如果业务要求更高的可用性，并且读一致性不是很重要，那么选择异步可能是更经济的方法。
If the business requires greater availability but read consistency is not a premium, then an asynchronous option may be a more cost-effective approach.
有很多改善最终一致性模型的实用方法，比如会话级别的一致性和单调读一致性，它们都为开发人员提供了更好的工具。
There are a number of practical improvements to the eventual consistency model, such as session-level consistency and monotonic reads, which provide better tools for the developer.
这样做会使管理负载平衡以及容错变得稍困难一些，但这是一种简单的方案……客户端有时会实现“读己之所写”一致性和单调读一致性。
This makes it slightly harder to manage load balancing and fault tolerance, but it is a simple solution... Sometimes the client implements read-your-writes and monotonic reads.
然而，这也是一个成本较高的选择，因为每当您跨集合内容进行迭代时，您就不得不同步所有操作，包括读和写，以此保证一致性。
That's also a costly option, however, because every time you iterate across the contents of the collection, you have to synchronize all operations, including read and write, to ensure consistency.
只有部分数据副本参与更新操作，且/或作为读操作的一部分与其它副本进行联系时，就会出现弱/最终一致性。
Weak/eventual consistency happens when not all data replicas participate in the update operation and/or contacted as part of read operation.
只要会话还存在，系统就保证“读己之所写”一致性。
As long as the session exists, the system guarantees read-your-writes consistency.
从纯数据的角度来看，eBay可以被描述为具有大量的读、写操作，但对容错性和一致性的要求非常严格。
From a pure data perspective, eBay can be described as having a huge number of frequent writes, as well as reads, with an extremely low tolerance for error or inconsistency.
如果每次都是同一台服务器，那么就比较容易保证“读己之所写”一致性和单调一致性。
If this is the same server every time than it is relatively easy to guarantee read-your-writes and monotonic reads.
如果每次都是同一台服务器，那么就比较容易保证“读己之所写”一致性和单调一致性。
If this is the same server every time, then it is relatively easy to guarantee read-your-writes and monotonic reads.
如果每次都是同一台服务器，那么就比较容易保证“读己之所写”一致性和单调一致性。
If this is the same server every time, then it is relatively easy to guarantee read-your-writes and monotonic reads.
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