scala.collection.immutable.Iterable[org.apache.spark.sql.Row

Question

我有一些sql.Row对象，我希望在Spark 1.6.x中转换为DataFrame

我的行如下所示：

events: scala.collection.immutable.Iterable[org.apache.spark.sql.Row] = List([14183197,Browse,80161702,8702170626376335,59,527780275219,List(NavigationLevel, Session)], [14183197,Browse,80161356,8702171157207449,72,527780278061,List(StartPlay, Action, Session)])

打印输出：

events.foreach(println)
[14183197,Browse,80161702,8702170626376335,59,527780275219,List(NavigationLevel, Session)]
[14183197,Browse,80161356,8702171157207449,72,527780278061,List(StartPlay, Action, Session)]

所以我为数据创建了一个架构;

 val schema = StructType(Array(
    StructField("trackId", IntegerType, true),
    StructField("location", StringType, true),
    StructField("videoId", IntegerType, true),
    StructField("id", StringType, true),
    StructField("sequence", IntegerType, true),
    StructField("time", StringType, true),
    StructField("type", ArrayType(StringType), true)
  ))

然后我尝试通过以下方式创建DataFrame：

val df = sqlContext.createDataFrame(events, schema)

但是我收到以下错误;

   error: overloaded method value createDataFrame with alternatives:
  (data: java.util.List[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
  (rdd: org.apache.spark.api.java.JavaRDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
  (rdd: org.apache.spark.rdd.RDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
  (rows: java.util.List[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
  (rowRDD: org.apache.spark.api.java.JavaRDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
  (rowRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame
 cannot be applied to (scala.collection.immutable.Iterable[org.apache.spark.sql.Row], org.apache.spark.sql.types.StructType)

我不确定为什么我会得到这个，是因为Row中的基础数据没有类型信息吗？

任何帮助都非常感谢

Answer 1

你必须parallelize：

val sc: SparkContext = ???
val df = sqlContext.createDataFrame(sc.parallelize(events), schema)