Question

当我偶然发现与Dataframe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)

使用Spark 2.0.0时，此代码成功

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？（从他们的回购开始，似乎他们并没有搞乱spark.sql所以也许没有）

谢谢！

Answer 1

以下答案由@ zero323评论组成。

CSV源不支持复杂对象。正如你从例外：CSV数据源不支持struct，values：array＆gt;数据类型。是预期的行为。它不适用于Spark 2.x，虽然它曾经在1.x中使用spark-csv，其中向量已被转换为字符串。

以下jira SPARK-16216中的此行为是正确的。

Answer 2

作为一种解决方法，您可以使用此fork中的VectorDisassembler类，或采用描述为here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。

代码看起来大致如下：

val disassembler = new org.apache.spark.ml.feature.VectorDisassembler()
val disassembledDF = disassembler.setInputCol("scaledFeatures").transform(df)
disassembledDF.show()

Spark 2.1无法在CSV上写入Vector字段

2 个答案: