Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 SQL Parser . . . . . . . . simplest quick start with Apache ShardingSphere. Example Codes: https://github.com/apache/shardingsphere/tree/master/examples 2.1 ShardingSphere-JDBC 2.1.1 Scenarios There are four ways you can configure Apache ShardingSphere‐JDBC is suitable for java application. Source Codes: https://github.com/apache/shardingsphere/tree/master/shardingsphere‐jdbc 3.1.2 ShardingSphere-Proxy ShardingSphere‐Proxy is the second product

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 SQL Parser Engine . . . . simplest quick start with Apache ShardingSphere. Example Codes: https://github.com/apache/shardingsphere/tree/master/examples 2.1 ShardingSphere-JDBC 2.1.1 Scenarios There are four ways you can configure Apache performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 SQL Parser Engine . . . . simplest quick start with Apache ShardingSphere. Example Codes: https://github.com/apache/shardingsphere/tree/master/examples 2.1 ShardingSphere-JDBC 2.1.1 Scenarios There are four ways you can configure Apache performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 SQL Parser Engine . . . . simplest quick start with Apache ShardingSphere. Example Codes: https://github.com/apache/shardingsphere/tree/master/examples 7.1 ShardingSphere-JDBC 7.1.1 Scenarios There are two ways you can configure Apache performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 SQL Parser Engine . . . . simplest quick start with Apache ShardingSphere. Example Codes: https://github.com/apache/shardingsphere/tree/master/examples 7.1 ShardingSphere-JDBC 7.1.1 Scenarios There are two ways you can configure Apache performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 SQL Parser . . . . . . . . language between users and databases. SQL Parse engine used to parse SQL into an abstract syntax tree to provide Apache ShardingSphere understand and implement the add‐ on features. It supports SQL performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access

本章节以尽量短的时间，为使用者提供最简单的 Apache ShardingSphere 的快速入门。 示例代码：https://github.com/apache/shardingsphere/tree/master/examples 2.1 ShardingSphere-JDBC 2.1.1 应用场景 Apache ShardingSphere‐JDBC 可以通过 Java，YAML，Spring 否 静态入口 无 有 ShardingSphere‐JDBC 的优势在于对 Java 应用的友好度。 源码：https://github.com/apache/shardingsphere/tree/master/shardingsphere‐jdbc 3.1.2 ShardingSphere-Proxy ShardingSphere‐Proxy 是 Apache ShardingSphere ShardingSphere‐Proxy 的优势在于对异构语言的支持，以及为 DBA 提供可操作入口。 源码：https://github.com/apache/shardingsphere/tree/master/shardingsphere‐proxy 3.1.3 混合架构 ShardingSphere‐JDBC 采用无中心化架构，与应用程序共享资源，适用于 Java 开发的高性能的轻量级

performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access C and Go, etc.). 3.1. Sharding 23 Apache ShardingSphere document, v5.0.0-beta Abstract Syntax Tree The parsing process can be divided into lexical parsing and syntactic parsing. Lexical parser is into abstract syntax tree by syntactic parser. For example, the following SQL: SELECT id, name FROM t_user WHERE status = 'ACTIVE' AND age > 18 Its parsing AST (Abstract Syntax Tree) is this: To better

本章节以尽量短的时间，为使用者提供最简单的 Apache ShardingSphere 的快速入门。 示例代码：https://github.com/apache/shardingsphere/tree/master/examples 2.1 ShardingSphere-JDBC 2.1.1 应用场景 Apache ShardingSphere‐JDBC 可以通过 Java，YAML，Spring 本地缓存最大容量 spring.shardingsphere.rules.sql-parser.parse-tree-cache.initial-capacity= # 解析树本 地缓存初始容量 spring.shardingsphere.rules.sql-parser.parse-tree-cache.maximum-size= # 解析树本地缓存 最大容量 操作步骤 1. 设置本地缓存配置 maximum-size=65535 spring.shardingsphere.rules.sql-parser.parse-tree-cache.initial-capacity=128 spring.shardingsphere.rules.sql-parser.parse-tree-cache.maximum-size=1024 相关参考 • JAVA API：SQL 解析 • YAML 配置：SQL

Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Abstract Syntax Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 SQL Parser . . . . . . . . language between users and databases. SQL Parse engine used to parse SQL into an abstract syntax tree to provide Apache ShardingSphere understand and implement the add‐ on features. It supports SQL performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When the data amount exceeds the threshold, deeper index will increase the disk IO access