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https://issues.apache.org/jira/browse/SPARK-10741?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14905327#comment-14905327
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Ian edited comment on SPARK-10741 at 9/23/15 9:36 PM:
------------------------------------------------------
Yes, going through all rules when resolve Sort on Aggregate is a correct
approach.
The main problem appeared that the execute call at
(https://github.com/apache/spark/blob/v1.5.0/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L571)
is resolving to different attribute ids, and causing confusion at
https://github.com/apache/spark/blob/v1.5.0/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala#L592-L611.
just for me to understand a bit more:
the second approach you are proposing is to remove the confusion by changing
how ids are resolved in Analyzer.scala#L571, right?
was (Author: ianlcsd):
Yes, going through all rules when resolve Sort on Aggregate is a correct
approach.
The main problem appeared that the execute call at
(hhttps://github.com/apache/spark/blob/v1.5.0/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L571)
is resolving to different attribute ids, and causing confusion at
https://github.com/apache/spark/blob/v1.5.0/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala#L592-L611.
just for me to understand a bit more:
the second approach you are proposing is to remove the confusion by changing
how ids are resolved in Analyzer.scala#L571, right?
> Hive Query Having/OrderBy against Parquet table is not working
> ---------------------------------------------------------------
>
> Key: SPARK-10741
> URL: https://issues.apache.org/jira/browse/SPARK-10741
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Ian
>
> Failed Query with Having Clause
> {code}
> def testParquetHaving() {
> val ddl =
> """CREATE TABLE IF NOT EXISTS test ( c1 string, c2 int ) STORED AS
> PARQUET"""
> val failedHaving =
> """ SELECT c1, avg ( c2 ) as c_avg
> | FROM test
> | GROUP BY c1
> | HAVING ( avg ( c2 ) > 5) ORDER BY c1""".stripMargin
> TestHive.sql(ddl)
> TestHive.sql(failedHaving).collect
> }
> org.apache.spark.sql.AnalysisException: resolved attribute(s) c2#16 missing
> from c1#17,c2#18 in operator !Aggregate [c1#17], [cast((avg(cast(c2#16 as
> bigint)) > cast(5 as double)) as boolean) AS
> havingCondition#12,c1#17,avg(cast(c2#18 as bigint)) AS c_avg#9];
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:37)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:44)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:154)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:49)
> {code}
> Failed Query with OrderBy
> {code}
> def testParquetOrderBy() {
> val ddl =
> """CREATE TABLE IF NOT EXISTS test ( c1 string, c2 int ) STORED AS
> PARQUET"""
> val failedOrderBy =
> """ SELECT c1, avg ( c2 ) c_avg
> | FROM test
> | GROUP BY c1
> | ORDER BY avg ( c2 )""".stripMargin
> TestHive.sql(ddl)
> TestHive.sql(failedOrderBy).collect
> }
> org.apache.spark.sql.AnalysisException: resolved attribute(s) c2#33 missing
> from c1#34,c2#35 in operator !Aggregate [c1#34], [avg(cast(c2#33 as bigint))
> AS aggOrder#31,c1#34,avg(cast(c2#35 as bigint)) AS c_avg#28];
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:37)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:44)
> {code}
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