Hi Matei,
At least in my case, the s3 bucket is in the same region. Running count()
works and so does generating synthetic data. What I saw was that the job
would hang for over an hour with no progress but tasks would immediately
start finishing if I cached the data.

- jerry


On Mon, Aug 25, 2014 at 1:26 PM, Matei Zaharia [via Apache Spark Developers
List] <ml-node+s1001551n8000...@n3.nabble.com> wrote:

> Was the original issue with Spark 1.1 (i.e. master branch) or an earlier
> release?
>
> One possibility is that your S3 bucket is in a remote Amazon region, which
> would make it very slow. In my experience though saveAsTextFile has worked
> even for pretty large datasets in that situation, so maybe there's
> something else in your job causing a problem. Have you tried other
> operations on the data, like count(), or saving synthetic datasets (e.g.
> sc.parallelize(1 to 100*1000*1000, 20).saveAsTextFile(...)?
>
> Matei
>
> On August 25, 2014 at 12:09:25 PM, amnonkhen ([hidden email]
> <http://user/SendEmail.jtp?type=node&node=8000&i=0>) wrote:
>
> Hi jerryye,
> Maybe if you voted up my question on Stack Overflow it would get some
> traction and we would get nearer to a solution.
> Thanks,
> Amnon
>
>
>
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