Is FAIR available for Spark Standalone cluster mode? - scala

Question

I'm having 2 node cluster with spark standalone cluster manager. I'm triggering more than one job using same sc with Scala multi threading.What I found is my jobs are scheduled one after another because of FIFO nature so I tried to use FAIR scheduling
conf.set("spark.scheduler.mode", "FAIR")
conf.set("spark.scheduler.allocation.file", sys.env("SPARK_HOME") + "/conf/fairscheduler.xml")
val job1 = Future {
val job = new Job1()
job.run()
}
val job2 =Future {
val job = new Job2()
job.run()
}
class Job1{
def run()
sc.setLocalProperty("spark.scheduler.pool", "mypool1")
}
}
class Job2{
def run()
sc.setLocalProperty("spark.scheduler.pool", "mypool2")
}
}
<pool name="mypool1">
<schedulingMode>FAIR</schedulingMode>
<weight>1</weight>
<minShare>2</minShare>
</pool>
<pool name="mypool2">
<schedulingMode>FAIR</schedulingMode>
<weight>1</weight>
<minShare>2</minShare>
</pool>
Job1 and Job2 will be triggered from an launcher class. Even after setting these properties, my jobs are handled in FIFO.
Is FAIR available for Spark Standalone cluster mode?Is there a page
where it's described in more details? I can't seem to find much about
FAIR and Standalone in Job Scheduling.I'm following this SOF question.am I missing anything here ?

Answer 1

I don't think standalone is the problem. You described creating only one pool, so I think your problem is that you need at least one more pool and assign each job to a different pool.
FAIR scheduling is done across pools, anything within the same pool will run in FIFO mode anyway.
This is based on the documentation here:
https://spark.apache.org/docs/latest/job-scheduling.html#default-behavior-of-pools