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> In general, you don't really get to compact tombstones meaningfully without consensus so you really are pushing at least remnants of the entire log around to each client indefinitely.

This is often much less of a problem in practice than people think. Git repositories also grow without bound, but nobody seems to really notice or care. For diamond types (my library), I lz4 compress the text content itself within the .dt files. The metadata is so small that in many cases the space savings from lz4 compression makes the resulting .dt file - including full change history - smaller than the document on disk.

If anyone really cares about this problem, there are a couple other approaches:

1. In our Eg-walker algorithm, we don't use stable guids to identify insert positions. Thats where other algorithms go wrong, because they need to store these guids indefinitely in case someone references them. For eg-walker, we just store relative positions. This makes the algorithm work more like git, where you can do shallow clones. And everything works, until you want to merge a branch which was split off earlier than your clone point. Then you should be able to download the earlier edits back to the common branch point and merge. To support merging, you also only need to store the metadata of earlier edits. You don't need the inserted content. The metadata is usually tiny (~1-4 bits per keystroke for text is common with compression.)

2. Mike Toomim's Antimatter algorithm is a distributed consensus protocol which can detect when its safe to purge the metadata for old changes. It works even in the presence of partitions in the network. (Its conservative by default - if a partition happens, the network will keep metadata around until that peer comes back, or until some timeout.)

> The same few guys (Martin Kleppman, Kevin Jahns, Joseph Gentle, probably others) pop up all over the more recent optimisations.

Alex Good is another. He's behind a lot of the huge improvements to automerge over the last few years. He's wicked smart.

The man himself. Yeah agreed you guys have solved it. It is more a misfired crud dev instinct for me that sees it as wasteful. Just a different paradigm and not big in practice.

I've got eg-walker & Diamond Types in my reading/youtube backlog. Diamond Types went further down the backlog because of "wip" on the repo! I will look into Antimatter too.

> It is more a misfired crud dev instinct for me that sees it as wasteful.

You're not alone. Lots of people bring this up. And it can be a real problem for some applications, if they store ephemeral data (like mouse cursor positions) within the CRDT itself.

re: git repositories

That's partly because repositories rarely need to be cloned in their entirety. As such, even when you need to do it and it's a couple hundreds mb taking a few minutes, it's tolerated.

In situations where a document needs to be cold loaded often, the size of the document is felt more acutely. Figma has a notion of GC-ing tombstones. But the tombstones in questions aren't even something that get created in regular editing, it happens in a much more narrow case having to do with local copies of shared components. Even that caused problems for a small portion of files -- but if a file got that large, it was also likely to be important.

> even when you need to do it and it's a couple hundreds mb taking a few minutes, it's tolerated.

Well written CRDTs should grow slower than git repositories.

> In situations where a document needs to be cold loaded often, the size of the document is felt more acutely.

With eg-walker (and similar algorithms) you can usually just load and store the native document snapshot at the current point-in-time, and work with that. And by that I mean, just the actual json or text or whatever that the application is interacting with.

Many current apps will first download an entire automerge document (with full history), then using the automerge API to fetch the data. Instead, using eg-walker and similar approaches, you can just download 2 things:

- Current state (eg a single string for a text file, or raw JSON for other kinds of data) alongside the current version

- History. This can be as detailed & go as far back as you want. If you download the full history (with data), you can reconstruct the document at any point in time. If you only download the metadata, you can't go back in time. But you can merge changes. You need history to merge changes. But you only need access to history metadata, and only as far back as the fork point with what you're merging.

If you're working online (eg figma), you can just download history lazily.

For client/server editing, in most cases you don't need any history at all. You can just fetch the current document snapshot (eg a text or json object). And users can start editing immediately. It only gets fancy when you try to merge concurrent changes. But thats quite rare in practice.

> If you're working online (eg figma), you can just download history lazily.

You can download the history lazily, that's a special case of incrementally loading the document.

If the history is only history, then sure. But I was understanding that we were talking about something that may need to be referenced but can eventually be GCed (e.g. tombstones-style). Then lazy loading makes user operations that need to reference that data go from synchronous operations to asynchronous operations. This is a massive jump in complexity. Incrementally loading data is not the hard part. The hard part is how product features need to be built on the assumption that the data is incrementally loaded.

Something that not all collaborative apps will care about, but Figma certainly did, is the concept that the client may go offline with unsynced changes for an indefinite amount of time. So the tombstones may need to be referenced by new-looking old changes, which increases the likelihood of hitting "almost-never" edge cases by quite a bit.

> But I was understanding that we were talking about something that may need to be referenced but can eventually be GCed (e.g. tombstones-style)

Yeah my point is that this is only a limitation of certain types of CRDTs. Automerge and Yjs both suffer from this.

However, it’s not a requirement for anything built on egwalker. Or generally any CRDTs in the pure operation log style. Egwalker (and by extension, loro and diamond types) can generate and broadcast edits synchronously. Even without any history loaded.

Thank you for your work. Diamond Types is probably my favorite piece of programming, transliterating the algo for line edits for a situation where I needed a a different algo for the line management itself, is probably the most rewarding deep dive on a algorithm I've ever done.

I’m glad you enjoyed it! What did you build?

Effectively a distributed ~OS that hands off between browser and cloud using websocket presence.

So its like the human facing side of an OS where time, space (structure) and minds are all the only first class types and any apps exist as apptributes on the data itself.

It's basically a document editor, cal, feed, chat app, etc. in one interface where temporal search and retrieve is built in.

Regarding the growing log in CRDTs, it doesn't have to be that way. Most of these popular CRDT libs use Merkle DAG only to build up the log of the changes. But there is a better way, you can combine a Merkle DAG for ordering + prolly trees for storing the actual state of the data. That gives you total ordering, an easy way to prune old data when you choose to, and an easy way to sync. Look into fireproof for how this is combined.

Regarding distributed schemas, I agree, there's a lot of complexity there but it's worth looking into projects like https://www.inkandswitch.com/cambria/ and https://github.com/grafana/thema, which try to solve the problem. It's a young space though. If anyone else knows about similar projects or efforts, please let me know. Very interested in the space.

Interesting! Do you mind explaining the idea in more detail?

As far as I understand, libraries like Automerge use the Merkle DAG to encode a document as an immutable bundle of state changes aka operation log + the causal ordering which enables conflict free merging between multiple peers. The final document is reconstructed by combining the state transitions. So the Merkle DAG is both the state and the causal relationship between mutations which allows the merge "magic".

Prolly trees allow you to store history independent data which is trivial to sync, diff and merge, regardless of insert order, merge order or originating peer. A Merkle DAG layered on top of prolly trees (event reference prolly tree roots) gives you causality so that peers can agree on a common view of history. So it's very useful because you can check integrity and travel in time, but you can keep as much of it as you want, because it's not necessary for constructing the current state. Prolly trees give you the current state and the easy syncing, diff,merge. So you can truncate the history as needed for your use case.

For a production ready implementation of prolly trees you can check Dolt. For a combination of Merkle DAG (https://github.com/storacha/pail) and prolly trees you can check https://github.com/fireproof-storage/fireproof

Those are lovely data structures and I know about them. But how are you planning on using those data structures? What CRDT are you building?

You might also be interested in Alex Good's Beelay algorithm: https://www.youtube.com/watch?v=neRuBAPAsE0

Prolly trees can act as CRDTs if you have a merge function that always merges and doesn’t block.

So my initial comment merely tried to make the point that there is a design space where you’re not stuck with the tradeoff of carrying the full Merkle DAG history just to be able to reconstruct the latest version of your document.

Thanks for the video, will check it out!

The tombstone problem is exactly why I built DocNode. You're right that you can't compact them without consensus, so DocNode just doesn't create them. It assumes a server decides the order, which is already the case in 99% of collaborative apps. The result: a doc stays the same size whether it was edited once or a thousand times. Yjs for the same doc grows every time someone types, pastes, or undoes. Check it out: https://docukit.dev

I love diamond types personally, which I think Loro uses in certain cases. I find not only is DT fast under normal usage, allows for easy archiving and retrieval of past data but it also quickly outputs a per user casuality rendering that LLMs can pretty easily rectify in most cases. Compact and archival to flat files works very well.