Michael - I'm not sure if you actually read my email, but spill has nothing to do with the shuffle files on disk. It was for the partitioning (i.e. sorting) process. If that flag is off, Spark will just run out of memory when data doesn't fit in memory. 


On Fri, Apr 1, 2016 at 3:28 PM, Michael Slavitch <slavitch@gmail.com> wrote:
RAMdisk is a fine interim step but there is a lot of layers eliminated by keeping things in memory unless there is need for spillover.   At one time there was support for turning off spilling.  That was eliminated.  Why? 


On Fri, Apr 1, 2016, 6:05 PM Mridul Muralidharan <mridul@gmail.com> wrote:
I think Reynold's suggestion of using ram disk would be a good way to
test if these are the bottlenecks or something else is.
For most practical purposes, pointing local dir to ramdisk should
effectively give you 'similar' performance as shuffling from memory.

Are there concerns with taking that approach to test ? (I dont see
any, but I am not sure if I missed something).


Regards,
Mridul




On Fri, Apr 1, 2016 at 2:10 PM, Michael Slavitch <slavitch@gmail.com> wrote:
> I totally disagree that it’s not a problem.
>
> - Network fetch throughput on 40G Ethernet exceeds the throughput of NVME
> drives.
> - What Spark is depending on is Linux’s IO cache as an effective buffer pool
> This is fine for small jobs but not for jobs with datasets in the TB/node
> range.
> - On larger jobs flushing the cache causes Linux to block.
> - On a modern 56-hyperthread 2-socket host the latency caused by multiple
> executors writing out to disk increases greatly.
>
> I thought the whole point of Spark was in-memory computing?  It’s in fact
> in-memory for some things but  use spark.local.dir as a buffer pool of
> others.
>
> Hence, the performance of  Spark is gated by the performance of
> spark.local.dir, even on large memory systems.
>
> "Currently it is not possible to not write shuffle files to disk.”
>
> What changes >would< make it possible?
>
> The only one that seems possible is to clone the shuffle service and make it
> in-memory.
>
>
>
>
>
> On Apr 1, 2016, at 4:57 PM, Reynold Xin <rxin@databricks.com> wrote:
>
> spark.shuffle.spill actually has nothing to do with whether we write shuffle
> files to disk. Currently it is not possible to not write shuffle files to
> disk, and typically it is not a problem because the network fetch throughput
> is lower than what disks can sustain. In most cases, especially with SSDs,
> there is little difference between putting all of those in memory and on
> disk.
>
> However, it is becoming more common to run Spark on a few number of beefy
> nodes (e.g. 2 nodes each with 1TB of RAM). We do want to look into improving
> performance for those. Meantime, you can setup local ramdisks on each node
> for shuffle writes.
>
>
>
> On Fri, Apr 1, 2016 at 11:32 AM, Michael Slavitch <slavitch@gmail.com>
> wrote:
>>
>> Hello;
>>
>> I’m working on spark with very large memory systems (2TB+) and notice that
>> Spark spills to disk in shuffle.  Is there a way to force spark to stay in
>> memory when doing shuffle operations?   The goal is to keep the shuffle data
>> either in the heap or in off-heap memory (in 1.6.x) and never touch the IO
>> subsystem.  I am willing to have the job fail if it runs out of RAM.
>>
>> spark.shuffle.spill true  is deprecated in 1.6 and does not work in
>> Tungsten sort in 1.5.x
>>
>> "WARN UnsafeShuffleManager: spark.shuffle.spill was set to false, but this
>> is ignored by the tungsten-sort shuffle manager; its optimized shuffles will
>> continue to spill to disk when necessary.”
>>
>> If this is impossible via configuration changes what code changes would be
>> needed to accomplish this?
>>
>>
>>
>>
>>
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>
>
--
Michael Slavitch
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