That’s some proper leadership right there
64 karma · joined December 5, 2022
I implemented this in a decentralized context and as a CRDT though, so that the properties hold (commutative, idempotent and associative).
But as aboodman says in a sibling comment, if there’s a server as an authority, it can simply reject messages from unauthorized clients.
If yes, I’m sorry and I understand your position. If not, gtfo.
The point was that “just use redis” is a poor and incorrect advice.
Is an in-process, in-memory database faster than making a query over network? Yes it is.
Use a single value register then in place where I said “register”.
> partition means that different subsets of nodes in a single system have different views on reality
Partition means that nodes of a (single) network are (temporarily) not connected.
In the example discussed here, blue and green were in different partitions thus had “different view” to the state. Once synchronized, their view on the state is consistent and both observe both values, blue being the latest state.
> is no longer represented in the history of v when the partition heals
Please review the above discussion again and the original article. You keep saying this but it’s shown in both that it’s is not true.
Not invalid. The observation was correct at that time and place, meaning, in the partition that it was observed. This is the P in AP.
It seems to me that you’re talking about and arguing for synchronization, that is, consensus about the values (=state), which takes the system to CP as opposed to AP.
> there is no straightforward way to module "a value"
I would recommend to look into the “Register” (CR)data structure.
Why is that?
Can you elaborate what do you mean by this? I was arguing that it’s possible as the original argument was “this is not possible in a real system and is only theoretical”.
> provide no meaningful consistency guarantees to users, because they allows "committed" writes to be lost
If I set the (shared) value to green and you set it to blue, what is the expected observed value? What if you set it to green and I set it blue, what is the observed value? More importantly, what is the consistency that was lost?
Review the description of Lamport Causal Clock in the article. Note that it carries “additional info” (additional to the example diagram). This “additional info” is what establishes the structure needed for total order.
> whichever concurrent updates you don't choose are lost, and that violates consistency
They’re not lost! The concurrent updates not chosen are still part of the “list of operations”, but the update to the data/value itself may not be observable if the subsequent update (the update that was sorted to be first) updates the same data/value (eg. both operations update the same key in a key-value structure). If the two operations update different data/value, then both updated values are observable. This isn’t cheating, rather it works exactly as expected: it is eventually consistent.
So if you have all messages and the two properties above, a total order can be derived.
You’re correct to say that causal order != total order as such but with the use of correct primitives, like Lamport Causal Clocks, we can get a nice and clean linear order of events :)
By having 1) causal order (eg. using what the article refers to as Lamport Causal Clock) and 2) a deterministic sorting function to sort ops that happened concurrently (from the perspective of causal order), we can derive total order.
It’s absolutely possible and used.
And with those two properties, almost any data structure can be turned into a (op-based) CRDT.
That is to say, thoughtlede has it correct in their comments above.
It doesn’t say anything about the correct answer to the question and all about your analysis process. Keep looking, good luck!
So I disagree this is a non-starter, because we didn’t find a solution in the past, but rather an ideal place to start and a great space to discover new Sybil resistance mechanisms (which we have over the recent years).
I can’t say there’s a simple solution to all of these, today, but my intuition and optimism says there’s a solution for all of these :)
With keys, validation can happen in several ways: attestation by reputable orgs, reputation systems, off-band, 2FA, “Hi, I’m John”, etc etc. Discovery is also highly context dependent in that it can and should happen “in the app/system” (=whatever the context of the use case is, eg. you know me by pubkey 123, the tax office knows me by pubkey xyz).
“anonymous registration services” from the perspective of self-sovereign identity is by definition not anonymous :)
Public/private keys have or enable all those properties.
While there a many problems with how domains work today as a public goods system, domain names as identities is infinitely better than what we have now, but I think we can have it much better and domain names are one step in that self-sovereign ownership.