Performance Evaluation of NoSQL Databases

Gandini, Andrea; Gribaudo, Marco; Knottenbelt, William J.; Osman, Rasha; Piazzolla, Pietro

doi:10.1007/978-3-319-10885-8_2

Cited by 33 publications

(29 citation statements)

References 17 publications

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“…Another reason might be that NoSQL stores became mainstream earlier than globally-distributed transactional databases and gathered attention from the research community to work on model-based performance analysis of NoSQL stores [7,12,21].…”

Section: Related Workmentioning

confidence: 99%

Exploring Design Alternatives for RAMP Transactions Through Statistical Model Checking

Liu

Ölveczky

Ganhotra³

et al. 2017

Formal Methods and Software Engineering

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Abstract. In this paper we explore and extend the design space of the recent RAMP (Read Atomic Multi-Partition) transaction system for large-scale partitioned data stores. Arriving at a mature distributed system design through implementation and experimental validation is a labor-intensive task, so that only a limited number of design alternatives can be explored in practice. The developers of RAMP did implement and validate three design alternatives for RAMP, and sketched three additional designs. This work addresses two questions: (1) How can the design space of a distributed transaction system such as RAMP be explored with modest effort, so that substantial knowledge about design alternatives can be gained before designs are implemented? and (2) How realistic and informative are the results of such design explorations? We answer the first question by: (i) formally modeling eight RAMP-like designs (five by the RAMP developers and three of our own) in Maude as probabilistic rewrite theories, and (ii) using statistical model checking of those models to analyze key performance metrics such as throughput, average latency, and degrees of strong consistency and read atomicity. We answer the second question by showing that our quantitative analyses: (i) are consistent with the experimental results obtained by the RAMP developers for their implemented designs; (ii) confirm the conjectures made by the RAMP developers for their other three unimplemented designs; and (iii) uncover some promising new designs that seem attractive for some applications.

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Section: Related Workmentioning

confidence: 99%

Exploring Design Alternatives for RAMP Transactions Through Statistical Model Checking

Liu

Ölveczky

Ganhotra³

et al. 2017

Formal Methods and Software Engineering

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“…Regarding performance, only response times and throughput are considered. The work in [20] benchmarks three NoSQL databases, namely Cassandra, MongoDB and HBase, by throughput and operation latency. Two simple high-level queuing network models are presented that are able to capture those performance characteristics.…”

Section: Model-based Performance Analysis Of Nosql Storesmentioning

confidence: 99%

Quantitative Analysis of Consistency in NoSQL Key-Value Stores

Liu

Nguyen

Ganhotra

et al. 2015

Quantitative Evaluation of Systems

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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 implementationbased evaluation of the source code; and (2) a statistical model checking analysis of our probabilistic model.

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“…Prior work has focused on comparing NoSQL systems in terms of read/write performance, scalability and flexibility [6,22,2,17]. To the best of our knowledge only a limited number of works have been conducted on performance modelling of NoSQL databases, and available models specific to Cassandra are few [13,25]. The models in [13] and [25] use two different modelling techniques due to the difficulty to represent, in the same model, the synchronization and the scheduling of the requests.…”

Section: Related Workmentioning

confidence: 99%

“…Gandini et al present a high level queueing network model for Cassandra [13]. Each node of the distributed setup is described using two queues: one for the CPU and one for the disk.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Queueing Network Model for Performance Prediction of Apache Cassandra

Dipietro¹,

Casale²,

Serazzi³

2017

Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools

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NoSQL databases such as Apache Cassandra have attracted large interest in recent years thanks to their high availability, scalability, flexibility and low latency. Still there is limited research work on performance engineering methods for NoSQL databases, which yet are needed since these systems are highly distributed and thus can incur significant cost/performance trade-offs. To address this need, we propose a novel queueing network model for the Cassandra NoSQL database aimed at supporting resource provisioning. The model defines explicitly key configuration parameters of Cassandra such as consistency levels and replication factor, allowing engineers to compare alternative system setups.Experimental results based on the YCSB benchmark indicate that, with a small amount of training for the estimation of its input parameters, the proposed model achieves good predictive accuracy across different loads and consistency levels. The average performance errors of the model compared to the real results are between 6% and 10%. We also demonstrate the applicability of our model to other NoSQL databases and other possible utilisation of it.

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Performance Evaluation of NoSQL Databases

Cited by 33 publications

References 17 publications

Exploring Design Alternatives for RAMP Transactions Through Statistical Model Checking

Exploring Design Alternatives for RAMP Transactions Through Statistical Model Checking

Quantitative Analysis of Consistency in NoSQL Key-Value Stores

A Queueing Network Model for Performance Prediction of Apache Cassandra

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