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"In practice, the uptime of systems that favor availability have not proven greater than what can be achieved with consistent systems."

This claim is much stronger than just saying AP systems sometimes fail. Therefore observing some AP systems failures is not enough to prove it. For the statement to hold true, AP systems would have to fail just as often as CP systems. However, such claim is totally unfounded. The article doesn't link to any research showing AP systems have the same (or worse) availability than CP systems.

On the other hand, most CP systems, even centralized ones, are not truly consistent as required by ACID either. Most RDBMS systems run at Read Committed level for performance reasons, which is ACD, not ACID.

It is also ignoring the fact that most systems are neither AP or CP, and that this distinction is not very useful to describe database systems.




Yep, the Yammer article is just opinion. It may or may not match your experiences/beliefs :) Check the Spanner paper for slightly more data (but its still pretty informal) from Google. They don't talk about other systems, but they explain why Spanner has such high availability. It would be interesting to see an empirical study comparing availability but I'm not holding my breath because doing that fairly and realistically would be challenging.

I'm not sure about the claim about CP systems, but I probably have a different opinion on "common" CP systems (etcd, consul, Spanner-likes, which do offer consistency in the sense of ACID.) But it's definitely worth considering things weaker than that - e.g. Azure Blob Storage and Google Cloud Storage (S3-likes) offer strong consistency but no cross-key transactions. These databases are very applicable but weaker than Spanner-likes like the one from TFA.

Agreed that AP vs. CP isn't a good way to describe databases :)


Note that this mostly just an opinion piece is also very guilty of attempting to poison the well.

"AP systems are not 100% available in practice"

Nothing is 100% available, it is like absolute zero, you can get close but never reach it.

I am not sure if the intent on this was to justify the uptime problems that yammer had this year, or if it was to just self justify engineering choices.

The real flaw is ignoring the horses for courses reality of systems needs. Automated Teller Machine's transactions are not strongly consistent, yet we manage to get by.

Spanner is a "shared nothing" architecture which has advantages availability and recoverablity. But you do pay a price for the high level of consistency.

Each write is mediated by a leader, and has to be committed on at least 2 of the three regional nodes which does have a cost.

Yammer (and I guess rayokota) chose to use PostgreSQL which is not a horrible choice for a traditional RDBMS these days, but we all know the pain of a master node failover, re-sync etc.. And with most streaming replication methods there will be a serious issue with the nodes that may have been on a partition with the original master and the fail-over node that is promoted.

Distributed systems are hard, but I am having a hard time really finding much value in this post outside of a fairly opaque opinion that there is no perfect product for all needs, which will always be true.

But as for why I chose to respond to your comment jacobparker. The thing to remember about strong consistency is that locks, two stage commits, single mediators etc...they will all limit the theoretical speedup in latency you can have by adding resources.

While not perfect this means that you should engineer with Amdahl's law and hope for Gustafson's law.

As a practical example on how this becomes an issue with scaling consider the following article about the limitations of single disk queues in the linux kernel on systems with multiple CPUs

kernel.dk/blk-mq.pdf

Note the extreme falloff on Figure 4.

This doesn't mean that you can't start out with a simple strong consistency model, but if you don't at least try to avoid the need for ACID type transactions it will be very hard to scale later. Worse most teams I have been on try to implement complex and fragile sharding schemes which in effect replicate a BASE style datastore. If we think managing distributed systems are hard, writing them is much more difficult.

But really there are no universal truths in this area, and all decisions should be made on use case and not by selecting the product before defining the need (which is our most common method it seems)


> For the statement to hold true, AP systems would have to fail just as often as CP systems.

I think all that would need to be true is that there exists one CP system that has uptime figures comparable to AP systems. Then you could say that targeting AP is pointless, when you could either just use that system; or engineer your own new CP system to do the same things to achieve uptime as that system.


You can achieve high uptime in a CP system by assuring P will never happen, ending up with some kind of an AC system, but in practice it may be very costly, hard to setup and operate.

Can you imagine every ATM having a redundant networking connectivity and two independent power lines?

There is no free lunch. Comparing just availability figures is therefore not enough. Still an AP system may be preferable if it has the same availability at a fraction of a cost of that unicorn available and consistent system.


Note that a "CP system with high uptime" isn't suddenly an AC system. It still doesn't attempt to have 100% uptime, and its ecosystem of clients still must be built around the idea that the server can go down, if just for a few minutes a year.

This is different from an AP system, because an AP system has to be built from the ground up to assume that stupid corruptions will happen during netsplits and that it'll have to re-integrate them; while the CP system can just assume it'll all go down, and not have to have any of that extra code or infrastructure.

A good example of a high-uptime CP system that I'm aware of, is telecom switches. They're usually architected as simple pairs: one live, and one hot standby. And—even in platforms that don't have "hot upgrade" functionality—you can still upgrade both nodes without downtime by just intentionally causing connections to fail-over back and forth between the nodes.

Telecom switches have very little downtime. But they do go down, rather than becoming inconsistent. And the resulting architecture is much cheaper (in both hardware/networking costs, ops costs, and software development/maintenance costs) than the equivalent architecture you'd need to serve calls under AP guarantees.


For many applications repeatable read, or even read committed, is plenty good enough Isolation.


For many applications BASE is plenty good consistency.




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