PNUTS: Yahoo!'s hosted data serving platform

By Brian F. Cooper, Raghu Ramakrishnan, Utkarsh Srivastava, Adam Silberstein, Philip Bohannon, Hans-Arno Jacobsen, Nick Puz, Daniel Weaver, Ramana Yerneni. Proceedings of the VLDB Endowment, 2008

1,149 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/876be80390bcaffb9b910ed05680b2e81a37d64d

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Citations in the corpus

Data Management in the Cloud: Limitations and Opportunities (2009, background): “Amazon’s SimpleDB [11] and Yahoo’s PNUTS [15] both implement shared-nothing databases over a wide-area network, but overcome the difﬁculties of distributed replication by relaxing the ACID guarantees of the system.”
Don't settle for eventual: scalable causal consistency for wide-area storage with COPS (2011, background): “Thus, either causal dependencies between keys are limited to the set of keys that can be stored on one node [10, 15, 30, 41], or a single node (or replicated state machine) must provide a commit ordering and log for all operations across a cluster.”
Relational Cloud: a Database Service for the cloud (2011, methodology): “Approaches include NoSQL systems [3, 4, 2, 14], which sacrifice a fair amount of expressive power and/or consistency in favor of extreme scalability, and SQL-based systems that limit the type of transactions allowed [11, 7].”
Calvin: fast distributed transactions for partitioned database systems (2012, background): “Systems such as Dynamo, SimpleDB, Cassandra, Voldemort, Riak, and PNUTS all lessen the consistency guarantees for replicated data [13, 1, 17, 2, 3, 12].”
Data management in cloud environments: NoSQL and NewSQL data stores (2013, background): “[62], Cassandra achieved the highest update throughput on an update heavy workload in comparison to HBase, MySQL, and Yahoo’s PNUTS [63].”
CockroachDB: The Resilient Geo-Distributed SQL Database (2020, background): “Over the years, many systems with reduced consistency levels have been proposed with the goal of overcoming the scalability challenges of traditional relational database systems [5, 15, 19, 32, 35, 44, 61, 64, 71].”
Finding a Needle in Haystack: Facebook's Photo Storage (2010, background): “Similarly, Sinfonia’s [1] mini-transactions and PNUTS’s [5] database functionality provide more features and stronger guarantees than Haystack needs.”
Benchmarking cloud serving systems with YCSB (2010, background): “Still other systems are used only within a particular company, such as Yahoo!’s PNUTS [17], Google’s BigTable [16], and Amazon’s Dynamo [18].”
Consistency Tradeoffs in Modern Distributed Database System Design: CAP is Only Part of the Story (2012, background): “Dynamo, Cassandra, and Riak switch more fully to data replication option (3) in the event of a network partition and deal with the resulting consistency problems using special reconciliation code that runs upon detection of replica divergence.”
Trends in big data analytics (2014, background): “PNUTS [51] proposes per-record consistency, where all replicas apply updates to a row in the same order.”
WiscKey (2016, background): “…al. 2010; Debnath et al. 2010], photo stores [Beaver et al. 2010], cloud data [Lai et al. 2015], social networking [Armstrong et al. 2013; Dong 2015; Sumbaly et al. 2012], online gaming [Debnath et al. 2011], messaging [George 2011; Harter et al. 2014], and online advertising [Cooper et al. 2008].”
Transactional storage for geo-replicated systems (2011, background): “For example, PNUTS [14] supports only one-record transactions.”
Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases (2017, background): “Weaker consistency (PACELC [17]) and isolation models [18][20] are well known in distributed databases and have led to optimistic replication techniques [19] as well as eventually consistent systems [21][22][23].”
The RAMCloud Storage System (2015, methodology): “Other NoSQL systems like Dynamo [DeCandia et al. 2007] and PNUTS [Cooper et al. 2008] also have simplified data models but do not service all reads from memory.”
Schism (2010, background): “Recently there has been significant interest in “simpler” distributed storage systems, such as BigTable [2] or PNUTS [3].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Timeline consistency refers to all replicas of a given record apply all updates to the record in the same order [155].”
Fast crash recovery in RAMCloud (2011, background): “Exampl es include Dynamo [9] and PNUTS [7].”