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)
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