The article explains that Spring's @Transactional annotation does not affect private methods because transactions are applied via proxy objects, but using AspectJ weaving or programmatic TransactionTemplate can overcome this limitation and ensure proper transaction handling.
This article walks through the ten most frequently asked Spring interview topics—including IoC/DI concepts, bean lifecycle, AOP mechanics, circular‑dependency resolution, @Transactional behavior, Spring Boot startup steps, auto‑configuration, @Autowired vs @Resource, bean scopes, and the differences between servlet filters and Spring interceptors—providing concrete code examples and key points that can earn interviewers' extra credit.
Drawing on a decade of production experience, the article presents eight concrete rules for Spring @Transactional—covering transaction duration, proxy limitations, rollback settings, exception handling, read‑only flags, method visibility, bean separation for retries, and logging—to prevent common bugs and ensure reliable database operations.
This article examines ten common pitfalls when using Spring Boot's @Transactional annotation, explains why each issue occurs—from self‑invocation and private methods to swallowed exceptions, read‑only settings, long transactions, lock interactions, and deadlocks—and provides concrete code examples and three‑step solutions to avoid them.
The article analyzes common mistakes when moving from MySQL to PostgreSQL—such as oversized connection pools, blind max_connections tweaks, oversized transactions, idle‑in‑transaction connections, and indiscriminate indexing—and offers concrete, step‑by‑step guidance to achieve a stable, well‑tuned PostgreSQL deployment.
The final installment of the Spring Boot source‑code series consolidates the previous six episodes—covering startup flow, auto‑configuration, IoC container, bean lifecycle, circular dependencies, and SPI—while providing a complete knowledge map, 20 interview‑question templates, practical study tips, and a roadmap for further learning and contribution.
As AI accelerates Java business code generation, developers must carefully define transaction boundaries, distinguishing atomic operations, avoiding oversized or overly fragmented transactions, and coordinating with messaging and caching to ensure consistency, recoverability, and reliable system behavior.
The article examines five high‑frequency Spring Boot anti‑patterns—field injection, returning entities from controllers, overusing @Transactional, generic exception handling, and embedding business logic in controllers—showing problematic code snippets, real‑world consequences, and concise refactored examples to improve testability, performance, and maintainability.
The article compares SpringBoot's declarative transaction management using @Transactional with programmatic transaction handling via TransactionTemplate, explaining their underlying mechanisms, code examples, advantages, disadvantages, and suitable scenarios, and provides a detailed side‑by‑side table to help developers choose the appropriate approach.
This article explains the fundamentals of transaction management in SpringBoot, detailing the ACID properties, the @Transactional annotation’s AOP‑based implementation, core attributes such as propagation, isolation, rollback rules, timeout and read‑only mode, and provides practical code examples for single‑ and multi‑datasource scenarios.
The article breaks down the complexities of payment processing by outlining business background, decomposing the workflow into modular steps, illustrating sequence diagrams, designing data structures, and addressing related domains such as product and coupon management, while highlighting key technical challenges like TCC transactions, locking, and retry mechanisms.
This guide explains what a distributed transaction is and walks through the complete setup of a Seata server (version 0.9.0), including downloading, configuring file.conf and registry.conf for Nacos, initializing the database, starting services, and creating the required undo_log table.
A mysterious payment failure where orders appeared successful but were never persisted was traced to a missing transaction commit in a special code path, leading to polluted connections that silently broke subsequent transactions, and the article explains the root cause, debugging steps, fix, and preventive measures.
This guide explains how to configure multiple data sources in SpringBoot to achieve basic read‑write separation, covering applicable scenarios, step‑by‑step setup of master and slave DataSources, SqlSessionFactory and SqlSessionTemplate beans, package‑based routing, transaction considerations, common pitfalls, and best‑practice recommendations.
An online payment service failed when a newly added business logic forgot to commit its transaction, leaving a polluted connection that silently blocked subsequent orders, and the post‑mortem explains the root cause, debugging steps, code fixes, and preventive measures.
This article explains why basic Spring Boot datasource settings are insufficient for production and walks through five advanced configurations—HikariCP tuning, transaction timeout and isolation, read‑write splitting, slow‑SQL monitoring, and security hardening—each illustrated with concrete code snippets and rationale.
A payment service failed to insert orders despite successful payments, showing no errors, occasional lock timeouts, and intermittent success, which was traced to a missing transaction commit that polluted reused connections, causing unrelated business failures until the bug was fixed and preventive measures were added.
The article explains why returning a generic Result wrapper from a Java service layer breaks responsibility separation, harms reusability, complicates exception handling and testing, and obscures transaction boundaries, advocating for returning pure domain objects instead.
The article explains that returning a Result wrapper from the Service layer mixes business logic with presentation concerns, leading to tighter coupling, reduced reusability, cumbersome error handling, harder testing, poorer DDD alignment, limited interface flexibility, and ambiguous transaction boundaries.
This article explains why Spring @Transactional may not work as expected, covering issues such as wrong method visibility, final methods, internal calls, missing bean registration, multithreading, unsupported table engines, incorrect propagation settings, swallowed exceptions, and how to resolve each problem.
This article presents a comprehensive, step‑by‑step guide to optimizing Java‑based API interfaces, covering batch processing, asynchronous execution, caching, pre‑processing, pooling, transaction handling, pagination, SQL tuning, lock granularity, and code restructuring, with practical code examples and diagrams.
This article examines the challenges of implementing payment functionality in business systems, breaking down the process into detailed steps, outlining sequence diagrams, proposing a data structure design, and discussing related e‑commerce components such as product and coupon management to ensure reliable, scalable transactions.
This guide explains how to configure Spring Boot applications to use multiple coexisting DataSource beans—such as MySQL and TDengine—without runtime switching, covering bean definitions, MyBatis mapper scanning, transaction manager setup, custom @Transactional annotations, and common pitfalls, enabling clean separation of business, log, and time‑series data.
This article introduces Spring Boot 3's TransactionExecutionListener, explains its purpose compared to traditional transaction hooks, provides a complete code example—including entity, service, custom listener, and test cases—and demonstrates how to observe transaction phases and handle both successful commits and rollbacks.
An in‑depth post‑mortem of a JED database incident reveals how its lock matrix and MVCC isolation caused metric data loss, explains the underlying lock granularity, transaction isolation levels, and MVCC visibility rules, and proposes short‑term distributed‑lock and long‑term read‑calc‑write solutions.
This article explores the challenges of maintaining data consistency in distributed microservice architectures, covering CAP theory, consistency models, replication strategies, transaction patterns like Saga and TCC, tooling choices, monitoring practices, and actionable best‑practice recommendations.
This article explains the challenges of manual JDBC transaction handling, introduces Spring's unified transaction API with PlatformTransactionManager, TransactionDefinition, and TransactionStatus, demonstrates code examples for programmatic and declarative transactions, and discusses common pitfalls such as non‑public methods, self‑invocation, and exception handling.
Spring Boot simplifies transaction management via PlatformTransactionManager and AOP, offering declarative and programmatic approaches, default rollback on runtime exceptions, customizable rollback rules, propagation behaviors, manual rollback techniques, multithreaded transaction handling, and solutions to common pitfalls such as swallowed exceptions and self-invocation issues.
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.
This article provides a comprehensive overview of the Spring framework, covering its core principles such as Inversion of Control, Dependency Injection, Aspect‑Oriented Programming, and transaction management, while illustrating each concept with diagrams and practical code examples for Java developers.
Dynamic‑datasource 4.3.1 offers a powerful AOP‑based, annotation‑driven solution for seamless multi‑database routing, read‑write separation, and load‑balancing in Spring Boot applications, while addressing common pitfalls such as transaction boundaries, connection‑pool configuration, health‑check failures, and version compatibility.
An in‑depth analysis of a Spring transaction failure caused by a custom AOP aspect interfering with the default TransactionInterceptor, detailing the symptom, root cause, and step‑by‑step solutions—including adjusting aspect order, rethrowing exceptions, and manual rollback—to ensure reliable transaction management.
This article presents a comprehensive, step‑by‑step guide to optimizing API latency by applying batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, code refactoring, pagination, SQL tuning, and fine‑grained locking techniques.
This article explains common distributed‑transaction solutions, describes the author’s own compensation‑plus‑local‑message‑table approach for merging customers across databases, outlines the TCC model with its Try‑Confirm‑Cancel phases, and details why TCC was rejected due to complexity, long resource locks, and rollback issues.
The article explains how Spring's PlatformTransactionManager and TransactionSynchronizationManager work together to provide both declarative and programmatic transaction management, detailing their core responsibilities, lifecycle handling, synchronization callbacks, practical use cases, and common troubleshooting steps.
This article dissects Spring's transaction management system, detailing its layered architecture, core interfaces such as PlatformTransactionManager, TransactionDefinition and TransactionSynchronization, the lifecycle of transaction start, commit and rollback, programmatic and declarative usage, advanced features like distributed and nested transactions, common pitfalls, and optimization recommendations.
This article presents a comprehensive set of practical techniques—including batch processing, asynchronous execution, caching, preprocessing, pooling, parallelization, indexing, pagination, SQL tuning, and lock granularity—to dramatically reduce API latency and improve overall backend system efficiency.
This article presents a comprehensive Spring Boot 3 tutorial that explains how the framework manages database connections, introduces eight built‑in DataSource control methods, and provides detailed code examples for configuring HikariCP, using DataSourceUtils, SmartDataSource, TransactionAwareDataSourceProxy, and transaction managers.
This article introduces the fundamentals of database sharding, outlines architectural evolution, explains vertical and horizontal splitting dimensions and strategies, discusses middleware choices, and presents real‑world case studies while addressing common challenges such as ID generation, join queries, pagination, and distributed transactions.
This article explains how @Transactional and @Async normally cooperate in Spring Boot, demonstrates cases where the transaction does not roll back when @EnableAsync order is changed, analyzes the underlying AOP proxy creation and bean post‑processor execution order, and introduces new Spring 6.2 transaction‑rollback configuration options.
This tutorial explains how Spring's @Transactional and @Async annotations work, explores their interaction in various scenarios such as bank‑transfer examples, discusses thread‑context propagation, highlights pitfalls when mixing them, and provides best‑practice recommendations for maintaining data integrity.
This article presents a systematic guide to improving API response times by applying batch processing, asynchronous execution, caching, pooling, parallelism, indexing, transaction management, pagination, SQL tuning, and proper lock granularity, supplemented with practical Java code examples and diagrams.
The article objectively compares NewSQL distributed databases with middleware‑plus‑sharding solutions, covering architectural differences, distributed transaction handling, high‑availability, scaling, SQL support, storage engines, maturity, and provides a decision‑making checklist to help engineers select the most suitable approach for their workloads.
This article explains the fundamentals of transaction handling in Spring Boot, covering both programmatic approaches with PlatformTransactionManager and TransactionTemplate and declarative management using @Transactional, while detailing common pitfalls, propagation and isolation settings, and providing concrete code examples.
This article examines seven typical challenges that arise after implementing database sharding—such as global ID conflicts, cross‑shard queries, distributed transactions, shard‑key design, data migration, pagination, and operational overhead—and provides practical solutions and code examples to address each issue.
This article compares Spring Boot's declarative @Transactional annotation with programmatic transaction management using TransactionTemplate, TransactionalOperator, and TransactionManager, explains their suitable scenarios, provides code examples, and presents JMeter performance tests that show how the right choice can dramatically improve throughput.
This article compiles twelve advanced Spring interview questions covering bean initialization and lifecycle, AOP implementation, transaction management, the differences between ApplicationContext and BeanFactory, type conversion mechanisms, dependency injection, circular dependency resolution with three‑level caches, the event system, @Transactional and @Async annotations, and the inner workings of DispatcherServlet.
The article outlines the payment business background, breaks down the payment process into modular components, presents sequence diagrams and structural designs, summarizes key technical considerations, and additionally promotes a ChatGPT community and related services.
This article explains the fundamentals of distributed transactions, the CAP theorem, various transaction patterns such as 2PC, 3PC, TCC, Saga, and introduces Seata's AT mode with step‑by‑step setup of the TC coordinator and microservice clients, culminating in a working e‑commerce example and key best‑practice takeaways.
This article analyzes the complexities of payment business logic, breaks down the workflow into modular components, illustrates sequence diagrams, discusses related domains such as product and coupon management, and summarizes key technical considerations like transaction handling, locking, and settlement.
This article explains how Spring Boot’s @Transactional annotation works, introduces programmatic transaction management, and demonstrates how to create custom transaction hook functions with TransactionSynchronizationManager and TransactionSynchronization to execute custom logic at each transaction phase, improving robustness and maintainability.
This guide explains why upgrading from Spring Boot 1.5 to 2.1 is essential for MongoDB transaction management, outlines version requirements, highlights key feature changes, and provides step‑by‑step instructions with code examples to enable reliable transactional operations in a Spring‑based backend.
This curated bibliography compiles seminal database‑system papers—spanning foundational relational theory, system design for RDBMS and NoSQL, SQL engine optimization, storage structures, transaction and scheduling mechanisms, plus workload, networking, quality, and tuning research—each entry listing title, authors, year and a PDF link for scholars and practitioners.
This article explains how to guarantee that when any asynchronous thread fails during a large Excel import, the main thread can roll back all related transactions, covering the limitations of @Transactional and @Async, thread‑local propagation, and practical solutions using Future, CompletableFuture, custom ForkJoinPool, and manual transaction management.
Spring 6.2 introduces asynchronous bean initialization, a new addSingletonCallback mechanism, global transaction rollback settings, and injection performance optimizations, eliminating blocking during multi‑threaded event publishing and dramatically reducing startup time, as demonstrated with code examples and performance test results.
The article explains how to handle distributed consistency when a financial reimbursement process involves MySQL writes and remote calls to BPM and SAP systems, analyzes failure scenarios, and proposes a split‑transaction approach using a task table, scheduled jobs, and Spring's afterCommit hook.
This article explains how Spring's @Transactional and @Async annotations can be applied to the same service methods, the implications for transaction boundaries across threads, and the precautions developers must take to maintain data consistency and proper transaction management.
The article explains how to analyze, decompose, and design a payment system by breaking the workflow into modules, illustrating pre‑payment, third‑party integration, and post‑payment stages with sequence diagrams, and proposing a data‑structure model that covers account management, transaction records, order handling, and related business components.
This article explains Spring's transaction mechanism, covering its reliance on database support, ACID properties, isolation levels, propagation attributes, programmatic and declarative management styles, and the underlying AOP implementation with concrete code examples.
This article examines common scenarios that cause Spring transaction management to fail—such as non‑public methods, swallowed exceptions, incorrect rollback settings, self‑invocation, final methods, wrong propagation, asynchronous threads, unsupported database engines, and misuse of @Transactional—and provides concrete code‑level solutions to ensure reliable transactional behavior.
This article explains how InnoDB determines transaction types based on the first SQL statement, describes the internal state changes when a transaction starts, and details the identification, ID allocation, and handling of read, read‑only, read‑write, and internal transactions in MySQL 8.0.32.
This article provides a detailed, global‑level review of MyBatis, covering its main features such as SQL mapping, dynamic SQL, parameter and result mapping, transaction management, connection‑pool integration, second‑level cache, as well as the three‑layer architecture, configuration parsing, proxy creation, and SQL execution process.
This guide explains how to handle various Spring bean injection and transaction challenges, including injecting prototype beans into singletons, using @Lazy, registering abstract beans, resolving multiple bean conflicts, forcing rollback without exceptions, injecting static fields, ensuring transaction consistency, enabling non‑public transactional methods, custom component scanning, and adding interfaces via AOP.
This article explains Spring's transaction core, showing how TransactionInterceptor and TransactionAspectSupport use AOP to bind a Connection to ThreadLocal, walks through key code snippets for creating, committing, and rolling back transactions, and demonstrates a JUC‑based solution for maintaining transaction consistency across multiple threads.
This guide walks through setting up a Spring Boot project with JDK 8, Atomikos, and JPA to manage two MySQL data sources, configure JTA transaction handling, define entity managers, services, and a controller, and demonstrates successful and failing transaction scenarios with code snippets and screenshots.
This article examines the ClassCastException that occurs when 100 threads concurrently query MyBatis using a shared SqlSession, analyzes the root cause in DefaultSqlSession and BaseExecutor, and explains how Spring Boot’s MyBatis integration employs SqlSessionTemplate, ThreadLocal binding, and transaction management to guarantee thread‑safe SqlSession usage.
This article provides a comprehensive overview of Seata, a distributed transaction framework that ensures data consistency across microservices through four transaction modes: AT, XA, TCC, and Saga, each with distinct characteristics and use cases.
This article explains why transaction management is essential, how Spring abstracts resource handling, supports nested transactions and propagation behaviors, and compares declarative (annotation) and programmatic approaches, providing practical code examples and a deep dive into the underlying TransactionManager model.
This guide explains Spring Boot's programmatic transaction management using TransactionTemplate, TransactionCallback, and PlatformTransactionManager, covering configuration, rollback handling, event listeners, and when to prefer programmatic versus declarative approaches, complete with code examples.
This article analyzes frequent scenarios where Spring's @Transactional annotation fails in Spring Boot projects—such as self-invocation, swallowed exceptions, misconfigured rollback rules, non‑public methods, wrong propagation settings, and long‑running transactions—and provides concrete fixes to prevent data inconsistency and performance issues.
This article demonstrates how using Spring Boot's TransactionTemplate to programmatically control transactions can prevent long‑running non‑transactional operations from blocking database connections, thereby increasing system throughput compared to the traditional @Transactional annotation, with detailed code examples and performance observations.
This article explains why transaction control is essential for database‑backed applications, compares native JDBC/MyBatis handling with Spring's unified approach, details the core transaction APIs, and walks through the AOP‑based mechanism—including @EnableTransactionManagement, proxy generation, and CGLIB interception—that automatically opens, commits, and rolls back transactions at runtime.
This article explains the concept of distributed transactions, compares common solutions such as 2PC, 3PC, TCC, and message‑based eventual consistency, and provides a detailed walkthrough of configuring and using the Seata middleware—including AT mode workflow, server and client setup, and real‑world testing—so developers can confidently implement reliable cross‑service transactions.
The article details Shopee’s seadt‑SAGA framework—a Go‑based distributed transaction solution that implements the SAGA pattern via two modes, orchestration with a central coordinator and collaboration with embedded logic, explains their state‑machine designs, compares rollback ordering using vector clocks versus sequence numbers, and discusses each mode’s trade‑offs.
The article explains why the error "Transaction rolled back because it has been marked as rollback‑only" occurs in a Java Spring service when using MyBatis‑Plus saveBatch, how hidden @Transactional annotations cause hidden nested transactions, and presents a practical workaround by replacing the batch operation with a custom mapper.
This article explains how to execute two dependent tasks in parallel with CompletableFuture, why the usual @Transactional annotation fails in a multi‑threaded environment, and provides a programmatic transaction solution that copies Spring's transaction resources between threads to guarantee atomic commit or rollback across all parallel operations.
The article describes how a finance team tackled a 50‑million‑row MySQL transaction table by analyzing the pain points, defining split goals, selecting sharding‑jdbc, designing multi‑source transaction handling, pagination across shards, data migration strategies, and a staged rollout to ensure stability and performance.
This article explains the concept, motivations, design methods, common patterns, and trade‑offs of database sharding (both table and database level), offering practical code examples, scalability criteria, and guidance on avoiding over‑engineering while considering alternatives like TiDB.
Discover a comprehensive set of ten practical techniques—including batch processing, asynchronous execution, caching, pooling, parallelization, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve efficiency in legacy backend systems, illustrated with real-world examples and code snippets.
This article explains Spring's transaction propagation behaviors, rollback mechanisms, common pitfalls such as self-invocation, and provides solutions using AOP proxies and @EnableAspectJAutoProxy, supplemented with code examples and a brief promotion of a related community.
This article outlines a comprehensive set of backend API performance optimization techniques—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction handling, SQL tuning, and lock granularity—to reduce latency and improve system efficiency.
This article outlines a comprehensive set of backend interface optimization techniques—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction sizing, code restructuring, deep pagination, SQL tuning, and lock granularity—to reduce latency and improve overall system efficiency.
This article summarizes practical techniques for reducing API latency, including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction size control, program structure refactoring, deep pagination handling, SQL tuning, and proper lock granularity.
The article explains how to dynamically manage multiple data sources and ensure transaction consistency in Spring applications by extending AbstractRoutingDataSource, using configuration‑file or database‑table approaches, and applying AOP‑based switching with custom transaction management.
This article explains how to dynamically manage multiple data sources and ensure transaction consistency across them in Spring applications, covering both configuration‑file and database‑table approaches, custom AOP switching, and a bespoke multi‑database transaction mechanism.
This article presents a comprehensive set of backend API optimization strategies—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction management, code restructuring, deep pagination, SQL tuning, and lock granularity—to dramatically reduce response times and improve system efficiency.
This article explains Spring's transaction abstraction, detailing the roles of PlatformTransactionManager and ReactiveTransactionManager, the TransactionDefinition attributes, TransactionStatus interface, resource synchronization techniques, declarative transaction management with AOP, rollback rules, and provides XML and Java configuration examples for both JDBC and Hibernate environments.
This article explains how Spring enables transaction management through @EnableTransactionManagement, details the underlying proxy mechanism, outlines the basic and detailed execution flow, describes transaction propagation types and their classifications, and shows how to force rollback and use TransactionSynchronization for custom callbacks.
This article explains how application services act as a façade for domain models, outlines key design principles, shows practical Go/Java implementation patterns, discusses transaction propagation and security handling, and provides concrete best‑practice recommendations for building thin, framework‑agnostic backend services.
The article explains the classic three‑layer MVC pattern, its shortcomings in complex Java projects, and proposes adding a Manager layer to separate generic business processing, reduce transaction scope, and improve code organization, illustrated with Spring‑based code examples.
Yanxuan migrated its monolithic trading system from a shared DDB cluster to an independent database by using Netease Data Canal for real‑time sync, a write‑stop switch with Pandora middleware, account and permission isolation, and extensive testing across three phases to ensure data consistency and minimal business impact.
This article explains how Spring enables transaction management through @EnableTransactionManagement, details the underlying advisor and proxy mechanisms, describes the basic and detailed execution steps, explores propagation types with practical code examples, and shows how to force rollbacks and use TransactionSynchronization for lifecycle callbacks.
This article provides a comprehensive guide to Spring transaction management, explaining the inner workings of @EnableTransactionManagement, the basic and detailed execution processes, various propagation mechanisms, forced rollback techniques, and TransactionSynchronization, supplemented with practical code examples and case analyses.
This article analyzes why a Spring service encounters "connection closed" errors during long‑running transactions, explains how MyBatis and Druid manage connections, and provides a programmatic‑transaction solution that splits large operations into smaller units to prevent connection leaks and timeouts.
This article walks through the fundamentals of Spring's transaction management, presenting a practical example, detailed execution workflow, and step‑by‑step source‑code analysis—including bean creation, AOP proxy generation, transaction attribute retrieval, transaction start, commit, and rollback – to help developers master Spring transaction internals.
This article explains common pitfalls of Spring's transaction management—including AOP proxy limitations, unchecked exception handling, self‑invocation, asynchronous execution, multiple data sources, and large transaction issues—and provides practical solutions such as using class‑based proxies, configuring rollback rules, and splitting transactions into smaller units.
This article provides an in‑depth overview of microservice concepts, RPC frameworks, serialization methods, distributed transaction models, CAP and BASE theories, monitoring solutions, high‑availability strategies, load‑balancing techniques, configuration management, service registration/discovery, and Dubbo fault‑tolerance clusters, offering practical examples and code snippets for Java‑based cloud native systems.
This article explains the challenges of distributed transactions in multi‑node systems, reviews common solutions such as 2PC, TCC, Saga, and focuses on implementing eventual consistency using message queues, detailing local message tables, MQ‑based transactions, and loss‑prevention techniques across RocketMQ, Kafka, and RabbitMQ.
The article explains how Go's database/sql pool retains connections until Rows, Stmt, or Tx objects are explicitly closed or a timeout context is cancelled, demonstrates experiments showing blocked goroutines when resources aren't released, and offers practical guidelines—using QueryContext with timeouts, deferring Close/Cancel, and always committing or rolling back transactions—to prevent pool exhaustion and improve MySQL reliability.
This article demonstrates how to accelerate massive MySQL data updates in a Spring Boot application by replacing naïve loops with manual transaction control, multithreaded processing, CountDownLatch synchronization, and UNION‑based SQL batching, achieving up to a five‑fold speed increase.
