Hi there,
You may want to look at setting the memory overhead settings higher. Spark will
then start containers with a higher memory limit (spark.executor.memory +
spark.executor.memoryOverhead, to be exact) while the heap is still locked to
spark.executor.memory. There’s some memory used by offheap storage from Spark
that won’t be accounted for in just the heap size.
Hope this helps,
-Matt Cheah
From: Jayesh Lalwani <jayesh.lalw...@capitalone.com>
Date: Thursday, August 2, 2018 at 12:35 PM
To: "user@spark.apache.org" <user@spark.apache.org>
Subject: Spark on Kubernetes: Kubernetes killing executors because of
overallocation of memory
We are running Spark 2.3 on a Kubernetes cluster. We have set the following
spark configuration options
"spark.executor.memory": "7g",
"spark.driver.memory": "2g",
"spark.memory.fraction": "0.75"
WHat we see is
a) In the SPark UI, 5G has been allocated to each executor, which makes sense
because we set spark.memory.fraction=0.75
b) Kubernetes reports the pod memory usage as 7.6G
WHen we run a lot of jobs on the Kubernetes cluster, Kubernetes starts killing
the executor pods, because it thinks that the pod is misbehaving.
We logged into a running pod, and ran the top command, and most of the 7.6G is
being allocated to the executor's java process
Why is Spark taking 7.6G instead of 7 G? Where is the 600MB being allocated to?
Is there some configuration that controls how much of the executor memory gets
allocated to Permgen vs the memory that gets allocated to the heap?
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