Hi all,
I have written a program and overridden two events onStageCompleted and
onTaskEnd. However, these two events do not provide information on when a
Task/Stage is completed.
What I want to know is which Task corresponds to which stage of a DAG (the
Spark history server only tells me how many stages a Job has and how many
Jobs a Stage has).
Can I print out the edges of the Tasks according to the DAGScheduler?
Below is the program I have written:
import org.apache.spark.rdd.RDD
import org.apache.spark.TaskContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext, TaskEndReason}
import org.apache.spark.scheduler.{SparkListener,
SparkListenerEnvironmentUpdate, SparkListenerStageCompleted,
SparkListenerTaskEnd}
import scala.collection.mutable
import org.apache.spark.sql.execution.SparkPlan
class CustomListener extends SparkListener {
override def onStageCompleted(stageCompleted:
SparkListenerStageCompleted): Unit = {
val rdds = stageCompleted.stageInfo.rddInfos
val stageInfo = stageCompleted.stageInfo
println(s"Stage ${stageInfo.stageId}")
println(s"Number of tasks: ${stageInfo.numTasks}")
stageInfo.rddInfos.foreach { rddInfo =>
println(s"RDD ${rddInfo.id} has ${rddInfo.numPartitions} partitions.")
}
}
override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
val stageId = taskEnd.stageId
val stageAttemptId = taskEnd.stageAttemptId
val taskInfo = taskEnd.taskInfo
println(s"Task: ${taskInfo.taskId}; Stage: $stageId; Duration:
${taskInfo.duration} ms.")
}
def wordCount(sc: SparkContext, inputPath: String): Unit = {
val data = sc.textFile(inputPath)
val flatMap = data.flatMap(line => line.split(","))
val map = flatMap.map(word => (word, 1))
val reduceByKey = map.reduceByKey(_ + _)
reduceByKey.foreach(println)
}
}
object Scenario1 {
def main(args: Array[String]): Unit = {
val appName = "scenario1"
val spark = SparkSession.builder()
.master("local[*]")
.appName(appName)
.getOrCreate()
val sc = spark.sparkContext
val sparkListener = new CustomListener()
sc.addSparkListener(sparkListener)
val inputPath = "s3a://data-join/file00"
sparkListener.wordCount(sc, inputPath)
sc.stop()
}
}
Best regards,
Truong
Vào CN, 16 thg 4, 2023 vào lúc 09:32 Trường Trần Phan An <
truong...@vlute.edu.vn> đã viết:
> Dear Jacek Laskowski,
>
> Thank you for your guide. I will try it out for my problem.
>
> Best regards,
> Truong
>
>
> Vào Th 6, 14 thg 4, 2023 vào lúc 21:00 Jacek Laskowski
> đã viết:
>
>> Hi,
>>
>> Start with intercepting stage completions
>> using SparkListenerStageCompleted [1]. That's Spark Core (jobs, stages and
>> tasks).
>>
>> Go up the execution chain to Spark SQL
>> with SparkListenerSQLExecutionStart [2] and SparkListenerSQLExecutionEnd
>> [3], and correlate infos.
>>
>> You may want to look at how web UI works under the covers to collect all
>> the information. Start from SQLTab that should give you what is displayed
>> (that should give you then what's needed and how it's collected).
>>
>> [1]
>> https://github.com/apache/spark/blob/8cceb3946bdfa5ceac0f2b4fe6a7c43eafb76d59/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala#L46
>> [2]
>> https://github.com/apache/spark/blob/24cdae8f3dcfc825c6c0b8ab8aa8505ae194050b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala#L44
>> [3]
>> https://github.com/apache/spark/blob/24cdae8f3dcfc825c6c0b8ab8aa8505ae194050b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala#L60
>> [4]
>> https://github.com/apache/spark/blob/c124037b97538b2656d29ce547b2a42209a41703/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala#L24
>>
>> Pozdrawiam,
>> Jacek Laskowski
>>
>> "The Internals Of" Online Books
>> Follow me on https://twitter.com/jaceklaskowski
>>
>>
>>
>>
>> On Thu, Apr 13, 2023 at 10:40 AM Trường Trần Phan An <
>> truong...@vlute.edu.vn> wrote:
>>
>>> Hi,
>>>
>>> Can you give me more details or give me a tutorial on "You'd have to
>>> intercept execution events and correlate them. Not an easy task yet doable"
>>>
>>> Thank
>>>
>>> Vào Th 4, 12 thg 4, 2023 vào lúc 21:04 Jacek Laskowski <
>>> ja...@japila.pl> đã viết:
>>>
Hi,
tl;dr it's not possible to "reverse-engineer" tasks to functions.
In essence, Spark SQL is an abstraction layer over RDD API that's made
up of partitions and tasks. Tasks are Scala functions (possibly with some
Python for PySpark). A simple-looking high-level operator like
DataFrame.join can end up with multiple RDDs, each with a set of partitions
(and hence tasks). What the tasks do is an implementation detail that you'd
have to know about by reading the source code of Spark SQL that produces
the "bytecode".
Just looking at the DAG or the tasks
