Abstract
Spark is a general distributed framework with the abstraction called resilient distributed datasets (RDD). Database analysis is one of the main kinds of workloads supported on Spark. The SQL component on Spark has evolved from Shark to Spark SQL, while the core components of Spark also have evolved a lot comparing with the original version. We analyzed on which aspects Spark have made efforts to support many workloads efficiently and whether the changes make the support for SQL achieve better performance.
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References
Big Data Benchmark. https://amplab.cs.berkeley.edu/benchmark/
Spark JIRA. https://issues.apache.org/jira/browse/SPARK/
Armbrust, M., Xin, R.S., Lian, C., Huai, Y., Liu, D., Bradley, J.K., Meng, X., Kaftan, T., Franklin, M.J., Ghodsi, A., Zaharia, M.: Spark SQL: relational data processing in spark. In: SIGMOD 2015, ACM, New York (2015)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: OSDI 2004, USENIX Association, Berkeley (2004)
Floratou, A., Minhas, U.F., Özcan, F.: SQL-on-hadoop: Full circle back to shared-nothing database architectures. Proc. VLDB Endowment 7(12), 1295–1306 (2014)
Huang, S., Huang, J., Dai, J., Xie, T., Huang, B.: The hibench benchmark suite: characterization of the mapreduce-based data analysis. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), pp. 41–51. IEEE (2010)
Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: distributed data-parallel programs from sequential building blocks. In: EuroSys 2007, New York (2007)
Kornacker, M., Behm, A., Bittorf, V., Bobrovytsky, T., Ching, C., Choi, A., Erickson, J., Grund, M., Hecht, D., Jacobs, M., et al.: Impala: a modern, open-source SQL engine for hadoop. In: Proceedings of the Conference on Innovative Data Systems Research CIDR 2015 (2015)
Li, M., Tan, J., Wang, Y., Zhang, L., Salapura, V.: Sparkbench: a comprehensive benchmarking suite for in memory data analytic platform spark. In: CF 2015, pp. 53:1–53:8. ACM, New York (2015)
Lu, L., Arpaci-Dusseau, A.C., Arpaci-Dusseau, R.H., Lu, S.: A study of linux file system evolution. In: FAST 2013, USENIX Association, Berkeley (2013)
Melnik, S., Gubarev, A., Long, J.J., Romer, G., Shivakumar, S., Tolton, M., Vassilakis, T.: Dremel: interactive analysis of web-scale datasets. Proc. VLDB Endowment 3(1–2), 330–339 (2010)
Saha, B., Shah, H., Seth, S., Vijayaraghavan, G., Murthy, A., Curino, C.: Apache tez: a unifying framework for modeling and building data processing applications. In: SIGMOD 2015, New York (2015)
Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P., Murthy, R.: Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2(2), 1626–1629 (2009)
Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., Gao, W., Jia, Z., Shi, Y., Zhang, S., Zheng, C., Lu, G., Zhan, K., Li, X., Qiu, B.: Bigdatabench: a big data benchmark suite from internet services. In: 20th IEEE International Symposium on High Performance Computer Architecture, HPCA 2014, Orlando, FL, USA, February 15–19, 2014, pp. 488–499 (2014)
Xin, R.S., Rosen, J., Zaharia, M., Franklin, M.J., Shenker, S., Stoica, I.: Shark: SQL and rich analytics at scale. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 13–24. ACM (2013)
Chen, Y., Qin, X., Bian, H., Chen, J., Dong, Z., Du, X., Gao, Y., Liu, D., Lu, J., Zhang, H.: A study of SQL-on-hadoop systems. In: Zhan, J., Rui, H., Weng, C. (eds.) BPOE 2014. LNCS, vol. 8807, pp. 154–166. Springer, Heidelberg (2014)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI 2012, USENIX Association, Berkeley (2012)
Acknowledgements
This work was supported by the National High Technology Research and Development Program of China (Grant No. 2015AA015308), the Major Program of National Natural Science Foundation of China (Grant No. 61432006), and the Key Technology Research and Development Programs of Guangdong Province, China (Grant No. 2015B010108006).
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Tian, X., Lu, G., Zhou, X., Li, J. (2016). Evolution from Shark to Spark SQL: Preliminary Analysis and Qualitative Evaluation. In: Zhan, J., Han, R., Zicari, R. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2015. Lecture Notes in Computer Science(), vol 9495. Springer, Cham. https://doi.org/10.1007/978-3-319-29006-5_6
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DOI: https://doi.org/10.1007/978-3-319-29006-5_6
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