Spark Excel:使用多行标头读取Excel文件会引发异常:方法抛出"scala.MatchError"异常 [英] Spark excel: reading excel file with multi line header throw an exception: Method threw 'scala.MatchError' exception

问题描述

我正在使用 spark-excel 来读取Excel文件，问题出在我什么时候如果使用具有多行标头的文件，则数据集的QueryExecution会引发异常 Method引发了'scala.MatchError'异常.无法评估org.apache.spark.sql.execution.QueryExecution.toString()

I'm using spark-excel to read excel files, the problem is whenever I use a file with multilines header, the QueryExecution of the dataset throw an exception 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，但是没有用，有没有解决这个问题的方法?

The only solution for now is to replace the multiline header with a one line, I also tried to replace the column name in the dataset using withColumnRenamed, but it didn't work, is there any way to fix this?

这是完整的堆栈:

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

复制步骤:

    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

推荐答案

此问题已通过 spark excel 0.9.17 解决，发布链接在

This issue is fixed with spark excel 0.9.17, issue link in github

这篇关于Spark Excel:使用多行标头读取Excel文件会引发异常:方法抛出"scala.MatchError"异常的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！