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From Michael Segel <>
Subject Re: HW imbalance
Date Mon, 26 Jan 2015 16:34:48 GMT
If you’re running YARN, then you should be able to mix and max where YARN is managing the
resources available on the node. 

Having said that… it depends on which version of Hadoop/YARN. 

If you’re running Hortonworks and Ambari, then setting up multiple profiles may not be straight
forward. (I haven’t seen the latest version of Ambari) 

So in theory, one profile would be for your smaller 36GB of ram, then one profile for your
128GB sized machines. 
Then as your request resources for your spark job, it should schedule the jobs based on the
cluster’s available resources. 
(At least in theory.  I haven’t tried this so YMMV) 



On Jan 26, 2015, at 4:25 PM, Antony Mayi <> wrote:

> should have said I am running as yarn-client. all I can see is specifying the generic
executor memory that is then to be used in all containers.
> On Monday, 26 January 2015, 16:48, Charles Feduke <> wrote:
> You should look at using Mesos. This should abstract away the individual hosts into a
pool of resources and make the different physical specifications manageable.
> I haven't tried configuring Spark Standalone mode to have different specs on different
machines but based on
> # - SPARK_WORKER_CORES, to set the number of cores to use on this machine
> # - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors
(e.g. 1000m, 2g)
> # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
> it looks like you should be able to mix. (Its not clear to me whether SPARK_WORKER_MEMORY
is uniform across the cluster or for the machine where the config file resides.)
> On Mon Jan 26 2015 at 8:07:51 AM Antony Mayi <> wrote:
> Hi,
> is it possible to mix hosts with (significantly) different specs within a cluster (without
wasting the extra resources)? for example having 10 nodes with 36GB RAM/10CPUs now trying
to add 3 hosts with 128GB/10CPUs - is there a way to utilize the extra memory by spark executors
(as my understanding is all spark executors must have same memory).
> thanks,
> Antony.

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