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SparkBench: a comprehensive benchmarking suite for in memory data analytic platform Spark

Published: 06 May 2015 Publication History

Abstract

Spark has been increasingly adopted by industries in recent years for big data analysis by providing a fault tolerant, scalable and easy-to-use in memory abstraction. Moreover, the community has been actively developing a rich ecosystem around Spark, making it even more attractive. However, there is not yet a Spark specify benchmark existing in the literature to guide the development and cluster deployment of Spark to better fit resource demands of user applications. In this paper, we present SparkBench, a Spark specific benchmarking suite, which includes a comprehensive set of applications. SparkBench covers four main categories of applications, including machine learning, graph computation, SQL query and streaming applications. We also characterize the resource consumption, data flow and timing information of each application and evaluate the performance impact of a key configuration parameter to guide the design and optimization of Spark data analytic platform.

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Published In

cover image ACM Conferences
CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers
May 2015
413 pages
ISBN:9781450333580
DOI:10.1145/2742854
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 May 2015

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Author Tags

  1. benchmarking
  2. cloud computing
  3. evaluation
  4. in memory data analytics
  5. spark

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  • Research-article

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CF'15
Sponsor:
CF'15: Computing Frontiers Conference
May 18 - 21, 2015
Ischia, Italy

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CF '15 Paper Acceptance Rate 33 of 96 submissions, 34%;
Overall Acceptance Rate 273 of 785 submissions, 35%

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Cited By

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  • (2024)Evaluating Stream Processing AutoscalersProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666036(110-122)Online publication date: 24-Jun-2024
  • (2024)Blaze: Holistic Caching for Iterative Data ProcessingProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3629558(370-386)Online publication date: 22-Apr-2024
  • (2024)CheckMate: Evaluating Checkpointing Protocols for Streaming Dataflows2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00309(4030-4043)Online publication date: 13-May-2024
  • (2024)Benchmarking scalability of stream processing frameworks deployed as microservices in the cloudJournal of Systems and Software10.1016/j.jss.2023.111879208:COnline publication date: 1-Feb-2024
  • (2023)SAC: Dynamic Caching upon Sketch for In-Memory Big Data Analytics2023 9th International Conference on Big Data Computing and Communications (BigCom)10.1109/BIGCOM61073.2023.00032(180-187)Online publication date: 4-Aug-2023
  • (2023)SPOAHA: Spark Program Optimizer Based on Artificial Hummingbird AlgorithmKnowledge Science, Engineering and Management10.1007/978-3-031-40289-0_26(317-331)Online publication date: 9-Aug-2023
  • (2023)SparkEdgeEmu: An Emulation Framework for Edge-Enabled Apache Spark DeploymentsEuro-Par 2023: Parallel Processing10.1007/978-3-031-39698-4_11(154-168)Online publication date: 24-Aug-2023
  • (2022)A Remote Memory Sharing System for Virtualized Computing InfrastructuresIEEE Transactions on Cloud Computing10.1109/TCC.2020.301808910:3(1532-1542)Online publication date: 1-Jul-2022
  • (2022)Optimal Resource Allocation of Cloud-Based Spark ApplicationsIEEE Transactions on Cloud Computing10.1109/TCC.2020.298568210:2(1301-1316)Online publication date: 1-Apr-2022
  • (2022)Towards Dependency-Aware Cache Management for Data Analytics ApplicationsIEEE Transactions on Cloud Computing10.1109/TCC.2019.294501510:1(706-723)Online publication date: 1-Jan-2022
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