搜到了答案，我觉得回答地不错。
根据Google发布的论文
MapReduce: Simplified Data Processing on Large Clusters
http://static.googleusercontent.com/media/research.google.com/zh-CN//archive/mapreduce-osdi04.pdf这里引用3.5 TaskGranularity的一小段，下文中M代表map任务数，R代表reduce任务数
Furthermore, R is often constrained by users because the output of each reduce task ends up in a separate output fi le. In practice, we tend to choose M so that each individual task is roughly 16 MB to 64 MB of input data(so that the locality optimization described above is most effective), and we make R a small multiple of the number of worker machines we expect to use. We often perform MapReduce computations with M = 200,000 and R = 5,000, using 2,000 worker machines.
总的来说
map任务数倾向于把输入文件可以分割成16MB到64MB之间，因为这刚好是GFS每个分块文件的大小，可以减少数据在网络中流动
reduce任务数通常是机器节点数的小倍数
至于机器节点数？有钱就要任性，多多益善。。
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作者：曾凌恒
链接：
http://www.zhihu.com/question/26816539/answer/34133965