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Flink SQL 1.0+ UT Cases

阿新 • • 發佈：2019-01-06
SELECT * FROM MyTable WHERE false LogicalProject(_1=[$0], _2=[$1], _3=[$2]) LogicalFilter(condition=[false]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[_1, _2, _3], where=[false]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 9000.0 cpu, 0.0 io}, id = 99 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0
, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 93 SELECT * FROM MyTable WHERE true LogicalProject(_1=[$0], _2=[$1], _3=[$2]) LogicalTableScan(table=[[MyTable]]) DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 13 SELECT * FROM MyTable WHERE
c LIKE '%world%' LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalFilter(condition=[LIKE($2, '%world%')]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_2, 'b') AS _2, AS(_3, 'c') AS _3], where=[LIKE(AS(_3, 'c'), '%world%')]): rowcount = 1000.0, cumulative cost = {2000.0
rows, 16000.0 cpu, 0.0 io}, id = 44 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 28 SELECT * FROM MyTable WHERE MOD(a,2)=0 LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalFilter(condition=[=(MOD($0, 2), 0)]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_2, 'b') AS _2, AS(_3, 'c') AS _3], where=[=(MOD(AS(_1, 'a'), 2), 0)]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 18000.0 cpu, 0.0 io}, id = 44 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 28 SELECT * FROM MyTable WHERE a < 2 OR a > 20 LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalFilter(condition=[OR(<($0, 2), >($0, 20))]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_2, 'b') AS _2, AS(_3, 'c') AS _3], where=[OR(<(AS(_1, 'a'), 2), >(AS(_1, 'a'), 20))]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 19000.0 cpu, 0.0 io}, id = 44 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 28 SELECT * FROM MyTable WHERE MOD(a,2)<>0 AND MOD(b,2)=0 LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalFilter(condition=[AND(<>(MOD($0, 2), 0), =(MOD($1, 2), 0))]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_2, 'b') AS _2, AS(_3, 'c') AS _3], where=[AND(<>(MOD(AS(_1, 'a'), 2), 0), =(MOD(AS(_2, 'b'), 2), 0))]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 21000.0 cpu, 0.0 io}, id = 44 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 28 /** Join **/ SELECT c, g FROM Table3, Table5 WHERE b = e LogicalProject(c=[$2], g=[$6]) LogicalFilter(condition=[=($1, $4)]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS b, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 24006.0 cpu, 40000.0 io}, id = 265 DataSetJoin(where=[=(b, e)], join=[b, c, e, g], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 24000.0 cpu, 40000.0 io}, id = 264 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 10000.0 cpu, 0.0 io}, id = 262 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 198 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 263 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 200 SELECT c, g FROM Table3, Table5 WHERE b = e AND b < 2 LogicalProject(c=[$2], g=[$6]) LogicalFilter(condition=[AND(=($1, $4), <($1, 2))]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS b, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 27006.0 cpu, 40000.0 io}, id = 425 DataSetJoin(where=[=(b, e)], join=[b, c, e, g], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 27000.0 cpu, 40000.0 io}, id = 424 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2], where=[<(AS(_2, 'b'), 2)]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 13000.0 cpu, 0.0 io}, id = 422 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 278 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 423 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 280 SELECT c, g FROM Table3, Table5 WHERE b = e AND a < 6 AND h < b LogicalProject(c=[$2], g=[$6]) LogicalFilter(condition=[AND(=($1, $4), <($0, 6), <($7, $1))]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS b, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 32007.0 cpu, 48000.0 io}, id = 208 DataSetJoin(where=[AND(=(b, e), <(h, b))], join=[b, c, e, g, h], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 32000.0 cpu, 48000.0 io}, id = 207 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2], where=[<(AS(_1, 'a'), 6)]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 15000.0 cpu, 0.0 io}, id = 205 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 65 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2, AS(_5, 'h') AS _3]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 15000.0 cpu, 0.0 io}, id = 206 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 67 SELECT c, g FROM Table3, Table5 WHERE a = d AND b = h LogicalProject(c=[$2], g=[$6]) LogicalFilter(condition=[AND(=($0, $3), =($1, $7))]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS a, g AS b]): rowcount = 1.0, cumulative cost = {6001.0 rows, 30008.0 cpu, 48000.0 io}, id = 129 DataSetJoin(where=[AND(=(a, d), =(b, h))], join=[a, b, c, d, g, h], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 30000.0 cpu, 48000.0 io}, id = 128 DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_2, 'b') AS _2, AS(_3, 'c') AS _3]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 13000.0 cpu, 0.0 io}, id = 126 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 65 DataSetCalc(select=[AS(_1, 'd') AS _1, AS(_4, 'g') AS _2, AS(_5, 'h') AS _3]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 15000.0 cpu, 0.0 io}, id = 127 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 67 SELECT c, g FROM Table3, Table5 WHERE a = g LogicalProject(c=[$2], g=[$6]) LogicalFilter(condition=[=($0, $6)]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS a, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 21005.0 cpu, 28000.0 io}, id = 132 DataSetJoin(where=[=(a, g)], join=[a, c, g], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 21000.0 cpu, 28000.0 io}, id = 131 DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_3, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 10000.0 cpu, 0.0 io}, id = 129 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 65 DataSetCalc(select=[AS(_4, 'g') AS _1]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 9000.0 cpu, 0.0 io}, id = 130 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 67 SELECT Table5.c, Table3.c FROM Table3, Table5 WHERE a = d AND a < 4 LogicalProject(c=[$7], c0=[$2]) LogicalFilter(condition=[AND(=($0, $3), <($0, 4))]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c0 AS a, c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 27006.0 cpu, 28000.0 io}, id = 212 DataSetJoin(where=[=(a, d)], join=[a, c, d, c0], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 27000.0 cpu, 28000.0 io}, id = 211 DataSetCalc(select=[AS(_1, 'a') AS _1, AS(_3, 'c') AS _2], where=[<(AS(_1, 'a'), 4)]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 13000.0 cpu, 0.0 io}, id = 209 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 65 DataSetCalc(select=[AS(_1, 'd') AS _1, AS(_5, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 210 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 67 SELECT COUNT(g), COUNT(b) FROM Table3, Table5 WHERE a = d LogicalAggregate(group=[{}], EXPR$0=[COUNT($0)], EXPR$1=[COUNT($1)]) LogicalProject(g=[$6], b=[$1]) LogicalFilter(condition=[=($0, $3)]) LogicalJoin(condition=[true], joinType=[inner]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetAggregate(select=[COUNT(g) AS EXPR$0, COUNT(b) AS EXPR$1]): rowcount = 1.0, cumulative cost = {6002.0 rows, 16008.0 cpu, 28020.0 io}, id = 175 DataSetCalc(select=[g AS a, b]): rowcount = 1.0, cumulative cost = {6001.0 rows, 16006.0 cpu, 28000.0 io}, id = 174 DataSetJoin(where=[=(a, d)], join=[a, b, d, g], joinType=[Join]): rowcount = 1.0, cumulative cost = {6000.0 rows, 16000.0 cpu, 28000.0 io}, id = 173 DataSetCalc(select=[a, b]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 6000.0 cpu, 0.0 io}, id = 171 DataSetScan(table=[[Table3]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 161 DataSetCalc(select=[d, g AS e]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 8000.0 cpu, 0.0 io}, id = 172 DataSetScan(table=[[Table5]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 162 SELECT c, g FROM Table3 FULL OUTER JOIN Table5 ON b = e LogicalProject(c=[$2], g=[$6]) LogicalJoin(condition=[=($1, $4)], joinType=[full]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS b, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 24006.0 cpu, 40000.0 io}, id = 74 DataSetJoin(where=[=(b, e)], join=[b, c, e, g], joinType=[FullOuterJoin]): rowcount = 1.0, cumulative cost = {6000.0 rows, 24000.0 cpu, 40000.0 io}, id = 73 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 10000.0 cpu, 0.0 io}, id = 71 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 49 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 72 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 51 SELECT c, g FROM Table5 LEFT OUTER JOIN Table3 ON b = e LogicalProject(c=[$7], g=[$3]) LogicalJoin(condition=[=($6, $1)], joinType=[left]) LogicalTableScan(table=[[Table5]]) LogicalTableScan(table=[[Table3]]) DataSetCalc(select=[c AS e, g]): rowcount = 1.0, cumulative cost = {6001.0 rows, 24006.0 cpu, 40000.0 io}, id = 74 DataSetJoin(where=[=(b, e)], join=[e, g, b, c], joinType=[LeftOuterJoin]): rowcount = 1.0, cumulative cost = {6000.0 rows, 24000.0 cpu, 40000.0 io}, id = 73 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 71 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 51 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 10000.0 cpu, 0.0 io}, id = 72 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 49 SELECT c, g FROM Table3 RIGHT OUTER JOIN Table5 ON b = e LogicalProject(c=[$2], g=[$6]) LogicalJoin(condition=[=($1, $4)], joinType=[right]) LogicalTableScan(table=[[Table3]]) LogicalTableScan(table=[[Table5]]) DataSetCalc(select=[c AS b, g AS c]): rowcount = 1.0, cumulative cost = {6001.0 rows, 24006.0 cpu, 40000.0 io}, id = 74 DataSetJoin(where=[=(b, e)], join=[b, c, e, g], joinType=[RightOuterJoin]): rowcount = 1.0, cumulative cost = {6000.0 rows, 24000.0 cpu, 40000.0 io}, id = 73 DataSetCalc(select=[AS(_2, 'b') AS _1, AS(_3, 'c') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 10000.0 cpu, 0.0 io}, id = 71 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 49 DataSetCalc(select=[AS(_2, 'e') AS _1, AS(_4, 'g') AS _2]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 12000.0 cpu, 0.0 io}, id = 72 DataSetScan(table=[[_DataSetTable_1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 51 /** Aggregations **/ SELECT sum(_1), min(_1), max(_1), count(_1), avg(_1) FROM MyTable LogicalAggregate(group=[{}], EXPR$0=[SUM($0)], EXPR$1=[MIN($0)], EXPR$2=[MAX($0)], EXPR$3=[COUNT($0)], EXPR$4=[AVG($0)]) LogicalProject(_1=[$0]) LogicalTableScan(table=[[MyTable]]) DataSetAggregate(select=[SUM(_1) AS EXPR$0, MIN(_1) AS EXPR$1, MAX(_1) AS EXPR$2, COUNT(_1) AS EXPR$3, AVG(_1) AS EXPR$4]): rowcount = 1000.0, cumulative cost = {3000.0 rows, 10000.0 cpu, 4000.0 io}, id = 26 DataSetCalc(select=[_1]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 5000.0 cpu, 0.0 io}, id = 25 DataSetScan(table=[[MyTable]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 23 SELECT avg(_1), avg(_2), avg(_3), avg(_4), avg(_5), avg(_6), count(_7), sum(CAST(_6 AS DECIMAL)) FROM MyTable LogicalAggregate(group=[{}], EXPR$0=[AVG($0)], EXPR$1=[AVG($1)], EXPR$2=[AVG($2)], EXPR$3=[AVG($3)], EXPR$4=[AVG($4)], EXPR$5=[AVG($5)], EXPR$6=[COUNT($6)], EXPR$7=[SUM($7)]) LogicalProject(_1=[$0], _2=[$1], _3=[$2], _4=[$3], _5=[$4], _6=[$5], _7=[$6], $f7=[CAST($5):DECIMAL(1073741823, 0)]) LogicalTableScan(table=[[MyTable]]) DataSetAggregate(select=[AVG(_1) AS EXPR$0, AVG(_2) AS EXPR$1, AVG(_3) AS EXPR$2, AVG(_4) AS EXPR$3, AVG(_5) AS EXPR$4, AVG(_6) AS EXPR$5, COUNT(_7) AS EXPR$6, SUM($f7) AS EXPR$7]): rowcount = 1000.0, cumulative cost = {3000.0 rows, 25000.0 cpu, 51000.0 io}, id = 24 DataSetCalc(select=[_1, _2, _3, _4, _5, _6, _7]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 17000.0 cpu, 0.0 io}, id = 23 DataSetScan(table=[[MyTable]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 21 SELECT avg(a + 2) + 2, count(b) + 5 FROM MyTable LogicalProject(EXPR$0=[+($0, 2)], EXPR$1=[+($1, 5)]) LogicalAggregate(group=[{}], agg#0=[AVG($0)], agg#1=[COUNT($1)]) LogicalProject($f0=[+($0, 2)], b=[$1]) LogicalTableScan(table=[[MyTable]]) DataSetCalc(select=[+($f0, 2) AS $f0, +($f1, 5) AS $f1]): rowcount = 1000.0, cumulative cost = {4000.0 rows, 17000.0 cpu, 16000.0 io}, id = 32 DataSetAggregate(select=[AVG($f0) AS $f0, COUNT(b) AS $f1]): rowcount = 1000.0, cumulative cost = {3000.0 rows, 9000.0 cpu, 16000.0 io}, id = 31 DataSetCalc(select=[+(a, 2) AS a, b]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 7000.0 cpu, 0.0 io}, id = 30 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 26 SELECT avg(a), sum(b), count(c) FROM (SELECT _1 as a, _2 as b, _3 as c FROM MyTable) LogicalAggregate(group=[{}], EXPR$0=[AVG($0)], EXPR$1=[SUM($1)], EXPR$2=[COUNT($2)]) LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalTableScan(table=[[MyTable]]) DataSetAggregate(select=[AVG(a) AS EXPR$0, SUM(b) AS EXPR$1, COUNT(c) AS EXPR$2]): rowcount = 1000.0, cumulative cost = {3000.0 rows, 14000.0 cpu, 7000.0 io}, id = 27 DataSetCalc(select=[_1, _2, _3]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 11000.0 cpu, 0.0 io}, id = 26 DataSetScan(table=[[_DataSetTable_0]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 20 SELECT sum(_1) as a, count(distinct _3) as b FROM MyTable LogicalAggregate(group=[{}], a=[SUM($0)], b=[COUNT(DISTINCT $1)]) LogicalProject(_1=[$0], _3=[$2]) LogicalTableScan(table=[[MyTable]]) /** distinct not support**/ SELECT _2, _3, avg(_1) as a FROM MyTable GROUP BY GROUPING SETS (_2, _3) LogicalProject(_2=[$0], _3=[$1], a=[$4]) LogicalProject(_2=[CASE($2, null, $0)], _3=[CASE($3, null, $1)], i$_2=[$2], i$_3=[$3], a=[$4]) LogicalAggregate(group=[{0, 1}], groups=[[{0}, {1}]], indicator=[true], a=[AVG($2)]) LogicalProject(_2=[$1], _3=[$2], _1=[$0]) LogicalTableScan(table=[[MyTable]]) /** grouping set not support **/ /** Set **/ SELECT c FROM t1 UNION ALL (SELECT c FROM t2) LogicalUnion(all=[true]) LogicalProject(c=[$2]) LogicalTableScan(table=[[t1]]) LogicalProject(c=[$2]) LogicalTableScan(table=[[t2]]) DataSetUnion(union=[c]): rowcount = 1.0, cumulative cost = {6000.0 rows, 10000.0 cpu, 0.0 io}, id = 40 DataSetCalc(select=[c AS a]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 5000.0 cpu, 0.0 io}, id = 38 DataSetScan(table=[[t1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 34 DataSetCalc(select=[c AS a]): rowcount = 1000.0, cumulative cost = {2000.0 rows, 5000.0 cpu, 0.0 io}, id = 39 DataSetScan(table=[[t2]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu, 0.0 io}, id = 35 SELECT c FROM (SELECT * FROM t1 UNION ALL (SELECT a, b, c FROM t2)) WHERE b < 2 LogicalProject(c=[$2]) LogicalFilter(condition=[<($1, 2)]) LogicalUnion(all=[true]) LogicalProject(a=[$0], b=[$1], c=[$2]) LogicalTableScan(table=[[t1]]) LogicalProject(a=[$0], b=[$1], c=[$3]) LogicalTableScan(table=[[t2]]) DataSetCalc(select=[c AS a], where=[<(b, 2)]): rowcount = 1.0, cumulative cost = {5001.0 rows, 10007.0 cpu, 0.0 io}, id = 93 DataSetUnion(union=[a, b, c]): rowcount = 1.0, cumulative cost = {5000.0 rows, 10000.0 cpu, 0.0 io}, id = 92 DataSetScan(table=[[t1]]): rowcount = 1000.0, cumulative cost = {1000.0 rows, 1000.0 cpu,

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   Apache Impala 3.1.0 已釋出，暫未發現更新內容的介紹，點此保持關注。 下載地址： https://impala.apache.org/downloads.html   Apache Impala 是一個高效能分散式

JDBC練習 學生學籍管理系統1.0（sql server）

import java.sql.*; import java.util.ArrayList; import java.util.List; import java.util.Scanner; public class SqlLink { static Scanner sc=new Sca

執行SQL語句的時候唯一約束欄位異常Duplicate entry '33382-1-0' for key xxx

前言：做專案的時候，執行SQL語句報了Duplicate entry '33382-1-0' for key xxx異常，後來發現是唯一約束導致，於是乎一通谷歌百度，後來解決了，記錄一下。 正文： 程式碼片段是這樣的： session.createSQLQuery("i

Spark SQL 1.3.0 DataFrame介紹、使用及提供了些完整的資料寫入

問題導讀1.DataFrame是什麼？2.如何建立DataFrame?3.如何將普通RDD轉變為DataFrame？4.如何使用DataFrame？5.在1.3.0中，提供了哪些完整的資料寫入支援API？ 自2013年3月面世以來，Spark SQL已經成為除Spark C

sql 查詢條件where 1=1 ,1=2和1=0有什麼區別

資料庫在進行查詢的時候，經常看到有的人使用where 1=1和1=0,1=2等的查詢， 這種條件在執行前，就會被計算出true 或者false， 1=2實際解釋為(NULL IS NOT NULL) true 則不影響，false則不會掃描 主要是一些程式設計師的為了拼湊動

Mac下安裝Flink的local模式（flink-1.0.2）

1.本地執行 下載 進入下載頁面。如果你想讓Flink與Hadoop進行互動（如HDFS或者HBase），請選擇一個與你的Hadoop版本相匹配的Flink包。當你不確定或者只是想執行在本地檔案系統上，請選擇Hadoop 1.2.x對應的包。 2.環境

MDF檔案檢視器(SQL MDF Viewer) 1.0 綠色版

當你想檢視一個 SQL Server 資料庫的 MDF 檔案的時候，而你卻沒有安裝 SQL Server （那麼龐大的一個軟體，安裝之後想解除安裝？根本解除安裝不幹靜），那怎麼辦呢？MDF檔案檢視器(S

（BUG）Kettle8.1.0.0-365轉換步驟輸出SQL檔案insert指令碼日期型別資料，格式不正確且沒有雙引號

浪費了“黃金五年”的Java程式設計師，還有救嗎？ >>>   

Bean Query 第一個版本號(1.0.0)已公布

tid artifact con blog sdn tail ont tails map BeanQuery 是一個把對象轉換為Map的Java工具庫。支持選擇Bean中的一些屬性。對結果進行排序和依照條件查詢。不只能夠作用於頂層對象，也能夠作用於子對象。很多其它具體

購物系統1.0

enc break 存在 efault close def art default while #!/usr/bin/python #-*- coding:utf-8 -*- import sys #讀取商品列表 goods_list = open(‘商品列表.txt‘

Oracle 12.1.0.2 對JSON的支持

使用 lin 1.5 text lob mysq 索引 acl var Oracle 12.1.0.2版本有一個新功能就是可以存儲、查詢、索引JSON數據格式，而且也實現了使用SQL語句來解析JSON，非常方便。JSON數據在數據庫中以VARCHAR2, CLOB或者BLO

HTTP/1.0+ "keep-alive" 連接

通過 保持 就會 無法 首部 報文 response line -a 一、keep-alive 連接 (1) 我們在使用串行連接的時候，比如加載四張圖片，當加載第一張圖片時，會建立連接，加載完後會關閉連接，加載第二張圖片時同樣會先建立連接再關閉連接，以此類推，這樣就會消耗

1+1=0.5的姿勢困局！誰讓美麗蘑菇的合並泛起泡沫

人民網 互聯網 淘寶 觀察者 探路者 自從2016年1月，美麗說、蘑菇街正式合並以來,裁員風聲就沒斷過。但這並不重要。重要的是，較之其他如滴滴快的、新美大之類的同領域執牛耳者的合並，不再火並。合並後的美麗說、蘑菇街只能用慘淡來形容。從合並前2015年兩家交易額合計近200億元，到2016年

ubuntu14.04 + GTX980ti + cuda 8.0 ---Opencv3.1.0配置

install release err idt rim cut fix module b- 狂踩坑，腦袋疼。 流程： 1.逛網下載opencv source Opencv3.1.0 zip 2.unzip解壓 3.安裝一堆先決必要的環境： sudo apt-get i