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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)




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