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Note that consistency as defined in the CAP theorem is quite different from the consistency guaranteed in ACID database transactions. [4] Availability Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. [1]
The CAP theorem is based on three trade-offs, one of which is "atomic consistency" (shortened to "consistency" for the acronym), about which the authors note, "Discussing atomic consistency is somewhat different than talking about an ACID database, as database consistency refers to transactions, while atomic consistency refers only to a property of a single request/response operation sequence.
The Algorand consensus protocol privileges consistency over availability (CAP theorem). [26] If the network is unable to reach consensus over the next step (or block), within a certain time, the protocol enters in a recovery mode, suspending the block production to prevent forks (contrary to what would happen in blockchains based on the ...
The CAP theorem is based on three trade-offs: consistency, availability, and partition tolerance. Partition tolerance, in this context, means the ability of a data processing system to continue processing data even if a network partition causes communication errors between subsystems.
The PACELC theorem was first described by Daniel Abadi from Yale University in 2010 in a blog post, [2] which he later clarified in a paper in 2012. [3] The purpose of PACELC is to address his thesis that "Ignoring the consistency/latency trade-off of replicated systems is a major oversight [in CAP], as it is present at all times during system operation, whereas CAP is only relevant in the ...
Eventual consistency, also called optimistic replication, [2] is widely deployed in distributed systems and has origins in early mobile computing projects. [3] A system that has achieved eventual consistency is often said to have converged , or achieved replica convergence . [ 4 ]
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Eric Allen Brewer is professor emeritus of computer science at the University of California, Berkeley [1] and vice-president of infrastructure at Google. [2] His research interests include operating systems and distributed computing. He is known for formulating the CAP theorem about distributed network applications in the late 1990s. [3]