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HyR-tree: a spatial index for hybrid flash/3D XPoint storage

  • S.I. : Information, Intelligence, Systems and Applications
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Abstract

Flash-based SSDs have become well established in the storage market, replacing magnetic disks in both enterprise and consumer computer systems. The performance characteristics of these new devices have prompted a considerable amount of research that aims at developing efficient data access methods. Early works attempted to reduce the expensive random writes, exploiting logging and batch write techniques, while more recent ones addressed query processing, taking advantage of the high internal parallelism of SSDs. 3D XPoint is a new nonvolatile memory technology that has emerged recently, featuring smaller access times and higher durability compared with flash. It is available both as block addressable secondary storage and as byte addressable persistent main memory. However, the high cost of 3D XPoint prevents, for the moment, its adoption in large scales. This renders hybrid storage systems utilizing NAND flash and 3D XPoint a sufficient alternative. In this work, we propose HyR-tree, a hybrid variant of R-tree that persists a part of the tree in the high performing 3D XPoint storage. HyR-tree identifies repeated access pattern to the data and uses these patterns to locate the most important nodes. The importance of a node is determined by the performance gain that derives from its placement within a 3D XPoint-based device. We experimentally evaluated HyR-tree using real devices and four different datasets. The acquired results show that our proposal achieves significant performance gains up to 40% in tree construction and up to 56% in range queries.

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Notes

  1. http://spatialhadoop.cs.umn.edu/datasets.html.

References

  1. Afshani P, Arge L, Larsen KD (2009) Orthogonal range reporting in three and higher dimensions. In: Proceedings of the 50th annual IEEE symposium on foundations of computer science, pp 149–158

  2. Beckmann N, Kriegel HP, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of the 1990 ACM SIGMOD international conference on management of data, pp 322–331

  3. Bozanis P, Nanopoulos A, Manolopoulos Y (2003) LR-tree: a logarithmic decomposable spatial index method. Comput J 46(3):319–331

    Article  MATH  Google Scholar 

  4. Canim M, Mihaila GA, Bhattacharjee B, Ross KA, Lang CA (2009) An object placement advisor for DB2 using solid state storage. Proc VLDB Endow 2(2):1318–1329

    Article  Google Scholar 

  5. Canim M, Mihaila GA, Bhattacharjee B, Ross KA, Lang CA (2010) SSD bufferpool extensions for database systems. Proc VLDB Endow 3(1–2):1435–1446

    Article  Google Scholar 

  6. Carniel AC, Ciferri RR, Ciferri CD (2018) A generic and efficient framework for flash-aware spatial indexing. Inf Syst 52:102–120

    Google Scholar 

  7. Chen F, Hou B, Lee R (2016) Internal parallelism of flash memory-based solid-state drives. ACM Trans Storage 12(3):13

    Article  Google Scholar 

  8. Fevgas A, Akritidis L, Alamaniotis M, Tsompanopoulou P, Bozanis P (2019) A study of R-tree performance in hybrid Flash/3D XPoint storage. In: Proceedings of the 10th international conference on information, intelligence, systems and applications (IISA), pp 1–6

  9. Fevgas A, Akritidis L, Bozanis P, Manolopoulos Y (2020) Indexing in flash storage devices: a survey on challenges, current approaches, and future trends. VLDB J 29(1):273–311

    Article  Google Scholar 

  10. Fevgas A, Bozanis P (2019) LB-Grid: an SSD efficient grid file. Data Knowl Eng 121:18–41. https://doi.org/10.1016/j.datak.2019.04.002

    Article  Google Scholar 

  11. Fevgas A, Bozanis P (2019) A spatial index for hybrid storage. In: Proceedings of the 23rd international database applications and engineering symposium, pp 1–8

  12. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on management of data, pp 47–57

  13. Hady FT, Foong A, Veal B, Williams D (2017) Platform storage performance with 3D XPoint technology. Proc IEEE 105(9):1822–1833

    Article  Google Scholar 

  14. Hu Y, Jiang H, Feng D, Tian L, Luo H, Zhang S (2011) Performance impact and interplay of SSD parallelism through advanced commands, allocation strategy and data granularity. In: Proceedings of the 25th international conference on supercomputing, pp 96–107

  15. Izraelevitz J, Yang J, Zhang L, Kim J, Liu X, Memaripour A, Soh YJ, Wang Z, Xu Y, Dulloor SR, et al (2019) Basic performance measurements of the intel optane DC persistent memory module. arXiv:1903.05714

  16. Jin P, Yang P, Yue L (2015) Optimizing B+-Tree for hybrid storage systems. Distrib Parallel Databases 33(3):449–475

    Article  Google Scholar 

  17. Kamel I, Faloutsos C (1994) Hilbert R-tree: an improved R-tree using fractals. In: Proceedings of the 20th international conference on very large data bases, pp 500–509

  18. Koltsidas I, Hsu V (2017) IBM storage and NVM express revolution. Technical reports, IBM

  19. Korda N, Szorenyi B, Li S (2016) Distributed clustering of linear bandits in peer to peer networks. In: Proceedings of The 33rd international conference on machine learning, pp 1301–1309

  20. Kourtis K, Ioannou N, Koltsidas I (2019) Reaping the performance of fast NVM storage with uDepot. In: Proceedings of the 17th USENIX conference on file and storage technologies, pp 1–15. Boston, MA

  21. Li S (2016) The art of clustering bandits. Ph.D. Thesis, Università degli Studi dell’Insubria

  22. Li S, Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, pp 539–548

  23. Lin S, Zeinalipour-Yazti D, Kalogeraki V, Gunopulos D, Najjar WA (2006) Efficient indexing data structures for flash-based sensor devices. ACM Trans Storage 2(4):468–503

    Article  Google Scholar 

  24. Liu X, Salem K (2013) Hybrid storage management for database systems. Proc VLDB Endow 6(8):541–552

    Article  Google Scholar 

  25. Mahadik K, Wu Q, Li S, Sabne A (2020) Fast distributed bandits for online recommendation systems. In: Proceedings of the 34th ACM international conference on supercomputing, pp 1–13

  26. Manolopoulos Y, Nanopoulos A, Papadopoulos AN, Theodoridis Y (2010) R-trees: theory and applications. Springer, Berlin

  27. Micheloni R (2016) 3D flash memories. Springer, Berlin

    Book  Google Scholar 

  28. Niu J, Xu J, Xie L (2018) Hybrid storage systems: a survey of architectures and algorithms. IEEE Access 6:13385–13406

    Article  Google Scholar 

  29. Roh H, Park S, Kim S, Shin M, Lee SW (2011) B+-tree index optimization by exploiting internal parallelism of flash-based solid state drives. Proc VLDB Endow 5(4):286–297

    Article  Google Scholar 

  30. Roh H, Park S, Shin M, Lee SW (2014) MPSearch: multi-path search for tree-based indexes to exploit internal parallelism of flash SSDs. IEEE Data Eng Bull 37(2):3–11

    Google Scholar 

  31. Roumelis G, Vassilakopoulos M, Corral A, Fevgas A, Manolopoulos Y (2018) Spatial batch-queries processing using xBR+-trees in solid-state drives. In: Proceedings of the 8th international conference on model and data engineering, pp 301–317

  32. Roussopoulos N, Kotidis Y, Roussopoulos M (1997) Cubetree: organization of and bulk incremental updates on the data cube. ACM SIGMOD Record 26(2):89–99

    Article  Google Scholar 

  33. Samet H (1990) Applications of spatial data structures: Computer graphics, image processing, and GIS. Addison-Wesley Longman Publishing Co., Inc., USA

  34. Schubert E, Zimek A, Kriegel HP (2013) Geodetic distance queries on R-trees for indexing geographic data. In: Proceedings of the 16th international symposium on spatial and temporal databases, pp 146–164

  35. Sellis T, Roussopoulos N, Faloutsos C (1987) The R+-tree: a dynamic index for multi-dimensional objects. In: Proceedings of the 13th VLDB conference, pp 507–518

  36. Tao Y, Papadias D (2001) Efficient historical R-trees. In: Proceedings of the 13th international conference on scientific and statistical database management, pp 223–232

  37. Vengroff DE, Vitter JS (1996) Efficient 3-D range searching in external memory. In: Proceedings of the 28th annual ACM symposium on theory of computing, pp 192–201

  38. Vietri G, Rodriguez LV, Martinez WA, Lyons S, Liu J, Rangaswami R, Zhao M, Narasimhan G (2018) Driving cache replacement with ml-based lecar. In: 10th \(\{\)USENIX\(\}\) workshop on hot topics in storage and file systems (HotStorage 18)

  39. Wu CH, Chang LP, Kuo TW (2003) An efficient R-tree implementation over flash-memory storage systems. In: Proceedings of the 11th ACM international symposium on advances in geographic information systems, pp 17–24

  40. Wu CH, Huang CW, Chang CY (2019) A data management method for databases using hybrid storage systems. ACM SIGAPP Appl Comput Rev 19(1):34–47

    Article  Google Scholar 

  41. Wu CH, Kuo TW, Chang LP (2007) An efficient B-tree layer implementation for flash-memory storage systems. ACM Trans Embed Comput Syst 6(3):19

    Article  Google Scholar 

  42. Wu Y, Park K, Sen R, Kroth B, Do J (2020) Lessons learned from the early performance evaluation of intel optane dc persistent memory in dbms. In: Proceedings of the 16th international workshop on data management on new hardware, DaMoN ’20. Association for Computing Machinery, New York. https://doi.org/10.1145/3399666.3399898

  43. Xu J, Swanson S (2016) NOVA: a log-structured file system for hybrid volatile/non-volatile main memories. In: 14th USENIX conference on file and storage technologies (FAST 16). USENIX Association, Santa Clara, pp 323–338. https://www.usenix.org/conference/fast16/technical-sessions/presentation/xu

  44. Yang J, Lilja DJ (2018) Reducing relational database performance bottlenecks using 3D XPoint storage technology. In: Proceedings of the 17th IEEE international conference on trust, security and privacy in computing and communications and 12th IEEE international conference on big data science and engineering, pp 1804–1808

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Authors and Affiliations

  1. Data Structuring and Eng. Lab., Department of Electrical and Computer Engineering, University of Thessaly, 38221, Volos, Greece

    Athanasios Fevgas & Panagiota Tsompanopoulou

  2. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, USA

    Miltiadis Alamaniotis

  3. School of Science and Technology, International Hellenic University, Thessaloniki, Greece

    Leonidas Akritidis & Panayiotis Bozanis

Authors
  1. Athanasios Fevgas
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  2. Leonidas Akritidis
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  3. Miltiadis Alamaniotis
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  4. Panagiota Tsompanopoulou
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  5. Panayiotis Bozanis
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Correspondence to Athanasios Fevgas.

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Fevgas, A., Akritidis, L., Alamaniotis, M. et al. HyR-tree: a spatial index for hybrid flash/3D XPoint storage. Neural Comput & Applic 35, 133–145 (2023). https://doi.org/10.1007/s00521-021-05804-2

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  • DOI: https://doi.org/10.1007/s00521-021-05804-2

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