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Ooi Beng Chin 黄铭钧

Databases, Machine Learning and Systems

 
 
 

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MemepiC -- 内存数据管理  

2014-07-17 21:24:24|  分类: 默认分类 |  标签: |举报 |字号 订阅

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In-memory databases have gained a lot of traction in recent years due to sustained drop in memory cost and also increase in memory size and speed.  Indeed, as has been said many years ago: "memory is the new disk, disk is the new tape".  Figure 1 shows the development of database systems in recent years. 

AnarchDB -- 内存数据管理 - 黄铭钧  - Ooi Beng Chin   黄铭钧

  Figure 1. Modern DB Systems

 

In-memory systems, being free from the disk access bottleneck, they become more sensitive to certain operations and parameters that are considered negligible in disk-based systems.  Besides, speed is its ultimate objective.  Simply transforming a disk-based system into an in-memory system does not work, as in addition to memory accesses, there are many h/w technologies that can also be exploited.  We have recently completed the first version of MemepiC, a system that has been designed without system calls as much as possible and supports "anti-caching" style memory management. It is being extended as a distributed platform and being integrated with the programming model of epiC.  We are also re-examining memory indexes and transaction management that exploit h/w features in a distributed environment.
 

References:
[1]: H. Zhang, G. Chen, B. C. Ooi, W.-F. Wong, S. Wu, Y. Xia: “Anti-Caching”-based Elastic Memory Management for Big Data. IEEE ICDE 2015.
 
[2] Q. Cai, H. Zhang, G. Chen, B. C. Ooi, and K.-L. Tan. Memepic: Towards a database system architecture without system calls. Technical report, NUS, 2015.
 

[4] H. Zhang, G. Chen, B. C. Ooi, K. L. Tan, M. Zhang: In-Memory Big Data Management and Processing: A Survey. IEEE Transactions on Knowledge and Data Engineering. Vol. 27, No. 7, p1920-1948, 2015.
 
[5] K.-L. Tan, Q. Cai, B. C. Ooi, W.F. Wong, C. Yao, H. Zhang: In-memory Databases – Challenges and Opportunities -- From Software and Hardware Perspective. ACM SIGMOD Record, Special Issue on Visionary Ideas in Data Management, June 2015.
 

[6] C. Yao, D. Agrawal, G. Chen, B. C. Ooi, S. Wu: Adaptive Logging: Optimizing Logging and Recovery Costs in Distributed In-memory Databases. ACM Int'l. Conference on Management of Data (SIGMOD), 2016.


[7] memepiC project web site: http://www.comp.nus.edu.sg/~dbsystem/memepic

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