Causal Consistency is “Available under Partition”, meaning that a process can read and write the memory (memory is Available) even while there is no functioning network connection (network is Partitioned) between processes; it is an asynchronous model.
[2] In these models, a distributed execution is represented as a partial order, based on Lamport's happened-before concept of potential causality.
[4] Also, causal consistency helps with the development of abstract data types such as queues or counters.
[5] Since time and ordering are so fundamental to our intuition, it is hard to reason about a system that does not enforce causal consistency.
[11] They can be summarised as follows: Transactional session guarantees for serialisability and snapshot isolation are presented by Daudjee and Salem.
This requires the implementation to maintain meta-data that represents the causal relationships between memory accesses.
(As a side effect of delivery, add the new event to the causal context of the receiving process.)
In the meantime, the implementation either passively waits to receive the missing events, or actively fetches them from their source.
This representation has a fixed size, and the ordering of events can be inferred by a simple comparison of the vectors.
To precisely determine which events are dependent and which are concurrent in a fully peer-to-peer system, the size of the metadata is at least proportional to the number of active writers.
[15] In the limit, a single scalar (a Lamport clock[3]) suffices, at the cost of removing any concurrency.