Cracking in-memory database index: A case study for Adaptive Radix Tree index

Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher qu...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Information systems (Oxford) Ročník 104; s. 101913
Hlavní autori:	Wu, Gang, Song, Yidong, Zhao, Guodong, Sun, Wei, Han, Donghong, Qiao, Baiyou, Wang, Guoren, Yuan, Ye
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.02.2022 Elsevier Science Ltd
Predmet:	Algorithms Antiretroviral therapy Case studies Columnar structure Data structures Database cracking Experiments Feasibility In-memory database Indexes Information systems Memory Performance indices Queries Query processing Reaction time Response time (computers) Time Database cracking ART In-memory database
ISSN:	0306-4379, 1873-6076
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structures for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed. This makes it possible to build up an ART index step by step with incessant queries, and hence avoids the poor instant availability of a complete index which is constructed once and for all, but is time consuming. Furthermore, updating a cracking ART index is considered as well. Extensive experiments show that the average initialization time of the ART cracker index is reduced by 75%, and the query response time gradually approaches the original ART algorithm with the coming queries. •In-memory database indexes have more extensive research and application space.•Database Cracking is used as a method applied to in-memory database indexes.•The algorithm has better performance advantages under random queries.
AbstractList	Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structures for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed. This makes it possible to build up an ART index step by step with incessant queries, and hence avoids the poor instant availability of a complete index which is constructed once and for all, but is time consuming. Furthermore, updating a cracking ART index is considered as well. Extensive experiments show that the average initialization time of the ART cracker index is reduced by 75%, and the query response time gradually approaches the original ART algorithm with the coming queries. •In-memory database indexes have more extensive research and application space.•Database Cracking is used as a method applied to in-memory database indexes.•The algorithm has better performance advantages under random queries. Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structures for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed. This makes it possible to build up an ART index step by step with incessant queries, and hence avoids the poor instant availability of a complete index which is constructed once and for all, but is time consuming. Furthermore, updating a cracking ART index is considered as well. Extensive experiments show that the average initialization time of the ART cracker index is reduced by 75%, and the query response time gradually approaches the original ART algorithm with the coming queries.
ArticleNumber	101913
Author	Sun, Wei Yuan, Ye Zhao, Guodong Song, Yidong Wang, Guoren Qiao, Baiyou Wu, Gang Han, Donghong
Author_xml	– sequence: 1 givenname: Gang orcidid: 0000-0002-9855-6300 surname: Wu fullname: Wu, Gang email: wugang@mail.neu.edu.cn organization: School of Computer Science and Engineering, Northeastern University, China – sequence: 2 givenname: Yidong surname: Song fullname: Song, Yidong email: 2488951516@qq.com organization: School of Computer Science and Engineering, Northeastern University, China – sequence: 3 givenname: Guodong surname: Zhao fullname: Zhao, Guodong organization: School of Computer Science and Engineering, Northeastern University, China – sequence: 4 givenname: Wei surname: Sun fullname: Sun, Wei organization: Baidu, China – sequence: 5 givenname: Donghong surname: Han fullname: Han, Donghong organization: School of Computer Science and Engineering, Northeastern University, China – sequence: 6 givenname: Baiyou surname: Qiao fullname: Qiao, Baiyou organization: School of Computer Science and Engineering, Northeastern University, China – sequence: 7 givenname: Guoren surname: Wang fullname: Wang, Guoren organization: School of Computer, Beijing Institute of Technology, China – sequence: 8 givenname: Ye surname: Yuan fullname: Yuan, Ye organization: School of Computer, Beijing Institute of Technology, China
BookMark	eNp1kM1LAzEQxYMo2FbvHgOet06S3U3TWyl-QUWQeg75mJWsdrcm29L-927ZXj0Nb3i_mccbk8umbZCQOwZTBqx8qKchTTlwdpKKiQsyYjMpshJkeUlGIKDMciHVNRmnVAMAL5QakbdlNO47NF80NNkGN208Um86Y03CfuXxMKcL6k4qdTt_pFUb6cKbbRf2SD-MDwe6jnj23pCryvwkvD3PCfl8elwvX7LV-_PrcrHKHJdFl-XGysKikpXzdmaYLVzuLFaGYWFczqV1OfoSzQysd1gxw5UsQEgBCB68mJD74e42tr87TJ2u211s-peal0yVUvCC9y4YXC62KUWs9DaGjYlHzUCfStO1Dj3Rl6aH0npkPiDYp98HjDq5gI1DHyK6Tvs2_A__Ae18dbs
Cites_doi	10.1145/1247480.1247527 10.1007/s00778-013-0345-7 10.1145/1559845.1559878 10.1145/781027.781063 10.1109/ICDE.2013.6544812 10.1109/ICDE.2017.54 10.1109/ICDE.2018.00064 10.1145/2168836.2168855 10.1145/2619228.2619232 10.1007/s00778-015-0397-y 10.1145/2933349.2933352 10.1007/s002360050048 10.1145/342009.335449 10.1109/ICDEW.2010.5452743 10.1007/978-3-642-18206-8_13 10.1145/2723372.2723719 10.1109/ICDE.2013.6544834 10.1145/1807167.1807206 10.1109/ICDE.2011.5767867 10.14778/3358701.3358705
ContentType	Journal Article
Copyright	2021 Elsevier Ltd Copyright Elsevier Science Ltd. Feb 2022
Copyright_xml	– notice: 2021 Elsevier Ltd – notice: Copyright Elsevier Science Ltd. Feb 2022
DBID	AAYXX CITATION 7SC 8FD E3H F2A JQ2 L7M L~C L~D
DOI	10.1016/j.is.2021.101913
DatabaseName	CrossRef Computer and Information Systems Abstracts Technology Research Database Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Library and Information Science Abstracts (LISA) ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional
DatabaseTitleList	Technology Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1873-6076
ExternalDocumentID	10_1016_j_is_2021_101913 S0306437921001228
GrantInformation_xml	– fundername: National Key Research and Development Program of China grantid: 2019YFB1405302 funderid: http://dx.doi.org/10.13039/501100012166 – fundername: State Key Laboratory of Computer Software New Technology Open Project Fund, China grantid: KFKT2018B05 – fundername: NSFC, China grantid: 61872072 funderid: http://dx.doi.org/10.13039/501100001809
GroupedDBID	--K --M -~X .DC .~1 0R~ 13V 1B1 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 63O 7-5 71M 77K 8P~ 9JN 9JO AAAKF AAAKG AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO AAYFN ABBOA ABFNM ABKBG ABMAC ABMVD ABTAH ABUCO ABXDB ABYKQ ACDAQ ACGFS ACHRH ACNNM ACNTT ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD AEBSH AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHZHX AI. AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HAMUX HF~ HLZ HVGLF HZ~ H~9 IHE J1W KOM LG9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SSB SSD SSL SSV SSZ T5K TN5 UHS VH1 WUQ XSW ZCG ZY4 ~G- 77I 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7SC 8FD E3H F2A JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c275t-4ab75be97fcdb8a1b5c4cbefa1e5ac427bc4ed6ea80bdcef1a297503730e0d0d3
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000718044500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0306-4379
IngestDate	Fri Nov 14 18:44:09 EST 2025 Sat Nov 29 07:22:04 EST 2025 Fri Feb 23 02:42:20 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Database cracking ART In-memory database
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c275t-4ab75be97fcdb8a1b5c4cbefa1e5ac427bc4ed6ea80bdcef1a297503730e0d0d3
Notes	ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3
ORCID	0000-0002-9855-6300
PQID	2619673252
PQPubID	2035446
ParticipantIDs	proquest_journals_2619673252 crossref_primary_10_1016_j_is_2021_101913 elsevier_sciencedirect_doi_10_1016_j_is_2021_101913
PublicationCentury	2000
PublicationDate	February 2022 2022-02-00 20220201
PublicationDateYYYYMMDD	2022-02-01
PublicationDate_xml	– month: 02 year: 2022 text: February 2022
PublicationDecade	2020
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford
PublicationTitle	Information systems (Oxford)
PublicationYear	2022
Publisher	Elsevier Ltd Elsevier Science Ltd
Publisher_xml	– name: Elsevier Ltd – name: Elsevier Science Ltd
References	Schuhknecht, Jindal, Dittrich (b15) 2013; 7 Yandong Mao, Eddie Kohler, Robert Tappan Morris, Cache craftiness for fast multicore key–value storage, in: EuroSys 2012, pp. 183–196. Richard A. Hankins, Jignesh M. Patel, Effect of node size on the performance of cache-conscious B+-trees, in: SIGMETRICS 2003, pp. 283–294. Changkyu Kim, Jatin Chhugani, et al. FAST: fast architecture sensitive tree search on modern CPUs and GPUs, in: SIGMOD Conference 2010, pp. 339–350. Martin L. Kersten, Stefan Manegold, Cracking the database store, in: CIDR 2005, pp 213–224. Justin J. Levandoski, David B. Lomet, Sudipta Sengupta, The Bw-Tree: A B-tree for new hardware platforms, in: ICDE 2013, pp. 302–313. Halim, Idreos, Karras, Yap (b20) 2012; 5 Holanda, Raasveldt, Manegold, Mühleisen (b28) 2019; 13 Stratos Idreos, Martin L. Kersten, Stefan Manegold, Self-organizing tuple reconstruction in column-stores, in: SIGMOD Conference 2009, pp. 297–308. Zhongle Xie, Qingchao Cai, Gang Chen, Rui Mao, Meihui Zhang, A comprehensive performance evaluation of modern in-memory indices, in: ICDE 2018, pp. 641–652. Idreos, Manegold, Kuno, Graefe (b21) 2011; 4 Viktor Leis, Florian Scheibner, Alfons Kemper, Thomas Neumann, The ART of practical synchronization. DaMoN 2016: 3:1-3:8. Zhongle Xie, Qingchao Cai, H.V. Jagadish, Beng Chin Ooi, Weng-Fai Wong, Parallelizing skip lists for in-memory multi-core database systems, in: ICDE 2017, pp. 119–122. Eleni Petraki, Stratos Idreos, Stefan Manegold, Holistic indexing in main-memory column-stores, in: SIGMOD Conference 2015, pp. 1153–1166. Cooper, Silberstein, Tam, Ramakrishnan, Sears (b13) 2010 Graefe, Halim, Idreos (b25) 2014; 23 Holger Pirk, Eleni Petraki, Stratos Idreos, Stefan Manegold, Martin L. Kersten, Database cracking: fancy scan, not poor man’s sort! DaMoN 2014, 4:1-4:8. Jun Rao, Kenneth A. Ross, Making B+-Trees cache conscious in main memory, in: SIGMOD Conference 2000, pp. 475–486. O’Neil, Cheng, Gawlick (b30) 1996; 33 Stratos Idreos, Martin L. Kersten, Stefan Manegold, Updating a cracked database. SIGMOD Conference 2007: 413-424. Goetz Graefe, Stratos Idreos, Harumi A. Kuno, Stefan Manegold, Benchmarking adaptive indexing, in: TPCTC 2010, pp. 169–184. Graefe, Kuno (b23) 2010 Graefe, Halim, Idreos, Kuno, Manegold (b31) 2012; 5 Jun Rao, Kenneth A. Ross, Cache conscious indexing for decision-support in main memory, in: VLDB 1999, pp. 78–89. Alfons Kemper, Thomas Neumann, HyPer: A hybrid OLTP & OLAP main memory database system based on virtual memory snapshots, in: ICDE 2011, pp. 195–206. Idreos, Groffen, Nes, Manegold, Sjoerd Mullender, Kersten (b19) 2012; 35 Viktor Leis, Alfons Kemper, Thomas Neumann, The adaptive radix tree: ARTful indexing for main-memory databases, in: ICDE 2013, pp. 38–49. Idreos, Kersten, Manegold (b10) 2007 Schuhknecht, Jindal, Dittrich (b12) 2016; 25 Goetz. Graefe, Harumi A. Kuno, Adaptive indexing for relational keys, in: ICDE Workshops, 2010, pp. 69–74. Tobin J. Lehman, Michael J. Carey, A study of index structures for main memory database management systems, in: VLDB, 1986, pp. 294–303. Schuhknecht (10.1016/j.is.2021.101913_b15) 2013; 7 Holanda (10.1016/j.is.2021.101913_b28) 2019; 13 Schuhknecht (10.1016/j.is.2021.101913_b12) 2016; 25 Graefe (10.1016/j.is.2021.101913_b31) 2012; 5 10.1016/j.is.2021.101913_b29 Graefe (10.1016/j.is.2021.101913_b25) 2014; 23 10.1016/j.is.2021.101913_b11 Cooper (10.1016/j.is.2021.101913_b13) 2010 Idreos (10.1016/j.is.2021.101913_b10) 2007 10.1016/j.is.2021.101913_b14 10.1016/j.is.2021.101913_b17 10.1016/j.is.2021.101913_b16 10.1016/j.is.2021.101913_b1 10.1016/j.is.2021.101913_b2 10.1016/j.is.2021.101913_b3 10.1016/j.is.2021.101913_b4 10.1016/j.is.2021.101913_b5 O’Neil (10.1016/j.is.2021.101913_b30) 1996; 33 10.1016/j.is.2021.101913_b6 10.1016/j.is.2021.101913_b7 10.1016/j.is.2021.101913_b8 Idreos (10.1016/j.is.2021.101913_b21) 2011; 4 10.1016/j.is.2021.101913_b9 10.1016/j.is.2021.101913_b18 10.1016/j.is.2021.101913_b22 Graefe (10.1016/j.is.2021.101913_b23) 2010 10.1016/j.is.2021.101913_b24 Idreos (10.1016/j.is.2021.101913_b19) 2012; 35 10.1016/j.is.2021.101913_b26 Halim (10.1016/j.is.2021.101913_b20) 2012; 5 10.1016/j.is.2021.101913_b27
References_xml	– reference: Zhongle Xie, Qingchao Cai, Gang Chen, Rui Mao, Meihui Zhang, A comprehensive performance evaluation of modern in-memory indices, in: ICDE 2018, pp. 641–652. – reference: Stratos Idreos, Martin L. Kersten, Stefan Manegold, Updating a cracked database. SIGMOD Conference 2007: 413-424. – reference: Jun Rao, Kenneth A. Ross, Cache conscious indexing for decision-support in main memory, in: VLDB 1999, pp. 78–89. – volume: 7 start-page: 97 year: 2013 end-page: 108 ident: b15 article-title: The uncracked pieces in database cracking publication-title: PVLDB – volume: 5 start-page: 656 year: 2012 end-page: 667 ident: b31 article-title: Concurrency control for adaptive indexing publication-title: PVLDB – start-page: 371 year: 2010 end-page: 381 ident: b23 article-title: Self-selecting, self-tuning, incrementally optimized indexes publication-title: EDBT – reference: Richard A. Hankins, Jignesh M. Patel, Effect of node size on the performance of cache-conscious B+-trees, in: SIGMETRICS 2003, pp. 283–294. – reference: Justin J. Levandoski, David B. Lomet, Sudipta Sengupta, The Bw-Tree: A B-tree for new hardware platforms, in: ICDE 2013, pp. 302–313. – start-page: 68 year: 2007 end-page: 78 ident: b10 article-title: Database cracking publication-title: CIDR – reference: Goetz. Graefe, Harumi A. Kuno, Adaptive indexing for relational keys, in: ICDE Workshops, 2010, pp. 69–74. – reference: Goetz Graefe, Stratos Idreos, Harumi A. Kuno, Stefan Manegold, Benchmarking adaptive indexing, in: TPCTC 2010, pp. 169–184. – volume: 4 start-page: 585 year: 2011 end-page: 597 ident: b21 article-title: Merging what’s cracked, cracking what’s merged: Adaptive indexing in main-memory column-stores publication-title: PVLDB – reference: Jun Rao, Kenneth A. Ross, Making B+-Trees cache conscious in main memory, in: SIGMOD Conference 2000, pp. 475–486. – reference: Martin L. Kersten, Stefan Manegold, Cracking the database store, in: CIDR 2005, pp 213–224. – start-page: 143 year: 2010 end-page: 154 ident: b13 article-title: Benchmarking cloud serving systems with YCSB publication-title: SoCC – volume: 25 start-page: 27 year: 2016 end-page: 52 ident: b12 article-title: An experimental evaluation and analysis of database cracking publication-title: VLDB J. – reference: Stratos Idreos, Martin L. Kersten, Stefan Manegold, Self-organizing tuple reconstruction in column-stores, in: SIGMOD Conference 2009, pp. 297–308. – reference: Viktor Leis, Alfons Kemper, Thomas Neumann, The adaptive radix tree: ARTful indexing for main-memory databases, in: ICDE 2013, pp. 38–49. – reference: Viktor Leis, Florian Scheibner, Alfons Kemper, Thomas Neumann, The ART of practical synchronization. DaMoN 2016: 3:1-3:8. – reference: Eleni Petraki, Stratos Idreos, Stefan Manegold, Holistic indexing in main-memory column-stores, in: SIGMOD Conference 2015, pp. 1153–1166. – reference: Zhongle Xie, Qingchao Cai, H.V. Jagadish, Beng Chin Ooi, Weng-Fai Wong, Parallelizing skip lists for in-memory multi-core database systems, in: ICDE 2017, pp. 119–122. – volume: 35 start-page: 40 year: 2012 end-page: 45 ident: b19 article-title: MonetDB: Two decades of research in column-oriented database architectures publication-title: IEEE Data Eng. Bull. – reference: Tobin J. Lehman, Michael J. Carey, A study of index structures for main memory database management systems, in: VLDB, 1986, pp. 294–303. – reference: Holger Pirk, Eleni Petraki, Stratos Idreos, Stefan Manegold, Martin L. Kersten, Database cracking: fancy scan, not poor man’s sort! DaMoN 2014, 4:1-4:8. – reference: Alfons Kemper, Thomas Neumann, HyPer: A hybrid OLTP & OLAP main memory database system based on virtual memory snapshots, in: ICDE 2011, pp. 195–206. – reference: Yandong Mao, Eddie Kohler, Robert Tappan Morris, Cache craftiness for fast multicore key–value storage, in: EuroSys 2012, pp. 183–196. – reference: Changkyu Kim, Jatin Chhugani, et al. FAST: fast architecture sensitive tree search on modern CPUs and GPUs, in: SIGMOD Conference 2010, pp. 339–350. – volume: 5 start-page: 502 year: 2012 end-page: 513 ident: b20 article-title: Stochastic database cracking: Towards robust adaptive indexing in main-memory column-stores publication-title: PVLDB – volume: 13 start-page: 2366 year: 2019 end-page: 2378 ident: b28 article-title: Progressive indexes: indexing for interactive data analysis publication-title: Proc. VLDB Endow. 12 – volume: 33 start-page: 351 year: 1996 end-page: 385 ident: b30 article-title: The log-structured merge-tree (LSM-tree) publication-title: Acta Inform. – volume: 23 start-page: 303 year: 2014 end-page: 328 ident: b25 article-title: Transactional support for adaptive indexing publication-title: VLDB J. – ident: 10.1016/j.is.2021.101913_b11 doi: 10.1145/1247480.1247527 – volume: 23 start-page: 303 issue: 2 year: 2014 ident: 10.1016/j.is.2021.101913_b25 article-title: Transactional support for adaptive indexing publication-title: VLDB J. doi: 10.1007/s00778-013-0345-7 – ident: 10.1016/j.is.2021.101913_b24 doi: 10.1145/1559845.1559878 – ident: 10.1016/j.is.2021.101913_b14 doi: 10.1145/781027.781063 – start-page: 371 year: 2010 ident: 10.1016/j.is.2021.101913_b23 article-title: Self-selecting, self-tuning, incrementally optimized indexes – volume: 7 start-page: 97 issue: 2 year: 2013 ident: 10.1016/j.is.2021.101913_b15 article-title: The uncracked pieces in database cracking publication-title: PVLDB – ident: 10.1016/j.is.2021.101913_b1 – ident: 10.1016/j.is.2021.101913_b3 – start-page: 143 year: 2010 ident: 10.1016/j.is.2021.101913_b13 article-title: Benchmarking cloud serving systems with YCSB – ident: 10.1016/j.is.2021.101913_b8 doi: 10.1109/ICDE.2013.6544812 – ident: 10.1016/j.is.2021.101913_b7 doi: 10.1109/ICDE.2017.54 – ident: 10.1016/j.is.2021.101913_b9 doi: 10.1109/ICDE.2018.00064 – ident: 10.1016/j.is.2021.101913_b5 doi: 10.1145/2168836.2168855 – volume: 5 start-page: 502 issue: 6 year: 2012 ident: 10.1016/j.is.2021.101913_b20 article-title: Stochastic database cracking: Towards robust adaptive indexing in main-memory column-stores publication-title: PVLDB – ident: 10.1016/j.is.2021.101913_b26 doi: 10.1145/2619228.2619232 – start-page: 68 year: 2007 ident: 10.1016/j.is.2021.101913_b10 article-title: Database cracking – volume: 25 start-page: 27 issue: 1 year: 2016 ident: 10.1016/j.is.2021.101913_b12 article-title: An experimental evaluation and analysis of database cracking publication-title: VLDB J. doi: 10.1007/s00778-015-0397-y – volume: 35 start-page: 40 issue: 1 year: 2012 ident: 10.1016/j.is.2021.101913_b19 article-title: MonetDB: Two decades of research in column-oriented database architectures publication-title: IEEE Data Eng. Bull. – ident: 10.1016/j.is.2021.101913_b17 doi: 10.1145/2933349.2933352 – volume: 33 start-page: 351 issue: 4 year: 1996 ident: 10.1016/j.is.2021.101913_b30 article-title: The log-structured merge-tree (LSM-tree) publication-title: Acta Inform. doi: 10.1007/s002360050048 – ident: 10.1016/j.is.2021.101913_b2 doi: 10.1145/342009.335449 – volume: 5 start-page: 656 issue: 7 year: 2012 ident: 10.1016/j.is.2021.101913_b31 article-title: Concurrency control for adaptive indexing publication-title: PVLDB – ident: 10.1016/j.is.2021.101913_b22 doi: 10.1109/ICDEW.2010.5452743 – volume: 4 start-page: 585 issue: 9 year: 2011 ident: 10.1016/j.is.2021.101913_b21 article-title: Merging what’s cracked, cracking what’s merged: Adaptive indexing in main-memory column-stores publication-title: PVLDB – ident: 10.1016/j.is.2021.101913_b29 doi: 10.1007/978-3-642-18206-8_13 – ident: 10.1016/j.is.2021.101913_b18 – ident: 10.1016/j.is.2021.101913_b27 doi: 10.1145/2723372.2723719 – ident: 10.1016/j.is.2021.101913_b6 doi: 10.1109/ICDE.2013.6544834 – ident: 10.1016/j.is.2021.101913_b4 doi: 10.1145/1807167.1807206 – ident: 10.1016/j.is.2021.101913_b16 doi: 10.1109/ICDE.2011.5767867 – volume: 13 start-page: 2366 year: 2019 ident: 10.1016/j.is.2021.101913_b28 article-title: Progressive indexes: indexing for interactive data analysis publication-title: Proc. VLDB Endow. 12 doi: 10.14778/3358701.3358705
SSID	ssj0002599
Score	2.3297505
Snippet	Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance....
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Index Database Publisher
StartPage	101913
SubjectTerms	Algorithms Antiretroviral therapy Case studies Columnar structure Data structures Database cracking Experiments Feasibility In-memory database Indexes Information systems Memory Performance indices Queries Query processing Reaction time Response time (computers) Time
Title	Cracking in-memory database index: A case study for Adaptive Radix Tree index
URI	https://dx.doi.org/10.1016/j.is.2021.101913 https://www.proquest.com/docview/2619673252
Volume	104
WOSCitedRecordID	wos000718044500002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6076 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002599 issn: 0306-4379 databaseCode: AIEXJ dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELag5UAPPAqIQkE-cEErI-fpuLdV1RYQVAgWdeES-RWRomZX-6iWf8_4kWy2qIgeuERZx2tZni8z48k3Y4RexZmhhU44ibkuiPXJCeeVIIKbnCeFjiJfxPUDOz0txmP-KdDG5u44AdY0xWrFp_9V1NAGwrapszcQdzcoNMA9CB2uIHa4_pPgD2dC_fSZKuTC8mh_DSwN1JqrgauN6JPRlf3tiss6puFQi6ljEX0Wul4NRjMTeve915C75CDjS0C7mK1POuzFFM6WLtwuglW0AZzA_P1W68m69fsP4SK1J8tJv_nL0qnCM1P3YxKwnaUdvyPkYtGc2EqHG3qWpj1NCaqA-yzUP5S4jyecw1Cwf4-jN-uum_Wyr9ixjl3YEtfOy3pe2hFKP8JttB2zjIPu2x6-Oxq_7yw2bAG5_9rkZx0-Z3se4OYsrnNfrhhy552MHqB7YVuBhx4OD9Et0-yi--2RHTho8F2006s_-Qh9bLGCO6zgFivYSf8AD7FFCnZIwSBn3CIFO6RgixTf9zH6enw0OnxLwvkaRME6LEgqJMuk4axSWhYikplKlTSViEwmVBozqVKjcyMKKrUyVSRsGjZNwCgYqqlOnqCtZtKYpwhTVVEh06iKdZaKlIHV5FqrSsmcMaWiPfS6XbZy6suolNeJaQ8l7bqWwQ307l0JAPnLv_ZbEZThbYTnORgYlsRZ_OwGE3iO7q5BvY-2FrOleYHuqMtFPZ-9DOD5DakRhKw
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Cracking+in-memory+database+index%3A+A+case+study+for+Adaptive+Radix+Tree+index&rft.jtitle=Information+systems+%28Oxford%29&rft.au=Wu%2C+Gang&rft.au=Song%2C+Yidong&rft.au=Zhao%2C+Guodong&rft.au=Sun%2C+Wei&rft.date=2022-02-01&rft.issn=0306-4379&rft.volume=104&rft.spage=101913&rft_id=info:doi/10.1016%2Fj.is.2021.101913&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_is_2021_101913
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-4379&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-4379&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-4379&client=summon