(looks like the list didn't like a HTML table on the previous email. My excuses for any duplicates)
Hi, We are observing with certain regularity that our Spark jobs, as Mesos framework, are hoarding resources and not releasing them, resulting in resource starvation to all jobs running on the Mesos cluster. For example: This is a job that has spark.cores.max = 4 and spark.executor.memory="3g" | ID |Framework |Host |CPUs |Mem …5050-16506-1146497 FooStreaming dnode-4.hdfs.private 7 13.4 GB …5050-16506-1146495 FooStreaming dnode-0.hdfs.private 1 6.4 GB …5050-16506-1146491 FooStreaming dnode-5.hdfs.private 7 11.9 GB …5050-16506-1146449 FooStreaming dnode-3.hdfs.private 7 4.9 GB …5050-16506-1146247 FooStreaming dnode-1.hdfs.private 0.5 5.9 GB …5050-16506-1146226 FooStreaming dnode-2.hdfs.private 3 7.9 GB …5050-16506-1144069 FooStreaming dnode-3.hdfs.private 1 8.7 GB …5050-16506-1133091 FooStreaming dnode-5.hdfs.private 1 1.7 GB …5050-16506-1133090 FooStreaming dnode-2.hdfs.private 5 5.2 GB …5050-16506-1133089 FooStreaming dnode-1.hdfs.private 6.5 6.3 GB …5050-16506-1133088 FooStreaming dnode-4.hdfs.private 1 251 MB …5050-16506-1133087 FooStreaming dnode-0.hdfs.private 6.4 6.8 GB The only way to release the resources is by manually finding the process in the cluster and killing it. The jobs are often streaming but also batch jobs show this behavior. We have more streaming jobs than batch, so stats are biased. Any ideas of what's up here? Hopefully some very bad ugly bug that has been fixed already and that will urge us to upgrade our infra? Mesos 0.20 + Marathon 0.7.4 + Spark 1.1.0 -kr, Gerard.
