Apache spark org.apache.spark.sql.AnalysisException:无法从总和中提取值（_c930）；

我正在使用spark sql选择一列以及另一列的总和： 以下是我的疑问：

scala> spark.sql("select distinct _c3,sum(_c9).as(sumAadhar)  from aadhar group by _c3 order by _c9 desc LIMIT 3").show

我的模式是：

    root
 |-- _c0: string (nullable = true)
 |-- _c1: string (nullable = true)
 |-- _c2: string (nullable = true)
 |-- _c3: string (nullable = true)
 |-- _c4: string (nullable = true)
 |-- _c5: string (nullable = true)
 |-- _c6: string (nullable = true)
 |-- _c7: string (nullable = true)
 |-- _c8: string (nullable = true)
 |-- _c9: double (nullable = true)
 |-- _c10: string (nullable = true)
 |-- _c11: string (nullable = true)
 |-- _c12: string (nullable = true)

我发现了一个错误：

org.apache.spark.sql.AnalysisException: Can't extract value from sum(_c9#30);
  at org.apache.spark.sql.catalyst.expressions.ExtractValue$.apply(complexTypeExtractors.scala:73)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9$$anonfun$applyOrElse$5.applyOrElse(Analyzer.scala:613)
  at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9$$anonfun$applyOrElse$5.applyOrElse(Analyzer.scala:605)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:308)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:308)
  at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:305)
  at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:328)
  at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:326)
  at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:305)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:269)
  at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:279)
  at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:283)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
  at scala.collection.immutable.List.map(List.scala:285)

---

任何想法，我做错了什么，或者是否有任何其他方法可以对以下列的值求和，该列在简化模式上进行了尝试：

scala> val df = Seq(("a", 2), ("a", 3), ("b", 4), ("a", 9), ("b", 1), ("c", 100)).toDF("_c3", "_c9") df: org.apache.spark.sql.DataFrame = [_c3: string, _c9: int]

scala> df.createOrReplaceTempView("aadhar")

scala> spark.sql("select _c3,sum(_c9) as sumAadhar from aadhar group by _c3 order by sumAadhar desc LIMIT 3").show
+---+---------+ 
|_c3|sumAadhar|
+---+---------+ 
|  c|      100| 
|  a|       14| 
|  b|        5|
+---+---------+

删除了distinct，因为它不是必需的，因为您的原始查询已按_c3分组。 将sum_c9.assumAadhar更改为sum_c9作为sumAadhar，因为我认为语法导致spark sql执行一些非预期的强制转换。

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获取相同错误：org.apache.spark.sql.AnalysisException:无法解析给定输入列的“\u c9”：[u c3，sumCAST\u c9为双精度]；第1行位置55；但当我使用alias时，它工作得很好：spark.sqlselect _c3，sum _c9作为adh组的总计，按_c3顺序，按总计限制3。show@KumarHarsh，从你上次的评论来看，这个解决方案似乎对你有效。如果是这样，考虑标记作为答案。