2014
DOI: 10.1145/2596624
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Don't settle for eventual consistency

Abstract: Stronger properties for low-latency geo-replicated storage.

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Cited by 20 publications
(15 citation statements)
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“…• For any execution α of R and e = do(del(a), _) ∈ α, if there does not exist event e = do(del(a), _) ∈ α that happens before e, then replica repl(e) has a message pending after e. The class of push-based protocols contains both op-based protocols [5], in which a message carries a description of the latest operations that the sender has performed (e.g., RGA), and statebased [5] protocols, in which a message describes all operations the sender knows about (i.e., its state). We also show ( [2, §B]) that the class of push-based protocols contains eventually consistent writepropagating protocols [3]-which model many deployed highly available eventually consistent protocols [4,5,9,11,12,18,26,33]under the natural assumption that sending a message does not affect the state of the list at the sending replica.…”
Section: Push-based Protocolsmentioning
confidence: 99%
“…• For any execution α of R and e = do(del(a), _) ∈ α, if there does not exist event e = do(del(a), _) ∈ α that happens before e, then replica repl(e) has a message pending after e. The class of push-based protocols contains both op-based protocols [5], in which a message carries a description of the latest operations that the sender has performed (e.g., RGA), and statebased [5] protocols, in which a message describes all operations the sender knows about (i.e., its state). We also show ( [2, §B]) that the class of push-based protocols contains eventually consistent writepropagating protocols [3]-which model many deployed highly available eventually consistent protocols [4,5,9,11,12,18,26,33]under the natural assumption that sending a message does not affect the state of the list at the sending replica.…”
Section: Push-based Protocolsmentioning
confidence: 99%
“…The traditional relational database management systems (RDBMSs) are unsuitable as horizontal partitioning due to relationships and dependencies among stored data is difficult [43]. Designed to guarantee consistency, RDBMSs have limited scalability and availability especially in case of network partitions [44,45], and cannot provide required latency and scalability for SNs with clusters replicating over data centers geographically dispersed [44]. Therefore, alternative data management solutions such as Cassandra [44] and Haystack [46] by Facebook, Bigtable [47] and Megastore [48] by Google were developed.…”
Section: Sn Computer Sciencementioning
confidence: 99%
“…Potential causality was inspired by early work on replicated databases, which attempt to maintain consistent information while serving different regions (Johnson and Thomas 1975). This application continues to draw attention to the concept due to the prevalence of large-scale social networks (Lloyd et al 2014).…”
Section: Potential Causalitymentioning
confidence: 99%
“…This property may be referred to as "weak consistency" (Rönngren and Liljenstam 1999), though this downplays its importance as the key requirement for preserving causal orderings (Lamport 1978). Moreover, "causal consistency" has an analogous meaning in the context of replicated databases (Lloyd et al 2014).…”
Section: Potential Causalitymentioning
confidence: 99%