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MapReduce: Simplified Data Processing on Large Clusters
Manage episode 487366647 series 3670304
The Binary Breakdown에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 The Binary Breakdown 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
MapReduce is a programming model that simplifies the process of processing large datasets on clusters of commodity machines. It allows users to define two functions: Map and Reduce, which are then automatically parallelized and executed across the cluster. The Map function processes key/value pairs from the input data and generates intermediate key/value pairs. The Reduce function merges all intermediate values associated with the same key to produce the final output. This paper, written by researchers at Google, describes the implementation of MapReduce on their large-scale computing infrastructure, highlighting its features, performance, fault tolerance, and real-world applications. The authors also discuss the benefits of using MapReduce, such as its simplicity, scalability, and flexibility, and compare it to other related systems. https://storage.googleapis.com/gweb-research2023-media/pubtools/4449.pdf
44 에피소드
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