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From Ramandeep Singh <>
Subject Re: Troubleshooting Spark OOM
Date Wed, 09 Jan 2019 23:41:55 GMT

Here are a few suggestions that you can try.

OOM Issues that, I have faced with Spark:
*Not enough shuffle partition*s.Increase them.
Less memory Overhead settings: Boosting it to around 12 percent. You
usually get this as a error message in your executors.
*Large Executor Configs*: They can be problematic, smaller and larger in
number executors are preferred over larger and fewer executors.
Changing GC algorithm

Here are a few tips

On Wed, Jan 9, 2019 at 1:55 PM Dillon Dukek <>

> Hi William,
> Just to get started, can you describe the spark version you are using and
> the language? It doesn't sound like you are using pyspark, however,
> problems arising from that can be different so I just want to be sure. As
> well, can you talk through the scenario under which you are dealing with
> this error? ie the order of operations for the transformations you are
> applying.
> However, if you're set on getting a heap dump, probably the easiest way
> would be to just monitor an active application through the spark UI then go
> grab a heap dump from the executor java process when you notice one that's
> having problems.
> On Wed, Jan 9, 2019 at 10:18 AM William Shen <>
> wrote:
>> Hi there,
>> We've encountered Spark executor Java OOM issues for our Spark
>> application. Any tips on how to troubleshoot to identify what objects are
>> occupying the heap? In the past, dealing with JVM OOM, we've worked with
>> analyzing heap dumps, but we are having a hard time with locating Spark
>> heap dump after a crash, and we also anticipate that these heap dump will
>> be huge (since our nodes have a large memory allocation) and may be
>> difficult to analyze locally. Can someone share their experience dealing
>> with Spark OOM?
>> Thanks!

Ramandeep Singh

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