准备数据
SELECT uid, SUM(COUNT) FROM logs GROUP BY uid;hive> SELECT * FROM logs; a 苹果 5 a 橙子 3 a 苹果 2 b 烧鸡 1 hive> SELECT uid, SUM(COUNT) FROM logs GROUP BY uid; a 10 b 1计算过程
默认设置了hive.map.aggr=true，所以会在mapper端先group by一次，最后再把结果merge起来，为了减少reducer处理的数据量。注意看explain的mode是不一样的。mapper是hash，reducer是mergepartial。如果把hive.map.aggr=false，那将groupby放到reducer才做，他的mode是complete.
Operator
Explain
hive> explain SELECT uid, sum(count) FROM logs group by uid; OK ABSTRACT SYNTAX TREE: (TOK_QUERY (TOK_FROM (TOK_TABREF (TOK_TABNAME logs))) (TOK_INSERT (TOK_DESTINATION (TOK_DIR TOK_TMP_FILE)) (TOK_SELECT (TOK_SELEXPR (TOK_TABLE_OR_COL uid)) (TOK_SELEXPR (TOK_FUNCTION sum (TOK_TABLE_OR_COL count)))) (TOK_GROUPBY (TOK_TABLE_OR_COL uid)))) STAGE DEPENDENCIES: Stage-1 is a root stage Stage-0 is a root stage STAGE PLANS: Stage: Stage-1 Map Reduce Alias -> Map Operator Tree: logs TableScan // 扫描表 alias: logs Select Operator //选择字段 expressions: expr: uid type: string expr: count type: int outputColumnNames: uid, count Group By Operator //这里是因为默认设置了hive.map.aggr=true，会在mapper先做一次聚合，减少reduce需要处理的数据 aggregations: expr: sum(count) //聚集函数 bucketGroup: false keys: //键 expr: uid type: string mode: hash //hash方式，processHashAggr() outputColumnNames: _col0, _col1 Reduce Output Operator //输出key，value给reducer key expressions: expr: _col0 type: string sort order: + Map-reduce partition columns: expr: _col0 type: string tag: -1 value expressions: expr: _col1 type: bigint Reduce Operator Tree: Group By Operator aggregations: expr: sum(VALUE._col0) //聚合 bucketGroup: false keys: expr: KEY._col0 type: