If you ask your cache for a value, it could choose to reply now, with the information that it has - favouring A.
Or it could wait and hope for more accurate information to return to you later, favouring C.
'Cache' seems to imply that it's built for availability purposes.
In a specific use case that might apply. For example, if two people edit the same document and fix the same typo, the visual outcome is the same, no matter who made the change first or last.
But that is very niche as if we would take a programming code, someone can change a line of code that someone else is changing as well and they might be the same, but then you have other lines of code as well that might not be and then you end up with a code that won't compile. In other words, if we focus on the singular change in insolation, this makes sense. But that is essentially never the case in distributed environments in this context and we have to look at broader picture where multiple changes made by someone are related or tied to each other and do not live insolation.
Either way, i see nothing useful here. You can "render" your local changes immediately vs wait for them to be propagated through the system and return back to you. There is very little difference here and in the end it is mostly just about proper diffing approach and has little to do with the distributed system itself.
PS: the problem here is not really the order of applied changes for local consumer, like in case of editing a shared word document. The problem here is if we have a database and we commit a change locally but then someone else commits different change elsewhere, like "update users set email = foo@bar where id = 5" and before we receive the other, later, change we serve clients invalid data. That is the main issue of eventual consistency here. As I am running a system like this, I have to use "waiters" to ensure I get the correct data. For example, when user creates some content via web ui and is redirected back to list of all content, this is so fast that the distributed system has not had enough time to propagate the changes. So this user will not see his new content in the list - yet. For this scenario, I use correlation id that i receive when content is created and i put it into the redirect so when user moves to the page that lists all the content, this correlation is detected and a network call is made to appropriate server whose sole purpose is to keep the connection open until that server's state is caught up to the provided correlation id. Then I refresh the list of content to present the user the correct information - all of this whilst there is some loading indicator present on the page. There is simply no way around this in distributed systems and so I find this article of no value(at least to me).
Just a basic example for a task tracker:
* first update sets task cancelled_at and cancellation_reason
* second update wants the task to be in progress, so sets started_at
If code just uses the timestamps to consider the task state, it would not assume the task is cancelled, unexpected since the later user update set it to in progress.
Easy fix, we just add a state field 'PENDING|INPROGRESS|CANCELLED|...'.
Okay, but now you have a task that is in progress, but also has a cancellation timestamp, which seems inconsistent.
The point is:
With CRDTs you have to consider how partial out of order merges affect the state, and make sure your logic is always written in a way so these are handled properly. That is *not easy*!
I'd love it if someone came up with a framework that allows defining application semantics on top of CRDTs, and have the framework ensure types remain consistent.
The point is that you always have to think about merging behaviour for every piece of state.
The difference is that coming up with a correct CRDT solution for application specific consistency requirements can be a research project. In many cases, no CRDT solution can exist.
In my experience, 95% of applications are handled just fine by the sort of JSON types built in to Yjs or automerge. The problems I hear people complain about are things like performance, size on disk and library ergonomics. And the long tail of features - like ephemeral data support and binary assets.
But data mapping seems mostly fine?
I know of a couple of exceptions. Arbitrary nested tree reparenting can be a nightmare. And there aren’t many good rich text implementations out there.
What problems are you actually running into?
One large class of problems I'm thinking of is simply outside the scope of CRDTs. The whole idea of _eventual_ consistency doesn't really work for things like payment systems or booking systems. A lot of OLTP applications have to be consistent at all times (hence the O). Money must not be double spent. Rooms or seats must not be double booked.
The other class of problems is more debatable. CRDTs can guarantee that collaborative text editing results in the same sequence of letters on all nodes. They cannot guarantee that this sequence makes sense. Authors can step on each other's toes.
Whether or not this is a problem depends on the specific workflow and I think it could be mitigated by choosing better units of storage/work (such as paragraphs rather than letters).
Yes! I think of it as owned data and shared data. Owned data is data that is owned by one process or node. Eg my bank balance, the position of my mouse cursor, the temperature of my CPU. For this stuff, you don’t want a crdt. Use a database. Or a variable in memory or a file on disk. Broadcast updates if you want, but route all write requests through the data’s owner.
Then there’s shared data - like the source code for a project or an apple note. There, CRDTs might make sense - especially if you get branching and merging support along for the ride.
> Authors can step on each other's toes.
Yeah when merging long lived branches, the workflow most people want is what git provides - of humans manually resolving conflicts. There’s no reason a crdt couldn’t provide this. CRDTs have a superset of the information available to git. It’s weird nobody has coded a system like that up yet.
That's an interesting idea. I have to think about this.
Any many CRDT implantations have already solved this for the styled text domain (e.g bold and cursive can be additive but color not etc).
But something user definable would be really useful
The gist is:
* Replicating intentions (actions, immutable function call definitions that advance state) instead of just replicating state.
* Hybrid logical clocks for total ordering.
* Some client side db magic to make action functions deterministic.
This ensures application semantics are always preserved with no special conflict resolution considerations while still having strong eventual consistency. Check out the readme for more info. I haven’t gotten to take it much further beyond an experiment but the approach seems promising.
I've had similar thoughts, but my concern was: if you have idempotent actions, then why not just encode them as actions in a log. Which just brings you to event sourcing, a quite well-known pattern.
If you go that route, then what do you need CRDTs for?
Event Sourcing is not strictly designed to achieve eventual consistency in the face of concurrent writes though. But that doesn't mean it can't be!
I've also been considering an intent based CRDT system for a while now (looking forward to checking out GPs link) and agree that it looks/sounds very much like Event Sourcing. It's worth while being clear on the definition/difference between the two though!
At least in my thinking/prototyping on the problem so far I think this solution offers some unique properties. It lets clients operate offline as long as they like. It delegates the heavy lifting of resolving state from actions/events to clients, requiring minimal server logic. It prevents unbounded growth of action logs by doing a sort of "rebase" for clients beyond a cutoff. It seems to me like it maximally preserves intentions without requiring specific conflict resolution logic. IMO worth exploring further.
Well, this all depends on the definition of «function properly». Convergence ensures that everyone observed the same state, not that it’s a useful state. For instance, The Imploding Hashmap is a very easy CRDT to implement. The rule is that when there’s concurrent changes to the same key, the final value becomes null. This gives Strong Eventual Consistency, but isn’t really a very useful data structure. All the data would just disappear!
So yes, CRDT is a massively useful property which we should strive for, but it’s not going to magically solve all the end-user problems.
One simple answer to this problem that works almost all the time is to just have a “conflict” state. If two peers concurrently overwrite the same field with the same value, they can converge by marking the field as having two conflicting values. The next time a read event happens, that’s what the application gets. And the user can decide how the conflict should be resolved.
In live, realtime collaborative editing situations, I think the system just picking something is often fine. The users will see it and fix it if need be. It’s really just when merging long running branches that you can get in hot water. But again, I think a lot of the time, punting to the user is a fine fallback for most applications.
The basic CRDT ideas are actually pretty easy to implement: add some metadata here, keep some history there. The difficulty, for the past 20 years or so, is making the overheads low, and the APIs understandable.
Many projects revolve around some JSON-ish data format that is also a CRDT:
- Automerge https://automerge.org (the most tested one, but feels like legacy at times, the design is ~10yrs old, there are more interesting new ways)
- JsonJoy https://jsonjoy.com/
- RDX (mine) https://replicated.wiki/ https://github.com/gritzko/go-rdx/
- Y.js https://yjs.dev/
Others are trying to retrofit CRDTs into SQLite or Postgres. IMO, those end up using last-write-wins in most cases. Relational logic steers you that way.
Conflict-free replicated data types (CRDTs) https://en.wikipedia.org/wiki/Conflict-free_replicated_data_...
ijaym•6h ago
Do people really distinguish "Strong Eventual Consistency" from "Eventual Consistency"? To me, when I say "Eventual Consistency" I alwayes mean "Strong Eventual Consisteny".
nl•4h ago
In an eventually consistent system replicas can diverge. A "last write" system can be eventually consistent, but a given point can read differently.
Eg: operations
1) Add "AA" to end of string 2) Split string in middle
Replicas R1 and R2 both have the string "ZZZZ"
If R1 sees operations (1) then (2) it will get "ZZZZAA", then "ZZZ", "ZAA"
If R2 sees (2) then (1) it will get:
"ZZ", "ZZ", then "ZZAA", "ZZ".
Strong Eventual Consistency doesn't have this problem because the operations have the time vector on them so the replicas know what order to apply them.
aatd86•3h ago
josephg•47m ago