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From Sandy Ryza <>
Subject Re: spark worker and yarn memory
Date Thu, 05 Jun 2014 21:50:27 GMT
Hi Xu,

As crazy as it might sound, this all makes sense.

There are a few different quantities at play here:
* the heap size of the executor (controlled by --executor-memory)
* the amount of memory spark requests from yarn (the heap size plus
384 mb to account for fixed memory costs outside if the heap)
* the amount of memory yarn grants to the container (yarn rounds up to
the nearest multiple of yarn.scheduler.minimum-allocation-mb or
yarn.scheduler.fair.increment-allocation-mb, depending on the
scheduler used)
* the amount of memory spark uses for caching on each executor, which
is (default 0.6) of the executor heap

So, with --executor-memory 8g, spark requests 8g + 384m from yarn,
which doesn't fit into it's container max.  With --executor-memory 7g,
Spark requests 7g + 384m from yarn, which fits into its container max.
 This gets rounded up to 8g by the yarn scheduler.  7g is still used
as the executor heap size, and .6 of this is about 4g, shown as the
cache space in the spark.


> On Jun 5, 2014, at 9:44 AM, "Xu (Simon) Chen" <> wrote:
> I am slightly confused about the "--executor-memory" setting. My yarn cluster has a maximum
container memory of 8192MB.
> When I specify "--executor-memory 8G" in my spark-shell, no container can be started
at all. It only works when I lower the executor memory to 7G. But then, on yarn, I see 2 container
per node, using 16G of memory.
> Then on the spark UI, it shows that each worker has 4GB of memory, rather than 7.
> Can someone explain the relationship among the numbers I see here?
> Thanks.

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