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Comment by naasking | Hacker News Reader

Consider a version control system where you had to communicate with the server for every commit, branch, merge, etc. That's how it used to be, and it was painful.

Distributed version control relaxes those constraints with better merging to ensure commits are commutative. Now take that property and extend it to general programming, and you have similar advantages with eventual consistency.

It's harder to see with typical concurrent abstractions, but Microsoft's concurrent revisions explicitly models their concurrency abstractions around distributed version control. Their papers are worth a read if you want ideas, data and inspiration that concurrency and distributed programming can actually be easy.

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I think I've done a bad job at communicating my design because it's perfectly consistent with what you've said. My proposed design is a client/server model, but that doesn't mean it isn't distributed. Each client can still operate on its own for as long as it wants, and it chooses to synchronize with the central server whenever it likes. (In fact, this is quite similar to most peoples' workflows with Git/Github, and I certainly wouldn't call that painful.)

In any case, thanks for the pointers about the Microsoft research. I don't think I've seen that. I'll check it out.

See here for a comprehensive review of CR: http://lambda-the-ultimate.org/node/4699

Each client can operate on its own what though? If they have some kind of private copy of the data, then how do you resolve conflicts? CRDTs are about doing this automatically by restricting the types of changes clients can make. Concurrent revisions permit arbitrary changes so long as you provide a merge function to make them commute.

I think you're just hand-waving a lot of the complexity away by using "synchronize" without explaining what is synchronized and how. That's where all the complexity in distributed computation stems from.

> Each client can operate on its own what though?

Imagine the fundamental representation of your data structure is a sequence of operations. From those operations, you can generate your model. A really simple application of this is a graph, which is just a set of vertices and edges. So if you have these operations

    VertexAdd { id: fooID, label: foo }
    VertexAdd { id: barID, label: bar }
    VertexAdd { id: quuxID, label: quux }
    EdgeAdd { src: fooID, dst: quuxID }

where the ids generated are unique (say, uuids). The corresponding model is just the graph:

    Graph {
        vertices: {foo, bar, quux},
        edges: {{foo, bar}},
    }

You might imagine operations to remove vertices and edges as well. Many operations are commutative (add vertex), but some pairs of operations don't commute, e.g., add vertex/remove vertex. As I understand that, that's the central problem this paper is trying to address via CRDTs. What I'm saying is that the total ordering imposed by the server makes this concern moot, because the server always picks one sequential ordering.

Let's say there are multiple simultaneous clients editing our graph structure above at the same time. Let's consider an example of a pair of operations that commute. One of them decides to remove vertex quux while the other adds an edge that connects quux to foo:

    client1:
        VertexDelete { id: quuxID }
    
    client2:
        EdgeAdd { src: quuxID, dst: fooID }

If both clients attempt to update the server with these new operations, then the server will declare one ordering as true and send the resulting operations back to the clients with the correct ordering. The clients must then adjust. In this case, the outcome is the same regardless of the ordering chosen (because you can't draw an edge to a vertex that doesn't exist).

What about the case when two operations are not commutative? Well, that means they are dependent, which in turn means that every client receives them in the order in which they were applied. An add/delete vertex is one such example, because the client doing the delete must acquire a vertex ID to give to the VertexDelete operation, but the only way to acquire the ID is to observer the VertexAdd first.

Another example of dependent operations is attaching meta data to vertices, e.g.,

    client1:
        VertexAdd { id: anotherID, label: Another }
        VertexLabel { id: anotherID, label: Changed }

In this case, VertexAdd/VertexLabel are dependent, but the Label operation requires the vertex ID.

Now of course, clients can misbehave. There's no reason why a client couldn't generate the ID and add operations in this order:

    client1:
        VertexLabel { id: anotherID, label: Changed }
        VertexAdd { id: anotherID, label: Another }

But if we can assume well behaving clients, then this should be OK! And even if clients are misbehaving, then the process of turning the operations into a model can simply drop operations like VertexLabel because they reference a vertex that doesn't exist.

> If both clients attempt to update the server with these new operations, then the server will declare one ordering as true and send the resulting operations back to the clients with the correct ordering. The clients must then adjust.

But this seems no different than "last write wins". The point is that clients want to keep reliably working on their copy without suddenly losing all of their work because of an incorrect/stale assumption that their changes would be accepted. This is typically considered undesirable.

So with non-commutative changes, either you would sync with the server after each change to ensure you can make meaningful progress, or you risk doing lots of meaningless work and having it all undone.

Yes, but that's natural conflict. One client can do something that the other client doesn't like, and they can go back and forth. The system can't prevent that because it's a natural part of collaboration.

And note that this only applies to the interactions between local operations and server operations. If a client does a bunch of local work and only a small component of it is tied to a previous operation (e.g., one edge in graph), then that doesn't mean the rest of the work gets dropped. That is, if I create a bunch of new vertexes and edges, and only one of those edges connects my new vertex to a vertex already present in the server's operations and that connection is deleted simultaneously by another client, then I don't lose all of my work---I just lose that one edge. The rest of my graph is in tact.

And of course, if this is an end user application (like, say, Google Docs), then the user should be able to view the history and undo what their collaborator did. But I don't see this as any different than editing a document on Google Docs where one collaborator keeps deleting stuff you did.

> Yes, but that's natural conflict. One client can do something that the other client doesn't like, and they can go back and forth. The system can't prevent that because it's a natural part of collaboration.

But this isn't necessarily true. The point is there's more concurrency and collaboration possible than what you describe, if you restrict the operations in specific ways. This is what CRDTs and CRs do.

> But I don't see this as any different than editing a document on Google Docs where one collaborator keeps deleting stuff you did.

It is different. The collaborator actually saw your changes and decided to remove them. This is very different than your changes arbitrarily being lost by the system itself because its merge behaviour is insufficient.

Hmm, OK, I guess I'm kind of lost then. In my mind, the system I described never loses anything. I'm OK nipping this convo because I kind of feel like we're going in circles, but if you do want to continue I'm not sure how because I don't quite understand where we're misunderstanding each other. Maybe show an explicit example of the kind of data loss you're talking about?

My best guess is that I've described my design poorly and I'm leaving something critical out. I think the only way for me to figure out what I've left out is to hear more of your objection. :-)

Not sure if I can respond properly on mobile but I'll do my best.

1. There are differences between system conflicts and user conflicts.

Let's say I'm collaborating on a Google doc. Both my collaborator and I start writing in the same part of the document. If the concurrent system is designed "poorly" (for example, it has last write wins semantics), then only one of our writing appears. You can store this history in the system and show that your changes were overwritten by their changes, but this can be frustrating if you are both collaborating on the end of the document.

2. Commutative operations give better guarantees.

Let's take for instance an append only DAG. We restrict this DAG to having 2 operations: creating a vertex (and associated edges) between 2 existing vertices and creating an edge between 2 existing vertices. In this case, even if both my collaborator and I add a vertex between 2 existing vertices, the system will converge on a DAG where both vertices are added. You can follow the same argument with adding edges.

This DAG is an example of a CRDT. It has a limited set of operations (addEdge and addBetween) which allow convergent semantics through concurrent operations

Happy to chat more about this stuff and hope my mobile response makes sense heh.

So w.r.t. to (1), yeah, I totally get that. And I agree that is a hard problem to solve in the context of document editing (or more precisely, anything that contains a sequence). But for a graph, the logic is considerably simpler because a graph is just two sets. So if the system "loses" work that the client has done, it's only when some other client has deleted or changed something. So if two clients are editing the same graph and one drops a vertex and the other creates an edge that connects to that vertex, what is the right outcome in that circumstance? It seems to me like the edge gets deleted, and that this is a natural user conflict, no? (Regardless of how you order the operations in this case, the edge gets dropped.)

W.r.t. to (2), yeah, sure, but if clients need to be able to remove things then you lose commutativity and you end up missing the requirements of the system itself. Or is there some other component of a CRDT system that's supposed to handle this? (My big picture takeaway from the OP paper was that it was specifically trying to handle the non-commutative operations.)

Yup! Removes are where a lot of these things get tricky. The typical strategy is as you say, removes take precedence over adds, so if one client adds vertices/edges onto a vertex that gets removed by another client, when the DAGs converge, the remove gets precedence and "drops" all edges/vertices attached. It seems like we're basically saying the same thing.

Removes are also tricky because if you're just transmitting the remove operation itself, you have to ensure that updates appear to the client in order, or else removes and adds could conflict, causing inconsistent state.

Yeah, in practice, each client maintains a persistent data structure, where each "mutation" is the application of a single operation. If a new operation comes in that occurs before other operations that have already been applied to the persistent data structure, then the client rewinds the structure to its state just before the new out-of-order operation, applies that operation, and then applies the rest of the operations that it already has. (I think of this as very similar to a `git rebase`.)

But yeah, this might not work at all for sequences. And of course, there are other issues with respect to the memory requirements of the persistent data structure in the client. Some sort of compaction is probably necessary.

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