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Comment by rdtsc | Hacker News Reader

> This leads to three kinds of systems: CA, CP and AP,

What is a distributed system that is CA? Can you build a distributed system which will never have a partition.

They answer your question just two lines under the one you are quoting:

> For distributed systems over a “wide area,” it's generally viewed that partitions are inevitable, although not necessarily common. If you believe that partitions are inevitable, any distributed system must be prepared to forfeit either consistency (AP) or availability (CP), which is not a choice anyone wants to make. In fact, the original point of the CAP theorem was to get designers to take this tradeoff seriously. But there are two important caveats: First, you only need to forfeit consistency or availability during an actual partition, and even then there are many mitigations. Second, the actual theorem is about 100% availability; a more interesting discussion is about the tradeoffs involved to achieve realistic high availability.

> If you believe that partitions are inevitable, any distributed system

How does that answer it? Are they implying that partitions will not happen if you don't believe in them?

A CA system is not a system that doesn't have partitions, it's a system that works under the condition that there are no partitions (ie. it is a non-partition-resistant system).

> , it's a system that works under the condition that there are no partitions

But that is not a choice with distributed systems, unless as soon as a partition happens the system shuts down immediately. That is it effectively disappears and never re-appears again. But that's not a CA system then?

Or saying that it is not partition resistant is also difficult because the system in case of a partition will do _something_. The typical choices is that it either responds to clients (trying to be AP) or it doesn't (trying to be CP).

That is why I understand CA systems as equivalent to the belief that "partitions can't happen". Which I think is unrealistic.

This is a system which is guaranteed to be consistent and available as long as there are no partitions. If there are partitions, the system isn't guaranteed to be either.

Yes, it's a pretty much useless system.

A memcache cluster might qualify. If there's a partition, just forget about the missing nodes. It's just a cache anyway.

The system you describe would typically be referred to as AP, because it maintains availability in the presence of partitions.

Trivially, by never allowing either half of a partition to make progress while the partition is in place. Since the CAP theorem, by itself, doesn't put a cap(oof) on latency, it is valid to consider a system CA, if it if always available to listen to requests while partitioned, but never able to fulfill them.

This, of course, is effectively useless in practice, and is dependent on an infinite buffer of pending operations, etc.

https://brooker.co.za/blog/2014/07/16/pacelc.html

> Trivially, by never allowing either half of a partition to make progress while the partition is in place.

Doesn't availability mean getting a response on success or failure. If during a partition there is no response on success of failure how is the system available? It seems re-writing a term like "x will happen" to "x will happen after an infinite timeout" should not be valid

It does, but within what bounds? The CAP theorem doesn't specify. One could assume that it means before the partition is restored, but that is only one possible valid interpretation. The PACELC theorem, which is by no means the last word on the story, clarifies this well:

https://en.wikipedia.org/wiki/PACELC_theorem

"PACELC builds on the CAP theorem. Both theorems describe how distributed databases have limitations and tradeoffs regarding consistency, availability, and partition tolerance. PACELC however goes further and states that a trade-off also exists, this time between latency and consistency, even in absence of partitions, thus providing a more complete portrayal of the potential consistency tradeoffs for distributed systems."

And I would take that argument one step further and say that latency and partitioning are effectively identical, and from the point of view of any given operation, it is impossible to say whether the system is in partitioned state until max lateny (timeout) has elapsed, because failure to make progress within timeout is the only meaningful definition of partion-induced unavailability.

Aphyr covers the impossibility of practical CA systems here: https://aphyr.com/posts/325-comments-on-you-do-it-too

Sometimes it's helpful to consider a distributed system through the lens of "harvest vs yield" where harvest is the proportion of information in a system reflected by a response, and yield which is the probability of receiving a response at all.

https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24...

That's a good summary by Aphyr he said it better than me of course.

Now I know it is Eric Brewer himself who wrote that announcement. So I make no claim disproving him or knowing better than, I am just struggling to understand the implications. That CA terms has caused a lot of confusion (at least to me) and so far from what I have read talking about CA systems doesn't make sense.

Never heard of harvest & yield that's an interesting perspective. Thanks for the link, I'll need to go and ponder that for a while. It seems to take a probabilistic approach to these constraints.

But even in that article the description of CA systems is confusing. They describe it it means system can work only in the absence of a partitions. Ok, but then what does it do in the case of partition? The system will presumably do something accept requests, refuse to reply, ... I don't see how it can be both C and A though. That is why I equate that choice with a statement of "I believe partitions will never happen", which I personally think is not realistic.

A useful perspective for me has been that C, A, and P are spectra rather than all-or-nothing. A system may bend a bit on availability when some of its replicas are partitioned, because there are fewer servers that can respond at all, smaller aggregate queue depth etc... It has been said that consistency was specified rather poorly in Brewer's original paper, and we've since seen work that dives into the consistency spectrum. Peter Balis's "HAT not CAP" paper is an often-cited example of such work. Aphyr again comes through with a summary of some of these ideas in https://aphyr.com/posts/313-strong-consistency-models .

We know that we can't have 100% harvest and 100% yield in the presence of partitions. We can play with different things though to shape the curve of these things as conditions worsen, however

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