Spark Application. Why the "executor computing time" is too long? - scala

Question

Spark Application
deploy mode:standalone
I want to know why the input data is same but the computing time is so different for a task between two different "WordCount" code.
For example:
1.The original "WordCount" code
object ScalaWordCount{
def main(args: Array[String]){
if (args.length < 2){
System.err.println(
s"Usage: $ScalaWordCount <INPUT_HDFS> <OUTPUT_HDFS>"
)
System.exit(1)
}
val sparkConf = new SparkConf().setAppName("ScalaWordCount")
val sc = new SparkContext(sparkConf)
val io = new IOCommon(sc)
val data = io.load[String](args(0))
val counts = data.flatMap(line => line.split(" "))
.map(word => (word, 1))
.reduceByKey(_ + _)
io.save(args(1), counts)
sc.stop()
}
}
the task result:
task duration
2.the other "WordCount" Code
object ScalaWordCount{
def main(args: Array[String]){
if (args.length < 2){
System.err.println(
s"Usage: $ScalaWordCount <INPUT_HDFS> <OUTPUT_HDFS>"
)
System.exit(1)
}
val sparkConf = new SparkConf().setAppName("ScalaWordCount")
val sc = new SparkContext(sparkConf)
val io = new IOCommon(sc)
val data = io.load[String](args(0))
val flatRdd = data.flatMap(line => line.split(" "))
val mapRdd = flatRdd.map(word => {
val pre = scala.util.Random.nextInt(10).toString
val key = pre + "_" + word
(key, 1)
})
val shuffleRdd = mapRdd.reduceByKey(_ + _)
val shuffleMapRdd = shuffleRdd.map{ case (k, v) => (k.split("_")(1), v) }
val counts = shuffleMapRdd.reduceByKey(_ + _)
io.save(args(1), counts)
sc.stop()
}
}
the task result:
task duration
So I want to know what will cause this.
Thanks a lot.