the pool after the completion of the operation. It reduces the initial connection and authentication time that required to create a new connection. JNDI − For the dataSource type JNDI, MyBatis will get the tutorials_point.com.mybatis_examples.Student. Instead of using this name to address the class every time, you can declare an alias to that class as shown below − points to the classpath of the XML file. The attribute url points to the fully qualified path of the xml file. We can use mapper interfaces instead of xml file, the attribute class points to the

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 20 3.1.5 Related References . . provides access to high‐availability computing services based on stateless ser‐ vices. At the same time, it can sense and use the underlying database’s HA solution to achieve its overall high availability suggest using cluster mode in production environment. 1.3. Deployment 10 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes:

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . are welcome to check out the mailing list and discuss via mail. 13 7 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase 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

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 18 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 19 3.1.5 Related References . . to check out the mailing list and discuss via mail. 1.5. Roadmap 9 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase 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

. . . . . . . . . . . . . . . . . . . . . . . 301 viii 1 Overview Stargazers over time Contributor over time Apache ShardingSphere is an open‐source ecosystem consisted of a set of distributed database Tracing & Observability Supported • Data Encryption 1.2. Features 4 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. 2.1 ShardingSphere-JDBC will increase 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

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . are welcome to check out the mailing list and discuss via mail. 13 7 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase 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

. . . . . . . . . . . . . . . . . . . . . . . . 391 x 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a new criterion Apache ShardingSphere document, v5.0.0 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. 2.1 ShardingSphere-JDBC Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

. . . . . . . . . . . . . . . . . . . . . . . 444 xii 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard Apache ShardingSphere document, v5.1.1 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. 2.1 ShardingSphere-JDBC Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

. . . . . . . . . . . . . . . . . . . . . . . 489 xii 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard Apache ShardingSphere document, v5.1.2 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

运算表达式中包含分片键 当分片键处于运算表达式中时，无法通过 SQL 字面提取用于分片的值，将导致全路由。 例如，假设 create_time 为分片键： SELECT * FROM t_order WHERE to_date(create_time, 'yyyy-mm-dd') = '2019-01-01'; 实验性支持 实验性支持特指使用 Federation 执行引擎提供支持。该 tbl_name GROUP BY col1 HAV‐ ING SUM(col2) > ? 慢 SQL 原因 SELECT * FROM tbl_name WHERE to_date(create_time, ‘yyyy‐ mm‐dd’) = ? 分片键在运算表达式中，导致全 路由 不支持的 SQL 原因 解决方 案 INSERT INTO tbl_name (col1, col2, ⋯) key-generator.props. = # 属性配置, 注意：使用 SNOWFLAKE 算法，需要配置 worker.id 与 max. tolerate.time.difference.milliseconds 属性。若使用此算法生成值作分片值，建议配置 max. vibration.offset 属性 spring.shardingsphere.sharding