如何理解CAP定理的“可用性”？-Howtounderstandthe“Availability”oftheCAPtheorem?

10  

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

我建议您避免使用维基百科，而是阅读Gilbert和Lynch提供的证明。

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

“系统中非故障节点收到的每个请求都必须产生响应”

And the related footnote which answers your question

以及回答您问题的相关脚注

"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"

“Brewer最初只需要几乎所有请求都能收到响应。由于允许概率可用性不会在发生任意故障时改变结果，为简单起见，我们要求100％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.

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

2  

The Availability in CAP means "All (non-failing) nodes are available for queries". It has NOTHING to do with the Wikipedia link, which is about "High Availability".

CAP中的可用性意味着“所有（非故障）节点可用于查询”。它与维基百科链接无关，这与“高可用性”有关。

For example, the PAXOS algorithm is CP (no Availability property) because the minority nodes "shut up" during a partition. But if you need Consistency, then PAXOS is considered "High Availability".

例如，PAXOS算法是CP（无可用性属性），因为少数节点在分区期间“关闭”。但如果您需要Consistency，那么PAXOS将被视为“高可用性”。

Conversely, a single-node MySQL database server is CA (has the CAP Availability property). It can reboot (and be down for hours while it does BIOS check, FileSystem check, DB Repair, etc). When it finishes booting, it starts responding to queries again. That's perfect Availability (as per the CAP theorem), but horrible availability for "High Availability".

相反，单节点MySQL数据库服务器是CA（具有CAP可用性属性）。它可以重新启动（并在BIOS检查，文件系统检查，数据库修复等时停机数小时）。完成启动后，它会再次开始响应查询。这是完美的可用性（根据CAP定理），但“高可用性”的可用性很差。

1  

I agree with Mark Burgess: The CAP Theorem Is Not a Theorem. The CAP properties in the conjecture by Brewer are simply not well-defined enough to provide a rigorous mathematical proof. So, the reason availability is hard to "understand" could be because it is simply not well-defined in this context.

我同意Mark Burgess：CAP定理不是一个定理。 Brewer猜想中的CAP属性根本没有明确定义，足以提供严格的数学证明。因此，可用性很难“理解”的原因可能是因为它在这种情况下根本没有明确定义。

0  

An interesting point of view by Coda Hale

Coda Hale有趣的观点

A system that chooses Availability will handle a network partition by continuing to serve all requests even if they lead to inconsistencies (e.g nodes in different network partitions both accept possibly conflicting writes).

选择可用性的系统将通过继续为所有请求提供服务来处理网络分区，即使它们导致不一致（例如，不同网络分区中的节点都接受可能冲突的写入）。

A system that chooses Consistency will limit the amount of legal operations during a network partition (e.g writes are not accepted as they might lead to conflicts/inconsistencies).

选择一致性的系统将限制网络分区期间的合法操作量（例如，不接受写入，因为它们可能导致冲突/不一致）。

It is not possible to choose both.

不可能同时选择两者。

I suppose a hypothetical system can also choose to just crash during network partitions but this does not seem like a very practical design...

我想一个假设的系统也可以选择在网络分区期间崩溃，但这似乎不是一个非常实用的设计......