我的设置

我正在使用以下组件

• Spark芯_2.10
• spark-sql_2.10

我的问题

这基本上是我的代码

Dataset<Row> rowsSource = spark.read()
    .option("header", "true")
    .option("delimiter", ";")
    .csv("source.csv");

Dataset<Row> rowsTarget = spark.read()
    .option("header", "true")
    .option("delimiter", ";")
    .csv("target.csv");

rowsSource.createOrReplaceTempView("source");
rowsTarget.createOrReplaceTempView("target");

Dataset<Row> result = spark.sql("SELECT source.Id FROM source" +
                                " LEFT OUTER JOIN target USING(Id)" +
                                " WHERE target.Id IS NULL");

result.show();

这里有一些测试数据：
资料来源：
“ID”;“状态”
“% 1”;“错误”
“2”;“OK”
目标：
“ID”;“状态”
“2”;“OK”
我希望SQL语句只找到一个ID，即“1”
但是如果我运行它，在执行SQL语句的行中发生异常，

2017-03-21 17:00:09,693 INFO  [main] com.materna.mobility.smart.selenium.Aaaa: starting
Exception in thread "main" org.apache.spark.sql.AnalysisException: USING column `Detail` cannot be resolved on the left side of the join. The left-side columns: ["Detail", Detailp, Detaild, Detailb, Amount - 2016 48 +0100/1, Amount - 2016 49 +0100/1, Amount - 2016 50 +0100/1, Amount - 2016 51 +0100/1, Amount - 2016 52 +0100/1];
    at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$90$$anonfun$apply$56.apply(Analyzer.scala:1977)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$90$$anonfun$apply$56.apply(Analyzer.scala:1977)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$90.apply(Analyzer.scala:1976)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$90.apply(Analyzer.scala:1975)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$commonNaturalJoinProcessing(Analyzer.scala:1975)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNaturalAndUsingJoin$$anonfun$apply$31.applyOrElse(Analyzer.scala:1961)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNaturalAndUsingJoin$$anonfun$apply$31.applyOrElse(Analyzer.scala:1958)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:58)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNaturalAndUsingJoin$.apply(Analyzer.scala:1958)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveNaturalAndUsingJoin$.apply(Analyzer.scala:1957)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
    at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
    at scala.collection.immutable.List.foldLeft(List.scala:84)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
    at scala.collection.immutable.List.foreach(List.scala:318)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
    at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:64)
    at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:62)
    at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50)
    at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:63)
    at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:592)
    at MyClass.main(MyClass.java:48)

如果我在ID之前插入一个额外的后缀（;），一切都按预期工作，下面是一个例子：
;“ID”;“状态”
我认为Spark会解析3个列，但由于第一个列无效，所以它会被忽略。