MapReduce的核心是shuffle,她对于mapreduce的效率起到了至关重要的作用,now,我把我对shuffle的理解过程简单介绍一下,如果有误还请指教阿。。
MapReduce的过程(针对一个map来说):
每个Map在内存中都有一个缓存区,map的输出结果会先放到这个缓冲区当中,缓冲区有一个spill percent,这里默认是80%(可以手动进行配置),也就是说当输出到缓冲区中的内容达到80%时,就会进行spill(溢出),溢出到磁盘的一个临时文件中,也就是说这80%的内容成为一个临时文件,这里还涉及到了一个partition的概念,一个临时文件里面是进行了分区的,并且分区的数量由reduce的数量决定,即不同的分区内容传给不同的reduce。当这80%的内容在溢出时,map会继续向那20%的缓冲中输出。插入一点,在缓冲区溢出到磁盘之前,会进行sort和combiner,然后才会写道磁盘中。这两个步骤很重要,尤其是combiner,它直接决定了MapReduce的效率。并且sort和combiner这两个处理发生在在shuffle的整个过程中。这样一个map执行下来,就会在磁盘上存储几个临时文件,然后会对这几个临时文件进行一个merge,合并程一个文件,这个文件中有n个partition,n是reduce的数量。说明一下:这些临时文件和合并的文件都是在本地文件系统上存储的。
每个Map输出这样一个文件,最后不同Map生成的文件按照不同的partition传给不同的reduce,然后reduce直接把结果输出到HDFS文件系统上了。
这个是官方的一个图:
有人认为这个图有问题,但在我个人开来,这个图描述的没有问题,如果认为确实有问题的还请指教阿。。
简单说一下combiner的一个很重要的作用:combiner会对相同key的值进行合并,比如<a,1><a,1><a,1>,combiner过后合并成<a,3>,即减少了数据量,那末传向reduce的数据量就减少了,进而提高了效率。。
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