An experimental evaluation and analysis of database cracking

Database cracking has been an area of active research in recent years. The core idea of database cracking is to create indexes adaptively and incrementally as a side product of query processing. Several works have proposed different cracking techniques for different aspects including updates, tuple...

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Vydáno v:The VLDB journal Ročník 25; číslo 1; s. 27 - 52
Hlavní autoři: Schuhknecht, Felix Martin, Jindal, Alekh, Dittrich, Jens
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2016
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ISSN:1066-8888, 0949-877X
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Abstract Database cracking has been an area of active research in recent years. The core idea of database cracking is to create indexes adaptively and incrementally as a side product of query processing. Several works have proposed different cracking techniques for different aspects including updates, tuple reconstruction, convergence, concurrency control, and robustness. Our 2014 VLDB paper “The Uncracked Pieces in Database Cracking” (PVLDB 7:97–108, 2013 /VLDB 2014) was the first comparative study of these different methods by an independent group. In this article, we extend our published experimental study on database cracking and bring it to an up-to-date state. Our goal is to critically review several aspects, identify the potential, and propose promising directions in database cracking. With this study, we hope to expand the scope of database cracking and possibly leverage cracking in database engines other than MonetDB. We repeat several prior database cracking works including the core cracking algorithms as well as three other works on convergence (hybrid cracking), tuple reconstruction (sideways cracking), and robustness (stochastic cracking), respectively. Additionally to our conference paper, we now also look at a recently published study about CPU efficiency (predication cracking). We evaluate these works and show possible directions to do even better. As a further extension, we evaluate the whole class of parallel cracking algorithms that were proposed in three recent works. Altogether, in this work we revisit 8 papers on database cracking and evaluate in total 18 cracking methods, 6 sorting algorithms, and 3 full index structures. Additionally, we test cracking under a variety of experimental settings, including high selectivity ( Low selectivity means that many entries qualify. Consequently, a high selectivity means, that only few entries qualify) queries, low selectivity queries, varying selectivity, and multiple query access patterns. Finally, we compare cracking against different sorting algorithms as well as against different main memory optimized indexes, including the recently proposed adaptive radix tree (ART). Our results show that: (1) the previously proposed cracking algorithms are repeatable, (2) there is still enough room to significantly improve the previously proposed cracking algorithms, (3) parallelizing cracking algorithms efficiently is a hard task, (4) cracking depends heavily on query selectivity, (5) cracking needs to catch up with modern indexing trends, and (6) different indexing algorithms have different indexing signatures.
AbstractList Database cracking has been an area of active research in recent years. The core idea of database cracking is to create indexes adaptively and incrementally as a side product of query processing. Several works have proposed different cracking techniques for different aspects including updates, tuple reconstruction, convergence, concurrency control, and robustness. Our 2014 VLDB paper “The Uncracked Pieces in Database Cracking” (PVLDB 7:97–108, 2013 /VLDB 2014) was the first comparative study of these different methods by an independent group. In this article, we extend our published experimental study on database cracking and bring it to an up-to-date state. Our goal is to critically review several aspects, identify the potential, and propose promising directions in database cracking. With this study, we hope to expand the scope of database cracking and possibly leverage cracking in database engines other than MonetDB. We repeat several prior database cracking works including the core cracking algorithms as well as three other works on convergence (hybrid cracking), tuple reconstruction (sideways cracking), and robustness (stochastic cracking), respectively. Additionally to our conference paper, we now also look at a recently published study about CPU efficiency (predication cracking). We evaluate these works and show possible directions to do even better. As a further extension, we evaluate the whole class of parallel cracking algorithms that were proposed in three recent works. Altogether, in this work we revisit 8 papers on database cracking and evaluate in total 18 cracking methods, 6 sorting algorithms, and 3 full index structures. Additionally, we test cracking under a variety of experimental settings, including high selectivity ( Low selectivity means that many entries qualify. Consequently, a high selectivity means, that only few entries qualify) queries, low selectivity queries, varying selectivity, and multiple query access patterns. Finally, we compare cracking against different sorting algorithms as well as against different main memory optimized indexes, including the recently proposed adaptive radix tree (ART). Our results show that: (1) the previously proposed cracking algorithms are repeatable, (2) there is still enough room to significantly improve the previously proposed cracking algorithms, (3) parallelizing cracking algorithms efficiently is a hard task, (4) cracking depends heavily on query selectivity, (5) cracking needs to catch up with modern indexing trends, and (6) different indexing algorithms have different indexing signatures.
Author Schuhknecht, Felix Martin
Jindal, Alekh
Dittrich, Jens
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10.1007/s00778-013-0345-7
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Issue 1
Keywords Adaptive indexing
Multi-threaded algorithms
Database cracking
Sorting
Language English
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References Kersten, M., et al.: Cracking the database store. In: CIDR, pp. 213–224 (2005)
Leis, V., et al.: The adaptive radix tree: ARTful indexing for main-memory databases. In: ICDE, pp. 38–49 (2013)
Bayer, R., McCreight, E.M.: Organization and maintenance of large ordered indices. Acta Inf. 1, 173–189 (1972)
Martinez-PalauXDominguez-SalDTwo-way replacement selectionPVLDB20103871881
FinchTIncremental Calculation of Weighted Mean and Variance2009CambridgeUniversity of Cambridge Computing Service
Idreos, S., et al.: Database cracking. In: CIDR, pp. 68–78 (2007)
Adelson-Velsky, G., et al.: An algorithm for the organization of information. In: USSR Academy of Sciences, pp. 263–266 (1962)
IdreosSManegoldSMerging what’s cracked, cracking what’s mergedPVLDB20114586597
Pirk, H., Petraki, E., Idreos, S., Manegold, S., Kersten, M.L.: Database cracking: fancy scan, not poor man’s sort! In: DaMoN, Snowbird, UT, USA, pp. 4:1–4:8 (2014)
HildebrandtPIsbitzHRadix exchange: an internal sorting method for digital computersJ. ACM1959621561630084.1200810163810.1145/320964.320972
HalimFIdreosSStochastic database cracking: towards robust adaptive indexing in main-memory column-storesPVLDB20125502513
Kim, C., et al.: FAST: Fast architecture sensitive tree search on modern CPUs and GPUs. In: SIGMOD, pp. 339–350 (2010)
Birkeland, O.R.: Searching large data volumes with MISD processing. Ph.D. Thesis (2008)
Idreos, S., Kersten, M., Manegold, S.: Self-organizing tuple reconstruction in column-stores. In: SIGMOD, pp. 297–308 (2009)
Graefe, G., Halim, F., Idreos, S., et al.: Transactional support for adaptive indexing. VLDB J. 23(2), 303–328 (2014)
Idreos, S., Kersten, M., Manegold, S.: Updating a cracked database. In: SIGMOD, pp. 413–424 (2007)
DeWitt, D.J., Naughton, J.F., et al.: Practical skew handling in parallel joins. In: VLDB, Proceedings, pp. 27–40 (1992)
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Alvarez, V., Schuhknecht, F.M., Dittrich, J., Richter, S.: Main memory adaptive indexing for multi-core systems. In: DaMoN, Snowbird, UT, USA, pp. 3:1–3:10 (2014)
SchuhknechtFMKhanchandaniPDittrichJOn the surprising difficulty of simple things: the case of radix partitioningPVLDB20158934937
Graefe, G., Halim, F., Idreos, S., et al.: Concurrency control for adaptive indexing. PVLDB 5, 656–667 (2012)
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References_xml – reference: Idreos, S., et al.: Database cracking. In: CIDR, pp. 68–78 (2007)
– reference: Graefe, G., Kuno, H.: Self-selecting, self-tuning, incrementally optimized indexes. In: EDBT, pp. 371–381 (2010)
– reference: Graefe, G., Halim, F., Idreos, S., et al.: Transactional support for adaptive indexing. VLDB J. 23(2), 303–328 (2014)
– reference: SchuhknechtFMJindalADittrichJThe uncracked pieces in database crackingPVLDB2013797108
– reference: Idreos, S., Kersten, M., Manegold, S.: Self-organizing tuple reconstruction in column-stores. In: SIGMOD, pp. 297–308 (2009)
– reference: Birkeland, O.R.: Searching large data volumes with MISD processing. Ph.D. Thesis (2008)
– reference: DeWitt, D.J., Naughton, J.F., et al.: Practical skew handling in parallel joins. In: VLDB, Proceedings, pp. 27–40 (1992)
– reference: Generalized Heap Impl. https://github.com/valyala/gheap
– reference: HalimFIdreosSStochastic database cracking: towards robust adaptive indexing in main-memory column-storesPVLDB20125502513
– reference: Pirk, H., Petraki, E., Idreos, S., Manegold, S., Kersten, M.L.: Database cracking: fancy scan, not poor man’s sort! In: DaMoN, Snowbird, UT, USA, pp. 4:1–4:8 (2014)
– reference: Adelson-Velsky, G., et al.: An algorithm for the organization of information. In: USSR Academy of Sciences, pp. 263–266 (1962)
– reference: HildebrandtPIsbitzHRadix exchange: an internal sorting method for digital computersJ. ACM1959621561630084.1200810163810.1145/320964.320972
– reference: IdreosSManegoldSMerging what’s cracked, cracking what’s mergedPVLDB20114586597
– reference: SchuhknechtFMKhanchandaniPDittrichJOn the surprising difficulty of simple things: the case of radix partitioningPVLDB20158934937
– reference: Rao, J., Ross, K.A.: Making B+-trees cache conscious in main memory. In: SIGMOD, pp. 475–486 (2000)
– reference: Bayer, R., McCreight, E.M.: Organization and maintenance of large ordered indices. Acta Inf. 1, 173–189 (1972)
– reference: Kersten, M., et al.: Cracking the database store. In: CIDR, pp. 213–224 (2005)
– reference: McCalpin, J.D.: STREAM benchmark, version from January 17. https://www.cs.virginia.edu/stream/FTP/Code/stream.c (2013)
– reference: HoareCARQuicksortCommun. ACM19614732110.1145/366622.366644
– reference: Idreos, S., Kersten, M., Manegold, S.: Updating a cracked database. In: SIGMOD, pp. 413–424 (2007)
– reference: Leis, V., et al.: The adaptive radix tree: ARTful indexing for main-memory databases. In: ICDE, pp. 38–49 (2013)
– reference: FinchTIncremental Calculation of Weighted Mean and Variance2009CambridgeUniversity of Cambridge Computing Service
– reference: Martinez-PalauXDominguez-SalDTwo-way replacement selectionPVLDB20103871881
– reference: Graefe, G., Halim, F., Idreos, S., et al.: Concurrency control for adaptive indexing. PVLDB 5, 656–667 (2012)
– reference: Kim, C., et al.: FAST: Fast architecture sensitive tree search on modern CPUs and GPUs. In: SIGMOD, pp. 339–350 (2010)
– reference: Alvarez, V., Schuhknecht, F.M., Dittrich, J., Richter, S.: Main memory adaptive indexing for multi-core systems. In: DaMoN, Snowbird, UT, USA, pp. 3:1–3:10 (2014)
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