[大牛翻译系列]Hadoop 翻译文章索引

原书章节 原书章节题目 翻译文章序号 翻译文章题目 链接
4.1 Joining Hadoop(1) MapReduce 连接:重分区连接(Repartition join) http://www.cnblogs.com/datacloud/p/3578509.html
4.1.1 Repartition join Hadoop(1) MapReduce 连接:重分区连接(Repartition join) http://www.cnblogs.com/datacloud/p/3578509.html
4.1.2 Replicated joins Hadoop(2) MapReduce 连接:复制连接(Replication join) http://www.cnblogs.com/datacloud/p/3579333.html
4.1.3 Semi-joins Hadoop(3) MapReduce 连接:半连接(Semi-join) http://www.cnblogs.com/datacloud/p/3579975.html
4.1.4 Picking the best join strategy for your data Hadoop(4) MapReduce 连接:选择最佳连接策略 http://www.cnblogs.com/datacloud/p/3582113.html
4.2 Sorting Hadoop(5) MapReduce 排序:次排序(Secondary sort) http://www.cnblogs.com/datacloud/p/3584640.html
4.2.1 Secondary sort Hadoop(5) MapReduce 排序:次排序(Secondary sort) http://www.cnblogs.com/datacloud/p/3584640.html
4.2.2 Total order sorting Hadoop(6) MapReduce 排序:总排序(Total order sorting) http://www.cnblogs.com/datacloud/p/3586761.html
4.3 Sampling Hadoop(7) MapReduce:抽样(Sampling) http://www.cnblogs.com/datacloud/p/3588120.html
6.1 Measuring MapReduce and your environment Hadoop(8) MapReduce 性能调优:性能测量(Measuring) http://www.cnblogs.com/datacloud/p/3589875.html
6.2 Determining the cause of your performance woes Hadoop(9) MapReduce 性能调优:理解性能瓶颈,诊断map性能瓶颈 http://www.cnblogs.com/datacloud/p/3591981.html
6.2.1 Understanding what can impact MapReduce job performance Hadoop(9) MapReduce 性能调优:理解性能瓶颈,诊断map性能瓶颈 http://www.cnblogs.com/datacloud/p/3591981.html
6.2.2 Map woes Hadoop(9) MapReduce 性能调优:理解性能瓶颈,诊断map性能瓶颈 http://www.cnblogs.com/datacloud/p/3591981.html
6.2.3 Reducer woes Hadoop(10) MapReduce 性能调优:诊断reduce性能瓶颈 http://www.cnblogs.com/datacloud/p/3595682.html
6.2.4 General task woes Hadoop(11) MapReduce 性能调优:诊断一般性能瓶颈 http://www.cnblogs.com/datacloud/p/3596294.html
6.2.5 Hardware woes Hadoop(12) MapReduce 性能调优:诊断硬件性能瓶颈 http://www.cnblogs.com/datacloud/p/3597909.html
6.4.3 Optimizing the shuffle and sort phase Hadoop(13) MapReduce 性能调优:优化洗牌(shuffle)和排序阶段 http://www.cnblogs.com/datacloud/p/3599920.html
6.4.4 Skew mitigation Hadoop(14) MapReduce 性能调优:减小数据倾斜的性能损失 http://www.cnblogs.com/datacloud/p/3601624.html
6.4.5 Optimizing user space Java in MapReduce Hadoop(15) MapReduce 性能调优:优化MapReduce的用户JAVA代码 http://www.cnblogs.com/datacloud/p/3603191.html
6.4.6 Data serialization Hadoop(16) MapReduce 性能调优:优化数据序列化 http://www.cnblogs.com/datacloud/p/3608591.html
6.5 Chapter summary  Hadoop(16)  MapReduce 性能调优:优化数据序列化 http://www.cnblogs.com/datacloud/p/3608591.html
5.1 Working with small files Hadoop(17) MapReduce 文件处理:小文件 http://www.cnblogs.com/datacloud/p/3611459.html
5.2 Efficient storage with compression(tech 25, 26) Hadoop(19) MapReduce 文件处理:基于压缩的高效存储(一) http://www.cnblogs.com/datacloud/p/3612817.html
5.2 Efficient storage with compression(tech 27) Hadoop(19) MapReduce 文件处理:基于压缩的高效存储(一) http://www.cnblogs.com/datacloud/p/3616544.html
Appendix A.10 LZOP Hadoop(20) 附录A.10 压缩格式LZOP编译安装配置 http://www.cnblogs.com/datacloud/p/3617586.html
Appendix D.1 An optimized repartition join framework Hadoop(21) 附录D.1 优化后的重分区框架 http://www.cnblogs.com/datacloud/p/3617079.html
Appendix D.2 A replicated join framework Hadoop(22) 附录D.2 复制连接框架  http://www.cnblogs.com/datacloud/p/3617078.html
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