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