NDLI: Stream Bench: Towards Benchmarking Modern Distributed Stream Computing Frameworks

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Publisher's Information

Stream Bench: Towards Benchmarking Modern Distributed Stream Computing Frameworks

Content Provider	ACM Digital Library
Author	Lu, Ruirui Wu, Gang Xie, Bin Hu, Jingtong
Abstract	While big data is becoming ubiquitous, interest in handling data stream at scale is also gaining popularity, which leads to the sprout of many distributed stream computing systems. However, complexity of stream computing and diversity of workloads expose great challenges to benchmark these systems. Due to lack of standard criteria, evaluations and comparisons of these systems tend to be difficult. This paper takes an early step towards benchmarking modern distributed stream computing frameworks. After identifying the challenges and requirements in the field, we raise our benchmark definition Stream Bench regarding the requirements. Stream Bench proposes a message system functioning as a mediator between stream data generation and consumption. It also covers 7 benchmark programs that intend to address typical stream computing scenarios and core operations. Not only does it care about performance of systems under different data scales, but also takes fault tolerance ability and durability into account, which drives to incorporate four workload suites targeting at these various aspects of systems. Finally, we illustrate the feasibility of Stream Bench by applying it to two popular frameworks, Apache Storm and Apache Spark Streaming. We draw comparisons from various perspectives between the two platforms with workload suites of Stream Bench. In addition, we also demonstrate performance improvement of Storm's latest version with the benchmark.
Starting Page	69
Ending Page	78
Page Count	10
File Format	PDF
ISBN	9781479978816
DOI	10.1109/UCC.2014.15
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-12-08
Access Restriction	Subscribed
Subject Keyword	Distributed stream computing benchmark big data
Content Type	Text
Resource Type	Article

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