Abstract
Embedded database libraries provide developers with a common and convenient data persistence layer. They are a key component of major mobile operating systems, and are used extensively on interactive devices like smartphones. Database performance affects the response times and resource consumption of millions of smartphone apps and billions of smartphone users. Given their wide use and impact, it is critical that we understand how embedded databases operate in realistic mobile settings, and how they interact with mobile environments. We argue that traditional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance only at saturation. To rectify this, we present PocketData, a new benchmark for mobile device database evaluation that uses typical workloads to produce representative performance results. We explain the performance measurement methodology behind PocketData, and address specific challenges. We analyze the results obtained, and show how different classes of workload interact with database performance. Notably, our study of mobile databases at non-saturated levels uncovers significant latency and energy variation in database workloads resulting from CPU frequency scaling policies called governors—variation that we show is hidden by typical benchmark measurement techniques.
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Available for download at http://pocketdata.info.
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Here, we follow [18], which observes lognormal delay with a 6ms mean in typical use.
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Complete benchmark results are available at http://www.pocketdata.info/.
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Acknowledgments
This work is supported by NSF Awards IIS-1617586, CNS-1629791 and CCF 1749539.
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Nuessle, C., Kennedy, O., Ziarek, L. (2020). Benchmarking Pocket-Scale Databases. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking for the Era of Cloud(s). TPCTC 2019. Lecture Notes in Computer Science(), vol 12257. Springer, Cham. https://doi.org/10.1007/978-3-030-55024-0_7
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