我正在使用 spark-excel 来读取 excel 文件，问题是每当我使用具有多行标头的文件时，数据集的 QueryExecute 都会抛出异常Method threw 'scala.MatchError' exception. Cannot evaluate org.apache.spark.sql.execution.QueryExecution.toString()
目前唯一的解决方案是用一行替换多行标题，我也尝试使用withColumnRenamed替换数据集中的列名，但没有用，有什么办法可以解决这个问题吗？
下面是完整的堆栈：
scala.MatchError: Nom de l'entité <-- Name of the header.
Name of the entity <-- Name of the header.
(of class java.lang.String)
at com.crealytics.spark.excel.ExcelRelation$$anonfun$2.apply(ExcelRelation.scala:122)
at com.crealytics.spark.excel.ExcelRelation$$anonfun$2.apply(ExcelRelation.scala:120)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
at com.crealytics.spark.excel.ExcelRelation.buildScan(ExcelRelation.scala:120)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$11.apply(DataSourceStrategy.scala:300)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$11.apply(DataSourceStrategy.scala:300)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$pruneFilterProject$1.apply(DataSourceStrategy.scala:338)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$pruneFilterProject$1.apply(DataSourceStrategy.scala:337)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy.pruneFilterProjectRaw(DataSourceStrategy.scala:393)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy.pruneFilterProject(DataSourceStrategy.scala:333)
at org.apache.spark.sql.execution.datasources.DataSourceStrategy.apply(DataSourceStrategy.scala:296)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:63)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:63)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:93)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:78)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:75)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:75)
at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:67)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:93)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:72)
at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:77)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:77)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3248)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2484)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2698)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:254)
at org.apache.spark.sql.Dataset.show(Dataset.scala:723)
at org.apache.spark.sql.Dataset.show(Dataset.scala:682)
at org.apache.spark.sql.Dataset.show(Dataset.scala:691)
at Main.main(Main.java:33)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at com.microsoft.azure.hdinsight.spark.mock.SparkLocalRunner.runJobMain(SparkLocalRunner.java:75)
at com.microsoft.azure.hdinsight.spark.mock.SparkLocalRunner.main(SparkLocalRunner.java:48)
更新
重现步骤：
SparkSession session = SparkSession.builder().getOrCreate();
String path = "testMultiLineHeader.xlsx";
Dataset<Row> dsBal = session.read().format("com.crealytics.spark.excel")
.option("location", path)
.option("sheetName", "Feuil1")
.option("useHeader", "true")
.option("treatEmptyValuesAsNulls", "true")
.option("inferSchema", "true")
.option("addColorColumns", "false")
.load(path);
dsBal.show();
导致此错误的文件： 文件
此问题已通过spark excel 0.9.17修复，github 中的问题链接