The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
BlinkDB: queries with bounded errors and bounded response times on very large data
Abstract
In this paper, we present BlinkDB, a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements. We evaluate BlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that manages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 x faster than Hive), within an error of 2-10%.
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Cites
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Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622
Self-citationImproving MapReduce performance in heterogeneous environments
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Self-citationHive: a warehousing solution over a map-reduce framework
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Reining in the outliers in map-reduce clusters using Mantri
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Self-citationSynopses for Massive Data: Samples, Histograms, Wavelets, Sketches
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A comparison of join algorithms for log processing in MaPreduce
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VideoEdge: Processing Camera Streams using Hierarchical Clusters
Hung, C.-C., Ananthanarayanan, G., Bodik, P., Golubchik, L., Yu, M., Bahl, P., & Philipose, M. (2018) · 2018 IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00016 · Cited by 287
A survey of data partitioning and sampling methods to support big data analysis
Mahmud, M. S., Huang, J. Z., Salloum, S., Emara, T. Z., & Sadatdiynov, K. (2020) · Big Data Mining and Analytics · doi:10.26599/bdma.2019.9020015 · Cited by 282
Low Latency Geo-distributed Data Analytics
Pu, Q., Ananthanarayanan, G., Bodik, P., Kandula, S., Akella, A., Bahl, P., & Stoica, I. (2015) · Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication · doi:10.1145/2785956.2787505 · Cited by 246
Self-citation
Metadata
- Authors
- Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., & Stoica, I.
- Venue
- Proceedings of the 8th ACM European Conference on Computer Systems
- Publisher
- ACM
- Published
- 2013-04-15
- Pages
- 29-42
- OpenAlex
- W2071989194
- Cited by
- 694
- References
- 18
- FWCI
- 52.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., & Stoica, I. (2013). BlinkDB: queries with bounded errors and bounded response times on very large data. Proceedings of the 8th ACM European Conference on Computer Systems. https://doi.org/10.1145/2465351.2465355
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