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Comment by enva2712 | Hacker News Reader

I don’t see why migrations on CRDTs would be categorically different from migrations on other data types, though maybe there’s less open source tooling to leverage at the moment. I’ve got some basic code written on automerge to handle them in a side project

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It’s more challenging because you need to accept updates in format v1 indefinitely even after you apply the v1->v2 migration, which makes it more like maintaining an API with backwards compatibility than the usual SQL migration pattern. For example, let’s say you start with a last-write-wins CRDT for an object of shape { name: string, bio: string } in v1, and then decide bio should use a convergent rich text data type like Peritext. If you do a naive migration and change the type of the field in-place, how do you handle updates from old peers doing a LWW set on bio, when now the data type you expect is a Peritext delta?

And if you’re really peer to peer, you’ll need older peers that don’t understand a new format in their user space software to avoid applying updates that break compatibility for them.

Geoffrey Litt documents the challenges in Cambria, see https://www.inkandswitch.com/cambria/

The lens solution is clever and, I’m glad to see lenses catching on outside the haskell ecosystem

I think the primary issue you raise comes down to whether or not the peers can be verified to be updated before applying the migration op. If this is the case, you can go the standard sql route of deploying a diff adding forward compatibility, applying migration op, deploying diff removing backward compatibility. This is the route I’ve gone since in my case the peers are web browsers so I’ve got a pretty reliable deployment system; though the lens solution is much better since it lifts this requirement

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