Question

这是一段有效的代码，但在我尝试从不同的Sparksession

创建scala object之后突然无法正常工作

val b = a.filter { x => (!x._2._1.isEmpty) && (!x._2._2.isEmpty) } 

val primary_ke = b.map(rec => (rec._1.split(",")(0))).distinct 

for (i <- primary_key_distinct) {    
  b.foreach(println)

}

错误：

 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 5)
org.apache.spark.SparkException: This RDD lacks a SparkContext. It could happen in the following cases: 
(1) RDD transformations and actions are NOT invoked by the driver, but inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
(2) When a Spark Streaming job recovers from checkpoint, this exception will be hit if a reference to an RDD not defined by the streaming job is used in DStream operations. For more information, See SPARK-13758.

即使在我撤销它并且我没有使用任何物体之后也无法工作。

代码已更新：

object try {

  def main(args: Array[String]) {



val spark = SparkSession.builder().master("local").appName("50columns3nodes").getOrCreate()

var s = spark.read.csv("/home/hadoopuser/Desktop/input/source.csv").rdd.map(_.mkString(","))
var k = spark.read.csv("/home/hadoopuser/Desktop/input/destination.csv").rdd.map(_.mkString(","))

val source_primary_key = s.map(rec => (rec.split(",")(0), rec))
val destination_primary_key = k.map(rec => (rec.split(",")(0), rec))

val a = source_primary_key.cogroup(destination_primary_key).filter { x => ((x._2._1) != (x._2._2)) }
val b = a.filter { x => (!x._2._1.isEmpty) && (!x._2._2.isEmpty) } 

var extra_In_Dest = a.filter(x => x._2._1.isEmpty && !x._2._2.isEmpty).map(rec => (rec._2._2.mkString(""))) 
var extra_In_Src = a.filter(x => !x._2._1.isEmpty && x._2._2.isEmpty).map(rec => (rec._2._1.mkString(""))) 

val primary_key_distinct = b.map(rec => (rec._1.split(",")(0))).distinct 
for (i <- primary_key_distinct) {

  var lengthofarray = 0
  println(i)
  b.foreach(println)

}
}
}

输入数据如下

s=1,david 2,ajay 3,jijo 4,abi 5,surendhar

k=1,david 2,ajay 3,jijoaa 4,abisdsdd 5,surendhar

val a包含{3，（jijo，jijoaa），5（abi，abisdsdd）}

Answer 1

如果您仔细阅读了第一条消息

（1）驱动程序不会调用RDD转换和操作，而是在其他转换中调用;例如，rdd1.map（x =＆gt; rdd2.values.count（）* x）无效，因为无法在rdd1.map转换内执行值转换和计数操作。有关更多信息，请参阅SPARK-5063。

它明确指出操作和转换无法在转换中执行。

primary_key_distinct 转换已在b上完成，而b本身就是在a上完成的转换。而b.foreach(println)是在<{1}}的转换中完成的操作

因此，如果您在驱动程序中收集primary_key_distinct或b，那么代码应该正常运行

primary_key_distinct

或

val b = a.filter { x => (!x._2._1.isEmpty) && (!x._2._2.isEmpty) }.collect

或如果您未在其他转化中使用操作，那么代码也应该正常运行

val primary_key_distinct = b.map(rec => (rec._1.split(",")(0))).distinct.collect

我希望解释清楚。

突然抛出这个RDD缺少一个SparkContext，它在每个代码都在main方法之前工作

1 个答案: