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在大数据处理中，Hadoop和MySQL是常见的技术组合。Hadoop提供了分布式计算框架，而MySQL是一种常用的关系型数据库。本文将详细介绍如何利用MapReduce来读写MySQL数据，以实现一个高效的数据处理服务器。
1. 概述
MapReduce是一种用于大规模数据处理的编程模型，它将任务分解为多个并行的Map和Reduce阶段。在本例中，我们将使用MapReduce来读取和写入MySQL数据库中的数据。
2. 准备工作
在开始之前，我们需要完成以下准备工作：
3. 读取MySQL数据
首先，我们将介绍如何使用MapReduce从MySQL数据库中读取数据。以下是一个示例代码：
import java.io.IOException大数据之 Hadoop （MapReduce）
MapReduce 概述 MapReduce：一个分布式运算程序的编程框架，是用户开发“基于 Hadoop 的数据分析 应用”的核心框架。 MapReduce 核心功能是将用户编写的业务逻辑代码和自带默认组件整合成一个完整的 分布式运算程序，并发运行在一个 Hadoop 集群上。 MapReduce 优缺点 优点 1）MapReduce 易于编程 它简单的实现一些接口，就可以完成一个分布式程序，这个分布式程序可以分布到大量廉价的 PC 机器上运行。也就是说你写一个分布式程序，跟写一个简单的串行程序是一模一样
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这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. 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