Suppose you have Spark + Standalone cluster manager. You opened spark session with some configs and want to launch SomeSparkJob
40 times in parallel with different arguments.
1) I pretty sure the 1st point is possible, since it's possible at spark local mode. I just don't know how to do that in standalone mode.
2-3) It's possible to hand listener on spark context like spark.sparkContext().addSparkListener(new SparkListener() {
. But seems SparkListener
lacks failure callbacks.
Also there is a bunch of methods with very poor documentation. I've never used them, but perhaps they could help to solve my problem.
spark.sparkContext().dagScheduler().runJob();
spark.sparkContext().runJob()
spark.sparkContext().submitJob()
spark.sparkContext().taskScheduler().submitTasks();
spark.sparkContext().dagScheduler().handleJobCancellation();
spark.sparkContext().statusTracker()
You can use SparkLauncher and control the flow.
import org.apache.spark.launcher.SparkLauncher;
public class MyLauncher {
public static void main(String[] args) throws Exception {
Process spark = new SparkLauncher()
.setAppResource("/my/app.jar")
.setMainClass("my.spark.app.Main")
.setMaster("local")
.setConf(SparkLauncher.DRIVER_MEMORY, "2g")
.launch();
spark.waitFor();
}
}
See API for more details.
Since it creates process you can check the Process status and retry e.g. try following:
public boolean isAlive()
If Process is not live start again, see API for more details.
Hoping this gives high level idea of how we can achieve what you mentioned in your question. There could be more ways to do same thing but thought to share this approach.
Cheers !