Why Sqls can be run via MapReduce?

　　in 《Core Model of Sql》, I showed that there are three driven operations in SQL: Group, Filter, Join. In this article, I'll illustrate why sqls can be executed by a MapReduce engine, by showing that all the 3 operations can be implemented via MapReduce.  A typical MapReduce goes this way:

　　Map:       each file row                                                -> (intermediary key, intermediary value)

　　Reduce:  (intermediary key, List of intermediary value) -> (final key, final value)

Now I show the 3 operations one by one.

Group:

　　Map:      each table row                                               ->(columns of "group by", other columns)

　　Reduce: (columns of "group by", List of other columns)  ->(columns of "group by", apply aggregate function to "List of other columns")

Filter:

　　Map:      each table row -> if this row does not pass the where condition, then drop it

                                              else (1, selected columns)

　　Reduce:  No reduce is needed

Join:

　　Split tableA into N partitions, split tableB into M partitions. Then there are N*M input pairs

　　For each input pair(part_i_A,part_j_B):

　　　　for rowA in part_i_A:

　　　　　　for rowB in part_j_B:

　　　　　　　　generate (1, rowA+rowB)