Perspectives on the CAP Theorem

Almost twelve years ago, in 2000, Eric Brewer introduced the idea that there is a fundamental trade-off between consistency, availability, and partition tolerance. This trade-off, which has become known as the CAP Theorem, has been widely discussed ever since. In this paper, we review the CAP Theorem and situate it within the broader context of distributed computing theory. We then discuss the practical implications of the CAP Theorem, and explore some general techniques for coping with the inherent trade-offs that it implies

CAP Theorem: Revision of its related consistency models

[EN] The CAP theorem states that only two of these properties can be simultaneously guaranteed in a distributed service: (i) consistency, (ii) availability, and (iii) network partition tolerance. This theorem was stated and proved assuming that "consistency" refers to atomic consistency. However, multiple consistency models exist and atomic consistency is located at the strongest edge of that spectrum. Many distributed services deployed in cloud platforms should be highly available and scalable. Network partitions may arise in those deployments and should be tolerated. One way of dealing with CAP constraints consists in relaxing consistency. Therefore, it is interesting to explore the set of consistency models not supported in an available and partition-tolerant service (CAP-constrained models). Other weaker consistency models could be maintained when scalable services are deployed in partitionable systems (CAP-free models). Three contributions arise: (1) multiple other CAP-constrained models are identified, (2) a borderline between CAP-constrained and CAP-free models is set, and (3) a hierarchy of consistency models depending on their strength and convergence is built.Muñoz-Escoí, FD.; Juan Marín, RD.; García Escriva, JR.; González De Mendívil Moreno, JR.; Bernabeu Aubán, JM. (2019). CAP Theorem: Revision of its related consistency models. The Computer Journal. 62(6):943-960. https://doi.org/10.1093/comjnl/bxy142S943960626Davidson, S. B., Garcia-Molina, H., & Skeen, D. (1985). Consistency in a partitioned network: a survey. ACM Computing Surveys, 17(3), 341-370. doi:10.1145/5505.5508Gilbert, S., & Lynch, N. (2002). Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. ACM SIGACT News, 33(2), 51-59. doi:10.1145/564585.564601Muñoz-Escoí, F. D., & Bernabéu-Aubán, J. M. (2016). A survey on elasticity management in PaaS systems. Computing, 99(7), 617-656. doi:10.1007/s00607-016-0507-8Brewer, E. (2012). CAP twelve years later: How the «rules» have changed. Computer, 45(2), 23-29. doi:10.1109/mc.2012.37Attiya, H., Ellen, F., & Morrison, A. (2017). Limitations of Highly-Available Eventually-Consistent Data Stores. IEEE Transactions on Parallel and Distributed Systems, 28(1), 141-155. doi:10.1109/tpds.2016.2556669Viotti, P., & Vukolić, M. (2016). Consistency in Non-Transactional Distributed Storage Systems. ACM Computing Surveys, 49(1), 1-34. doi:10.1145/2926965Burckhardt, S. (2014). Principles of Eventual Consistency. Foundations and Trends® in Programming Languages, 1(1-2), 1-150. doi:10.1561/2500000011Herlihy, M. P., & Wing, J. M. (1990). Linearizability: a correctness condition for concurrent objects. ACM Transactions on Programming Languages and Systems, 12(3), 463-492. doi:10.1145/78969.78972Lamport. (1979). How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs. IEEE Transactions on Computers, C-28(9), 690-691. doi:10.1109/tc.1979.1675439Ladin, R., Liskov, B., Shrira, L., & Ghemawat, S. (1992). Providing high availability using lazy replication. ACM Transactions on Computer Systems, 10(4), 360-391. doi:10.1145/138873.138877Yu, H., & Vahdat, A. (2002). Design and evaluation of a conit-based continuous consistency model for replicated services. ACM Transactions on Computer Systems, 20(3), 239-282. doi:10.1145/566340.566342Curino, C., Jones, E., Zhang, Y., & Madden, S. (2010). Schism. Proceedings of the VLDB Endowment, 3(1-2), 48-57. doi:10.14778/1920841.1920853Das, S., Agrawal, D., & El Abbadi, A. (2013). ElasTraS. ACM Transactions on Database Systems, 38(1), 1-45. doi:10.1145/2445583.2445588Chen, Z., Yang, S., Tan, S., He, L., Yin, H., & Zhang, G. (2014). A new fragment re-allocation strategy for NoSQL database systems. Frontiers of Computer Science, 9(1), 111-127. doi:10.1007/s11704-014-3480-4Kamal, J., Murshed, M., & Buyya, R. (2016). Workload-aware incremental repartitioning of shared-nothing distributed databases for scalable OLTP applications. Future Generation Computer Systems, 56, 421-435. doi:10.1016/j.future.2015.09.024Elghamrawy, S. M., & Hassanien, A. E. (2017). A partitioning framework for Cassandra NoSQL database using Rendezvous hashing. The Journal of Supercomputing, 73(10), 4444-4465. doi:10.1007/s11227-017-2027-5Muñoz-Escoí, F. D., García-Escrivá, J.-R., Sendra-Roig, J. S., Bernabéu-Aubán, J. M., & González de Mendívil, J. R. (2018). Eventual Consistency: Origin and Support. Computing and Informatics, 37(5), 1037-1072. doi:10.4149/cai_2018_5_1037Fischer, M. J., Lynch, N. A., & Paterson, M. S. (1985). Impossibility of distributed consensus with one faulty process. Journal of the ACM, 32(2), 374-382. doi:10.1145/3149.21412

Consistency vs. Availability in Distributed Real-Time Systems

In distributed applications, Brewer's CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We have extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design real-time systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on the Lingua Franca coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable software.Comment: 12 pages. arXiv admin note: text overlap with arXiv:2109.0777

A Primer on NoSQL Databases for Enterprise Architects: The CAP Theorem and Transparent Data Access with MongoDB and Cassandra

MongoDB and Apache Cassandra are the dominant Not Only SQL (NoSQL) database management systems for persisting structured records. Moreover, the pair are respectively in the top-five and top-ten of database management systems generally. Therefore this work seeks to present the two leading systems, along with the underlying principle of the CAP Theorem, in the context of creating transparent data access tiers capable of supporting flexible enterprise architectures

A Critique of the CAP Theorem

The CAP Theorem is a frequently cited impossibility result in distributed systems, especially among NoSQL distributed databases. In this paper we survey some of the confusion about the meaning of CAP, including inconsistencies and ambiguities in its definitions, and we highlight some problems in its formalization. CAP is often interpreted as proof that eventually consistent databases have better availability properties than strongly consistent databases; although there is some truth in this, we show that more careful reasoning is required. These problems cast doubt on the utility of CAP as a tool for reasoning about trade-offs in practical systems. As alternative to CAP, we propose a "delay-sensitivity" framework, which analyzes the sensitivity of operation latency to network delay, and which may help practitioners reason about the trade-offs between consistency guarantees and tolerance of network faults