Editorial: Stonebraker is, imo, and as usual, Right Again. His biggest problem is that he's boring that way. He doesn't open his mouth in contexts like this but to be Right.
Summary: People say "No SQL is right because of the CAP theorem." The CAP "theorem" says of DBs that: Consistency, high Availability, or Partition-tolerance .... pick any two. Quite true! So one of the pro no-SQL arguments is that high availability and partition tolerance are often the priorities ... so toss out consistency! SQL assumes consistency. Thus you need No SQL. Stonebraker correctly points out that, hey, you know what? Partitions are pretty rare and tossing out consistency really didn't increase your accessibility average by much .... so you tossed out consistency for no reason whatsoever. If you think the Cap "theorem" justifies NoSQL... you're just wrong.
Stonebraker's rant is nearly boring because it makes such an obvious point.
New Thought: I don't think NoSQL is popular because of the CAP theorem. I think it is popular because it is easier to get started with (even if that means using it poorly) than SQL. SQL is a little hard to learn. It's a little bit awkward to use in some "scripting" language or other HLL language. NoSQL may be bad engineering in many of its uses ... but its easier. A lot easier. And, people aren't much asking about engineering quality until sites start failing often. Which a heck of a lot of them do but by then the NoSQL architects have collected their money and are out of town or else are still around but able to point fingers of blame away from the abandonment of ACID.
An ACID DB that gave a more simple-minded logical model than SQL ... including, sure, relaxing ACID constraints where that was really desirable ... could go a long way fixing the confusion around NoSQL.
p.s.: given a typical distributed NoSQL DB, one thing you could do is regard that as the "physical model", implement proper transactions, and build a library that gave you ACID properties. Build up a high level way of using that library so that you have a logical model of the data that is independent of exactly how it is laid out in the underlying thing.... and you've got a Codd-style DB. Great thing to do. SQL 2.0
You make a good point that NoSQL is about so much more than the CAP theorem. That doesn't mean there aren't a ton of people (some very smart) out there citing the CAP theorem as proof that you have to give up consistency to be "web-scale".
Yeah, there are three things in play (I would say):
1. The language design of SQL is often icky. A different logical language could be nice.
2. The logical / physical separation is sometimes right on and sometimes... not so much. At the very least, there doesn't seem to be One True Logical Model, hence exposing a physical model with transactions seems justified.
3. Often, people are quick to give up consistency and while that is not always the wrong thing to do, it's done more than it ought to be. But... a lot of people hacking up big sites and similar these days... their standards are low and the "customer's" tolerance for resulting bugs and glitches is startlingly high (for now).
All three of those get lumped under the "NoSQL" heading and it is helpful (I think) to tease them apart.
This sounds like a serious false economy. That's the story of our industry, though.
An ACID DB that gave a more simple-minded logical model than SQL ... including, sure, relaxing ACID constraints where that was really desirable ... could go a long way fixing the confusion around NoSQL.
As Stonebraker says, you can easily layer a key-value API on SQL. Or there's Berkeley DB; maybe they should rebrand it as Berkeley NoSQL.
neo4j currently is also such a db, a non sql db, a NoSQL.
Normally, though, people expect something more from this NoSQL "movement".
People want "scaling", and elasticity.
We live in the world where today you have 2 users, tomorrow you have 2 million of users, the day after you have only 1000. We also live in a world were people are expecting everything to be working always, and are pretty pissed of if things doesn't respond in seconds (watch Luis C.K http://www.youtube.com/watch?v=8r1CZTLk-Gk)
It's not a surprise that all this hype about NoSQL came out when a number of db implementation were developed which handled replication, sharding, dynamic resizing (add remove nodes) etc
Now put a little bit aside the issue with the word "SQL" per se. Let's focus on the "partition tolerance" feature.
I was always frustrated with the fact that no matter how great my product could be, how perfect the implementations would be, how great my db would be; if my machine/rack/datacenter or section of datacenter wen't down, switches break, network connectivity goes down etc. the users of my application are not able to use it, for them it's down.
This kind of NoSQL, the one that handles partition tolerance, gives you the hope that eventually you will be able to make great software, resisting to this kind of events.
I'm not sure whether by these tools (cassandra, riak etc) I would be able to write an application that actually works better, even in the other cases. Stonebraker is right when he says that there are other more probable causes of errors, and that probably the compromises imposed by the partition tolerance will make your software development so complex you will probably make a lot of other errors and make the production unusable.
But at least there's hope that by using this tools you can make things that survive severe conditions. At least this is why I think people get's so excited about all this.
Solving the problems of distributed systems is incredibly hard and not necessary if all you need is scalability and high availability. Better think of your problems in terms of engineering trade-offs and not distributed theory and understand what you are giving up and what this gains you.
When you decide to give up consistency, your application can no longer assume consistency ever. Giving up availability in case of network partition means few extra minutes of downtime a year.
I don't think he completely misunderstood distributed systems - I think he decided to completely side-step the entire field.
Once one scales beyond a single node the system becomes distributed. Then by definition one must deal with distributed systems problems in order to achieve scale beyond the capabilities of a single node.
> When you decide to give up consistency, your application can no longer assume consistency ever. Giving up availability in case of network partition means few extra minutes of downtime a year.
Depends on the application. Giving up availability might mean cascading failures throughout your entire application. For instance if the datastore is unavailable for writes then any kind of queueing systems built around the DB (a common design pattern) run the risk of overflow during the downtime.
And I would make the argument that once an application scales beyond a single datacenter it cannot help but give up strict consistency under error conditions.
> I don't think he completely misunderstood distributed systems - I think he decided to completely side-step the entire field.
If he didn't misunderstand them then he is purposefully ignoring the hard problems. Which is worse?
There is an extreme case of partition tolerance that must be considered: disconnected operation.
For users at the edge of the network, latency can be the biggest performance killer. If it takes 1 second or more for each user action to be reflected in application state due to round trip time (mobile web) those seconds add up and users can be frustrated.
However, if you move the database and web application to the mobile device itself, users no longer see network latency as part of the user experience critical path. Latency has been proven to be correlated directly to revenue, because users engage much more readily with snappy interfaces.
Once data is being operated on by the user on the local device, the key becomes synchronization. Asynchronous multi-master replication demands a different approach to consistency, than the traditional model which assumes the database is being run by a central service.
The MVCC document model is designed for synchronization. It's a different set of contraints than the relational model, but since it's such a highly constrained problem space it also admits of general solutions and protocols.
It's my belief that the MVCC document model is closer to the 80% solution for a large class of applications. Storing strongly typed and normalized representations of data is an artifact of our historically constrained computing resources, so it will always be a good way to optimize certain problems.
But for many human-scale data needs, schemaless documents are a very good fit. They optimize for the user, not the computer.
eg if you get the Etag wrong CouchDB rejects the save.
(edited to add) the difference is that CouchDB makes the MVCC semantics visible to the client.
