Benchmarking Replication in Cassandra and MongoDB NoSQL Datastores

Haughian, Gerard; Osman, Rasha; Knottenbelt, William J.

doi:10.1007/978-3-319-44406-2_12

Cited by 30 publications

(41 citation statements)

References 9 publications

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“…Cassandra is an open-source shared-nothing NoSQL column-store database developed and used in Facebook [10,52,56]. It is based on the ideas behind Google BigTable [3] and Amazon Dynamo [14].…”

Section: Cassandramentioning

confidence: 99%

“…Conflicts are resolved based on timestamps (the newest record is kept). The database operates in master-master mode [52], where no node is different from another, and combines disk-persistence with in-memory caching of results, resulting in high write throughput operations [52,56]. The master-master architecture makes it easy for horizontal scalability to happen [56].…”

Section: Cassandramentioning

confidence: 99%

“…The database operates in master-master mode [52], where no node is different from another, and combines disk-persistence with in-memory caching of results, resulting in high write throughput operations [52,56]. The master-master architecture makes it easy for horizontal scalability to happen [56]. There are several different partitioning techniques and replication can be automatically managed by the database [56].…”

Section: Cassandramentioning

confidence: 99%

“…The master-master architecture makes it easy for horizontal scalability to happen [56]. There are several different partitioning techniques and replication can be automatically managed by the database [56].…”

Section: Cassandramentioning

confidence: 99%

“…It uses JSON (data is stored and transferred in a binary, more compact form named BSON), allowing for a schemaless data model where the only requirement is that an id is always present [10,56].…”

Section: Mongodbmentioning

confidence: 99%

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Choosing the right NoSQL database for the job: a quality attribute evaluation

Lourenço

Cabral

Carreiro

et al. 2015

Journal of Big Data

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For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NoSQL technology. The rising interest in NoSQL technology, as well as the growth in the number of use case scenarios, over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies. While most research work mostly focuses on performance evaluation using standard benchmarks, it is important to notice that the architecture of real world systems is not only driven by performance requirements, but has to comprehensively include many other quality attribute requirements. Software quality attributes form the basis from which software engineers and architects develop software and make design decisions. Yet, there has been no quality attribute focused survey or classification of NoSQL databases where databases are compared with regards to their suitability for quality attributes common on the design of enterprise systems. To fill this gap, and aid software engineers and architects, in this article, we survey and create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use case scenarios from the software engineer point of view and the quality attributes that each of them is most suited to.

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Section: Cassandramentioning

confidence: 99%

Section: Cassandramentioning

confidence: 99%

Section: Cassandramentioning

confidence: 99%

Section: Cassandramentioning

confidence: 99%

Section: Mongodbmentioning

confidence: 99%

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Choosing the right NoSQL database for the job: a quality attribute evaluation

Lourenço

Cabral

Carreiro

et al. 2015

Journal of Big Data

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Predicting Replication Time in Cassandra

Lingamaneni

Mishra

Sitaram

2020

EAI/Springer Innovations in Communication and Computing

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Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

Gorbenko

Romanovsky

Tarasyuk

2020

Communications in Computer and Information Science

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This experience report analyses performance of the Cassandra NoSQL database and studies the fundamental trade-off between data consistency and delays in distributed data storages. The primary focus is on investigating the interplay between the Cassandra performance (response time) and its consistency settings. The paper reports the results of the read and write performance benchmarking for a replicated Cassandra cluster, deployed in the Amazon EC2 Cloud. We present quantitative results showing how different consistency settings affect the Cassandra performance under different workloads. One of our main findings is that it is possible to minimize Cassandra delays and still guarantee the strong data consistency by optimal coordination of consistency settings for both read and write requests. Our experiments show that (i) strong consistency costs up to 25% of performance and (ii) the best setting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data consistency under mixed workloads.

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Benchmarking Replication in Cassandra and MongoDB NoSQL Datastores

Cited by 30 publications

References 9 publications

Choosing the right NoSQL database for the job: a quality attribute evaluation

Choosing the right NoSQL database for the job: a quality attribute evaluation

Predicting Replication Time in Cassandra

Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

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