An Interactive Intro to CRDTs (2023)
jakelazaroff.com
jakelazaroff.com
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. You also can't easily upgrade your db you're stuck looking after legacy data structures indefinitely - or you have to impose arbitrary cut off points.
List CRDTs - which text CRDTs are built from are probably unavoidable except for really really simple applications. Over the last 15 years they have evolved, roughly: WOOT (paper) -> RGA (paper) -> YATA (paper) / YJS (js + later rust port) -> Peritext (paper) / Automerge (rust/js/swift) -> Loro (Rust/js). Cola (rust) is another recent one. The big libs (yjs, automerge, loro) offer full doc models.
Mostly the later ones improve on space, time & intent capture (not interleaving concurrent requests).
The same few guys (Martin Kleppman, Kevin Jahns, Joseph Gentle, probably others) pop up all over the more recent optimisations.
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.
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.
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.
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.
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.
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.
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.
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 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.
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
You might also be interested in Alex Good's Beelay algorithm: https://www.youtube.com/watch?v=neRuBAPAsE0
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!
1) Counters
While not really useful, they demonstrate this well:
- mutations are +n and -n
- their order do not matter
- converging the state is a matter of applying the operations of remote peers locally
Useful for accounting, or replication of time-series/logs with no master/slave relationship between nodes (where writes would be accepted only on a "master" node).
- the only mutation is "append"
- converging the state is applying the peers operations then sorting by timestamp
3) Multi Value registers (and maps)
Similar to Last-Write-Win registers (and maps), but all writes are kept, the value becomes a set of concurrent values.
4) Many more...
Each is useful for specific use cases. And since not everybody is making collaborative tools, but many are working on distributed systems, I think it's worth it to mention this.
On another note, the article talks about state based CRDTs, where you need to share the whole state. In the examples I gave above, they are operation based CRDTs, where you need to share the operations done on the state and recompute it when needed.
For example, in the Elixir ecosystem, we have Horde ( https://hexdocs.pm/horde/readme.html ) which allows distributing a worker pool over multiple nodes, it's backed by DeltaCrdt ( https://hexdocs.pm/delta_crdt/DeltaCrdt.html ).
Delta-CRDTs are an optimization over state based CRDTs where you share state diffs instead of the whole state (described in this paper: https://arxiv.org/pdf/1603.01529 ).
Edit: I had an excerpt here which I completely misread. Sorry.
Downside: harder to think about it all.
Upside: a rocket may hit the datacenter.
From what I remember about Figma, it can be proclaimed CRDT. Google Docs got their sync algorithm before CRDT was even known (yep, I remember those days!).
I (obviously) care a lot about fixing these sort of bugs. But its important to remember that in many applications, it matters a lot less than people think.
If I implemented google docs today, I'd use a CRDT between all the servers. This would let you have multi-master server scaling, and everything on the server side would be lock-free.
Between the server and the client, CRDTs would be fine. With eg-walker, you can just send the current document state to the user with no history (until its actually needed). In eg-walker, you can merge any remote change so long as you have history going back to a common fork point. If merges happen and the client is missing history, just have them fetch the server for historical data.
Alternately, you can bridge from a CRDT to OT for the client/server comms. The client side code is a bit simpler that way, but clients may need server affinity - which would complicate your server setup.
> OTs power most collaborative text-based apps such as Google Docs. They’re the most well-known technique but in researching them, we quickly realized they were overkill for what we wanted to achieve ...
> Figma's tech is instead inspired by something called CRDTs, which stands for conflict-free replicated data types. ... Figma isn't using true CRDTs though. CRDTs are designed for decentralized systems where there is no single central authority to decide what the final state should be
https://www.figma.com/blog/how-figmas-multiplayer-technology...
[0] https://www.inkandswitch.com/keyhive/notebook/
[1] https://spritely.institute/news/composing-capability-securit...
Also a really well written piece.
An interactive intro to CRDTs - https://news.ycombinator.com/item?id=37764581 - Oct 2023 (130 comments)
It would be interesting to try again now.