This article demonstrates how to implement dynamic data source switching in Spring Boot using ThreadLocal and AbstractRoutingDataSource, covering configuration, annotation-based switching, and runtime data source registration with Druid.
This article shares production-hardened patterns for multi-tenant SaaS using Spring Boot, covering isolation mode selection, ThreadLocal-based context propagation, MyBatis-Plus SQL interception for automatic tenant_id injection, Feign and async thread propagation, HikariCP tuning, composite indexing, cursor pagination, and real-world pitfalls like interceptor ordering, transaction-bound data source switching, and ThreadLocal leakage.
This tutorial explains how to implement dynamic data source switching in Spring Boot by combining ThreadLocal with AbstractRoutingDataSource, covering custom context holders, configuration, annotation‑driven switching, dynamic addition of data sources, and complete code examples with test results.
This article demonstrates how to implement MySQL master‑slave dynamic data source switching in a SpringBoot 3.0.4 project using AOP, custom annotations, and configuration files, ensuring automatic failover to the master when a slave becomes unavailable.
This guide explains how to use Spring Boot, AOP, and custom annotations to implement dynamic MySQL master‑slave data source switching, automatically falling back to the master when a slave fails, covering configuration, Maven dependencies, enum definitions, data source beans, routing logic, and usage examples.
This guide shows how to implement dynamic MySQL master‑slave switching in a SpringBoot 3 project using AOP and a custom @DataSource annotation, configuring Druid pools, defining a routing datasource, and automatically falling back to the master when a slave fails for high availability.
This guide explains how to implement dynamic data source switching in SpringBoot by leveraging ThreadLocal and AbstractRoutingDataSource, covering basic concepts, code examples for context holder, routing datasource, configuration, annotation‑driven switching, and runtime addition of new data sources with practical testing steps.
This technical guide explains how to scale a sharded database horizontally by introducing five expansion schemes—including shutdown, write‑stop, log‑based, dual‑write, and smooth 2N approaches—covers MariaDB installation, master‑master replication setup, dynamic data‑source configuration with ShardingJDBC, and detailed Keepalived high‑availability configuration for seamless service continuity.
This session explains why SaaS applications need tenant data routing, compares multi‑tenant sharing models, details dynamic data source architecture, routing plugin design, tenant identifier propagation, and future sidecar solutions, helping developers choose appropriate isolation strategies, configure flexible data sources, and avoid cross‑tenant data contamination.
This article explains why dynamic data sources are needed in high‑traffic Spring Boot applications, shows how to configure multiple databases via Maven, YAML and Java, and demonstrates a custom annotation‑driven AOP solution for seamless runtime routing.
The article explains how to transition from client‑side sharding with Sharding‑JDBC to proxy‑side sharding using Rainbow Bridge DAL by adding new, switchable data sources, employing AbstractRoutingDataSource and configuration flags for gradual migration, while addressing read‑write separation, unsupported SQL functions, and ensuring backward compatibility.
This tutorial explains how to configure Spring Boot with a single Druid data source, extend it to multiple dynamic data sources using AbstractRoutingDataSource, and seamlessly integrate MyBatis, including custom annotations, AOP switching, thread‑local isolation, and transaction management for robust multi‑database applications.
This article examines the challenges of using transactions with database read/write splitting, including potential master‑slave switching errors, connection‑pool deadlocks caused by isolation levels and propagation, and presents Spring‑based dynamic data source and transaction management solutions.
This article describes the design and implementation of a five‑layer one‑stop fund processing platform, covering architecture, rapid business support, hotspot data handling, storage optimization, and query improvements, including sharding, asynchronous processing, and dynamic data source strategies to ensure scalability and reliability.
MTDDL, Meituan’s in‑house client‑side sharding middleware, was created to address rapid product‑database growth by offering dynamic data‑source routing, read‑write separation, distributed ID generation, configurable database‑and‑table sharding, connection‑pool management, SQL monitoring, and live configuration updates via annotations and extensible strategies.
