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当我偶然发现与数据帧保存相关的问题时，我正在将我的代码从 Spark 2.0 迁移到 2.1。

这是代码

import org.apache.spark.sql.types._
import org.apache.spark.ml.linalg.VectorUDT
val df = spark.createDataFrame(Seq(Tuple1(1))).toDF("values")
val toSave = new org.apache.spark.ml.feature.VectorAssembler().setInputCols(Array("values")).transform(df)
toSave.write.csv(path)

此代码在使用 Spark 2.0.0 时成功

使用 Spark 2.1.0.cloudera1，我收到以下错误：

java.lang.UnsupportedOperationException: CSV data source does not support struct<type:tinyint,size:int,indices:array<int>,values:array<double>> data type.
  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.org$apache$spark$sql$execution$datasources$csv$CSVFileFormat$$verifyType$1(CSVFileFormat.scala:233)
  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$verifySchema$1.apply(CSVFileFormat.scala:237)
  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anonfun$verifySchema$1.apply(CSVFileFormat.scala:237)
  at scala.collection.Iterator$class.foreach(Iterator.scala:893)
  at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
  at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
  at org.apache.spark.sql.types.StructType.foreach(StructType.scala:96)
  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.verifySchema(CSVFileFormat.scala:237)
  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.prepareWrite(CSVFileFormat.scala:121)
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:108)
  at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:101)
  at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
  at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
  at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:114)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:135)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:132)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:113)
  at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:87)
  at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:87)
  at org.apache.spark.sql.execution.datasources.DataSource.writeInFileFormat(DataSource.scala:484)
  at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:520)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:215)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:198)
  at org.apache.spark.sql.DataFrameWriter.csv(DataFrameWriter.scala:579)
  ... 50 elided

这只是在我身边吗？

这与 Spark 2.1 的 cloudera 版本有关吗？（从他们的仓库来看，他们似乎没有弄乱 spark.sql 所以也许不是）

谢谢 ！

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2 回答 2

3

以下答案由@zero323 的评论组成。

CSV 源不支持复杂对象。与您的例外情况完全相同：CSV 数据源不支持 struct,values:array‌​> 数据类型。是预期的行为。它不适用于 Spark 2.x，尽管它曾经在 1.x 中与 spark-csv 一起使用，其中向量已转换为字符串。

此行为在以下 jira SPARK-16216中是正确的。

于 2017-06-01T23:04:41.290 回答
-1

作为一种解决方法，您可以使用此fork中的 VectorDisassembler 类，或采用此处描述的解决方案。

我使用 VectorDisassembler 将 ml.feature.StandardScaler.fit 方法的结果数据帧存储到 CSV 中。

代码大致如下：

val disassembler = new org.apache.spark.ml.feature.VectorDisassembler()
val disassembledDF = disassembler.setInputCol("scaledFeatures").transform(df)
disassembledDF.show()
于 2017-07-04T15:00:14.027 回答