That's why I think it's the OOM killer. There are several cases of
memory overuse / errors :
1 - The application tries to allocate more than the Heap limit and GC
cannot free more memory = OutOfMemory : Java Heap Space exception from JVM
2 - The jvm is configured with a max heap size larger
Any idea what this means, many thanks
==
logs/spark-.-org.apache.spark.deploy.worker.Worker-1-09.out.1
==
15/04/13 07:07:22 INFO Worker: Starting Spark worker 09:39910 with 4
cores, 6.6 GB RAM
15/04/13 07:07:22 INFO Worker: Running Spark version 1.3.0
15/04/13 07:07:22 INFO
Very likely to be this :
http://www.linuxdevcenter.com/pub/a/linux/2006/11/30/linux-out-of-memory.html?page=2
Your worker ran out of memory = maybe you're asking for too much memory
for the JVM, or something else is running on the worker
Guillaume
Any idea what this means, many thanks
==
Linux OOM throws SIGTERM, but if I remember correctly JVM handles heap
memory limits differently and throws OutOfMemoryError and eventually sends
SIGINT.
Not sure what happened but the worker simply received a SIGTERM signal, so
perhaps the daemon was terminated by someone or a parent process.
