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Comment by antirez | Hacker News Reader

From Martin post: "However, the storage server remembers that it has already processed a write with a higher token number (34), and so it rejects the request with token 33."

This is not eventual consistency, this is refusing any new write with ID < past_ID, which requires linearizability.

Second, about eventual consistency, the Token ID even when increasing may not be casually related with the work performed while the lock is hold. if you need just a random order when there are concurrent accesses, you can lexically order the random token IDs, for what is worth.

Compare & Swap: could you argument that? If you want to avoid races in a read-modify-write scenario, you can set the token, do your work, and only write if the token is still the same, so that if your write succeed is because the state of the shared resource is still the same to when you started operating on it.

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Martin made no mention of strong vs. eventual consistency.

If you write to a store like cassandra using a monotonic token, the result will converge correctly. If you require to never read "stale" data, you can read / write w/ quorum. There is no linearization in the storage layer in this case.

In this case the lock is not superfluous and you have safety.

Regarding CAS, I said that your argument (in support of redlock) was wrong. I would be happy to respond to a proposed algorithm, but as someone else mentioned, either the storage layer can provide a CAS operation, in which case your lock is not required and the workers read, mutate, CAS, OR if the storage layer has any weaker semantics then it is not possible to ensure safety with a token.

>the storage layer can provide a CAS operation, in which case your lock is not required and the workers read, mutate, CAS

Yeah, so Optimistic Concurrency Control. Possibly a Redlock-like lease scheme could be used to improve performance in the high-contention case.

you don't need a token service if you are going to set the token on the locked resource, perform work and then unset the token. your lock is completely superfluous in that scenario

That's my point in the blog post I wrote. Please read it, I would love your feedback. Or better, you could do even with a best-effort lock, given that race conditions from time to time are harmless.

  2. If your data store can always accept the write only if 
  your token is greater than all the past tokens, than it’s a 
  linearizable store. If you have a linearizable store, you 
  can just generate an incremental ID for each Redlock 
  acquired, so this would make Redlock equivalent to 
  another distributed lock system that provides an 
  incremental token ID with every new lock. However in the 
  next point I’ll show how this is not needed.

you confuse which system is the linearizable one in this point. your storage system is just a dumb storage system that simply returns the object version with the highest clock. what makes it linearizable to an outside observer is that the system generating the lock ids is itself linearizable. if every insert is accompanied by an authentic lock id the system is linearizable. if you generate this lock id via the storage system itself then the storage system must also be linearizable

  3. However “2” is not a sensible choice anyway: most of the times the 
  result of working to a shared resource is not writing to a linearizable 
  store, so what to do? Each Redlock is associated with a large random 
  token (which is generated in a way that collisions can be ignored. The 
  Redlock specification assumes textually “20 bytes from /dev/urandom”). 
  What do you do with a unique token? For example you can implement Check 
  and Set. When starting to work with a shared resource, we set its state 
  to “`<token>`”, then we operate the read-modify-write only if the token 
  is still the same when we write.

would you say this sequence of events is correct:

* process A acquires lock

* process B acquires a lock on the same resource after a time out and sets it's lock id on the resource

* process A sets it's lock id on the resource

* process A writes a new version of the resource

* process B attempts and fails to write a new version of the resource

if it's not correct, would you say redlock prevents it? how?

now consider what happens if the resource is only one of a group of resources that need to be transactionally updated

the bottom line is that distributed transactional locking requires linearization and redlock is plainly not linearizable. please reconsider it's continued development and steer users towards something safer like zookeeper

> your storage system is just a dumb storage system that simply returns the object version with the highest clock. what makes it linearizable to an outside observer is that the system generating the lock ids is itself linearizable.

I don't think you understand Martin motivations. He says that the system has to reject the write with a lower ID, in order to protect from concurrent accesses, and to do so in a way that the client attempting a stale write is notified.

If you mount an eventually consistent system returning the object with higher version, there is nothing of linearizable about it. If the shared resource is a list of names, one client will try to add "Hellen" with version 8, one will try to add "Josh" with version 9. Only one name will end in the list in your schema, and the client adding "Josh" will not even know that the write failed.

So the goal is, when race conditions happen, to avoid them in some way. While I believe that when you use a distributed lock the problem is very often that you don't have other controls over the shared resource, if you really need to do that, a random token is equivalent: using CAS you can reject one of the writes if two clients are writing at the same time.

Note also that when it happens that, because of delays, the clients are accessing concurrently, the lock ID has little to do with the order in which the operations were indented to happen. Back to "add name to a list" problem:

T0: Client A receives new name to add from web.

T0: Client B is idle

T1: Client A is experiencing pauses.

T1: Client B receives new name to add from web.

T2: Client A is experiencing pauses.

T2: Client B receives a lock with ID 1

T3: Client A receives a lock with ID 2

...

So the client that received the name before, can do the operation after, or you can have delays in any other part of the system.

i disagree that i don't understand martin kleppmann's method and also that his method is 'eventually consistent'.

using a random token is not equivalent to using a linearizable lock, see: https://medium.com/@talentdeficit/redlock-unsafe-at-any-time...

i am not sure what your point about delays and concurrent clients is, of course a system can't infer order from events that happen outside of it's visibility. the lock system can only provide synchronization at the point the lock is acquired

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