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"result": {"pageContext":{"next":{"id":38,"attributes":{"feature":true,"title":"金鹰系统数据库分库分表策略","content":"为了全面监测区域内企业的金融风险，金鹰系统汇集了工商、舆情、投诉举报等多维度的大量数据。随着时间的推移，积攒的数据量越来越大，对系统的数据处理效率提出了更高的要求。目前金鹰系统单表最大数据量已达到亿级别，传统的数据库性能优化手段已无法满足系统的性能需求。为了减少数据库的负担，提升数据库响应速度，缩短查询时间，防控中心技术团队对数据进行分库分表。\n\n**什么是分库分表？**\n\n分库指从单个数据库拆分成多个数据库的过程，将数据散落在多个数据库中。而分表指从单张表拆分成多张表的过程，将数据散落在多张表内。\n\n**分库分表能解决什么问题？**\n\n如果数据库的查询压力过大，就需要考虑拆库，通过分库来分担单个数据库的连接压力。比如，数据库每秒需处理3500笔请求，假设单库每秒能处理的请求数最大为1000笔，那么就可以考虑拆分成4个库，来分散查询连接压力。\n\n如果单表数据量过大，当数据量超过一定量级后，无论是对于数据查询还是数据更新，在经过索引优化等纯数据库层面的传统优化手段之后，还是可能存在性能问题。这时候就需要去换个思路来解决问题。比如：从数据生产源头、数据处理源头来解决问题，既然数据量很大，那我们就来个分而治之，化整为零。可以通过分表把数据按照一定的规则拆分成多张表，来解决单表环境下无法解决的存取性能问题。\n\n►►►\n\n**金鹰系统数据库分库分表策略**\n\n金鹰系统目前采用ShardingSphere（一款分库分表框架）作为数据库中间件。通过对企业名称、法人等多个关键字进行组合散列运算得到企业ID。以企业ID作为分片键，ShardingSphere将根据企业ID与配置的路由信息决定该企业的数据落于哪个数据库分片、哪张表上，合理的算法使数据均匀分布在各数据库分片上，不同分片保存的数据量大致相同，从而确保数据处理效果能得到最大的优化。 \n\n","date":"2022-04-21","author":"广州金融科技","excerpt":"金鹰系统通过对监管企业的海量信息数据采用数据分库分表方式治理，提高了数据查询、数据处理效率","metaDescription":null,"createdAt":"2023-03-29T07:12:54.670Z","updatedAt":"2023-03-29T07:12:56.574Z","publishedAt":"2023-03-29T07:12:56.571Z","locale":"zh","caseStudiesType":{"data":null},"cover":{"data":null},"metaLogo":{"data":null},"localizations":{"data":[]}}},"prev":{"id":13,"attributes":{"feature":true,"title":"ShardingSphere 助力当当 WMS：订单效率提升 30%、节约成本上千万","content":">Apache ShardingSphere 简单易用且成熟稳定，当当自研 WMS 如虎添翼，在供应链系统改革中起到了至关重要的作用，是我们的杀手锏。\n——李勇，当当 WMS 技术负责人\n\n>Apache ShardingSphere 5.0.0 GA 已于 2021 年 11 月发布，5.1.0 版本也于上周发布。历经两年的迭代与打磨，可插拔生态趋近成熟，标志着 Apache ShardingSphere 从分库分表中间件蜕变为以 Database Plus 为理念的分布式数据库生态系统。\n\n近年来，随着“建设社会主义文化强国”政策的持续落实，全民阅读在移动互联快速发展中得到大力倡导，阅读给当代人提供了丰富的精神食粮。当当成立于 1999 年底，作为新技术与传统行业的结合体，当当融合了互联网新技术和传统图书行业，二十余年深耕图书领域助力全民阅读。\n\n当当在刚成立的几年，也是中国互联网腾飞的阶段，电商逐渐多元化发展的趋势迎来了行业野蛮生长期，各电商平台的发展如逆水行舟，不进则退。当当对战略和管理等方面进行调整的同时，也对技术架构层进行了硬核升级。\n\n当当成立之初的仓储物流系统并非自研，随着业务体量、技术实力等多方面的变化，为了满足业务快速发展需要，当当决定重构 WMS（Warehouse Management System）和 TMS（Transportation Management System）。硬件层使用 x86 取代小机，数据层由传统集中式转型为更灵活的分布式架构。如何解决海量仓储货品信息问题，是重构待解决的核心问题之一。互联网技术栈比较常用的分库分表被率先尝试使用，但由于当时开源数据库中间件的成熟度及功能方面不满足预期，当当决定自研分库分表产品，Sharding-JDBC 就这样应运而生，为“连接”带来更多可能。\n\n**自研 WMS 的上线完成了仓储层智能改革，五年、十余仓，Apache ShardingSphere 同 WMS 完美支撑了数个“读书节”、“618”和“11.11”等大型促销活动。**\n\n\n## 业务挑战\n\n当当在自研 WMS 之前使用的是第三方仓储管理系统，其数据库是基于 IBM 小机的 Oracle，结合当当业务体量以及电商仓储物流在促销期间密集请求的业务特点，传统集中式数据库架构给 WMS 带来了极大的挑战。\n\n在业务快速发展的冲击下，数据库计算和存储能力的上限越发明显，Scale-up 方案难以满足超级促销期间的业务需求，技术团队需要对业务层多次做拆分调整来缓解“存算”瓶颈，产能存在受限的风险。\n\n- 计算、存储能力双受限\n\n传统集中式架构扩展能力相对较差，数据库的计算和存储能力成为明显瓶颈；\n\n- 业务开发维护成本高昂\n\n在扩展能力相对差的基础上，业务侧需要不断的去让步做拆分，开发和维护成本持续攀升；\n\n- 系统架构开放度不足\n\n系统架构开放度不足将会使系统面临灵活度欠佳、功能缺失以及转型难度高等问题，基于现有架构难以快速响应新业务的开展及相关政策的调整，如云原生、SQL 审计、数据脱敏以及分布式治理等多方面需求。\n\n\n## 解决方案\n\n当当技术部结合现状及未来发展等多维度的调研，最终确定了自研 WMS 的方案。在硬件方面全面采用通用的 x86 设备替换 IBM 小型机，数据库层面引入开源数据库 MySQL 来替换 Oracle。当时已有多个开源数据库中间件，但成熟度和功能方面与预期不符，因此选择了自研数据库中间件，Sharding-JDBC 闪亮登场。\n\nShardingSphere-JDBC 定位为轻量级 Java 框架，在 Java 的 JDBC 层提供额外服务。具备轻量、高效、易用以及高兼容等特点。它使用客户端直连数据库，以 jar 包形式提供服务，无需额外部署和依赖，可理解为增强版的 JDBC 驱动，完全兼容 JDBC 和各种 ORM 框架。\n\n- 适用于任何基于 JDBC 的 ORM 框架，如：JPA、Hibernate、Mybatis、Spring JDBC Template 或直接使用 JDBC； \n\n- 支持任何第三方的数据库连接池，如：DBCP、C3P0、BoneCP、HikariCP 等；\n\n- 支持任意实现 JDBC 规范的数据库，目前支持 MySQL、PostgreSQL、Oracle、SQL Server 以及任何可使用 JDBC 访问的数据库。\n\n\n\nShardingSphere-JDBC 方案\n\n目前 Apache ShardingSphere 由 JDBC、Proxy 和 Sidecar（规划中）这 3 款既能够独立部署，又支持混合部署配合使用的产品组成，在当当自研 WMS 中使用的是 ShardingSphere-JDBC。\n\n那么 ShardingSphere-JDBC 在 WMS 中具体是如何应用的呢？\n\n在 WMS 系统中，城市的“仓”就是仓储系统的管理单位，每个仓有对应的业务系统和数据库，单个仓使用 3 套 MySQL 主从集群承载指定城市仓库的数据。目前，当当在全国各地设有十余个自建的仓库，主要分布在订单量较大的城市，这种自建仓库模式在具备灵活管理等特性的同时，也可以降低企业的长期仓储成本。\n\n在架构规划上，WMS 通过 ShardingSphere-JDBC 按业务类型进行分库， 每套集群负责存储指定的业务数据，单个仓的 3 套 MySQL 集群分工如下：\n\n- 基础库集群：User，Area，Menu；\n\n- 业务库集群：Order，Package；\n\n- 库存库集群：Stock，Working。\n\n上线前，系统会根据仓库的基础数据进行初始化，如货位等信息，这里不做深入讨论。\n\n分布式数据库中间件的架构解决了 WMS 存算受限、开发维护成本高以及系统灵活度不足的问题，以成熟的底座和熟悉的技术栈去承载业务，架构相对透明化，兼容度良好且没有入侵性。\n\n\n\n当当 WMS 系统拓扑图（单仓）\n\n\n## 用户收益\n\n在当当自研 WMS 的过程中发挥了非常重要的作用，带来了五方面的收益如下：\n\n1、性能极致\n\nShardingSphere-JDBC 轻量级的框架使其性能接近原生 JDBC，提供分库能力的同时性能可发挥极致，WMS 产能不再有受限的风险；\n\n2、系统稳定\n\n自 2016 年 WMS 上线至今，数年来系统一直稳定运行；\n\n3、低风险、零侵入\n\n当当底层系统为 2000 年演进而来，Apache ShardingSphere 凭借自身零侵入性的特点，用很小的改动就即完成了兼容适配，满足了业务需求；\n\n4、专注业务开发\n\n研发团队无需再关心业务拆分的工作，可将更多的精力聚焦于业务需求；\n\n5、降本增效\n\n高兼容的特点可以在无需大量的重构和改造前提下满足业务增长的需求，改造迁移成本低； \n\n通过仓店智能化、入库商品自动匹配最优货位的设计，仓店订单效率提升了 30%，人力成本节约上千万。\n\n\n## Apache ShardingSphere 的前世今生\n\n有人说 ShardingSphere 是当当的产品，其实更准确的说：ShardingSphere 起源于当当，在 2018 年 11 月 10 日捐赠给 Apache，经 17 个月的孵化于 2020 年 4 月 15 毕业，是 2020 年度第一个从 Apache 孵化器毕业的顶级项目。\n\n前段时间，在 ShardingSphere 进入 Apache 基金会三周年的纪念日（2021/11/10）里，社区发布了期待已久的 5.0 正式版，借着这个机会，一起回顾 Apache ShardingSphere 的前世今生。\n\n- 2014 年，当当针对电商平台推出了统一开发框架 dd-frame，目的是通过分离业务和技术代码，实现开发框架统一化、技术组件标准化以及跨组沟通高效化，使研发人员的精力最大化的聚焦在业务开发上。开发框架中名为 dd-rdb 的关系型数据库模块专门用于负责数据访问，可实现简单的分库分表功能，它就是 Sharding-JDBC 的前身，也是 dd-frame 2.x 中重点规划的部分。 \n \n- 2015 年，当当决定重构 WMS 和 TMS，设计上需要引用分库分表方案，天时地利人和，当当架构部在 9 月正式启动了项目，同年 12 月份 Sharding-JDBC 1.0.0 在当当内部发布，正式开启了 ShardingSphere 的征途。\n \n- 2016 年初，Sharding-JDBC 从 dd-rdb 中剥离，向社区开源，产品形态是增强版 JDBC 驱动，以 jar 包形式提供服务。\n \n- 2017 年底，发布 2.0.0 版本，打开了数据库治理功能。\n\n- 2018 年，进入 Apache 软件基金会。3.0.0 版本是一个华丽的转身，独立服务形态的 Sharding-Proxy 面世，实现了异构开发语言的需求，同时项目名称从 Sharding-JDBC 更名为 ShardingSphere，迈出了构建数据库上层标准和生态的第一步。 \n \n- 2019 年，发布 4.0.0 版本，可支持更多的数据库产品。\n \n- 2020 年，毕业成为 Apache 软件基金会的顶级项目。 \n\n- 2021 年 11 月 10 日，发布 5.0.0 GA 版，作为加入 Apache 软件基金会三周年纪念日的礼物，呈现给社区及整个分布式数据库和安全生态领域。 \n\n\n Apache ShardingSphere - RoadMap\n\n从 5.0.0 版本开始，Apache ShardingSphere 正式开启了以可插拔架构为核心的全新阶段，实现了由过去数据分片单一应用场景到现在复杂应用环境下综合数据治理的身份转变与能力提升。在新版本中，Apache ShardingSphere 具备了更多新特性，并拥有了更加成熟的大数据解决方案。\n\n首先，基于可插拔架构对内核进行了全面改造，内核中的各个功能可以任意组合并叠加使用。其次，为了提升 SQL 分布式查询能力，5.0.0 GA 版打造了全新的 Federation 执行引擎，来满足用户复杂的业务场景。此外，5.0.0 GA 版在内核功能 API 层面也进行了大量优化，旨在降低用户使用这些功能的成本。\n\n## 写在结尾\n\n全链条数字化驱动着当当高质量发展，让全民阅读触手可及。在必不可少的自研 WMS 一环中，ShardingSphere 在数据层提供了强力支撑。\n\nApache ShardingSphere 历经两年的迭代与打磨，5.0.0 GA 版本已面世。趋近成熟的可插拔生态，标志着 Apache ShardingSphere 从分库分表中间件工具蜕变为以 Database Plus 理念为新航标。重塑分布式可插拔体系，助力整个数据库行业带来新风向和更有价值的解决方案。\n\n更多内容欢迎点击阅读原文链接访问中文社区 OpenSEC（Open SphereEx Community），一起畅聊开源。\n\n","date":"2022-04-29","author":"SphereEx","excerpt":"历经两年的迭代与打磨，可插拔生态趋近成熟，标志着 Apache ShardingSphere 从分库分表中间件蜕变为以 Database Plus 为理念的分布式数据库生态系统。","metaDescription":null,"createdAt":"2022-04-29T08:49:24.970Z","updatedAt":"2022-11-02T05:36:20.351Z","publishedAt":"2022-04-29T08:49:28.304Z","locale":"zh","caseStudiesType":{"data":null},"cover":{"data":null},"metaLogo":{"data":null},"localizations":{"data":[{"id":14,"attributes":{"feature":false,"title":"Asia’s E-Commerce Giant Dangdang Increases Order Processing Speed by 30% — Saves Over Ten Million in Technology Budget with Apache ShardingSphere","content":">Apache ShardingSphere is an easy-to-use and stable product, making Dangdang’s warehouse management system (WMS) even more powerful. Integrated with WMS, ShardingSphere plays a vital role in reforming the supply chain system.\n>-- Li Yong, Head of WMS Technology, Dangdang\n\nFollowing Apache ShardingSphere 5.0.0 GA release in November 2021, the 5.1.0 version was released last month. Having gone through over two years of polishing, ShardingSphere’s plugin-oriented ecosystem is beginning to take shape, and the project embarks on the evolution from a simple data sharding middleware to a mature distributed database ecosystem driven by the concept of Database Plus.\n\nDangdang, established at the end of 1999, has become a leading e-commerce platform selling books of any kind, and by integrating new Internet technologies with the traditional book industry. Dangdang was founded during the surge in China’s Internet industry in the early 2000s.\n\nLater, the e-commerce industry became exremely competitive in the country, and the market saturated. Facing fierce market competition, e-commerce platforms had to adapt to remain competitive.\n\nIn response, Dangdang not only adjusted its business strategies and management approaches, but also upgraded its technology architecture. Dangdang didn’t have its warehouse management and transportation system at that time. However, with growing business volume and technological capabilities, Dangdang needed to rebuild its warehouse management system and transportation management system (TMS) to better satisfy its business needs. For instance, in terms of hardware, it replaced mini-computer with x86, while its old centralized system was transformed to a distributed system with more flexibility.\n\nOne of the biggest challenges was massive warehousing data storage. The engineers wanted to adopt the data sharding technology that was often chosen by other big Internet companies. Disappointingly, they failed to find a mature and versatile open source database middleware in the marketplace, and therefore,started to develop a new data sharding product. That’s the origin of Sharding-JDBC. The product was created to bring more possibilities to data services.\n\nDangdang released its new WMS five years ago, which meant that it completed its intelligent warehousing transformation. Since then, with Apache ShardingSphere, the WMS has enabled Dangdang to hold large online shopping events every year such as Dangdang’s April Reading Festival, Double-Eleven Online Shopping Festival (aka Singles Day), and Mid-Year Shopping Festival, and to manage over a dozen of smart warehouses.\n\n## Business Challenges\n\nWhen Dangdang adopted a third-party WMS, the database in use was Oracle based on IBM minicomputers.\n\nHowever, considering the increasing business volume and warehouse order requests, especially during the online shopping festivals, the traditional centralized database architecture of the old WMS became inadequate because the computing and storage capabilities were limited. Additionally, the scale-up solution couldn’t support the system during online shopping festivals, and therefore the developers must do scale-up and adjust the business layer several times to alleviate the storage and computing limits and avoid production risks.\n\n- **Limited computing and storage capabilities**\n\nA centralized architecture is less scalable, making database computing and storage capabilities become the bottleneck.\n\n- **Expensive development and maintenance cost**\n\nBecause of poor scalability, developers have to make concessions and scale-up, which increases system development and maintenance costs.\n\n- **Exclusiveness**\n\nIf the architecture is not open enough, the system is less flexible, with fewer functions, and difficult to be transformed. The current architecture makes it difficult to quickly adopt new business services such as cloud native ones, SQL audit, data encryption, and distributed governance.\n\n## The Solution\n\nBased on the situation described above, Dangdang’s tech team proposed a warehouse management system solution: in terms of hardware, IBM minicomputer would be replaced with all-purpose x86, and MySQL would replace Oracle.\n\nHowever, at that time, there wasn’t a versatile and mature enough open source database middleware living up to Dangdang’s expectations, so they created one and named it Sharding-JDBC.\n\nShardingSphere-JDBC is positioned as a lightweight Java framework that provides additional services at the Java Database Connectivity (JDBC) layer. It is lightweight, efficient, easy to use, and compatible.\n\nWith ShardingSphere providing services in the form of the .jar files package, users can connect the client directly to the database without additional deployment and dependencies. It can be seen as an enhanced JDBC driver, fully compatible with JDBC and all ORM frameworks.\n\n- Compatible with JDBC and any JDBC-based ORM framework such as JPA, Hibernate, Mybatis, Spring JDBC Template.\n\n- Supports all third-party database connection pools such as DBCP, C3P0, BoneCP, HikariCP, etc.\n\n- Supports all databases implementing JDBC standards. Currently, ShardingSphere-JDBC supports MySQL, PostgreSQL, Oracle, SQL Server, and any database that can be accessed via JDBC.\n\n\nThe ShardingSphere-JDBC Topography\n\nCurrently, Apache ShardingSphere consists of three products, i.e. JDBC, Proxy, and Sidecar (TODO). ShardingSphere-JDBC and ShardingSphere-Proxy can be deployed independently or together.\n\nIt is ShardingSphere-JDBC that is used in Dangdang’s warehouse management system.\n\n\n>So how is ShardingSphere-JDBC exactly utilized?\n\nIn the warehouse management system, each warehouse positioned in a physical city is referred to as a unit with its corresponding business system and databases. Each warehouse has three sets of MySQL primary-secondary clusters to load the warehousing data of the designated city. So far, Dangdang has more than ten self-built warehouses all over China, mostly in cities where customers place a large number of orders. This self-built warehouse model is flexible for warehouse management and helps reduce storage costs in the long run.\n\nIn terms of architecture, the WMS uses ShardingSphere-JDBC to do database sharding according to their business types, and each cluster stores specified business data. The three MySQL clusters of a single warehouse are divided into three types as follows:\n\n-** Basic:** stores user, area, and menu data.\n- **Business:** stores order and package data.\n- **Inventory:** stores stock and working data.\n\nBefore the release, the system was initialized based on the basic data of a warehouse, such as storage locations.\n\nNext, by deploying the distributed database middleware, Dangdang successfully solved a series of problems such as limited storage and computing capabilities, high costs, and lack of flexibility.\n\n\nTopography of Dangdang’s Warehouse Management System (of a Single Warehouse)\n\n## User Advantages\n\nApache ShardingSphere played a significant role in helping Dangdang develop its WMS. There are five main benefits:\n\n- **Extraordinary performance**\n\nThe ShardingSphere-JDBC lightweight framework makes its performance close to a native JDBC. Apart from its great database sharding capability, it helps database performance be taken to the extreme, which allows the WMS to work at full capacity.\n\n- **Keep the system stable**\n\nWMS has been functioning well since its release in 2016.\n\n- **Low risk & zero invasion**\n\nThe underlying system of Dangdang has been evolving since 2000. Thanks to its zero-intrusion nature, Apache ShardingSphere can be compatible with others with small modifications to meet Dangdang’s business requirements.\n\n- **Allow developers to focus on the business side**\n\nThe developer team does not need to worry about sharding any more and can concentrate on developing the system to meet business needs.\n\n- **Cost effective and efficient**\n\nSince ShardingSphere is known for its high compatibility, to satisfy increasing business needs, developers don’t have to reconstruct or upgrade the system, minimizing the migration cost.\n\nWarehousing order processing speed is increased by 30% and accordingly, tens of millions of manpower costs are reduced due to the smart warehouses and the auto storage location matching technology.\n\n**Apache ShardingSphere’s Roadmap**\n\nSome said that ShardingSphere is a product created by Dangdang. To be precise, ShardingSphere was derived from the company and it also donated ShardingSphere to the Apache Software Foundation (ASF) on November 10, 2018.\n\nAfter 17-months in the ASF incubator, Apache ShardingSphere successfully graduated on April 15, 2020, as a Top-Level Apache Project.\n\nRecently, to celebrate the third anniversary of ShardingSphere entering Apache Software Foundation(ASF), the community released ShardingSphere 5.0.0. Below is a brief review of Apache ShardingSphere.\n\n- In 2014, Dangdang introduced a centralized development framework targeting at its e-commerce platform called dd-frame. It was created to unify the development framework, standardize its technical components, and achieve efficient cross-team communication by separating business code from technical code. In this way, engineers can devote all their efforts to the business side. The relational database module named dd-rdb in the framework was developed to handle data access and implement the data sharding function. It was the precursor of Sharding-JDBC, as well as a major part of dd-frame 2.x.\n\n- In 2015, Dangdang decided to rebuild its WMS and TMS. As it needed a data sharding plan, the team launched the project in September. In December, 2015, Sharding-JDBC 1.0.0 was released and used within Dangdang.\n\n- In early 2016, Sharding-JDBC was separated from dd-rdb and became open source. The product is an enhanced JDBC driver providing service in .jar files.\n\n- At the end of 2017, Version 2.0.0 was released with the new data governance function.\n\n- In 2018, ShardingSphere was enrolled into Apache Incubator. The release of Version 3.0.0 was a notable turnaround: Sharding-Proxy was released as an independent service. It supported heterogeneous languages, and the project was renamed from Sharding-JDBC to ShardingSphere. It’s in 2018 that the community decided to build the criteria and ecosystem above databases.\n\n- In 2019, Version 4.0.0 was released capable of supporting more database products.\n\n- In 2020, ShardingSphere graduated as a Top-Level Project of the ASF.\n\n- On November 10, 2021, Version 5.0.0 GA was released as a third-anniversity celebration with the whole Apache ShardingSphere community, and the distributed database industry.\n\n\nApache ShardingSphere—Roadmap\n\nSince Version 5.0.0, Apache ShardingSphere has embarked on its new journey: with the plugin oriented architect at its core, it evloved from a data sharding application to a comprehensive and enhanced data governance tool applicable to various complex application scenarios. Concurrently, Apache ShardingSphere also has more features, and big data solutions.\n\n## Conclusion\n\nDigitization motivated Dangdang to achieve high-quality development and fulfill its mission. ShardingSphere is glad to support Dangdang’s WMS with its cutting-edging data services.\n\nHaving gone through two years‘ development, Apache ShardingSphere 5.0.0 GA has been released. The pluggable ecosystem marks an evolution from a data sharding middleware tool to a pioneer in the industry following the “Database Plus” concept.","date":"2022-03-11","author":"SphereEx","excerpt":"Apache ShardingSphere is easy-to-use and stable, making Dangdang’s warehouse management system (WMS) even more powerful.","metaDescription":"Dangdang is a B2C-based e-commerce company in China offering a large selection of products both online and offline.","createdAt":"2022-04-29T10:52:10.330Z","updatedAt":"2022-09-28T02:42:43.896Z","publishedAt":"2022-04-29T10:52:12.009Z","locale":"en"}}]}}},"article":{"id":40,"attributes":{"feature":true,"title":"基于Sharding-JDBC的订单分库⽅案","content":"# **前言** \n\n随着业务的发展，数据量迅速增⻓，单实例 Mysql 数据库的磁盘存储容量很快就会达到上限（阿⾥云 RDB 最⼤上限6T）。因此为了解决⽣产环境的存储瓶颈，需要对数据库进⾏分库设计，加⼤存储空间。**本⽅案基于Apache开源项⽬ ShardingSphere 采 Sharding-JDBC 进⾏订单⽔平分库设计（数据分⽚）缓解存储压⼒。**\n\n \n\n# **方案对比**\n\n⽬前主流的分库分表⽅案就是 Sharding-JDBC 和 Mycat，下⾯主要对⽐这两种。\n\n\n\n经上述对⽐，可以发现 Sharding-JDBC 是直连数据库、没有中间层的⽅式，性能上更优，同时由于 Sharding-JDBC 集成在应⽤代码内，并不会增加额外的运维成本，使开发者可以专注于⾃⾝代码逻辑。另外，Sharding-JDBC ⽬前仅⽀持 Java，考虑到⽬前团队技术栈主要以 Java 为主，所以采⽤ Sharding-JDBC 的分库⽅案成为了⼀个不错的选择。 下图反映了 Sharding-JDBC 在应⽤中所处的位置：\n\n\n\n \n\n# **原理**\n\nSharding-JDBC 提供了精确分⽚扩展接⼝ org.apache.shardingsphere.api.sharding.standard.PreciseShardingAlgorithm. PreciseShardingAlgorithm，**只需要实现其doSharding ⽅法，基于分⽚键做⼀定的算法转换，返回指定的数据源，便可以达到分库的效果。** ⽬前我们使⽤的是⼀致性 hash 算法，⽤于定位分⽚键所在数据源，同时引⼊虚拟节点，可以避免数据倾斜问题（即有的数据源数据多，有的数据源数据少，导致数据源设备压⼒不均衡）算法原理如图： \n\n\n\n **然后基于该分库的分⽚键&分⽚算法，我们还需在 application.yml 中增加 sharding 数据源配置；同时需要增加分表相关的配置信息。**最后项⽬启动时，sharding 会⾃动加载这些配置初始化数据源。在请求时从请求参数中提取到分⽚键，应⽤分⽚算法，找到⽬标数据源，操作该数据源。 \n\n# **实战**\n\n在原有订单数据源 datasource_name 的基础上，新加3台订单数据库实例 datasource_name_1&datasource_name_2&datasource_name_3，扩充后共4台订单数据库实例，磁盘存储最⼤可以达到原有容量的4倍。\n\n\n\n**4.1引⼊maven依赖**\n\n```xml\n\n\torg.apache.shardingsphere\n\tsharding-jdbc-spring-boot-starter\n\t4.1.1.001\n\n```\n\n这⾥的4.1.1.001版本是在官⽹ sharding 4.1.1版本上⾃⾏扩充了⼀部分内容：本⾝根据业务分⽚键对业务数据进⾏分⽚后，同⼀类型的数据只会在⼀个数据源中，sharding 只能操作这⼀个数据源的数据，但对于⼀部分分⽚键落⼊新数据源后，意味着数据源发⽣了变动，⽽在旧库数据未迁⼊到新库时，会存在⼀定的兼容时期，此时，改造源码实现了如下双操作（除插⼊外）：a、双查询：查询新旧数据源的数据，然后业务项⽬⾥使⽤切⾯进⾏查询汇总去重，根据 id+updateDatetime 进⾏去重，保留最新数据。b、双更新：更新数据时，会同时更新新旧数据源中满⾜条件的数据。c、双删除：删除数据时，会同时删除新旧数据源中满⾜条件的数据。双删的⽬的是，为了避免只 删除了⼀个库的数据，⽽由于双查导致另⼀个库的数据被查询出来误⽤。d、单插⼊：插⼊数据时，只会插⼊新分⽚算法所落⼊的数据源中。这样做的好处是，在不影响业 务的情况下，也减少了⼀部分数据迁移的⼯作量，即分库上线后新数据直接插⼊新库，不⽤做任何处理。\n\n改造源码如下：\n\n```java\npackage org.apache.shardingsphere.core.rule;\n\n/**\n * Databases and tables sharding rule.\n */\n@Getter\npublic class ShardingRule implements BaseRule {\n\n // 定义：旧的分库分片策略\n private final ShardingStrategy oldDatabaseShardingStrategy;\n \n public ShardingRule(final ShardingRuleConfiguration shardingRuleConfig, final Collection dataSourceNames) {\n Preconditions.checkArgument(null != shardingRuleConfig, \"ShardingRuleConfig cannot be null.\");\n Preconditions.checkArgument(null != dataSourceNames && !dataSourceNames.isEmpty(), \"Data sources cannot be empty.\");\n this.ruleConfiguration = shardingRuleConfig;\n shardingDataSourceNames = new ShardingDataSourceNames(shardingRuleConfig, dataSourceNames);\n tableRules = createTableRules(shardingRuleConfig);\n broadcastTables = shardingRuleConfig.getBroadcastTables();\n bindingTableRules = createBindingTableRules(shardingRuleConfig.getBindingTableGroups());\n // 加载旧的分库分片策略\n ShardingStrategyConfiguration oldDatabaseShardingStrategyConfig = shardingRuleConfig.getOldDatabaseShardingStrategyConfig();\n if (oldDatabaseShardingStrategyConfig != null) {\n oldDatabaseShardingStrategy = createDefaultShardingStrategy(oldDatabaseShardingStrategyConfig);\n } else {\n oldDatabaseShardingStrategy = null;\n }\n defaultDatabaseShardingStrategy = createDefaultShardingStrategy(shardingRuleConfig.getDefaultDatabaseShardingStrategyConfig());\n defaultTableShardingStrategy = createDefaultShardingStrategy(shardingRuleConfig.getDefaultTableShardingStrategyConfig());\n defaultShardingKeyGenerator = createDefaultKeyGenerator(shardingRuleConfig.getDefaultKeyGeneratorConfig());\n masterSlaveRules = createMasterSlaveRules(shardingRuleConfig.getMasterSlaveRuleConfigs());\n encryptRule = createEncryptRule(shardingRuleConfig.getEncryptRuleConfig());\n }\n}\n```\n\n\n\n```java\npackage org.apache.shardingsphere.sharding.route.engine;\n\n/**\n * Sharding route decorator.\n */\npublic final class ShardingRouteDecorator implements RouteDecorator {\n \n @SuppressWarnings(\"unchecked\")\n @Override\n public RouteContext decorate(final RouteContext routeContext, final ShardingSphereMetaData metaData, final ShardingRule shardingRule, final ConfigurationProperties properties) {\n SQLStatementContext sqlStatementContext = routeContext.getSqlStatementContext();\n // SQL参数集合\n List