Spark DataFrame 不尊重模式并将所有内容都视为字符串

Question

我遇到了一个问题，我已经好久没解决了。

我正在使用 Spark 1.4 和 Scala 2.10。我现在无法升级（大型分布式基础设施）
我有一个包含几百列的文件，其中只有 2 列是字符串，其余都是长列。我想将此数据转换为 Label/Features 数据帧。
我已经能够将其转换为 LibSVM 格式。
我无法将其转换为 Label/Features 格式。

原因是

我无法使用此处所示的 toDF() https://spark.apache.org/docs/1.5.1/ml-ensembles.html
```
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
```
它在 1.4 中不受支持

所以我首先将 txtFile 转换为 DataFrame，我使用了类似这样的东西

def getColumnDType(columnName:String):StructField = {

        if((columnName== "strcol1") || (columnName== "strcol2")) 
            return StructField(columnName, StringType, false)
        else
            return StructField(columnName, LongType, false)
    }
def getDataFrameFromTxtFile(sc: SparkContext,staticfeatures_filepath: String,schemaConf: String) : DataFrame = {
        val sfRDD = sc.textFile(staticfeatures_filepath)//
        val sqlContext = new org.apache.spark.sql.SQLContext(sc)
         // reads a space delimited string from application.properties file
        val schemaString = readConf(Array(schemaConf)).get(schemaConf).getOrElse("")

        // Generate the schema based on the string of schema
        val schema =
          StructType(
            schemaString.split(" ").map(fieldName => getSFColumnDType(fieldName)))

        val data = sfRDD
        .map(line => line.split(","))
        .map(p => Row.fromSeq(p.toSeq))

        var df = sqlContext.createDataFrame(data, schema)

        //schemaString.split(" ").drop(4)
        //.map(s => df = convertColumn(df, s, "int"))

        return df
    }

当我对这个返回的数据帧执行 df.na.drop() df.printSchema() 时，我得到了完美的 Schema Like this

root
 |-- rand_entry: long (nullable = false)
 |-- strcol1: string (nullable = false)
 |-- label: long (nullable = false)
 |-- strcol2: string (nullable = false)
 |-- f1: long (nullable = false)
 |-- f2: long (nullable = false)
 |-- f3: long (nullable = false)
and so on till around f300

但是 - 可悲的是无论我尝试用 df 做什么（见下文），我总是收到 ClassCastException（java.lang.String 无法转换为 java.lang.Long）

val featureColumns = Array("f1","f2",....."f300")
assertEquals(-99,df.select("f1").head().getLong(0))
assertEquals(-99,df.first().get(4))
val transformeddf = new VectorAssembler()
        .setInputCols(featureColumns)
        .setOutputCol("features")
        .transform(df)

所以 - 不好的是 - 即使架构显示 Long - df 仍在内部将所有内容视为字符串。

编辑

添加一个简单的例子

假设我有这样一个文件

1,A,20,P,-99,1,0,0,8,1,1,1,1,131153
1,B,23,P,-99,0,1,0,7,1,1,0,1,65543
1,C,24,P,-99,0,1,0,9,1,1,1,1,262149
1,D,7,P,-99,0,0,0,8,1,1,1,1,458759

和

sf-schema=f0 strCol1 f1 strCol2 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11

（列名真的不重要所以你可以忽略这个细节）

我想要做的就是创建一个 Label/Features 类型的数据框，其中我的第 3 列成为标签，第 5 到第 11 列成为特征 [Vector] 列。这样我就可以按照 https://spark.apache.org/docs/latest/ml-classification-regression.html#tree-ensembles 中的其余步骤进行操作。

我也按照 zero323

的建议投射了这些专栏

val types = Map("strCol1" -> "string", "strCol2" -> "string")
        .withDefault(_ => "bigint")
df = df.select(df.columns.map(c => df.col(c).cast(types(c)).alias(c)): _*)
df = df.drop("f0")
df = df.drop("strCol1")
df = df.drop("strCol2")

但是断言和VectorAssembler 仍然失败。 featureColumns = Array("f2","f3",....."f11") 这是我在 df

之后想要做的整个序列

    var transformeddf = new VectorAssembler()
    .setInputCols(featureColumns)
    .setOutputCol("features")
    .transform(df)

    transformeddf.show(2)

    transformeddf = new StringIndexer()
    .setInputCol("f1")
    .setOutputCol("indexedF1")
    .fit(transformeddf)
    .transform(transformeddf)

    transformeddf.show(2)

    transformeddf = new VectorIndexer()
    .setInputCol("features")
    .setOutputCol("indexedFeatures")
    .setMaxCategories(5)
    .fit(transformeddf)
    .transform(transformeddf)

来自 ScalaIDE 的异常跟踪 - 就在它命中 VectorAssembler 时如下

java.lang.ClassCastException: java.lang.String cannot be cast to java.lang.Long
    at scala.runtime.BoxesRunTime.unboxToLong(BoxesRunTime.java:110)
    at scala.math.Numeric$LongIsIntegral$.toDouble(Numeric.scala:117)
    at org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToDouble.apply(Cast.scala:364)
    at org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToDouble.apply(Cast.scala:364)
    at org.apache.spark.sql.catalyst.expressions.Cast.eval(Cast.scala:436)
    at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct$$anonfun$eval.apply(complexTypes.scala:75)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct$$anonfun$eval.apply(complexTypes.scala:75)
    at scala.collection.TraversableLike$$anonfun$map.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map.apply(TraversableLike.scala:244)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct.eval(complexTypes.scala:75)
    at org.apache.spark.sql.catalyst.expressions.CreateStruct.eval(complexTypes.scala:56)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun.apply(ScalaUdf.scala:72)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf$$anonfun.apply(ScalaUdf.scala:70)
    at org.apache.spark.sql.catalyst.expressions.ScalaUdf.eval(ScalaUdf.scala:960)
    at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
    at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:68)
    at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:52)
    at scala.collection.Iterator$$anon.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon.next(Iterator.scala:328)
    at scala.collection.Iterator$$anon.next(Iterator.scala:312)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:143)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:143)
    at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1767)
    at org.apache.spark.SparkContext$$anonfun$runJob.apply(SparkContext.scala:1767)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
    at org.apache.spark.scheduler.Task.run(Task.scala:70)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

Answer 1

你得到 ClassCastException 因为这正是应该发生的事情。模式参数不用于自动转换（一些 DataSources 可能会以这种方式使用模式，但不是像 createDataFrame 这样的方法）。它只声明存储在行中的值的类型。您有责任传递与模式匹配的数据，而不是相反。

虽然 DataFrame 显示您声明的架构仅在运行时验证，因此运行时 exception.If 您希望将数据转换为您明确拥有 cast 数据的特定数据。

首先读取所有列为StringType:

val rows = sc.textFile(staticfeatures_filepath)
  .map(line => Row.fromSeq(line.split(",")))

val schema = StructType(
  schemaString.split(" ").map(
    columnName => StructField(columnName, StringType, false)
  )
)

val df = sqlContext.createDataFrame(rows, schema)

接下来将选定的列转换为所需的类型：

import org.apache.spark.sql.types.{LongType, StringType}

val types = Map("strcol1" -> StringType, "strcol2" -> StringType)
  .withDefault(_ => LongType)

val casted = df.select(df.columns.map(c => col(c).cast(types(c)).alias(c)): _*)

使用汇编程序：

val transformeddf = new VectorAssembler()
  .setInputCols(featureColumns)
  .setOutputCol("features")
  .transform(casted)

您可以使用 spark-csv:

简单地执行第 1 步和第 2 步

// As originally 
val schema = StructType(
  schemaString.split(" ").map(fieldName => getSFColumnDType(fieldName)))


val df = sqlContext
  .read.schema(schema)
  .format("com.databricks.spark.csv")
  .option("header", "false")
  .load(staticfeatures_filepath)

另见

Spark DataFrame 不尊重模式并将所有内容都视为字符串

Spark DataFrame not respecting schema and considering everything as String

scala

scala-collections

apache-spark

apache-spark-sql

apache-spark-mllib