Causal consistency is one of the major memory consistency models. In concurrent programming, where concurrent processes are accessing a shared memory, a consistency model restricts which accesses are legal. This is useful for defining correct data structures in distributed shared memory or distributed transactions.
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. Contrast to strong consistency models, such as sequential consistency or linearizability, which cannot be both safe and live under partition, and are slow to respond because they require synchronisation.
Causal consistency was proposed in 1990s [1] as a weaker consistency model for shared memory models. Causal consistency is closely related to the concept of Causal Broadcast in communication protocols.[2] In these models, a distributed execution is represented as a partial order, based on Lamport's happened-before concept of potential causality. [3]
Causal consistency is a useful consistency model because it matches programmers' intuitions about time, is more available than strong consistency models, yet provides more useful guarantees than eventual consistency. For instance, in distributed databases, causal consistency supports the ordering of operations, in contrast to eventual consistency.[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.
However, many distributed databases lack this guarantee, even ones that
provide serialisability.[6]
Spanner does guarantee causal consistency, but it also forces strong consistency, thus eschewing availability under partition.
More available databases that ensure causal consistency include MongoDB
and AntidoteDB.
^Ahamad, Mustaque; Neiger, Gil; Burns, James E.; Kohli, Prince; Hutto, Phillip W. (March 1995), "Causal memory: definitions, implementation, and programming", Distributed Computing, 9 (1): 37–49, doi:10.1007/bf01784241, hdl:1853/6781, S2CID 6435056
^Birman, Kenneth P.; Joseph, Thomas A. (January 1987), "Reliable communication in the presence of failures", ACM Transactions on Computer Systems, 5 (1): 47–76, doi:10.1145/7351.7478, hdl:1813/6534, S2CID 11224827
^Lamport, Leslie (1978), "Time, clocks, and the ordering of events in a distributed system", Communications of the ACM, 21 (7): 558–565, doi:10.1145/359545.359563, S2CID 215822405
^Elbushra, Mawahib Musa; Lindström, Jan (2015), "Causal consistent databases", Open Journal of Databases, 2 (1): 17–35
^Perrin, Matthieu; Mostéfaoui, Achour; Jard, Claude (2016), "Causal consistency: beyond memory", in Asenjo, Rafael; Harris, Tim (eds.), Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2016, Barcelona, Spain, March 12-16, 2016, pp. 26:1–26:12, arXiv:1603.04199, doi:10.1145/2851141.2851170, S2CID 3010991
^Daudjee, Khuzaima; Salem, Kenneth (2004), "Lazy database replication with ordering guarantees", in Özsoyoglu, Z. Meral; Zdonik, Stanley B. (eds.), Proceedings of the 20th International Conference on Data Engineering, ICDE 2004, 30 March – 2 April 2004, Boston, MA, USA, IEEE Computer Society, pp. 424–435, CiteSeerX 10.1.1.564.1562, doi:10.1109/ICDE.2004.1320016, S2CID 1850131
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