Is elasticity of scalable databases a Myth?

Seybold, Daniel; Wagner, Nicolas; Erb, Benjamin; Domaschka, Jörg

doi:10.1109/bigdata.2016.7840931

Cited by 12 publications

(21 citation statements)

References 13 publications

(30 reference statements)

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“…Several published results evaluate the performance of Apache Cassandra and Couchbase with the YCSB [2,19,23,24,38,39]. Yet, only a few evaluate their scalability based on different cluster sizes [11,30] and by using cloud resources [26,37], which consolidates the need for Mowgli in order ease the DBMS evaluation by portable and reproducible evaluation scenarios. In the following these results are analysed and compared to our results in chronological order.…”

Section: Evaluation Results Verificationmentioning

confidence: 99%

“…In a preliminary version of Mowgli, the scalability of Cassandra and Couchbase was evaluated by read-heavy and read-update workloads by using one VM type and one storage backend [37] on a dedicated host in the OpenStack cloud at Ulm. The results confirm the scalability of Couchbase and Cassandra with growing cluster sizes.…”

Section: Evaluation Results Verificationmentioning

confidence: 99%

“…Furthermore, both DBMSs have already been subject to scalability evaluations with respect to read-update workloads and achieved promising results [24,26,37]. Also, the availability of other evaluation scenarios allows us to cross-check the results reported by Mowgli for read-update workloads (not part of this paper but carried out with a preliminary version of Mowgli [37]). Apache Cassandra applies the column-oriented data model, while Couchbase applies the document-oriented data model [8].…”

Section: Dbms Specificationmentioning

confidence: 99%

“…Consequently, thorough domain specific knowledge is required as the Mowgli components can only provide the conceptual technical abstraction but the domain specific commands are still required. The extensibility of Mowgli has been demonstrated by extending the current evaluation scenario (targeting performance and scalability) to elasticity [37] and availability [36].…”

Section: Mowgli Feature Analysismentioning

confidence: 99%

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Mowgli

Seybold

Keppler

Gründler

et al. 2019

Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

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Big Data and IoT applications require highly-scalable database management system (DBMS), preferably operated in the cloud to ensure scalability also on the resource level. As the number of existing distributed DBMS is extensive, the selection and operation of a distributed DBMS in the cloud is a challenging task. While DBMS benchmarking is a supportive approach, existing frameworks do not cope with the runtime constraints of distributed DBMS and the volatility of cloud environments. Hence, DBMS evaluation frameworks need to consider DBMS runtime and cloud resource constraints to enable portable and reproducible results. In this paper we present Mowgli, a novel evaluation framework that enables the evaluation of non-functional DBMS features in correlation with DBMS runtime and cloud resource constraints. Mowgli fully automates the execution of cloud and DBMS agnostic evaluation scenarios, including DBMS cluster adaptations. The evaluation of Mowgli is based on two IoT-driven scenarios, comprising the DBMSs Apache Cassandra and Couchbase, nine DBMS runtime configurations, two cloud providers with two different storage backends. Mowgli automates the execution of the resulting 102 evaluation scenarios, verifying its support for portable and reproducible DBMS evaluations. The results provide extensive insights into the DBMS scalability and the impact of different cloud resources. The significance of the results is validated by the correlation with existing DBMS evaluation results.Since the era of RDBMS, their selection is guided by domain-specific benchmarks that have evolved together with distributed DBMSs.

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Section: Evaluation Results Verificationmentioning

confidence: 99%

Section: Evaluation Results Verificationmentioning

confidence: 99%

Section: Dbms Specificationmentioning

confidence: 99%

Section: Mowgli Feature Analysismentioning

confidence: 99%

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Mowgli

Seybold

Keppler

Gründler

et al. 2019

Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering

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“…Our initial DDBMSs evaluation addresses scalability and elasticity of DDBMSs in the cloud [12]. Our results show significant differences with respect to elasticity and the need for orchestrated DDBMS evaluation in order to provide adaptive and reproducible evaluation scenarios.…”

Section: Approachmentioning

confidence: 99%

Towards a framework for orchestrated distributed database evaluation in the cloud

Seybold

2017

Proceedings of the 18th Doctoral Symposium of the 18th International Middleware Conference

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The selection and operation of a distributed database management system (DDBMS) in the cloud is a challenging task as supportive evaluation frameworks miss orchestrated evaluation scenarios, hindering comparable and reproducible evaluations for heterogeneous cloud resources. We propose a novel evaluation approach that supports orchestrated evaluation scenarios for scalability, elasticity and availability by exploiting cloud resources. We highlight the challenges in evaluating DDBMSs in the cloud and introduce a cloud-centric framework for orchestrated DDBMS evaluation, enabling reproducible evaluations and significant rating indices.

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The Impact of the Storage Tier: A Baseline Performance Analysis of Containerized DBMS

Seybold¹,

Hauser²,

Eisenhart³

et al. 2018

Lecture Notes in Computer Science

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Containers emerged as cloud resource offerings. While the advantages of containers, such as easing the application deployment, orchestration and adaptation, work well for stateless applications, the feasibility of containerization of stateful applications, such as database management system (DBMS), still remains unclear due to potential performance overhead. The myriad of container operation models and storage backends even raises the complexity of operating a containerized DBMS. Here, we present an extensible evaluation methodology to identify performance overhead of a containerized DBMS by combining three operational models and two storage backends. For each combination a memory-bound and disk-bound workload is applied. The results show a clear performance overhead for containerized DBMS on top of virtual machines (VMs) compared to physical resources. Further, a containerized DBMS on top of VMs with different storage backends results in a tolerable performance overhead. Building upon these baseline results, we derive a set of open evaluation challenges for containerized DBMSs.

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Is elasticity of scalable databases a Myth?

Cited by 12 publications

References 13 publications

Mowgli

Mowgli

Towards a framework for orchestrated distributed database evaluation in the cloud

The Impact of the Storage Tier: A Baseline Performance Analysis of Containerized DBMS

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