Whats your spark-submit commands in both cases? Is it Spark Standalone or YARN (both support client and cluster)? Accordingly what is the number of executors/cores requested?

TD

On Wed, Dec 31, 2014 at 10:36 AM, Enno Shioji <eshioji@gmail.com> wrote:
Also the job was deployed from the master machine in the cluster.

On Wed, Dec 31, 2014 at 6:35 PM, Enno Shioji <eshioji@gmail.com> wrote:
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).



On Wed, Dec 31, 2014 at 6:21 PM, Sean Owen <sowen@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 <eshioji@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