pg_catalog). When connecting to Proxy through some graph database clients, the client or proxy may have an er‐ ror prompt. You can use command‐line clients (mysql, psql, gsql, etc.) to connect to the Proxy’s databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single

pg_catalog). When connecting to Proxy through some graph database clients, the client or proxy may have an er‐ ror prompt. You can use command‐line clients (mysql, psql, gsql, etc.) to connect to the Proxy’s databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single

pg_catalog). When connecting to Proxy through some graph database clients, the client or proxy may have an er‐ ror prompt. You can use command‐line clients (mysql, psql, gsql, etc.) to connect to the Proxy’s databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single

pg_catalog). When connecting to Proxy through some graph database clients, the client or proxy may have an er‐ ror prompt. You can use command‐line clients (mysql, psql, gsql, etc.) to connect to the Proxy’s databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single

databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. 4.2. Sharding 22 Apache ShardingSphere document, v5.0.0 Theoretically, horizontal sharding has as data sharding, complicating developer and operator’s maintenance and operation. The following diagram has shown the complex topological rela‐ tions between applications and database groups when sharding

databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single Apache ShardingSphere document, v5.0.0-beta This diagram illustrates how the order‐by merger works when using next invocation. We can see from the diagram that when using next invocation, t_score_0 at the

databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single as data sharding, complicating developer and operator’s maintenance and operation. The following diagram has shown the complex topological rela‐ tions between applications and database groups when sharding

databases according to business, and the pressure is also separated into different databases. The diagram below has presented the solution to assign user tables and order tables to different databases by odd primary keys are put into the 1 database (or table), which is illustrated as the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single as data sharding, complicating developer and operator’s maintenance and operation. The following diagram has shown the complex topological rela‐ tions between applications and database groups when sharding

${proxy_password} 配置示例 完整配置请参考 ShardingSphere 仓库中的示例：https://github.com/apache/shardingsphere/tree/mast er/examples/shardingsphere‐proxy‐example 使用 Docker 背景信息 本节主要介绍如何通过 Docker 启动 ShardingSphere‐Proxy。 使用二进制发布包。 配置示例 完整配置请参考 ShardingSphere 仓库中的示例：https://github.com/apache/shardingsphere/tree/mast er/examples/shardingsphere‐proxy‐example 使用 Helm 背景信息 使用 Helm 在 Kubernetes 集群中引导 ShardingSphere‐Proxy JVM 采集器 true、false true SER‐ VICE_NAME 链 路 跟 踪 的 服 务名称 自定义 shardi ngsphere‐ agent JAEG ER_SAMPLER_TYPE Jaeger 采 样 率 类型 const、proba bilistic、ratel imiting、remote const JAEGE R_SAMPLER_PARAM

OU NT ER 按类型（INSERT、UPDATE、DELETE、SELECT、DDL、DCL、D AL、TCL、RQL、RDL、RAL、RUL）分类的解析总数 routed_sql_total C OU NT ER 按类型（INS ERT、UPDATE、DELETE、SELECT）分类的路由总 数 ro uted_result_total C OU NT ER 路由结果总数 C OU NT ER 语句执行总数 j dbc_statement_exe cute_errors_total C OU NT ER 语句执行错误总数 jdb c_statement_execu te_latency_millis H IS TO GR AM 语句执行耗时 jdbc_t ransac‐ tions_total C OU NT ER 事务总数，按 commit，rollback 持久卷（PersistentVolume）的访问模式 ["Rea dWriteO nce"] governance.zookeep er. persistence.size 持久卷（PersistentVolume）大小 8Gi governance.zookeep er. resources.limits ZooKeeper 容器的资源限制 {} govern ance.zookeeper