Distributed ACID transactions across multiple DBs or clusters has been possible for a while if you have been willing to pay the latency penalty for the transaction to complete, hasn't it? As far as I know, Google systems have to pay this latency cost too. From an analysis of Spanner and Calvin [1]:

> The cost of two-phase commit is particularly high in Spanner because the protocol involves three forced writes to a log that cannot be overlapped with each other. In Spanner, every write to a log involves a cross-region Paxos agreement, so the latency of two-phase commit in Spanner is at least equal to three times the latency of cross-region Paxos.

I suspect that Google's innovation in making these cross-region or cross-cluster transactions viable is partly in their network: having an extremely reliable network with consistent low latency, a lot of which allegedly uses first-party network fiber between disparate locations. This is touched on in a paper that Google published [4]:

> Many assume that Spanner somehow gets around CAP via its use of TrueTime, which is a service that enables the use of globally synchronized clocks. Although remarkable, TrueTime does not significantly help achieve CA; its actual value is covered below. To the extent there is anything special, it is really Google’s wide-area network, plus many years of operational improvements, that greatly limit partitions in practice, and thus enable high availability.

I'm also not sure that technologies like Spanner necessarily address the same use-case as SMP machines. Aren't those use-cases concerned with achieving the highest possible transaction throughput for transactions that are potentially highly contentious and require low latency? Systems that solve that problem today still largely look like Oracle DBs from 2000 -- or a fleet of replicas reading executing the same state machine on a transaction log.

Transactions against Spanner likely have high latency compared to traditional DBs[2]:

> On the downside to Spanner, Zweben noted, that the price of Spanner’s high availability is latency at levels unacceptable in some applications. If you’re executing banking transactions, like in airline reservations, and you can afford a minute of delay while you make sure they’re committed in multiple data centers, Spanner’s probably a good database for that, he said.

> Zweben says, however, if you’ve got a customer-service app that requires you to be extraordinarily responsive over millions of transactions and at the same time analyze profitability of a customer and maybe train a machine learning model, those simultaneous transactional and analytical requirements are better for a database [...]

One benchmark I found suggests that read and update latencies can be quite high [3]. The graphs are missing units, but it seems to suggest update latency is higher than with traditional DBs. I've had difficulty finding other benchmarks just now, but the one other I found reported the same finding [5]:

> An interesting pattern above is that queries 14-18, which are all updates, perform with higher latency on Spanner than the easy selects and non-bulk inserts.

It's definitely innovative technology in that it allows you to scale read-oriented SQL workloads to a far greater degree, with better availability, but from what I've read it does not tackle all of the same problems and use-cases that traditional relational DBs are best at solving: high transaction throughput and low latency for contentious data sets.

[1] https://fauna.com/blog/spanner-vs-calvin-distributed-consist...

[2] https://thenewstack.io/google-cloud-spanner-view-field/

[3] https://www.nuodb.com/techblog/benchmarking-google-cloud-spa...

[4] https://research.google.com/pubs/pub45855.html

[5] https://quizlet.com/blog/quizlet-cloud-spanner