the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency amount, but cannot solve them completely. After vertical sharding, if the data amount in the table XA based distributed trans‐ actions since they are not able to ensure its performance in high‐concurrency situations. They usually replace strongly consistent transactions with eventually consistent soft

the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency amount, but cannot solve them completely. After vertical sharding, if the data amount in the table XA based distributed trans‐ actions since they are not able to ensure its performance in high‐concurrency situations. They usually replace strongly consistent transactions with eventually consistent soft

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis in OLAP the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency 3.1. Sharding 17 Apache ShardingSphere document, v5.2.0 amount, but cannot solve them completely

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency 8.1. Sharding 20 Apache ShardingSphere document amount, but cannot solve them completely. After

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 18 Mass data real‐time analysis in OLAP the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency 3.1. Sharding 16 Apache ShardingSphere document, v5.2.1 amount, but cannot solve them completely

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency 8.1. Sharding 20 Apache ShardingSphere document amount, but cannot solve them completely. After

the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency amount, but cannot solve them completely. After vertical sharding, if the data amount in the table XA based distributed trans‐ actions since they are not able to ensure its performance in high‐concurrency situations. They usually replace strongly consistent transactions with eventually consistent soft

the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the system. In availability really solve the single‐node problem. it can ease problems brought by the high data amount and concurrency 4.3. Sharding 25 Apache ShardingSphere document, v5.1.2 amount, but cannot solve them completely XA based distributed trans‐ actions since they are not able to ensure its performance in high‐concurrency situations. They usually replace strongly consistent transactions with eventually consistent soft

sql-statement-cache.maximum-size= # SQL 语句 本地缓存最大容量 spring.shardingsphere.rules.sql-parser.sql-statement-cache.concurrency-level= # SQL 语句本地缓存并发级别，最多允许线程并发更新的个数 spring.shardingsphere.rules.sql-parser.parse-tree-cache parse-tree-cache.maximum-size= # 解析树本地缓存 最大容量 spring.shardingsphere.rules.sql-parser.parse-tree-cache.concurrency-level= # 解析树本 地缓存并发级别，最多允许线程并发更新的个数 5.1.4 Spring 命名空间 简介 ShardingSphere‐JDBC 提供官方的 Spring 命名空间，使开发者可以非常便捷的整合 名称 类型 说明 id 属性 本地缓存配置项名称 initial‐capacity 属性 本地缓存初始容量 maximum‐size 属性 本地缓存最大容量 concurrency‐level 属性 本地缓存并发级别，最多允许线程并发更新的个数 混合规则 混合配置的规则项之间的叠加使用是通过数据源名称和表名称关联的。 如果前一个规则是面向数据源聚合的，下一个规则

sql-statement-cache.maximum-size= # SQL 语句 本地缓存最大容量 spring.shardingsphere.rules.sql-parser.sql-statement-cache.concurrency-level= # SQL 语句本地缓存并发级别，最多允许线程并发更新的个数 spring.shardingsphere.rules.sql-parser.parse-tree-cache parse-tree-cache.maximum-size= # 解析树本地缓存 最大容量 spring.shardingsphere.rules.sql-parser.parse-tree-cache.concurrency-level= # 解析树本 地缓存并发级别，最多允许线程并发更新的个数 5.1.4 Spring 命名空间 简介 ShardingSphere‐JDBC 提供官方的 Spring 命名空间，使开发者可以非常便捷的整合 名称 类型 说明 id 属性 本地缓存配置项名称 initial‐capacity 属性 本地缓存初始容量 maximum‐size 属性 本地缓存最大容量 concurrency‐level 属性 本地缓存并发级别，最多允许线程并发更新的个数 混合规则 混合配置的规则项之间的叠加使用是通过数据源名称和表名称关联的。 如果前一个规则是面向数据源聚合的，下一个规则