这正是水平分区起作用的地方。
可以基于承租者ID轻松地对数据库进行水平分区。
The database can be easily partitioned horizontally based on tenant ID.
取水平分区剪三角堆积层次并与第一分区不连接。
Take horizontal section cut a triangular graduation which disconnected with first section.
数据库分片是指对数据库进行水平分区，其中每个分区称为一个碎片。
Database sharding refers to the horizontal partitioning of a database with each individual partition called a shard.
分区策略必须借助功能分割或水平分区来支持底层数据库的水平扩展。
Partitioning strategies must support scaling out of the underlying databases, usually by functional segmentation or horizontal partitioning.
Slice将OpenJPA扩展用于一个分布式的、水平分区的数据库环境。
Slice extends OpenJPA for a distributed, horizontally partitioned database environment.
虽然水平分区会根据行来分割一个或多个表，但这通常是在相同的数据库实例中进行的。
While horizontal partitioning splits one or more tables by row, it is usually within the same database instance.
通过垂直分层将管理和控制从网络上分离，水平分区将不同的子系统之间从网络上隔离；
The vertical stratification will manage and control the separation from the network, the level of partition between different subsystems from the network isolation;
如果数据库的大部分经常都需要更新，那么您应该考虑分区方案：硬件分区、水平分区或者垂直分区。
If most parts of the database require frequent updates, you should consider a partitioning scheme: hardware, horizontal, or vertical.
HibernateShards是一个旨在通过向HibernateCore添加水平分区支持来封装和最小化这种复杂性的框架。
Hibernate Shards is a framework designed to encapsulate and minimize this complexity by adding support for horizontal partitioning to Hibernate Core.
除了硬件成本之外，水平分区特性和需要企业版或者数据中心版本的许可，这两个版本对于每个处理器的零售价分别是27,495和54,990美元。
In addition to the hardware costs, the horizontal partitioning feature requires Enterprise or Datacenter licenses which have a retail price of 27,495 and 54,990 per processor.
因为SQLAzure目前有每个实例50gb的限制，因此无法水平扩展到更大的尺寸，开发者必须要使用该技术才能实现水平分区以进行应用的水平扩展。
As SQL Azure currently has a limit of 50gb per instance to scale horizontally to larger sizes, one must employ this technique of horizontal partitioning to achieve application scale-out.
因为SQLAzure目前有每个实例50gb的限制，因此无法水平扩展到更大的尺寸，开发者必须要使用该技术才能实现水平分区以进行应用的水平扩展。
As SQL Azure currently has a limit of 50gb per instance to scale horizontally to larger sizes, one must employ this technique of horizontal partitioning to achieve application scale-out.
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