将输入文档分割为M个(每个16m~64m大小)。 选取一个程序副本为master,用于分配map和reduce任务。 分配到map任务的worker,读取分割好的输入,分析(key,value)对,传递给map函数处理,并生成中间结果缓存在内存中。 缓存的中间结果被定时的写到本地磁盘,这些中间结果在本地磁盘的位置会被传到master,用以分配到相应的reduc...
MapReduce: Simplified Data Processing on Large Clusters是6.824: Distributed Systems中所介绍的第一篇论文。它提出了一种针对大数据处理的编程模型和实现,使得编程人员无需并行和分布式系统经验就可以轻松构建大数据处理应用。该模型将大数据处理问题拆解为两步,即map和reduce,map阶段将一组输入的键值对转化为中间结果键...
In addition, we do the performance analysis of the existing distributed systems in terms of execution time for various scientific applications which require iterative data processing. Finally, based on the performance analysis, we discuss some requirements for a new MapReduce-based distributed system ...
Each METADATA row stores approximately 1KB of data in memory(因为访问量比较大,元数据表是放在内存里的,这个优化在论文的locality groups中提到).This feature(将locality group放到内存中的特性) is useful for small pieces of data that are accessed frequently: we use it internally for the location column...
[2] RemziH. Arpaci-Dusseau, Eric Anderson, Noah Treuhaft, David E. Culler, Joseph M.Hellerstein, David Patterson, and Kathy Yelick. Cluster I/O with River: Makingthe fast case common. In Proceedings of the Sixth Workshop on Input/Output inParallel and Distributed Systems (IOPADS '99), ...
课程b站视频地址:MIT 6.824 Distributed Systems Spring 2020 分布式系统 推荐伴读读物: 极客时间 – 大数据经典论文解读 DDIA – 数据密集型应用 大数据相关论文中译版本 本节预习作业: MapReduce 论文(原版 - 英译) MapReduce 论文(中译) 引言 为什么我们需要使用分布式系统: ...
Making the fast case common. In Proceedings of the Sixth Workshop on Input/Output in Parallel and Distributed Systems (IOPADS ’99), pages 10.22, Atlanta, Georgia, May 1999. [3] Arash Baratloo, Mehmet Karaul, Zvi Kedem, and Peter Wyckoff. Charlotte: Metacomputing on the web. In ...
[2] Remzi H. Arpaci-Dusseau, Eric Anderson, NoahTreuhaft, David E. Culler, Joseph M. Hellerstein, David Patterson, and Kathy Yelick. Cluster I/O with River:Making the fast case common. In Proceedings of the Sixth Workshop on Input/Output in Parallel and Distributed Systems (IOPADS ’99)...
However, the input data is usually large and the computations have to be distributed across hundreds or thousands of machines in order to finish in a reasonable amount of time. The issues of how to parallelize the computation, distribute the data, ...
[2] Remzi H. Arpaci-Dusseau, Eric Anderson, Noah Treuhaft, David E. Culler, Joseph M. Hellerstein, David Patterson, and Kathy Yelick. Cluster I/O with River:Making the fast case common. InProceedings of the Sixth Workshop on Input/Output in Parallel and Distributed Systems (IOPADS '99),...