Saving all output to "!!{outputDirectory}!!/groupby1_map.q.raw". Enter "record" with no arguments to stop it.
>>>  !run !!{qFileDirectory}!!/groupby1_map.q
>>>  set hive.map.aggr=true;
No rows affected 
>>>  set hive.groupby.skewindata=false;
No rows affected 
>>>  set mapred.reduce.tasks=31;
No rows affected 
>>>  
>>>  CREATE TABLE dest1(key INT, value DOUBLE) STORED AS TEXTFILE;
No rows affected 
>>>  
>>>  EXPLAIN 
FROM src INSERT OVERWRITE TABLE dest1 SELECT src.key, sum(substr(src.value,5)) GROUP BY src.key;
'Explain'
'ABSTRACT SYNTAX TREE:'
'  (TOK_QUERY (TOK_FROM (TOK_TABREF (TOK_TABNAME src))) (TOK_INSERT (TOK_DESTINATION (TOK_TAB (TOK_TABNAME dest1))) (TOK_SELECT (TOK_SELEXPR (. (TOK_TABLE_OR_COL src) key)) (TOK_SELEXPR (TOK_FUNCTION sum (TOK_FUNCTION substr (. (TOK_TABLE_OR_COL src) value) 5)))) (TOK_GROUPBY (. (TOK_TABLE_OR_COL src) key))))'
''
'STAGE DEPENDENCIES:'
'  Stage-1 is a root stage'
'  Stage-0 depends on stages: Stage-1'
'  Stage-2 depends on stages: Stage-0'
''
'STAGE PLANS:'
'  Stage: Stage-1'
'    Map Reduce'
'      Alias -> Map Operator Tree:'
'        src '
'          TableScan'
'            alias: src'
'            Select Operator'
'              expressions:'
'                    expr: key'
'                    type: string'
'                    expr: value'
'                    type: string'
'              outputColumnNames: key, value'
'              Group By Operator'
'                aggregations:'
'                      expr: sum(substr(value, 5))'
'                bucketGroup: false'
'                keys:'
'                      expr: key'
'                      type: string'
'                mode: hash'
'                outputColumnNames: _col0, _col1'
'                Reduce Output Operator'
'                  key expressions:'
'                        expr: _col0'
'                        type: string'
'                  sort order: +'
'                  Map-reduce partition columns:'
'                        expr: _col0'
'                        type: string'
'                  tag: -1'
'                  value expressions:'
'                        expr: _col1'
'                        type: double'
'      Reduce Operator Tree:'
'        Group By Operator'
'          aggregations:'
'                expr: sum(VALUE._col0)'
'          bucketGroup: false'
'          keys:'
'                expr: KEY._col0'
'                type: string'
'          mode: mergepartial'
'          outputColumnNames: _col0, _col1'
'          Select Operator'
'            expressions:'
'                  expr: _col0'
'                  type: string'
'                  expr: _col1'
'                  type: double'
'            outputColumnNames: _col0, _col1'
'            Select Operator'
'              expressions:'
'                    expr: UDFToInteger(_col0)'
'                    type: int'
'                    expr: _col1'
'                    type: double'
'              outputColumnNames: _col0, _col1'
'              File Output Operator'
'                compressed: false'
'                GlobalTableId: 1'
'                table:'
'                    input format: org.apache.hadoop.mapred.TextInputFormat'
'                    output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
'                    serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
'                    name: groupby1_map.dest1'
''
'  Stage: Stage-0'
'    Move Operator'
'      tables:'
'          replace: true'
'          table:'
'              input format: org.apache.hadoop.mapred.TextInputFormat'
'              output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
'              serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
'              name: groupby1_map.dest1'
''
'  Stage: Stage-2'
'    Stats-Aggr Operator'
''
''
90 rows selected 
>>>  
>>>  FROM src INSERT OVERWRITE TABLE dest1 SELECT src.key, sum(substr(src.value,5)) GROUP BY src.key;
'_col0','_col1'
No rows selected 
>>>  
>>>  SELECT dest1.* FROM dest1;
'key','value'
'0','0.0'
'10','10.0'
'100','200.0'
'103','206.0'
'104','208.0'
'105','105.0'
'11','11.0'
'111','111.0'
'113','226.0'
'114','114.0'
'116','116.0'
'118','236.0'
'119','357.0'
'12','24.0'
'120','240.0'
'125','250.0'
'126','126.0'
'128','384.0'
'129','258.0'
'131','131.0'
'133','133.0'
'134','268.0'
'136','136.0'
'137','274.0'
'138','552.0'
'143','143.0'
'145','145.0'
'146','292.0'
'149','298.0'
'15','30.0'
'150','150.0'
'152','304.0'
'153','153.0'
'155','155.0'
'156','156.0'
'157','157.0'
'158','158.0'
'160','160.0'
'162','162.0'
'163','163.0'
'164','328.0'
'165','330.0'
'166','166.0'
'167','501.0'
'168','168.0'
'169','676.0'
'17','17.0'
'170','170.0'
'172','344.0'
'174','348.0'
'175','350.0'
'176','352.0'
'177','177.0'
'178','178.0'
'179','358.0'
'18','36.0'
'180','180.0'
'181','181.0'
'183','183.0'
'186','186.0'
'187','561.0'
'189','189.0'
'19','19.0'
'190','190.0'
'191','382.0'
'192','192.0'
'193','579.0'
'194','194.0'
'195','390.0'
'196','196.0'
'197','394.0'
'199','597.0'
'2','2.0'
'20','20.0'
'200','400.0'
'201','201.0'
'202','202.0'
'203','406.0'
'205','410.0'
'207','414.0'
'208','624.0'
'209','418.0'
'213','426.0'
'214','214.0'
'216','432.0'
'217','434.0'
'218','218.0'
'219','438.0'
'221','442.0'
'222','222.0'
'223','446.0'
'224','448.0'
'226','226.0'
'228','228.0'
'229','458.0'
'230','1150.0'
'233','466.0'
'235','235.0'
'237','474.0'
'238','476.0'
'239','478.0'
'24','48.0'
'241','241.0'
'242','484.0'
'244','244.0'
'247','247.0'
'248','248.0'
'249','249.0'
'252','252.0'
'255','510.0'
'256','512.0'
'257','257.0'
'258','258.0'
'26','52.0'
'260','260.0'
'262','262.0'
'263','263.0'
'265','530.0'
'266','266.0'
'27','27.0'
'272','544.0'
'273','819.0'
'274','274.0'
'275','275.0'
'277','1108.0'
'278','556.0'
'28','28.0'
'280','560.0'
'281','562.0'
'282','564.0'
'283','283.0'
'284','284.0'
'285','285.0'
'286','286.0'
'287','287.0'
'288','576.0'
'289','289.0'
'291','291.0'
'292','292.0'
'296','296.0'
'298','894.0'
'30','30.0'
'302','302.0'
'305','305.0'
'306','306.0'
'307','614.0'
'308','308.0'
'309','618.0'
'310','310.0'
'311','933.0'
'315','315.0'
'316','948.0'
'317','634.0'
'318','954.0'
'321','642.0'
'322','644.0'
'323','323.0'
'325','650.0'
'327','981.0'
'33','33.0'
'331','662.0'
'332','332.0'
'333','666.0'
'335','335.0'
'336','336.0'
'338','338.0'
'339','339.0'
'34','34.0'
'341','341.0'
'342','684.0'
'344','688.0'
'345','345.0'
'348','1740.0'
'35','105.0'
'351','351.0'
'353','706.0'
'356','356.0'
'360','360.0'
'362','362.0'
'364','364.0'
'365','365.0'
'366','366.0'
'367','734.0'
'368','368.0'
'369','1107.0'
'37','74.0'
'373','373.0'
'374','374.0'
'375','375.0'
'377','377.0'
'378','378.0'
'379','379.0'
'382','764.0'
'384','1152.0'
'386','386.0'
'389','389.0'
'392','392.0'
'393','393.0'
'394','394.0'
'395','790.0'
'396','1188.0'
'397','794.0'
'399','798.0'
'4','4.0'
'400','400.0'
'401','2005.0'
'402','402.0'
'403','1209.0'
'404','808.0'
'406','1624.0'
'407','407.0'
'409','1227.0'
'41','41.0'
'411','411.0'
'413','826.0'
'414','828.0'
'417','1251.0'
'418','418.0'
'419','419.0'
'42','84.0'
'421','421.0'
'424','848.0'
'427','427.0'
'429','858.0'
'43','43.0'
'430','1290.0'
'431','1293.0'
'432','432.0'
'435','435.0'
'436','436.0'
'437','437.0'
'438','1314.0'
'439','878.0'
'44','44.0'
'443','443.0'
'444','444.0'
'446','446.0'
'448','448.0'
'449','449.0'
'452','452.0'
'453','453.0'
'454','1362.0'
'455','455.0'
'457','457.0'
'458','916.0'
'459','918.0'
'460','460.0'
'462','924.0'
'463','926.0'
'466','1398.0'
'467','467.0'
'468','1872.0'
'469','2345.0'
'47','47.0'
'470','470.0'
'472','472.0'
'475','475.0'
'477','477.0'
'478','956.0'
'479','479.0'
'480','1440.0'
'481','481.0'
'482','482.0'
'483','483.0'
'484','484.0'
'485','485.0'
'487','487.0'
'489','1956.0'
'490','490.0'
'491','491.0'
'492','984.0'
'493','493.0'
'494','494.0'
'495','495.0'
'496','496.0'
'497','497.0'
'498','1494.0'
'5','15.0'
'51','102.0'
'53','53.0'
'54','54.0'
'57','57.0'
'58','116.0'
'64','64.0'
'65','65.0'
'66','66.0'
'67','134.0'
'69','69.0'
'70','210.0'
'72','144.0'
'74','74.0'
'76','152.0'
'77','77.0'
'78','78.0'
'8','8.0'
'80','80.0'
'82','82.0'
'83','166.0'
'84','168.0'
'85','85.0'
'86','86.0'
'87','87.0'
'9','9.0'
'90','270.0'
'92','92.0'
'95','190.0'
'96','96.0'
'97','194.0'
'98','196.0'
309 rows selected 
>>>  !record