Paper: The Case for Determinism in Database Systems

Can you have your ACID cake and eat your distributed database too? Yes explains Daniel Abadi, Assistant Professor of Computer Science at Yale University, in an epic post, The problems with ACID, and how to fix them without going NoSQL, coauthored with Alexander Thomson, on their paper The Case for Determinism in Database Systems. We've already seen VoltDB offer the best of both worlds, this sounds like a completely different approach.

The solution, they propose, is: architecture and execution model that avoids deadlock, copes with failures without aborting transactions, and achieves high concurrency. The paper contains full details, but the basic idea is to use ordered locking coupled with optimistic lock location prediction, while exploiting deterministic systems' nice replication properties in the case of failures.

The problem they are trying to solve is:

In our opinion, the NoSQL decision to give up on ACID is the lazy solution to these scalability and replication issues. Responsibility for atomicity, consistency and isolation is simply being pushed onto the developer. What is really needed is a way for to ACID systems to scale on shared-nothing architectures, and that is what we address in the research paper that we will present at VLDB this month.
Our view (and yes, this may seem counterintuitive at first), is that the problem with ACID is not that its guarantees are too strong (and that therefore scaling these guarantees in a shared-nothing cluster of machines is too hard), but rather that its guarantees are too weak, and that this weakness is hindering scalability.
The root of these problems lies in the isolation property within ACID. In particular, the serializability property (which is the standard isolation level for fully ACID systems) guarantees that execution of a set of transactions occurs in a manner equivalent to some sequential, non-concurrent execution of those transactions, even if what actually happens under the hood is highly threaded and parallelized. So if three transactions (let's call them A, B and C) are active at the same time on an ACID system, it will guarantee that the resulting database state will be the same as if it had run them one-by-one. No promises are made, however, about which particular order execution it will be equivalent to: A-B-C, B-A-C, A-C-B, etc.
The repercussions of a deterministic system are broad, but one advantage is immediately clear: active replication is trivial, strongly consistent, and suffers none of the drawbacks described above. There are some less obvious advantages too. For example, the need for distributed commit protocols in multi-node transactions is eliminated, which is a critical step towards scalability.

I certainly don't understand how this works yet. But whenever you say coordination it means a protocol which must run across multiple nodes, which means latency, which means it's sensitive to node failure, it's slow, and doesn't scale as nodes are increased. So I'll be very curios to see how it works as all the details come out.