Yes, HDFS has serious problems with creating lots of files. But we can
always just create a single merged file on HDFS per task.
On Apr 28, 2016 11:17 AM, "Reynold Xin" wrote:
Hm while this is an attractive idea in theory, in practice I think you are
substantially overestimating HDFS' ability to
Ah, got it. While that would be useful, it doesn't address the more
general (and potentially even more beneficial) case where the total number
of worker nodes is fully elastic. That already starts to push you into the
direction of spitting Spark worker and HDFS data nodes into disjoint sets,
and
Not disjoint. Colocated. By "shrinking", I don't mean any nodes are going
away. I mean executors are decreasing in number, which is the case with
dynamic allocation. HDFS nodes aren't decreasing in number though, and we
can still colocate on those nodes, as always.
On Thu, Apr 28, 2016 at 11:1
Yea, it's an open question. I'm willing to create some benchmarks, but I'd
first like to know that the feature would be accepted assuming the results
are reasonable. Can a committer give me a thumbs up?
On Thu, Apr 28, 2016 at 11:17 AM, Reynold Xin wrote:
> Hm while this is an attractive idea
So you are only considering the case where your set of HDFS nodes is
disjoint from your dynamic set of Spark Worker nodes? That would seem to
be a pretty significant sacrifice of data locality.
On Thu, Apr 28, 2016 at 11:15 AM, Michael Gummelt
wrote:
> > if after a work-load burst your cluster
Hm while this is an attractive idea in theory, in practice I think you are
substantially overestimating HDFS' ability to handle a lot of small,
ephemeral files. It has never really been optimized for that use case.
On Thu, Apr 28, 2016 at 11:15 AM, Michael Gummelt
wrote:
> > if after a work-load
> if after a work-load burst your cluster dynamically changes from 1
workers to 1000, will the typical HDFS replication factor be sufficient to
retain access to the shuffle files in HDFS
HDFS isn't resizing. Spark is. HDFS files should be HA and durable.
On Thu, Apr 28, 2016 at 11:08 AM, M
Yes, replicated and distributed shuffle materializations are key
requirement to maintain performance in a fully elastic cluster where
Executors aren't just reallocated across an essentially fixed number of
Worker nodes, but rather the number of Workers itself is dynamic.
Retaining the file interfac
> Why would you run the shuffle service on 10K nodes but Spark executors
on just 100 nodes? wouldn't you also run that service just on the 100
nodes?
We have to start the service beforehand, out of band, and we don't know a
priori where the Spark executors will land. Those 100 executors could lan
Why would you run the shuffle service on 10K nodes but Spark executors
on just 100 nodes? wouldn't you also run that service just on the 100
nodes?
What does plumbing it through HDFS buy you in comparison? There's some
additional overhead and if anything you lose some control over
locality, in a c
> Are you suggesting to have shuffle service persist and fetch data with
hdfs, or skip shuffle service altogether and just write to hdfs?
Skip shuffle service altogether. Write to HDFS.
Mesos environments tend to be multi-tenant, and running the shuffle service
on all nodes could be extremely wa
> On 27 Apr 2016, at 04:59, Takeshi Yamamuro wrote:
>
> Hi, all
>
> See SPARK-1529 for related discussion.
>
> // maropu
I'd not seen that discussion.
I'm actually curious about why the 15% diff in performance between Java NIO and
Hadoop FS APIs, and, if it is the case (Hadoop still uses t
Hi, all
See SPARK-1529 for related discussion.
// maropu
On Wed, Apr 27, 2016 at 12:27 PM, Saisai Shao
wrote:
> Quite curious about the benefits of using HDFS as shuffle service, also
> what's the problem of using current shuffle service?
>
>
> Thanks
> Saisai
>
> On Wed, Apr 27, 2016 at 4:31
Quite curious about the benefits of using HDFS as shuffle service, also
what's the problem of using current shuffle service?
Thanks
Saisai
On Wed, Apr 27, 2016 at 4:31 AM, Timothy Chen wrote:
> Are you suggesting to have shuffle service persist and fetch data with
> hdfs, or skip shuffle servi
Are you suggesting to have shuffle service persist and fetch data with hdfs, or
skip shuffle service altogether and just write to hdfs?
Tim
> On Apr 26, 2016, at 11:20 AM, Michael Gummelt wrote:
>
> Has there been any thought or work on this (or any other networked file
> system)? It would
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