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 [英] suddenly throwing This RDD lacks a SparkContext it was working before every code was in main method
问题描述
这是一段有效的代码,但在我尝试从不同的 scala 对象
It was a working piece of code but suddenly its not working after I tried creating Sparksession
from different scala object
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.
即使在我撤销它之后也无法工作,而且我没有使用任何对象.
Not working even after I revoked it and I'm not using any objects.
代码更新:
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,david2、杰3、地久4、阿比5、苏兰哈
k=1,david2、杰3,jijoaa4、abisdsdd5、苏兰哈
val a 包含 {3,(jijo,jijoaa),5(abi,abisdsdd)}
val a contains {3,(jijo,jijoaa),5(abi,abisdsdd)}
推荐答案
如果你仔细阅读第一条信息
If you read carefully the first message
(1) RDD 的转换和动作不是由驱动调用,而是在其他转换内部;例如,rdd1.map(x => rdd2.values.count() * x) 无效,因为值转换和计数操作不能在 rdd1.map 转换内部执行.有关详细信息,请参阅 SPARK-5063.
(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.
它明确指出动作和转换不能在转换中执行.
It clearly states that actions and transformations cannot be performed inside a transformation.
primary_key_distinct
是对 b
进行的转换,而 b
本身就是一个 转换在 a
上完成.而 b.foreach(println)
是在 primary_key_distinct
primary_key_distinct
is transformation done on b
and b
itself is a transformation done on a
. And b.foreach(println)
is an action done inside transformation of primary_key_distinct
因此,如果您在驱动程序中收集了b
或primary_key_distinct
,那么代码应该可以正常运行
So if you collect b
or primary_key_distinct
inside driver, then the code should run properly
val b = a.filter { x => (!x._2._1.isEmpty) && (!x._2._2.isEmpty) }.collect
或
val primary_key_distinct = b.map(rec => (rec._1.split(",")(0))).distinct.collect
或如果您不在另一个转换中使用action,那么代码也应该像
or if you don't use action inside another transformation then the code should run properly too as
for (i <- 1 to 2) {
var lengthofarray = 0
println(i)
b.foreach(println)
}
我希望解释清楚.
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