common sharding algorithms such as modulo, hash, range, and time. Customized Sharding Algorithm Provides a portal for application developers to implement their sharding algorithms that are closely allowing users to manage the physical distribution of actual tables themselves. Customized sharding algorithms are further divided into: ‐ Standard Sharding Algo‐ rithm Used to deal with scenarios where sharding $->{ expression } in the config‐ uration to identify the row expressions. Data nodes and sharding algorithms are currently supported. The content of row expressions uses Groovy syntax, and all operations

common sharding algorithms such as modulo, hash, range, and time. Customized Sharding Algorithm Provides a portal for application developers to implement their sharding algorithms that are closely allowing users to manage the physical distribution of actual tables themselves. Customized sharding algorithms are further divided into: ‐ Standard Sharding Algo‐ rithm Used to deal with scenarios where sharding $->{ expression } in the config‐ uration to identify the row expressions. Data nodes and sharding algorithms are currently supported. The content of row expressions uses Groovy syntax, and all operations

themselves with the physical distribution of actual tables. Includes implementations of common sharding algorithms such as modulo, hash, range, and time. 8.1. Sharding 26 Apache ShardingSphere document Customized sharding algorithms that are closely related to their business operations, while allowing users to manage the physical distribution of actual tables themselves. Customized sharding algorithms are further $->{ expression } in the config‐ uration to identify the row expressions. Data nodes and sharding algorithms are currently supported. The content of row expressions uses Groovy syntax, and all operations

themselves with the physical distribution of actual tables. Includes implementations of common sharding algorithms such as modulo, hash, range, and time. 8.1. Sharding 26 Apache ShardingSphere document Customized sharding algorithms that are closely related to their business operations, while allowing users to manage the physical distribution of actual tables themselves. Customized sharding algorithms are further $->{ expression } in the config‐ uration to identify the row expressions. Data nodes and sharding algorithms are currently supported. The content of row expressions uses Groovy syntax, and all operations

ShardingSphere document, v5.0.0 Sharding Algorithm Data sharding can be achieved by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. It can be implemented by developers themselves, or using Java codes are not helpful in the unified management of common configurations. Writing sharding algorithms with inline expressions, users can store rules together, making them easier to be browsed and identify them. ShardingSphere currently supports the configurations of data nodes and sharding algorithms. Inline expressions use Groovy syntax, which can support all kinds of operations, including inline

supports multiple sharding columns. Sharding Algorithm Data sharding can be achieved by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. It can be implemented by developers themselves, or using Java codes are not helpful in the unified management of common configurations. Writing sharding algorithms with inline expressions, users can store rules together, making them easier to be browsed and identify them. ShardingSphere currently supports the configurations of data nodes and sharding algorithms. Inline expressions use Groovy syntax, which can support all kinds of operations, including inline

supports multiple sharding columns. Sharding Algorithm Data sharding can be achieved by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. It can be implemented by developers themselves, or using Java codes are not helpful in the unified management of common configurations. Writing sharding algorithms with inline expressions, users can store rules together, making them easier to be browsed and identify them. ShardingSphere currently supports the configurations of data nodes and sharding algorithms. Inline expressions use Groovy syntax, which can support all kinds of operations, including inline

by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. They need to be implemented by developers themselves and can be highly flexible. Currently, 3 kinds of sharding algorithms are available extracts all kinds of scenarios by sharding strategies, instead of pro‐ viding built‐in sharding algorithms. Therefore, it can provide higher abstraction and the interface for developers to implement sharding in SQL. StandardShardingStrategy only supports single sharding keys and pro‐ vides two sharding algorithms of PreciseShardingAlgorithm and RangeShardingAlgorithm. PreciseShardingAlgorithm is compulsory

spring.shardingsphere.rules.sharding.sharding-algorithms.. type= # 分片算法类型 spring.shardingsphere.rules.sharding.sharding-algorithms.. props.xxx= # 分片算法属性配置 ake spring.shardingsphere.rules.sharding.sharding-algorithms.database-inline. type=INLINE spring.shardingsphere.rules.sharding.sharding-algorithms.database-inline.props. algorithm-expression=ds-$->{user_id % 2} spring.shardingsphere.rules.sharding.sharding-algorithms.t-order-inline.type=INLINE spring.shardingsphere.rules.sharding.sharding-algorithms.t-order-inline.props. 4.1. ShardingSphere-JDBC 110

分片算法 spring.shardingsphere.rules.sharding.sharding-algorithms.database_inline. type=INLINE spring.shardingsphere.rules.sharding.sharding-algorithms.database_inline.props. algorithm-expression=ds_${user_id % 2} spring.shardingsphere.rules.sharding.sharding-algorithms.table_inline.type=INLINE spring.shardingsphere.rules.sharding.sharding-algorithms.table_inline.props. algorithm-expression=t_order_${order_id spring.shardingsphere.rules.sharding.sharding-algorithms.. type= # 分片算法类型 spring.shardingsphere.rules.sharding.sharding-algorithms.. props.xxx=# 分片算法属性配置