1、中文 3700 字， 2000 英文单词， 10500 英文字符 文献出处： Dean J, Ghemawat S. MapReduce: simplified data processing on large clustersJ. Communications of the ACM, 2008, 51(1): 107-113. MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat , Google, Inc. Abstract MapReduce is a progr
2、amming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate
3、 key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling t
4、he programs execution across a set of machines, handling machine failures, and managing the required inter-machine ommunication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of M
5、apReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers and the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce
6、 jobs are executed on Googles clusters every day. 1 Introduction Over the past five years, the authors and many others at Google have implemented hundreds of that process large amounts of raw data,such as crawled documents, web request logs, etc., to compute various kinds of derived data, such as inverted indices, various representations of the graph structure of web documents, summaries of the number of pages crawled per host, the set of most frequent q
