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Research for Practice: Convergence

Martin Kleppmann and Peter Alvaro

Communications of the ACM, volume 65, issue 11, pages 104–106, November 2022.


In distributed systems, there are—broadly speaking—two approaches to data consistency: consensus or convergence. The consensus approach can be implemented with algorithms such as Paxos or Raft, and it ensures strong consistency, which means making the distributed system appear as if it were not distributed (linearizable) and as if there were no concurrency (serializable). This approach makes the system easy to use, but it comes at the cost of performance, scalability, and the kinds of faults that can be tolerated, because every update needs to wait for a reply from other nodes before it can complete.

The alternative approach, convergence, is more commonly known as eventual consistency. In this model, different nodes are allowed to process updates independently, without waiting for each other. This is typically faster, more robust, and more scalable, but it leads to nodes having temporarily inconsistent versions of the data. As those nodes communicate with each other, those inconsistencies must be resolved—that is, they should converge toward the same state.

Convergence is such a useful idea that different research communities have developed several ways of achieving it. This article looks at four variations on the theme of convergence, drawn from four areas of computer science. I have selected five fairly recent articles that provide introductions to each of the techniques for convergence.