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Posted to issues@drill.apache.org by "James Turton (Jira)" <ji...@apache.org> on 2023/02/22 13:18:00 UTC
[jira] [Updated] (DRILL-8403) Generate aggregate function calls are missing required filters when used with PIVOT
[ https://issues.apache.org/jira/browse/DRILL-8403?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
James Turton updated DRILL-8403:
--------------------------------
Summary: Generate aggregate function calls are missing required filters when used with PIVOT (was: Generate aggregate function calls are incorrectly filtered when used with PIVOT)
> Generate aggregate function calls are missing required filters when used with PIVOT
> -----------------------------------------------------------------------------------
>
> Key: DRILL-8403
> URL: https://issues.apache.org/jira/browse/DRILL-8403
> Project: Apache Drill
> Issue Type: Bug
> Affects Versions: 1.21.0
> Reporter: James Turton
> Assignee: Vova Vysotskyi
> Priority: Major
> Fix For: 1.21.1
>
>
> The following query should generate aggregates grouped by education_level and containing filters on marital_status but the requisite filters are lost during function rewriting.
> apache drill> SELECT
> 2..semicolon> *
> 3..semicolon> FROM
> 4..semicolon> (SELECT
> 5..........)> education_level,
> 6..........)> salary,
> 7..........)> marital_status,
> 8..........)> extract(year from age(birth_date)) age
> 9..........)> FROM
> 10.........)> cp.`employee.json`)
> 11.semicolon> PIVOT (
> 12.........)> avg(salary) avg_salary, avg(age) avg_age FOR marital_status IN ('M' married, 'S' single)
> 13.........)> );
> {+}--------------------{-}{-}{+}------------------{-}++{-}------------------{-}{-}------------------{-}++{-}-------------------
> |education_level|married_avg_salary|married_avg_age|single_avg_salary|single_avg_age|
> {+}--------------------{-}{-}{+}------------------{-}++{-}------------------{-}{-}------------------{-}++{-}-------------------
> |Graduate Degree|4392.823529411765|100.32352941176471|4392.823529411765|100.32352941176471|
> |Bachelors Degree|4492.404181184669|102.22996515679442|4492.404181184669|102.22996515679442|
> |Partial College|4047.1180555555557|100.10069444444444|4047.1180555555557|100.10069444444444|
> |High School Degree|3516.1565836298932|103.12811387900356|3516.1565836298932|103.12811387900356|
> |Partial High School|3511.0852713178297|102.30232558139535|3511.0852713178297|102.30232558139535|
> {+}--------------------{-}{-}{+}------------------{-}++{-}------------------{-}{-}------------------{-}++{-}-------------------
> 5 rows selected (0.285 seconds)
>
> 00-00 Screen : rowType = RecordType(ANY education_level, ANY married_min_salary, DOUBLE married_avg_age, ANY single_min_salary, DOUBLE single_avg_age): rowcount = 46.3, cumulative cost = \{1486.23 rows, 35748.229999999996 cpu, 474630.0 io, 0.0 network, 8148.800000000001 memory}, id = 812
> 00-01 Project(education_level=[$0], married_min_salary=[$1], married_avg_age=[$2], single_min_salary=[$3], single_avg_age=[$4]) : rowType = RecordType(ANY education_level, ANY married_min_salary, DOUBLE married_avg_age, ANY single_min_salary, DOUBLE single_avg_age): rowcount = 46.3, cumulative cost = \{1481.6 rows, 35743.6 cpu, 474630.0 io, 0.0 network, 8148.800000000001 memory}, id = 811
> 00-02 Project(education_level=[$0], married_min_salary=[divide(CastHigh(CASE(=($2, 0), null:NULL, $1)), $2)], married_avg_age=[divide(CastHigh(CASE(=($4, 0), null:NULL, $3)), $4)], single_min_salary=[divide(CastHigh(CASE(=($2, 0), null:NULL, $1)), $2)], single_avg_age=[divide(CastHigh(CASE(=($4, 0), null:NULL, $3)), $4)]) : rowType = RecordType(ANY education_level, ANY married_min_salary, DOUBLE married_avg_age, ANY single_min_salary, DOUBLE single_avg_age): rowcount = 46.3, cumulative cost = \{1435.3 rows, 35512.1 cpu, 474630.0 io, 0.0 network, 8148.800000000001 memory}, id = 808
> 00-03 HashAgg(group=[\\{0}], agg#0=[$SUM0($2)], agg#1=[COUNT($2)], agg#2=[$SUM0($3)], agg#3=[COUNT($3)]) : rowType = RecordType(ANY education_level, ANY $f1, BIGINT $f2, BIGINT $f3, BIGINT $f4): rowcount = 46.3, cumulative cost = \{1389.0 rows, 34725.0 cpu, 474630.0 io, 0.0 network, 8148.800000000001 memory}, id = 807
> 00-04 Project(education_level=[$0], marital_status=[$1], salary=[$2], age=[EXTRACT(FLAG(YEAR), AGE($3))], $f4=[IS TRUE(=($1, 'M'))], $f5=[IS TRUE(=($1, 'S'))]) : rowType = RecordType(ANY education_level, ANY marital_status, ANY salary, BIGINT age, BOOLEAN $f4, BOOLEAN $f5): rowcount = 463.0, cumulative cost = \{926.0 rows, 8797.0 cpu, 474630.0 io, 0.0 network, 0.0 memory}, id = 806
> 00-05 Scan(table=[[cp, employee.json]], groupscan=[EasyGroupScan [selectionRoot=classpath:/employee.json, numFiles=1, columns=[`education_level`, `marital_status`, `salary`, `birth_date`], files=[classpath:/employee.json], usedMetastore=false, limit=-1, formatConfig=JSONFormatConfig [extensions=[json]]]]) : rowType = RecordType(ANY education_level, ANY marital_status, ANY salary, ANY birth_date): rowcount = 463.0, cumulative cost = \{463.0 rows, 1852.0 cpu, 474630.0 io, 0.0 network, 0.0 memory}, id = 805
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