Diving Deep on S3 Consistency

So, causal consistency in this context means that 1) it does not matter if a write to object A or object B came first, because they are seen as "unrelated". This obviously allows for performance improvements over general "strong consistency" but still offers more guarantees than eventual consistency.

Second, for every "writer" (which would be a 1:1 relationship to the number of S3 objects) an amount of metadata needs to be kept in-memory for such cases where the access to an object _might_ lead to an outdated read or where an "older" write would potentially overwrite a "newer" write.

All that being said, there still is one single instance that, if it goes down, makes the whole system unavailable for the S3 objects it manages until it is replaced. So that means a lower availability compared to a solution that uses eventual consistency (like Cassandra).

Would this be equivalent to having an in-memory SQL database to store the metadata for some other system (such Cassandra) with a quick failover but still single point of failure to enhance the consistency guarantees - just more optimized/customized so that it can work with a huge system like S3?

for example imagine that you have N writers and you want only the first of them to write something in a n object.

each writer writes their content into "_foo" and each tries to copy that to "foo", with "x-amz-copy-source-if-match". Only one of them will succeed and "foo" will have one consistent value, which all observers (including other writers) can agree who has won.