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From Matt Cheah <mch...@palantir.com>
Subject Re: Spark on Kubernetes: Kubernetes killing executors because of overallocation of memory
Date Fri, 03 Aug 2018 00:36:33 GMT
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.lalwani@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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