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.nieh...@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!
