Incremental join view maintenance on distributed log-structured storage

Huichao DUAN , Huiqi HU , Weining QIAN , Aoying ZHOU

Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (4) : 154607

PDF (1502KB)
Front. Comput. Sci. ›› 2021, Vol. 15 ›› Issue (4) : 154607 DOI: 10.1007/s11704-020-9310-y
RESEARCH ARTICLE

Incremental join view maintenance on distributed log-structured storage

Author information +
History +
PDF (1502KB)

Abstract

Modern database systems desperate for the ability to support highly scalable transactions and efficient queries simultaneously for real-time applications. One solution is to utilize query optimization techniques on the on-line transaction processing (OLTP) systems. The materialized view is considered as a panacea to decrease query latency. However, it also involves the significant cost of maintenance which trades away transaction performance. In this paper, we examine the design space and conclude several design features for the implementation of a view on a distributed log-structured merge-tree (LSMtree), which is a well-known structure for improving data write performance. As a result, we develop two incremental view maintenance (IVM) approaches on LSM-tree. One avoids join computation in view maintenance transactions. Another with two optimizations is proposed to decouple the view maintenance with the transaction process. Under the asynchronous update, we also provide consistency queries for views. Experiments on TPC-H benchmark show our methods achieve better performance than straightforward methods on different workloads.

Keywords

materialized views / asynchronous maintenance / hybrid transaction and analytical process / LSM-tree

Cite this article

Download citation ▾
Huichao DUAN, Huiqi HU, Weining QIAN, Aoying ZHOU. Incremental join view maintenance on distributed log-structured storage. Front. Comput. Sci., 2021, 15(4): 154607 DOI:10.1007/s11704-020-9310-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Abadi D J, Madden S, Hachem N. Column-stores vs. row-stores: how different are they really? In: Proceedings of 2008 ACM International Conference on Management of Data. 2008, 967–980

[2]

Zhan C Q, Su M M, Wei C X, Peng X Q, Lin L, Wang S, Chen Z, Li F F, Pan Y, Zheng F, Chai C L. Analyticdb: real-time OLAP database system at alibaba cloud. Proceedings of the VLDB Endowment, 2019, 12(12): 2059–2070

[3]

Kemper A, Neumann T. Hyper: a hybrid oltp&olap main memory database system based on virtual memory snapshots. In: Proceedings of the 27th IEEE International Conference on Data Engineering. 2011, 195–206

[4]

Chirkova R, Yang J. Materialized views. Foundations and Trends in Databases, 2012, 4(4): 295–405

[5]

Duan H C, Hu H Q, Qian W N, Ma H X, Wang X L, Zhou A Y. Incremental materialized view maintenance on distributed log-structured mergetree. In: Proceedings of the 23rd International Conference on Database Systems for Advanced Applications. 2018, 682–700

[6]

Chang F, Dean J, Ghemawat S, Hsieh W C, Wallach D A, Burrows M, Chandra T, Fikes A, Gruber R E. Bigtable: a distributed storage system for structured data. ACM Transactions on Computer Systems, 2008, 26(2): 4

[7]

Lakshman A, Malik P. Cassandra: a decentralized structured storage system. Operating Systems Review, 2010, 44(2): 35–40

[8]

Huang G, Cheng X T, Wang J Y, Wang Y J, He D C, Zhang T Y, Li F F, Wang S, Cao W, Li Q. X-engine: an optimized storage engine for largescale e-commerce transaction processing. In: Proceedings of the 2019 ACM International Conference on Management of Data. 2019, 651–665

[9]

Ghemawat S, Gobioff H, Leung S. The google file system. In: Proceedings of the 19th ACM Symposium on Operating Systems Principles. 2003, 29–43

[10]

Levandoski J J, Lomet D B, Sengupta S. The Bw-tree: a B-tree for new hardware platforms. In: Proceedings of the 29th IEEE International Conference on Data Engineering. 2013, 302–313

[11]

Berenson H, Bernstein P A, Gray J, Melton J, O’Neil E J, O’Neil P E. A critique of ANSI SQL isolation levels. In: Proceedings of the 1995 ACM International Conference on Management of Data. 1995, 1–10

[12]

Garcia-Molina H, Ullman J D, Widom J. Database System Implementation. New Jersey: Prentice Hall, 2000

[13]

Galindo-Legaria C A. Outerjoins as disjunctions. In: Proceedings of 1994 ACM International Conference on Management of Data. 1994, 348–358

[14]

Bello R G, Dias K, Downing A, Jr J J F, Finnerty J L, Norcott W D, Sun H, Witkowski A, Ziauddin M. Materialized views in oracle. In: Proceedings of the 24th International Conference on Very Large Data Bases. 1998, 659–664

[15]

Zaharioudakis M, Cochrane R, Lapis G, Pirahesh H, Urata M. Answering complex SQL queries using automatic summary tables. In: Proceedings of the 2000 ACM International Conference on Management of Data. 2000, 105–116

[16]

Goldstein J, Larson P. Optimizing queries using materialized views: a practical, scalable solution. In: Proceedings of the 2001 ACM International Conference on Management of Data. 2001, 331–342

[17]

Agrawal S, Chaudhuri S, Narasayya V R. Automated selection of materialized views and indexes in SQL databases. In: Proceedings of the 26th International Conference on Very Large Data Bases. 2000, 496–505

[18]

Agrawal S, Chu E, Narasayya V R. Automatic physical design tuning: workload as a sequence. In: Proceedings of 2006 ACMInternational Conference on Management of Data. 2006, 683–694

[19]

Chaudhuri S, Narasayya V R. Self-tuning database systems: a decade of progress. In: Proceedings of the 33rd International Conference on Very Large Data Bases. 2007, 3–14

[20]

Zhou J R, Larson P, Elmongui H G. Lazy maintenance of materialized views. In: Proceedings of the 33rd International Conference on Very Large Data Bases. 2007, 231–242

[21]

Agrawal P, Silberstein A, Cooper B F, Srivastava U, Ramakrishnan R. Asynchronous view maintenance for VLSD databases. In: Proceedings of 1994 ACM International Conference on Management of Data. 2009, 179–192

[22]

Lomotey R K, Deters R. Terms analytics service for CouchDB: a document-based NoSQL. International Journal of Big Data Intelligence, 2015, 2(1): 23–36

[23]

Larson P, Zhou J R. Efficient maintenance of materialized outer-join views. In: Proceedings of the 29th IEEE International Conference on Data Engineering. 2007, 56–65

[24]

Katsis Y, Ong K W, Papakonstantinou Y, Zhao K K. Utilizing IDs to accelerate incremental view maintenance. In: Proceedings of 2015 ACM International Conference on Management of Data. 2015, 1985–2000

[25]

Ahmad Y, Kennedy O, Koch C, Nikolic M. Dbtoaster: higher-order delta processing for dynamic, frequently fresh views. Proceedings of the VLDB Endowment, 2012, 5(10): 968–979

[26]

Nikolic M, Dashti M, Koch C. How to win a hot dog eating contest: distributed incremental view maintenance with batch updates. In: Proceedings of 2016 ACM International Conference on Management of Data. 2016, 511–526

[27]

O’Neil P, Cheng E, Gawlick D, O’Neil E. The log-structured merge-tree (LSM-tree). Acta Informatica, 1996, 33(4): 351–385

[28]

DeCandia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W. Dynamo: amazon’s highly available key-value store. ACM SIGOPS Operating Systems Review, 2007, 41(6): 205–220

[29]

Sears R, Ramakrishnan R. BLSM: a general purpose log structured merge tree. In: Proceedings of 2012 ACM International Conference on Management of Data. 2012, 217–228

[30]

Tan W, Tata S, Tang Y Z, Fong L L. Diff-index: differentiated index in distributed log-structured data stores. In: Proceedings of the 17th International Conference on Extending Database Technology. 2014, 700–711

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (1502KB)

Supplementary files

Highlights

1406

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/