arXiv (Cornell University) · 2015 · 11 citations · 17 references
ComputingCloud ComputingComputer ScienceDatabasesFault-tolerant ComputingFault-tolerant SystemsConsistency ModelsData ReplicationDistributed Database ArchitectureFault Tolerance DesignHigh-availability DesignReliable Distributed SystemsConsistencyConsistency In Cloud DatabasesFault-tolerant Database SystemsGeo-replicationInvariantsScalabilityStrong ConsistencyWeak Consistency
Geo-replicated databases often operate under the principle of eventual consistency to offer high-availability with low latency on a simple key/value store abstraction. Recently, some have adopted commutative data types to provide seamless reconciliation for special purpose data types, such as counters. Despite this, the inability to enforce numeric invariants across all replicas still remains a key shortcoming of relying on the limited guarantees of eventual consistency storage. We present a new replicated data type, called bounded counter, which adds support for numeric invariants to eventually consistent geo-replicated databases. We describe how this can be implemented on top of existing cloud stores without modifying them, using Riak as an example. Our approach adapts ideas from escrow transactions to devise a solution that is decentralized, fault-tolerant and fast. Our evaluation shows much lower latency and better scalability than the traditional approach of using strong consistency to enforce numeric invariants, thus alleviating the tension between consistency and availability.
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