))
MapReduce: Simplified Data Processing on Large Clusters
Manage episode 487366647 series 3670304
A tartalmat a The Binary Breakdown biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a The Binary Breakdown vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.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 epizódok
Megosztás
