SwiftCloud: Fault-Tolerant Geo-Replication Integrated all the Way to the Client Machine

Preguiça, Nuno; Zawirski, Marek; Bieniusa, Annette; Duarte, Sérgio; Balegas, Valter; Baquero, Carlos; Shapiro, Marc

doi:10.1109/srdsw.2014.33

Cited by 24 publications

(21 citation statements)

References 8 publications

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“…SwiftCloud [57] and Antidote [1] provide a weak form of transactions that is highly available [8] and never abort, with reads observing a snapshot of the database and concurrent writes being merged using CRDT's defined concurrency semantics.…”

Section: Transactions and Other Programming Modelsmentioning

confidence: 99%

“…The following commercial systems use CRDTs: Riak 11 , Redis [18] and Akka 12 . A number of research prototypes have also used CRDTs, including Walter [66], SwiftCloud [57] and AntidoteDB 13 [1].…”

Section: Applicationsmentioning

confidence: 99%

“…The protocols implemented in most systems [57,1,66,46] use operation-based CRDTs, with all updates being replayed in all replicas. Second, in some geo-replicated systems [57,1], it is possible that new updates are received from remote replicas while a local transaction is running. To allow transactions to access a consistent snapshot, the system must be able to maintain multiple versions for each object.…”

Section: Crdts and Transactionsmentioning

confidence: 99%

“…For example, when associating a new object with a key, it is necessary that the new object is created in all replicas before adding the reference to it. This can be achieved by resorting to causal consistency [47,5,57] or to atomic updates (as discussed in Section 3.3.1).…”

Section: Support For Large Objectsmentioning

confidence: 99%

“…An alternative approach is to rely on weaker consistency models, such as eventual consistency [26,38,43] or causal consistency [47,5,57], where any replica can accept updates, which are propagated asynchronously to other replicas. These models are also interesting for supporting applications running in mobile devices, to mask the high latency of mobile communications and the period of disconnection or poor connectivity.…”

Section: Introductionmentioning

confidence: 99%

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Conflict-Free Replicated Data Types CRDTs

Preguiça

Baquero

Shapiro

2018

Encyclopedia of Big Data Technologies

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Section: Transactions and Other Programming Modelsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Crdts and Transactionsmentioning

confidence: 99%

Section: Support For Large Objectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Conflict-Free Replicated Data Types CRDTs

Preguiça

Baquero

Shapiro

2018

Encyclopedia of Big Data Technologies

Self Cite

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Checking Causal Consistency of Distributed Databases

et al. 2019

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The CAP Theorem shows that (strong) Consistency, Availability, and Partition tolerance are impossible to be ensured together. Causal consistency is one of the weak consistency models that can be implemented to ensure availability and partition tolerance in distributed systems. In this work, we propose a tool to check automatically the conformance of distributed/concurrent systems executions to causal consistency models. Our approach consists in reducing the problem of checking if an execution is causally consistent to solving Datalog queries. The reduction is based on complete characterizations of the executions violating causal consistency in terms of the existence of cycles in suitably defined relations between the operations occurring in these executions. We have implemented the reduction in a testing tool for distributed databases, and carried out several experiments on real case studies, showing the efficiency of the suggested approach.

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