eventual consistency

SQS follows the model of" eventual consistency," meaning you can send a message to the queue, but a consumer of that queue may not see the message for some significant period of time.

SQS采用“最终一致性（eventual consistency）”模型，这意味着您可以向队列发送消息，但是队列的消费者不一定能够在特定的时间段内看到它。

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The eventual consistency model states that given a sufficiently long period of time over which no changes are sent, all updates can be expected to propagate eventually through the system and the replicas will be consistent.

最终一致性模型声明如果很长一段时间内都没发出任何改变，那么所有更新可能会最终通过系统传递，让副本具有很好的一致性。

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Croll espoused the virtues of eventual consistency.

Croll认可了最终一致性的优点。

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Weak/ eventual consistency happens when not all data replicas participate in the update operation and/ or contacted as part of read operation.

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。

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In this context, the concept of eventual consistency started to win the ground.

在此前提下，最终一致性（eventual consistency）的概念开始赢得关注。

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The important models for this article are eventual and strong consistency.

本文重点介绍的模型是最终一致性和强一致性。

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Eventual consistency is a powerful scaling mechanism for data with low volatility, because local reads are much faster than remote ones.

最终一致性是一种强大的扩展机制，适用于易变性较低的数据，因为本地读取比远程读取快得多。

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The second limitation is the eventual consistency approach that Amazon takes with the queue.

第二个限制是Amazon应用于队列的最终一致性方法。

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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.

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。

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The second important characteristic of Amazon SQS is the concept of eventual consistency.

Amazon SQS的第二大特征是最终一致性。

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Vogels also outlines for the eventual consistency model different variations that can be combined

Vogels还概要说明了最终一致性模型的各种变体

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Access by process C that has no causal relationship to process A is subject to the normal eventual consistency rules.

与进程A无因果关系的进程C的访问遵守一般的最终一致性规则。

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