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From Silvio Fiorito <silvio.fior...@granturing.com>
Subject RE: Dynamic Resource Allocation with Spark Streaming (Standalone Cluster, Spark 1.5.1)
Date Mon, 26 Oct 2015 21:26:12 GMT
Hi Matthias,

Unless there was a change in 1.5, I'm afraid dynamic resource allocation is not yet supported
in streaming apps.

Thanks,
Silvio

Sent from my Lumia 930
________________________________
From: Matthias Niehoff<mailto:matthias.niehoff@codecentric.de>
Sent: ‎10/‎26/‎2015 4:00 PM
To: user@spark.apache.org<mailto:user@spark.apache.org>
Subject: Dynamic Resource Allocation with Spark Streaming (Standalone Cluster, Spark 1.5.1)

Hello everybody,

I have a few (~15) Spark Streaming jobs which have load peaks as well as long times with a
low load. So I thought the new Dynamic Resource Allocation for Standalone Clusters might be
helpful (SPARK-4751).

I have a test "cluster" with 1 worker consisting of 4 executors with 2 cores each, so 8 cores
in total.

I started a simple streaming application without limiting the max cores for this app. As expected
the app occupied every core of the cluster. Then I started a second app, also without limiting
the maximum cores. As the first app did not get any input through the stream, my naive expectation
was that the second app would get at least 2 cores (1 receiver, 1 processing), but that's
not what happened. The cores are still assigned to the first app.
When I look at the application UI of the first app every executor is still running. That explains
why no executor is used for the second app.

I end up with two questions:
- When does an executor getting idle in a Spark Streaming application? (and so could be reassigned
to another app)
- Is there another way to compete with uncertain load when using Spark Streaming Applications?
I already combined multiple jobs to a Spark Application using different threads, but this
approach comes to a limit for me, because Spark Applications get to big to manage.

Thank You!



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