Abstract
Over the past few decades, researchers have done massive research on data storage structures of relational databases. The existing research mainly focuses on the analysis and optimization of row and column storage structures in relational databases. It is discovered that row and column storage techniques are acceptable for relational database operations in a variety of goal and usage scenarios. However, with the generation of massive time series data, researchers ignore experimental analysis for specific storage structures for time series data in databases. In order to provide comprehensive verification, we compare and analyze the space and time consumed in the process of bulk loading and insertion, range query, and aggregate calculation of time series data under openGauss-based row and column storage. The purpose is to provide a storage design basis for extending time series data management capabilities in relational databases or developing time series databases. The results show that the choice of storage for time series should be based on the specific application scenarios.
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References
Chaalal, H., Hamdani, M., Belbachir, H.: Finding the best between the column store and row store Databases. In: Proceedings of the 10th International Conference on Information Systems and Technologies, pp. 1–4 (2020)
Ordonez, C., Bellatreche, L.: A survey on parallel database systems from a storage perspective: rows versus columns. In: Elloumi, M., et al. (eds.) DEXA 2018. CCIS, vol. 903, pp. 5–20. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99133-7_1
Bhagat, V., Gopal, A.: Comparative study of row and column oriented database. In: Fifth International Conference on Emerging Trends in Engineering & Technology. IEEE (2013)
Abadi, D.J., Madden, S.R., Hachem, N.: Column-stores vs. row-stores: how different are they really? In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 967–980 (2008)
Halverson, A., Beckmann, J.L., Naughton, J.F., et al.: A comparison of c-store and row-store in a common framework. University of Wisconsin-Madison Department of Computer Sciences (2006)
Ramamurthy, R., DeWitt, D.J., Su, Q.: A case for fractured mirrors. VLDB J. 12, 89–101 (2003)
Stonebraker, M., et al.: C-store: a column-oriented DBMS. In: VLDB, pp. 553–564 (2005)
Harizopoulos, S., Liang, V., Abadi, D.J., Madden, S.: Performance tradeoffs in read-optimized databases. In: VLDB, pp. 487–498 (2006)
Abadi, D., Madden, S., Ferreira, M.: Integrating compression and execution in column-oriented database systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Chicago, USA. ACM (2006)
Plattner, H.: A common database approach for OLTP and OLAP using an in-memory column database. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, USA (2009)
Rösch, P., Dannecker, L., Hackenbroich, G., et al.: A storage advisor for hybrid-store databases. Proc. VLDB Endow. 5(12) (2012)
Tahmassebpour, M.: A new method for time-series big data effective storage. IEEE Access 5, 10694–10699 (2017)
Wang, C., Huang, X., Qiao, J., et al.: Apache IoTDB: time-series database for internet of things. Proc. VLDB Endow. 13(12), 2901–2904 (2020)
Waddington, D.G., Lin, C.: A fast lightweight time-series store for IoT data. arXiv preprint arXiv:1605.01435 (2016)
Fouad, T., Mohamed, B.: Model transformation from object relational database to NoSQL column based database. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security, pp. 1–5 (2020)
Rhea, S., Wang, E., Wong, E., et al.: LittleTable: a time-series database and its uses. In: ACM International Conference, pp.125–138. ACM (2017)
Tsubouchi, Y., Wakisaka, A., Hamada, K., et al.: HeteroTSDB: an extensible time series database for automatically tiering on heterogeneous key-value stores. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), pp. 264–269. IEEE (2019)
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The paper is sponsored by CCF-Huawei Populus euphratica Innovation Research Funding.
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Li, L., Pu, F., Li, Y., Xu, J. (2023). A Comparative Study of Row and Column Storage for Time Series Data. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_16
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DOI: https://doi.org/10.1007/978-3-031-32910-4_16
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