This algorithm has another obscure downside: It has interleaving problems if you insert items backwards. If two users do a series of inserts in reverse order, their inserts will get interleaved in a weird, unpredictable way. Eg, if I type "aaaaa" (as a series of prepended inserts) and you type "bbbb" in the same way, we can end up with "ababababa" or "aabbabbaa" some combination like that. We generally want CRDTs to be non-interleaving - so, "aaaaabbbb" or "bbbbaaaaa" should be the only possible results.
This problem is fixed by FugueMax, described in "The Art of the Fugue" paper[1]. If you're thinking of implementing a text CRDT, I recommend starting there. Fuguemax is a tiny change from RGA. We swap out the sequence numbers for a "right parent" pointer and the problem goes away. Coincidentally, the algorithm is also a 1 line change away from Yjs's CRDT algorithm.
And its really not that complicated. Most of the complexity in the fuguemax paper comes about because - like with RGA - they describe the algorithm in terms of inserts into a tree. If you ask me, this is a mistake. The algorithm is simpler if you primarily think of it as inserts into a list. (Thanks Kevin Jahns for this insight!) I programmed Fuguemax up live on camera a few months ago like this. You can fit a simple reference implementation of fuguemax in ~200 lines of code[2]. (The video is linked from the readme in that repository. In the video I explain the algorithm and all the code along the way).
[1] https://arxiv.org/abs/2305.00583
[2] https://github.com/josephg/crdt-from-scratch/blob/master/crd...
The big benefit of eg-walker is that you don't need to load any history from disk to be able to do collaborative editing. There's no need to keep around and load the whole history of a document to be able to merge changes and send edits to other peers. Its also much faster in most editing situations - though modern optimizations mean text based CRDTs are crazy fast now anyway.
The downside is that eg-walker is more complex to implement. Compare - this "from scratch" traditional CRDT implementation of FugueMax:
https://github.com/josephg/crdt-from-scratch/blob/master/crd...
With the same ordering algorithm implemented on top of egwalker:
https://github.com/josephg/egwalker-from-scratch/blob/master...
Eg-walker takes about twice as much code. In this case, ~600 lines instead of 300. Its more complex, but its not crazy. It also embeds a traditional CRDT inside the algorithm. If you want to understand eg-walker, you should start with fuguemax anyway.
- Using a b-tree instead of an array to store data
- Use internal run-length encoding. Humans usually type in runs of characters. So store runs of operations instead of individual operations. (Eg {insert "abc", pos 0} instead of [{insert "a", pos 0}, {insert "b" pos 1}, {insert "c" pos 2}]).
But these two ideas also affect one another. Its not enough to just use a b-tree. You need a b-tree which also stores runs. And you also need to be able to insert in the middle of a run. And so on. You need some custom collections.
If you do run-length encoding properly, all iteration throughout your code needs to make use of the compressed runs. If any part of the code works character-by-character, it'll become a bottleneck. Oh and did I mention that it works even better if you use columnar encoding, and break the data up into a bunch of small arrays? Yeahhhh.
So thats why diamond types - my optimized egwalker implementation - is tens of thousands of lines of code instead of a few hundred. (Though in my defence, it also includes custom binary serialization, testing, wasm bindings, and so on.)
Rust makes the implementation way easier to implement thanks to traits. I have simple traits for data that can be losslessly compressed into runs[1]. A whole bunch of code takes advantage of that, by providing tooling that can work with a wide variety of actual data. For example, I have a custom vec wrapper that automatically compresses items when you call push(). I have a "zip" iterator which glues together other iterators over run-length encoded data. And so on. Its great.
Though now that I think about it, maybe all that trait foo is what makes it headache inducing. I swear its worth it.
[1] Eg MergableSpan: https://github.com/josephg/diamond-types/blob/00f722d6ebdc9f...
Yeah thats right. Its a GUID, because they need to be sent over the wire. For text editing, we usually use {site ID, transaction sequence} because they compress better than random IDs.
> One alternative way to approach it that comes to mind is to introduce transaction semantics so that you can consider a node to be identified by a [Lamport timestamp, site ID, transaction sequence] and the parent, and use a sequence number within the transaction to sort, ...
Maybe? I don't fully understand what you mean. And even if I did, I'm not clever enough to infer all the implications of that construction. But yes, I suspect you're right that in the best case, it would be equivalent to fuguemax. And in the worst case, it would introduce new bugs.
Can you elaborate? This sounds a little tautological, so I must be missing something.
But if you actually implement your CRDT like this, you'll find the tree is incredibly unbalanced. You'll end up with runs of thousands of items where you have (x)->(y)->(z)->(q) and so on. It resembles a linked list more than anything. Performance is abysmal as a result. This is one of the causes for the terrible performance of early versions of automerge.
Here's the trick: Flatten the tree. Store all items in a list instead, in the order all the items show up in the document. But this presents a new problem: how do you correctly handle inserts? We need to insert new items in the list in the correct location, as if we inserted into a tree then flattened it afterwards. But it turns out that this translation is quite simple in practice. Its like ~10-20 lines of code.
Interestingly, the fugue paper first describes fugue (as a tree). Then it identifies & fixes a problem in the algorithm to produce fuguemax. If you do the list insertion order translation on both fugue and fuguemax, fugue ends up with an extra if() statement that causes this problem. If you remove that if statement, you get the (better) fuguemax algorithm.
This transformation results in much better performance, and much lower memory usage. Counter-intuitively, you get another order of magnitude improved performance if you then store this flattened list once more in a b-tree.
If you're curious, here's the equivalent insertion code for fuguemax, rga and yjs. These are all fuzz tested against their upstream reference implementations to verify equivalence. Fugue is also somewhere in this file, if you want to compare.
Here's FugueMax[1]: https://github.com/josephg/reference-crdts/blob/c53947408770...
RGA: https://github.com/josephg/reference-crdts/blob/c53947408770...
And Yjs: https://github.com/josephg/reference-crdts/blob/c53947408770...
As I said, I didn't come up with this idea. Kevin Jahns figured out this trick for Yjs. I adapted it to the other algorithms.
[1] Fuguemax is called "yjsmod" in this repository because this code predates the fugue paper. It turns out our algorithms are equivalent.
j0e1•5mo ago