A receiver occupies a cpu core, an executor is simply a jvm instance and as
such it can be granted any number of cores and ram
So check how many cores you have per executor
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<div>-------- Original message --------</div><div>From: Mike Trienis
<mike.trie...@orcsol.com> </div><div>Date:2015/05/22 21:51 (GMT+00:00)
</div><div>To: user@spark.apache.org </div><div>Subject: Re: Spark Streaming:
all tasks running on one executor (Kinesis + Mongodb) </div><div>
</div>I guess each receiver occupies a executor. So there was only one executor
available for processing the job.
On Fri, May 22, 2015 at 1:24 PM, Mike Trienis <mike.trie...@orcsol.com> wrote:
Hi All,
I have cluster of four nodes (three workers and one master, with one core each)
which consumes data from Kinesis at 15 second intervals using two streams (i.e.
receivers). The job simply grabs the latest batch and pushes it to MongoDB. I
believe that the problem is that all tasks are executed on a single worker node
and never distributed to the others. This is true even after I set the number
of concurrentJobs to 3. Overall, I would really like to increase throughput
(i.e. more than 500 records / second) and understand why all executors are not
being utilized.
Here are some parameters I have set:
spark.streaming.blockInterval 200
spark.locality.wait 500
spark.streaming.concurrentJobs 3
This is the code that's actually doing the writing:
def write(rdd: RDD[Data], time:Time) : Unit = {
val result = doSomething(rdd, time)
result.foreachPartition { i =>
i.foreach(record => connection.insert(record))
}
}
def doSomething(rdd: RDD[Data]) : RDD[MyObject] = {
rdd.flatMap(MyObject)
}
Any ideas as to how to improve the throughput?
Thanks, Mike.
