边google边测试，目前将HIVE的小文件问题基本解决。
下为个人事件中的解决方案，请大家多指正~
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个人理解：造成小文件数过多的直接原因是reduce数过多，每个reduce在每次DML操作时候都会产生至少一个小文件。
我这边遇到reduce数过多的情况，往往是利用动态分区方式、跨比较大的时间范围查询并写入报表。
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个人解决方案（结合了百度到的几种方案，测试可有效解决问题）
设置参数：
set hive.input.format=org.apache.hadoop.hive.ql.io.CombineHiveInputFormat;
set hive.hadoop.supports.splittable.combineinputformat = true;
set hive.merge.mapredfiles = true; /* Merge small files at the end of a map-only job. */
set hive.merge.mapfiles=true; /* Merge small files at the end of a map-reduce job. . */
另外，在代码的最外层select 后加一个distribute by rand()，据说可以比较均匀的重新分配写入的文件数，我也每次都带上了。
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