Java Spark excel:读取带有多行标题的excel文件时引发异常：方法引发'；scala.MatchError'；例外

我正在使用读取excel文件，问题是每当我使用具有多行标题的文件时，数据集的QueryExecution抛出异常

方法抛出“scala.MatchError”异常。无法计算org.apache.spark.sql.execution.QueryExecution.toString（）

目前唯一的解决方案是将多行标题替换为一行，我还尝试使用

withColumnRenamed

替换数据集中的列名，但没有效果，有没有办法解决这个问题

以下是完整的堆栈：

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)

---

scala.MatchError:Nom de l'entité此问题已通过
spark excel 0.9.17
中的问题链接修复

    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();