星火：转换RDD [行]到数据帧，其中行中的一列是一个列表

Question

我有一个RDD [行]有每行的以下数据

[guid, List(peopleObjects)] 
["123", List(peopleObjects1, peopleObjects2, peopleObjects3)] 

我想将其转换为一个数据帧
我使用下面的代码

val personStructureType = new StructType() 
    .add(StructField("guid", StringType, true)) 
    .add(StructField("personList", StringType, true)) 
val personDF = hiveContext.createDataFrame(personRDD, personStructureType) 

我应该使用不同的数据类型为我的架构，而不是StringType？

如果我的名单只是它工作的字符串，但是当它是一个列表，我得到以下错误

scala.MatchError: List(personObject1, personObject2, personObject3) (of class scala.collection.immutable.$colon$colon) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:295) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StringConverter$.toCatalystImpl(CatalystTypeConverters.scala:294) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:260) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102) 
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401) 
    at org.apache.spark.sql.SQLContext$$anonfun$7.apply(SQLContext.scala:445) 
    at org.apache.spark.sql.SQLContext$$anonfun$7.apply(SQLContext.scala:445) 
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) 
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) 
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) 
    at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:219) 
    at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:73) 
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) 
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) 
    at org.apache.spark.scheduler.Task.run(Task.scala:88) 
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) 
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) 
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
    at java.lang.Thread.run(Thread.java:745) 

Answer 1

这不是完全清楚你正在尝试做的，但更好的方法，你是什么试图做的是创建一个case class，然后将您的RDD行映射到case class，然后调用toDF。

喜欢的东西：

case class MyClass(guid: Int, peopleObjects: List[String]) 

val rdd = sc.parallelize(Array((123,List("a","b")),(1232,List("b","d")))) 

val df = rdd.map(r => MyClass(r._1, r._2)).toDF 
df.show 
+----+-------------+ 
|guid|peopleObjects| 
+----+-------------+ 
| 123|  [a, b]| 
|1232|  [b, d]| 
+----+-------------+ 

或者你也可以做到这一点的长手的方式，但不使用的情况下类，像这样：

val df = sqlContext.createDataFrame(
    rdd.map(r => Row(r._1, r._2)), 
    StructType(Array(
    StructField("guid",IntegerType), 
    StructField("peopleObjects", ArrayType(StringType)) 
)) 
)