Oh sorry that was a edit mistake. The code is essentially:
val msgStream = kafkaStream
.map { case (k, v) => v}
.map(DatatypeConverter.printBase64Binary)
.saveAsTextFile("s3n://some.bucket/path", classOf[LzoCodec])
I.e. there is essentially no original code (I was calling saveAsTextFile in
a "save" function but that was just a remnant from previous debugging).
ᐧ
On Wed, Dec 31, 2014 at 6:21 PM, Sean Owen <so...@cloudera.com> wrote:
> -dev, +user
>
> A decent guess: Does your 'save' function entail collecting data back
> to the driver? and are you running this from a machine that's not in
> your Spark cluster? Then in client mode you're shipping data back to a
> less-nearby machine, compared to with cluster mode. That could explain
> the bottleneck.
>
> On Wed, Dec 31, 2014 at 4:12 PM, Enno Shioji <eshi...@gmail.com> wrote:
> > Hi,
> >
> > I have a very, very simple streaming job. When I deploy this on the exact
> > same cluster, with the exact same parameters, I see big (40%) performance
> > difference between "client" and "cluster" deployment mode. This seems a
> bit
> > surprising.. Is this expected?
> >
> > The streaming job is:
> >
> > val msgStream = kafkaStream
> > .map { case (k, v) => v}
> > .map(DatatypeConverter.printBase64Binary)
> > .foreachRDD(save)
> > .saveAsTextFile("s3n://some.bucket/path", classOf[LzoCodec])
> >
> > I tried several times, but the job deployed with "client" mode can only
> > write at 60% throughput of the job deployed with "cluster" mode and this
> > happens consistently. I'm logging at INFO level, but my application code
> > doesn't log anything so it's only Spark logs. The logs I see in "client"
> > mode doesn't seem like a crazy amount.
> >
> > The setup is:
> > spark-ec2 [...] \
> > --copy-aws-credentials \
> > --instance-type=m3.2xlarge \
> > -s 2 launch test_cluster
> >
> > And all the deployment was done from the master machine.
> >
> > ᐧ
>
