MapReduce:Simplified DataProcessing on LargeClusters What's the main point of view of this paper? ■Abstract ■Conclusion
MapReduce: Simplified DataProcessing on LargeClusters ◼ What’s the main point of view of this paper? ◼ Abstract ◼ Conclusion
Abstract MapReduce is a programming model and an associ- ated 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 key.Many real world tasks are expressible in this model,as shown in the paper
Programs written in this functional style are automati- cally parallelized and executed on a large cluster of com- modity machines.The run-time system takes care of the details of partitioning the input data,scheduling the pro- gram's execution across a set of machines,handling ma- chine failures,and managing the required inter-machine communication.This allows programmers without any experience with parallel and distributed systems to eas- ily utilize the resources of a large distributed system
Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many ter- abytes of data on thousands of machines.Programmers find the system easy to use:hundreds of MapReduce pro- grams have been implemented and upwards of one thou- sand MapReduce jobs are executed on Google's clusters every day
Are these view right? How do the authors prove their results? ·Highly scalable? ·Easy to use? Are they solid approaches?
Are these view right? ◼ How do the authors prove their results? ◼ Highly scalable? ◼ Easy to use? ◼ Are they solid approaches?