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Comment by Luker88 | Hacker News Reader

I only wrote that quorum is not a transaction (no transactions in cassandra), and that is not a consensus.

While quorum looks a lot like consensus, it is not since what is returned to the client is the latest timestamp. Different nodes could have and return different data. So Quorum write+local_quorum read might fail even if you are in the datacenter that accepted the write. Quorum is also on the total copies of the data, not a quorum of DC, so in certain (weird) multi-DC setups you could have a quorum in a single DC.

In general though I think that the data consistency options of cassandra (quorum/local/N) are a good idea but underdeveloped.

Anyway my point was: cassandra has too many pitfalls and eliminating those restricts the use case by a lot more than people realize. Plus the naming of all features look designed to trick you into thinking soewthing else

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No, you wrote that you have to write and wait several minutes for data to replicate. This is straight-up false.

Yes, if you mix consistency levels incorrectly you will do it wrong and maybe get stale data, but that is a different criticism. I agree that it is easy for unsophisticated users to incorrectly use consistency levels in complex topologies, and I hope we will introduce mechanisms to prevent users making such mistakes in future. But that was not your claim, and in my experience users do understand consistency levels just fine.

There are lots of valid criticisms to point at various use cases with Cassandra, but this was just incorrect.

it’s worse than waiting actually. Because client A might write value « 1 » and after client B write value « 2 ». Then client C can read Value 2 for 5 minute and then cassandra internal read-repair eventually put value 1 on all replica and value 2 is lost forever.

No, that is literally impossible.

If value 2 is newer than value 1, value 1 will never overwrite value 2. If a client reads value 2 at QUORUM then it will always be seen by all future queries.

Client À write value 1 on 2 of 3 node with timestamp=3

then later client B write value 2 on 2 of the 3 node with timestamp=1

then read repair happen and (value=1 timestamp=3) is written to all 3 nodes.

this is only one of many scenarios where this stupid design fail.

Client B’s write will only be successfully read from 1 of the nodes it wrote to, and read-repair only runs on QUORUM reads. So, no, it will not be possible to read value 2 for 5m - it will never be visible to operations at QUORUM.

This would have been a valid criticism of LWW (and there are other more contrived examples), but I think (or hope) this is an explicit trade off made by anyone using Cassandra in eventual consistency mode. There are strategies to prevent this being a problem for workloads where it matters, some discussed elsewhere in the thread.

Since "client B write" was successfully written on 2 of the 3 nodes. Any read request that read only from 2 of the 3 nodes instead of using quorum read, will be able to see what Client B wrote until it magically disappears.

Quorum Read-repair is only one reason for why the value would randomly disappear. Another one is periodic anti Entropy repair!

No, it won’t. Successfully written does not mean what you think it means. If there is “newer” (by timestamp) data on disk or in memtable then it will not be returned to a client, regardless of which order that data arrived. It is unlikely even to be written to disk (except the commit log).

Since at least one of those nodes has the “newer” value, only one node can serve this “older” value

> Different nodes could have and return different data.

Only if they miss some writes, and eventually they will converge. But if you do quorum writes and quorum reads (or local quorum W + local quorum R), this guarantees you'll read from at last one node that received all the writes issued before the read, so you get the converged value immediately, regardless of which node you ask. All nodes will eventually agree on the value, because timestamps are assigned by the coordinator or by the application, not at the replica. A single write will get the same timestamp across all replicas.

Incorrect timestamps can cause a different problem - a write that happened at physical time T2 > T1 might be considered to be older than T1 by the cluster, if it was accepted by the coordinator whose clock was set in the past. Such write might simply not take any effect, as old updates would be considered newer. However, again, the resolution would be consistent on all replicas once they get all updates.

eventually they will converge on a « random » value! that’s why we say it’s write quorum is not consensus it’s offer very weak guarantee

No, they will converge on a value with higher timestamp. And timestamps are controllable by your app, so you can guarantee they are monotonic.

timestamp is coming from the operating system where the client library is running. so unless all your write request are issued by the same machine then

you are guaranteed to face the problem where the client machine clocks are not in sync.

just comparing timestamp is obviously a design from someone that didn’t review the academic literature on distributed transaction and consensus

>> timestamp is coming from the operating system where the client library is running.

The client can set the timestamp to any value of its choice. It does not have to correspond to clock time.

>> so unless all your write request are issued by the same machine then you are guaranteed to face the problem where the client machine clocks are not in sync.

It's not about all writes. It's about ensuring that all writes to a single partition (specifically, a single column within a single partition) are done using a source of monotonic integers to ensure ordering.

You are correct that if you have a singleton server returning monotonic time to clients this would work.

But this Monotonic time server is not part of Cassandra itself and for this reason majority of the people using Cassandra will use the OS time without knowing this is silently corrupting the database.

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