如何理解“可用性”？定理的概念？ [英] How to understand the "Availability" of the CAP theorem?

问题描述

我不知道这是否是一个适当的问题。

I don't know if this is a proper question here.

我们知道，在CAP定理中， A表示可用性。在维基百科上，可用性的解释为：

As we know, in the CAP theorem, "A" means "Availability". On wikipedia, the explanation of "Availability" is:

Availability: a guarantee that every request receives a response about whether it was successful or failed

但是，从工程学的角度来看，没有绝对可用性。我们只能说系统的可用性是5'9'（99.999％），甚至是8'9'，但是我们不能说系统的可用性是100％，而100％的系统实际上并不会退出，即使系统中有数百万个重复的节点，对吗？

However, from the perspective of engineering, there is no ABSOLUTE availability. We can only say that the availability of a system is 5'9'(99.999%), or even 8'9', but we cannot say that the availability of a system is 100%, 100% available system does not exit in reality, even if the system has millions of duplicated nodes, right?

CAP定理证明，没有一个系统可以同时满足这三个要求。我的问题是，如果系统声称同时满足 A和 P，那么 A的准确含义是什么？ 6'9'或更高？

The CAP theorem proves that no system could satisfy the 3 requirements simultaneously. My question is, if a system claims to satisfy both "A" and "P", what is the accurate meaning of this "A"? 6'9' or even higher?

推荐答案

我建议您避免使用Wikipedia，而应阅读证明中提​​供的定义， 吉尔伯特和林奇。

I recommend that you avoid Wikipedia and instead read the definition as provided in the proof by Gilbert and Lynch.

系统中非失败节点收到的每个请求都必须
产生响应

"every request received by a non-failing node in the system must result in a response"

相关的脚注可以回答您的问题

And the related footnote which answers your question

Brewer最初只需要几乎所有请求来接收
响应。由于允许概率可用性不会在发生任意故障时更改
结果，为简单起见，我们要求
100％可用性

"Brewer originally only required almost all requests to receive a response. As allowing probabilistic availability does not change the result when arbitrary failures occur, for simplicity we are requiring 100% availability"

因此，如果几乎所有请求都收到响应，或者我们允许发生任意故障，则可以认为该系统具有高可用性。

So if almost all requests receive a response or we allow for arbitrary failures to occur, a system can be considered to have high availability.

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