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Quantitative Analysis of Consistency in NoSQL Key-Value Stores

Authors Si Liu, Jatin Ganhotra, Muntasir Raihan Rahman, Son Nguyen, Indranil Gupta, José Meseguer

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Author Details

Si Liu
  • Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA
Jatin Ganhotra
  • Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA
Muntasir Raihan Rahman
  • Microsoft, Redmond, WA, USA
Son Nguyen
  • Addepar Inc., Sunnyvale, CA, USA
Indranil Gupta
  • Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA
José Meseguer
  • Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA

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Si Liu, Jatin Ganhotra, Muntasir Raihan Rahman, Son Nguyen, Indranil Gupta, and José Meseguer. Quantitative Analysis of Consistency in NoSQL Key-Value Stores. In LITES, Volume 4, Issue 1 (2017). Leibniz Transactions on Embedded Systems, Volume 4, Issue 1, pp. 03:1-03:26, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LITES-v004-i001-a003

Abstract

The promise of high scalability and availability has prompted many companies to replace traditional relational database management systems (RDBMS) with NoSQL key-value stores. This comes at the cost of relaxed consistency guarantees: key-value stores only guarantee eventual consistency in principle. In practice, however, many key-value stores seem to offer stronger consistency. Quantifying how well consistency properties are met is a non-trivial problem.  We address this problem by formally modeling key-value stores as probabilistic systems and quantitatively analyzing their consistency properties by both statistical model checking and implementation evaluation. We present for the first time a formal probabilistic model of Apache Cassandra, a popular NoSQL key-value store, and quantify how much Cassandra achieves various consistency guarantees under various conditions. To validate our model, we evaluate multiple consistency properties using two methods and compare them against each other. The two methods are: (1) an implementation-based evaluation of the source code; and (2) a statistical model checking analysis of our probabilistic model.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Cloud computing
  • Information systems → Key-value stores
  • Software and its engineering → Model checking
  • Theory of computation → Rewrite systems
Keywords
  • NoSQL Key-value Store
  • Consistency
  • Statistical Model Checking
  • Rewriting Logic
  • Maude

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