑forced conflict exclusion mechanisms similar to 2PC, and showing how this expands Paxos’s applicability to multi‑dimensional transaction ordering and simplifies distributed database architectures.
The article details a step‑by‑step investigation of an intermittent deadlock occurring during session creation in a video‑conference system, explains how InnoDB lock types caused the conflict between DELETE and INSERT statements on the session and session_endpoint tables, reproduces the issue, debugs MySQL source code, and presents a simple code‑order fix to eliminate the deadlock.
The article examines common microservice transaction consistency techniques—including blocking retries, asynchronous queues, TCC compensation transactions, local message tables, and MQ transactions—explaining their mechanisms, advantages, drawbacks, and practical code examples for ensuring data integrity across distributed services.
The article explains various techniques for ensuring data consistency in distributed microservice architectures, including blocking retries, asynchronous queues, TCC compensation transactions, local message tables, and MQ transactions, while discussing their advantages, drawbacks, and practical implementation details.
This article explains Spring's @Transactional annotation, detailing its propagation options, rollback mechanisms, common pitfalls such as self‑invocation and non‑public methods, and demonstrates how to correctly invoke transactional methods via proxy objects with practical code examples.
This article reveals ten common Spring transaction pitfalls—including private methods, final modifiers, self‑invocation, missing @Service, wrong propagation, non‑transactional databases, swallowed or wrong exceptions, multi‑threaded calls, and nested transaction rollbacks—that can silently disable transaction management in Java backend projects.
