互联网环境下分布式事务处理系统现状与趋势

doi:10.11959/j.issn.2096-0271.2018021

Abstract

Abstract:

Due to the gradual failure of Moore’s law,traditional centralized data processing systems begin to show performance bottlenecks,which can’t effectively support the rapid development of current business.At the same time,due to the security,cost,policy and other reasons,the transformation of the original centralized data processing system into a distributed data processing system has become the only way.The significance,present situation and prospect of distributed transaction data processing system from its development background,advantages and challenges,technical characteristics,application points,future development direction were introduced.A theoretical basis and guidance for the application and development of distributed transaction data processing system was provided.

Key words: distributed, database, transaction

CLC Number:

TP392

Pengwei MA, Kai WEI, Chunyu JIANG. State of the art and future research on distributed processing system in Internet environment[J]. Big Data Research, 2018, 4(2): 86-94.

Figures/Tables 5

References 12

[1]	CORBETT J C , DEAN J , EPSTEIN M ,et al. Spanner:Google's globally-distributed database[C]// USENIX Conference on Operating Systems Design and Implementation,October 8-10,2012,Hollywood,USA. Berkeley:USENIX Association, 2012: 251-264.
[2]	CHEUNG D W , NG V T , FU A W ,et al. Efficient mining of association rules in distributed databases[J]. IEEE Transactions on Knowledge ＆ Data Engineering, 1996,8(6): 911-922.
[3]	?ZSU M T , VALDURIEZ P . Principles of distributed database systems[M]. Berlin: Springer, 1999,24(1): 31-41.
[4]	BERNSTEIN P A . Concurrency control in distributed database systems[J]. ACM Computing Surveys, 1989,13(2): 185-221.
[5]	PAIVA J , RUIVO P , Romano P ,et al. AutoPlacer:scalable self-tuning data placement in distributed key-value stores[J]. ACM Transactions on Autonomous ＆Adaptive Systems, 2014,9(4): 19.
[6]	KUMAR R , GUPTA N , CHARU S ,et al. Manage big data through NewSQL[C]// National Conference on Innovation in Wireless Communication and Networking Technology,April 6-9,2014,Istanbul,Turkey. Piscataway:IEEE Press, 2014: 1-6.
[7]	KUMAR R , PARASHAR B B , GUPTA S ,et al. Apache Hadoop,NoSQL and NewSQL solutions of big data[J]. International Journal of Advance Foundation and Research in Science ＆ Engineering, 2014,1(6): 28-36.
[8]	ARDEKANI M S , TERRY D B . A selfconfigurable geo-replicated cloud storage system[C]// USENIX Conference on Operating Systems Design and Implementation,October 6-8,2014,Broomfield,USA. Berkeley:USENIX Association, 2015: 367-381.
[9]	LING G U , WENG H X,HUA-LIANG H U ,et al. OceanBase schema design for OLAP application[J]. Journal of East China Normal University, 2014(5): 301-310.
[10]	RAMANATHAN S , GOEL S , ALAGUMALAI S . Comparison of cloud database:Amazon's simpleDB and Google's bigtable[C]// International Conference on Recent Trends in Information Systems,June 3-5,2011,Chennai,India. Piscataway:IEEE Press, 2011: 165-168.
[11]	SUKHWANI B , MIN H , THOENNES M ,et al. Database analytics acceleration using FPGAs[C]// International Conference on Parallel Architectures and Compilation Techniques,September 9-13,2017,Portland,USA. Piscataway:IEEE Press, 2017: 411-420.
[12]	ABBAS Q , SHAFIQ H , AHMAD I ,et al. Concurrency control in distributed database system[C]// International Conference on Computer Communication and Informatics,January 7-9,2016,Coimbatore,India. Piscataway:IEEE Press, 2016: 1-4.

Metrics

Recommended 0

No Suggested Reading articles found!

对比项	中间件方案	共享存储方案	计算存储均分布式方案
应用场景	OLTP业务为主	OLTP业务为主	OLAP/OLTP
扩展能力	受中间件能力限制	受存储设备能力限制	理论上无限制
高可用性	中间件存在单点故障	存储存在单点故障	理论上无单点故障
使用影响	增加业务规划能力要求	与集中式数据库相比无大变化	与集中式数据库相比无大变化
生态情况	方案稳定，应用案例丰富	成熟商业产品与创新开源产品并存	产品较少，但发展迅速
典型产品	Cobar、MyCat、TDDL	Aurora、Oracle RAC	Spanner、TiDB、OceanBase

[1]	Hongrun REN, Yangyong ZHU. Data pipeline model: exploration of the overthe-counter form of streaming data [J]. Big Data Research, 2023, 9(3): 15-28.
[2]	Jinglei HUANG, Jinpu LI, Ke TANG. Data trust: a trustworthy data transaction model [J]. Big Data Research, 2023, 9(2): 67-78.
[3]	Rui ZHU, Hongzhi WANG, Shuangshuang CUI, Kaixin ZHANG, Yu YAN. Cloud-edge-end collaborative big data management for metaverse [J]. Big Data Research, 2023, 9(1): 63-77.
[4]	Zhipeng LU. Research on the economic analytical model of three-tier data factor market [J]. Big Data Research, 2022, 8(4): 85-93.
[5]	Lihua HUANG, Yifan DOU, Mengke GUO, Qifeng TANG, Gen LI. Features and transaction modes of data products in data markets [J]. Big Data Research, 2022, 8(3): 3-14.
[6]	Qifeng TANG, Zhiqing SHAO, Yazhen YE. Authenticating and licensing architecture of data rights in data trade [J]. Big Data Research, 2022, 8(3): 40-53.
[7]	Yingzi WEN, Yang QU, Xudong ZHANG, Jun XU, Jianping LI. Comparative study on laws and regulations related to data transaction [J]. Big Data Research, 2022, 8(3): 66-77.
[8]	Kai ZHANG, Yang CHE. Method of accelerating deep learning with optimized distributed cache in containers [J]. Big Data Research, 2021, 7(5): 150-163.
[9]	Yuan LI, Ning GAO, Jing SUN, Huiqun ZHAO. Research and exploration of big data transaction model based on blockchain [J]. Big Data Research, 2021, 7(4): 37-48.
[10]	Qiang CHEN, Shiya DAI. Recognition method of accounting fraud risk based on financial knowledge graph [J]. Big Data Research, 2021, 7(3): 116-129.
[11]	Guangjun QIN, Limin XIAO, Guangyan ZHANG, Beifang NIU, Zhiguang CHEN. Virtual data space system for national highperformance computing environment [J]. Big Data Research, 2021, 7(2): 101-122.
[12]	Qinglin YANG, Guiyong WU, Guangyan ZHANG. An approach to buffering data efficiently in distributed storage systems [J]. Big Data Research, 2021, 7(2): 147-157.
[13]	Zhenlong DENG, Zhiguang CHEN. An optimization of MPI-IO interface for non-volatile memory [J]. Big Data Research, 2021, 7(2): 172-181.
[14]	Yuzhe LIN, Weihua ZHANG. A research on GPU transactional memory [J]. Big Data Research, 2020, 6(4): 3-17.
[15]	Xiangqian DONG, Bing GUO, Yan SHEN, Xuliang DUAN, Yuncheng SHEN, Hong ZHANG. Data assets value evaluation model based on profit maximization [J]. Big Data Research, 2020, 6(3): 13-20.

State of the art and future research on distributed processing system in Internet environment

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 12

Related Articles 15

Metrics

Recommended 0