May 2024
Intermediate to advanced
110 pages
1h 19m
Chinese
Content preview from 面向MapReduce的Hadoop优化
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第6章 优化MapReduce任务
大多数MapReduce程序都是以数据分析为目的的，因此通常需要花的时间比较长。许多公司正在使用Hadoop在大规模数据集上做高级数据分析，这对运行时间提出了更高的要求。效率问题，尤其是MapReduce的I/O开销，仍然需要高度重视。
在本章中，我们将讨论一些优化技术，比如使用压缩和 Combiner 改善作业的执行。而且，本章还要讲述优化mapper和reducer的代码的基本准则和实用规则，以及采用及复用对象实例的技术。
本章将介绍以下内容：
使用Combiner 的好处；
使用压缩技术的重要性；
学习正确使用Writable 类型；
如何明智地复用类型；
如何优化mapper和reducer的代码。
6.1 使用Combiner
通过使用Combiner可以使MapReduce整体性能得到提升。Combiner等同于本地Reduce操作，可以有效提升全局Reduce操作效率。Combiner可以从根本上优化和最小化键值对的数量，而键值对是通过网络在mapper和reducer之间传输的。Combiner使用Map操作处理键值对输出的中间结果，并不影响map和reduce函数中编写的转换逻辑。
习惯上，把reducer函数当作Combiner是使用Combiner的标准方式。计算逻辑应该符合交换律（运算次序不影响运算结果，如加法）和结合律（应用加法运算的次序不影响结果）。
要了解更多关于交换律和结合律属性的信息，可以浏览以下链接：
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