Transactional support for adaptive indexing

Graefe, Goetz; Halim, Felix; Idreos, Stratos; Kuno, Harumi; Manegold, Stefan; Seeger, Bernhard

doi:10.1007/s00778-013-0345-7

Cited by 23 publications

(37 citation statements)

References 32 publications

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“…[25] Sideways cracking, Covered cracking [15], this paper resp. [25] Sorting: Quick(_insert) sort, Radix(_insert) [12,13] Full index: AVL-tree, B+-tree, ART [1,3,20] Parallel standard cracking P-SC [8,9] Parallel coarse-granular index P-CGI [2] Parallel-chunked standard cracking P-CSC [2] Parallel-chunked vectorized cracking P-CVC Variant of [23] Parallel-chunked coarse-granular index…”

Section: Parallel Cracking Methodsmentioning

confidence: 99%

“…This research group has proposed several different indexing techniques to address different dimensions of database cracking, including updates [14], tuple reconstruction [15], convergence [16], concurrency control [8,9], and robustness [11]. In this paper, we critically review database cracking in several aspects.…”

Section: Our Focusmentioning

confidence: 99%

“…Additionally, we inspect a recently published work [23], which identifies CPU efficiency problems in the standard cracking algorithm and proposes alternatives. Furthermore, we investigate the current state-of-the-art in parallel cracking algorithms [2,8,9,23] and compare them against each other. Finally, we also extend the experimental parameters previously used in database cracking, e.g.…”

Section: Our Focusmentioning

confidence: 99%

“…Parallelizing cracking algorithms As exploiting modern hardware implies using the multi-threading capabilities of the system, we investigate in this section how the cracking algorithms can be parallelized. To do so, we first re-evaluate a lock-based parallel standard cracking algorithm [8,9] that serializes the crack-in-two operation at the granularity of partitions of data for inter-query parallelism. Additionally, we add the algorithms we proposed in our study on parallel adaptive indexing algorithms [2] and put the methods under a new set-up to the test.…”

Section: Extending Cracking Algorithmsmentioning

confidence: 99%

“…In particular, Database Cracking has emerged as an attractive approach for adaptive indexing in recent years [8,11,[14][15][16][17][18]. Since the release of our conference paper [25] on which this work builds upon, three more studies have been published [2,9,23]. Database cracking proposes to create indexes adaptively and as a side product of query processing.…”

Section: Introductionmentioning

confidence: 99%

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An experimental evaluation and analysis of database cracking

Schuhknecht

Jindal²,

Dittrich

2015

The VLDB Journal

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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, B Felix Martin Schuhknecht felix.schuhknecht@infosys.uni-saarland.de 1 Information Systems Group, Saarland University, Saarbrücken, Germany 2 CSAIL, MIT, Cambridge, MA, USA 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.

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