Searched on Google with the first line of a JAVA stack trace?

We can recommend more relevant solutions and speed up debugging when you paste your entire stack trace with the exception message. Try a sample exception.

Recommended solutions based on your search

Solutions on the web

via mail-archive.com by Unknown author, 1 year ago
Failed in [SELECT from_unixtime(floor(xyz.whenrequestreceived/1000.0 - 25200), '/MM/dd') as pst_date, count(*) as num_xyzs FROM all_matched_abc GROUP BY from_unixtime(floor(xyz.whenrequestreceived/1000.0 - 25200), '/MM/dd
thriftserver.SparkSQLDriver: Failed in [SELECT
from_unixtime(floor(xyz.whenrequestreceived/1000.0 - 25200),
  '/MM/dd') as pst_date,
count(*) as num_xyzs
  FROM
all_matched_abc
  GROUP BY
from_unixtime(floor(xyz.whenrequestreceived/1000.0 - 25200),
  '/MM/dd')
]
org.apache.spark.sql.catalyst.errors.package$TreeNodeException:
Expression not in GROUP BY:
HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFFromUnixTime(HiveGenericUdf#org.apache.hadoop.hive.ql.udf.generic.GenericUDFFloor(((CAST(xyz#183.whenrequestreceived
AS whenrequestreceived#187L, DoubleType) / 1000.0) - CAST(25200,
DoubleType))),/MM/dd) AS pst_date#179, tree:
Aggregate 
[HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFFromUnixTime(HiveGenericUdf#org.apache.hadoop.hive.ql.udf.generic.GenericUDFFloor(((CAST(xyz#183.whenrequestreceived,
DoubleType) / 1000.0) - CAST(25200, DoubleType))),/MM/dd)],
[HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFFromUnixTime(HiveGenericUdf#org.apache.hadoop.hive.ql.udf.generic.GenericUDFFloor(((CAST(xyz#183.whenrequestreceived
AS whenrequestreceived#187L, DoubleType) / 1000.0) - CAST(25200,
DoubleType))),/MM/dd) AS pst_date#179,COUNT(1) AS num_xyzs#180L]
 MetastoreRelation default, all_matched_abc, None	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$$anonfun$apply$3$$anonfun$applyOrElse$6.apply(Analyzer.scala:127)	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$$anonfun$apply$3$$anonfun$applyOrElse$6.apply(Analyzer.scala:125)	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$$anonfun$apply$3.applyOrElse(Analyzer.scala:125)	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$$anonfun$apply$3.applyOrElse(Analyzer.scala:115)	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:144)	at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:135)	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$.apply(Analyzer.scala:115)	at org.apache.spark.sql.catalyst.analysis.Analyzer$CheckAggregation$.apply(Analyzer.scala:113)	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:61)	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1$$anonfun$apply$2.apply(RuleExecutor.scala:59)	at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)	at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)	at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34)	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:59)	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$apply$1.apply(RuleExecutor.scala:51)	at scala.collection.immutable.List.foreach(List.scala:318)