数据库，针对性能进行优化；而自动扩展能够让用户弹性扩展 计算资源、存储资源和 MySQL 副本。 MySQL 数据库 MySQL 凭借经济高效、可靠、高性能且可扩展的电子商务、联机事务处理和嵌入式数据库应用 成为全球使用广泛的开源数据库。它是一种事务安全、符合 ACID 标准的集成式数据库，支持全 面的提交、回滚、崩溃恢复和行锁定功能。MySQL 不仅简便易用、易于扩展、性能卓越，而且 是全球广受欢迎的开源数据库软件。全球许多发展迅猛的行业巨头都使用 MySQL 来支持 其高流量网站、业务关键型系统和软件包，从中节省了大量时间和资金。 MySQL 客户清单（部分） 互联网客户/最终客户 嵌入式软件开发商/ISV 电信客户  Amazon.com  BBC News  Disney  Facebook  Google  Lufthansa  NASA

RukPak 系统的其余部分的不可变工件。这个行为强制 捆绑包代表集群中要 source 的一些唯一静态内容。用户可以放心，特定捆绑包指向特定的清单集合，在 没有创建新捆绑包的情况下无法更新。对于由嵌入式 BundleTemplate 对象创建的单机捆绑包和动态捆绑 包，此属性为 true。 捆绑包的不可变性由核心 RukPak webhook 强制执行。此 webhook 监视 Bundle 对象事件，并对捆绑包 RukPak BundleDeployment API 指向 Bundle 对象，并表明它应当处于活动状态。这包括从活跃捆绑包 的旧版本获取。BundleDeployment 对象可能还包括所需捆绑包的嵌入式 spec。 与 pod 生成容器镜像实例一样，捆绑包部署会生成捆绑包部署的版本。捆绑包部署可被视为 pod 概念的 一般化。 捆绑包部署如何根据引用的捆绑包对集群进行更改，具体由配置为监视该捆绑包部署的置备程序定义。 RukPak 系统的其余部分的不可变工件。这个行为强制 捆绑包代表集群中要 source 的一些唯一静态内容。用户可以放心，特定捆绑包指向特定的清单集合，在 没有创建新捆绑包的情况下无法更新。对于由嵌入式 BundleTemplate 对象创建的单机捆绑包和动态捆绑 包，此属性为 true。 捆绑包的不可变性由核心 RukPak webhook 强制执行。此 webhook 监视 Bundle 对象事件，并对捆绑包

}; 1 2 3 4 5 6 7 8 9 10 11 50RUNTIME POLYMORPHISM using bi = boost::intrusive; class dialog_widget : bi::list_base_hook<bi::link_mode<bi::auto_unlink>> { dialog_widget(dialog& parent) { parent dialog { bi::list widgets; }; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 51RUNTIME POLYMORPHISM using bi = boost::intrusive; class dialog_widget : bi::list_base_hook<bi::link_mode<bi::auto_unlink>> dialog { bi::list widgets; }; 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 using bi = boost::intrusive; class dialog_widget : bi::list_base_hook<bi::link_mode<bi::auto_unlink>> { }; bi::list

Message Broker Daemons Pipeline mt-nano mt-log mt-giga Key-Value Store Dictionary Data DB Java API BI Tool Site Owner Data analyst 10 / 97 ClickHouse in Production: Yandex.Metrika Site visitor Embedded Message Broker Daemons Pipeline mt-nano mt-log mt-giga Key-Value Store Dictionary Data DB Java API BI Tool Site Owner Data analyst 11 / 97 ClickHouse in Production: Yandex.Metrika Site visitor Embedded Message Broker Daemons Pipeline mt-nano mt-log mt-giga Key-Value Store Dictionary Data DB Java API BI Tool Site Owner Data analyst 12 / 97 ClickHouse in Production: Yandex.Metrika Site visitor Embedded

org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi Documentation 1. Access Kyuubi with Hive JDBC and ODBC Drivers 2. Access Kerberized Kyuubi with Beeline & BI Tools Integrations 1. Kyuubi On Apache Kudu 2. Kyuubi On Delta Lake 3. Kyuubi On Delta Lake With 3. JDBC URL 1.4. Example 1.5. Unsupported Hive Features 2. Access Kerberized Kyuubi with Beeline & BI Tools 2.1. Instructions 2.2. Install Kerberos Client 2.3. Configure Kerberos Client 2.4. Get Kerberos

org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi Documentation 1. Access Kyuubi with Hive JDBC and ODBC Drivers 2. Access Kerberized Kyuubi with Beeline & BI Tools Integrations 1. Kyuubi On Apache Kudu 2. Kyuubi On Delta Lake 3. Kyuubi On Delta Lake With 3. JDBC URL 1.4. Example 1.5. Unsupported Hive Features 2. Access Kerberized Kyuubi with Beeline & BI Tools 2.1. Instructions 2.2. Install Kerberos Client 2.3. Configure Kerberos Client 2.4. Get Kerberos

org/] to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi Documentation 1. Access Kyuubi with Hive JDBC and ODBC Drivers 2. Access Kerberized Kyuubi with Beeline & BI Tools Integrations 1. Kyuubi On Apache Kudu 2. Kyuubi On Delta Lake 3. Kyuubi On Delta Lake With 3. JDBC URL 1.4. Example 1.5. Unsupported Hive Features 2. Access Kerberized Kyuubi with Beeline & BI Tools 2.1. Instructions 2.2. Install Kerberos Client 2.3. Configure Kerberos Client 2.4. Get Kerberos

Iceberg to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi fully compatible with Hive JDBC and ODBC drivers that let you connect to popular Business Intelligence (BI) tools to query, analyze and visualize data though Spark SQL engines. Install Hive JDBC For programing 'hive-jdbc', version: '2.3.8' For BI tools, please refer to Quick Start to check the guide for the BI tool used. If you find there is no specific document for the BI tool that you are using, don’t worry

Iceberg to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi fully compatible with Hive JDBC and ODBC drivers that let you connect to popular Business Intelligence (BI) tools to query, analyze and visualize data though Spark SQL engines. Install Hive JDBC For programing 'hive-jdbc', version: '2.3.8' For BI tools, please refer to Quick Start to check the guide for the BI tool used. If you find there is no specific document for the BI tool that you are using, don’t worry

Iceberg to build and manage Data Lake with pure SQL for both data processing e.g. ETL, and analytics e.g. BI. All workloads can be done on one platform, using one copy of data, with one SQL interface. Kyuubi fully compatible with Hive JDBC and ODBC drivers that let you connect to popular Business Intelligence (BI) tools to query, analyze and visualize data though Spark SQL engines. Install Hive JDBC For programing 'hive-jdbc', version: '2.3.8' For BI tools, please refer to Quick Start to check the guide for the BI tool used. If you find there is no specific document for the BI tool that you are using, don’t worry