My colleague oleg has actually got something better, with spark hooked up
to the Tez dataflow engine, so giving dynamic node flexing during a run,
automatically, including re-arranging bits of the flow and reacting to
failures.

https://issues.apache.org/jira/browse/SPARK-3561

The spark team don't want it though; it's a disagreement about where plugin
points go (if any).

We've had that in hadoop core, and tend to take the path of "define the
extensions points strictly (filesystem, shuffle, scheduler, ...)", write
the reference tests, then let anyone else implementing them deal with the
compatibility problems. Hopefully the Spark team will choose to do
something similar.

Anyway: the source for that patch is available —why not have a play with it
and see what you think?


On 13 January 2015 at 00:58, Yong Feng <[email protected]> wrote:

> Hi Slider team
>
> I also played with "Spark/SparkR on Yarn" recently, and think we even could
> run Spark on Slider. After that we could enhance Slider to have API scale
> out/in Spark cluster horizontally or vertically. It is a little bit
> overlapped with the enhancement on YarnClusterScheduler of Spark. The
> Slider also may not have enough knowledge of Spark workload to make
> allocate request with data locality. However it will be a more general
> solution. After that Slider actually is a meta-app manager on Yarn. It will
> be similar as the Marathon in Mesos.
>
> Does it make sense? or anyone else has similar idea to run Spark on Slider?
> Does the data locality matter for Spark?
>
> Thanks,
>
> Yong
>

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