像SSIS这样的ETL工具就可以实现这些功能，如，在不同的系统间进行数据的移动、数据的再格式化、完整性校验、关键值查询、衍生跟踪等，SSIS也证明了它是一个有能力和有着多方面用途的ETL工具。
ETL tools like SSIS can perform these functions such as moving data between systems, reformatting data, integrity checking, key lookups, tracking lineage, and more.
首先在逻辑上将表与其自身连接，然后将更具有选择性的非起始索引键作为索引绑定过滤器，应用于起始键值的每个惟一的组合。
The table is logically joined to itself, and the more selective non-leading index keys are applied as index bound filters to each unique combination of the leading key values.
这就是说，程序不需要为程序之中的永久性对象生成单独的键值，而允许将注意力放到核心业务逻辑以及业务规则之上。
That is, not having to generate unique key values for persistent objects within the application allows the focus to remain on core business logic and business rules.
优化器将根据行中列的函数依赖性来推断关键值选择。
The optimizer will deduce key value selection based on the functional dependency of the column within the row.
解决方案 有：sharding （基于主键值讲数据表分散到多个数据库中），复制，利用弱语义一致性的简化数据库。
Solutions include sharding (splitting up a table across multiple databases based on the primary key), replication, and usage of simplified databases with weakened consistency semantics.
这个类别为以其他物件值为其索引键值基础的类别提供支援，同时传入的物件可确保产生之资源索引键的唯一性。
This class provides support for classes that base key values on other object values, where the objects passed in assure uniqueness of the generated resource keys.
传统的服务发现通常是以服务的功能性描述信息作为关键值进行服务发现，往往忽略了服务的非功能性描述信息。
The traditional service discovery mechanism used functional described information as a key to discover a service, it usually ignores the non-functional service descriptions.
传统的服务发现通常是以服务的功能性描述信息作为关键值进行服务发现，往往忽略了服务的非功能性描述信息。
The traditional service discovery mechanism used functional described information as a key to discover a service, it usually ignores the non-functional service descriptions.
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