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https://issues.apache.org/jira/browse/SPARK-32790?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17195288#comment-17195288
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Rosie Bloxsom commented on SPARK-32790:
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Thanks [~dongjoon],
Re. docs:
* +1 on the snippet you posted
* Also the descriptions of the FAILED and FINISHED states themselves imply
that receiving one of those states means the application finished with that
state (e.g. "The application finished with a successful status.") so I think
that would also need clarifying.
I was not sure what documentation changes to suggest, because I have only been
able to verify that this is what's happening in *client mode*, and the docs do
not distinguish between client and cluster modes.
I don't have access to a cluster to test with, but I have heard from colleagues
that error handling has not been a problem in cluster mode - so that implies
that this only is a problem with client mode? I'm not sure, as I don't fully
understand the differences between the modes, where processes run, and how they
handle errors. So therefore I'm not sure whether the docs on this need
completely changing, or just changing to clarify that with cluster mode the
states work as intended, but in client mode the FINISHED state is not
necessarily final.
> FINISHED state of application is not final
> ------------------------------------------
>
> Key: SPARK-32790
> URL: https://issues.apache.org/jira/browse/SPARK-32790
> Project: Spark
> Issue Type: Bug
> Components: Spark Submit
> Affects Versions: 2.4.4
> Environment: Spark 2.4.4 (and probably every version since, from
> looking at the code?)
> On a local machine.
> Reporter: Rosie Bloxsom
> Priority: Minor
> Labels: application, spark-submit
>
> If you launch an application with SparkLauncher.startApplication, and pass a
> listener to listener to the returned state, there are supposed to be two
> possible "final" states:
> * FINISHED, denoting success
> * FAILED, denoting a failure
> Because they are final, if you receive a FINISHED signal you should be able
> to proceed as if there was no error.
> Unfortunately, this code:
> https://github.com/apache/spark/blob/233c214a752771f5d8ca9fb2aea93cf1776a552d/launcher/src/main/java/org/apache/spark/launcher/ChildProcAppHandle.java#L128
> which I think is related to decisions from this previous issue:
> https://github.com/apache/spark/pull/18877
> means that in case of an error, a FINISHED event is sent, followed shortly
> thereafter by a FAILED event, and both of these events are "final".
> I'm not sure if there's a way to fix it so that only one event is sent -
> ideally when the child process fails, we would only send FAILED, rather than
> sending "FINISHED" first? If we can't change it, then at least we should
> update the docs to explain what happens, and maybe change the definition of
> isFinal?
> To reproduce, install spark 2.4.4 and run this scala code using one of the
> spark example jars. It shows the transition between the states for a
> trivially erroring spark application. The states received are:
> {noformat}
> Received event updating state to CONNECTED
> Received event updating state to RUNNING
> Received event updating state to FINISHED
> Received event updating state to FAILED
> {noformat}
> {code:scala}
> package foo
> import org.apache.spark.launcher.{SparkAppHandle, SparkLauncher}
> import org.scalatest.flatspec.AnyFlatSpecLike
> import org.scalatest.matchers.should.Matchers
> import scala.concurrent.duration._
> import scala.concurrent.{Await, Promise}
> class FinishedStateNotFinalSpec extends AnyFlatSpecLike with Matchers {
> "it" should "enter FAILED state without entering into FINISHED state" in {
> val examplesJar =
> "file:/C:/spark/spark-2.4.4-bin-hadoop2.7/spark-2.4.4-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.4.4.jar"
> val launcher = new SparkLauncher()
>
> .setSparkHome("""C:\spark\spark-2.4.4-bin-hadoop2.7\spark-2.4.4-bin-hadoop2.7""")
> .setAppResource(examplesJar)
> .redirectError()
> .redirectOutput(java.io.File.createTempFile("spark-error", "log"))
> .setAppName("Test")
> .setMaster("local[1]")
> .setMainClass("org.apache.spark.examples.SparkPi")
> .addAppArgs("This causes an error, because it should be a number not a
> string")
> val sparkCompletionPromise = Promise[Unit]()
> launcher.startApplication(new SparkAppListener(sparkCompletionPromise))
> Await.result(sparkCompletionPromise.future, 100000 millis)
> // check in the console output to see which states were entered
> }
> }
> class SparkAppListener(promise: Promise[Unit]) extends
> SparkAppHandle.Listener {
> def stateChanged(handle: SparkAppHandle): Unit = {
> val appState = handle.getState
> println(s"Received event updating state to $appState")
> if (appState.isFinal && appState == SparkAppHandle.State.FINISHED) {
> // Without this sleep, my program continues as if the spark-submit was
> a success.
> // With this sleep, there is a chance for the correct "FAILED" state to
> be registered.
> // But we shouldn't need this sleep, we should receive the FAILED state
> as the only "final" state.
> Thread.sleep(1000)
> promise.success(Unit)
> }
> else if (appState.isFinal && appState == SparkAppHandle.State.FAILED) {
> promise.failure(new RuntimeException("Spark run failed"))
> }
> }
> override def infoChanged(handle: SparkAppHandle): Unit = {}
> }
> {code}
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