Hi Tathagata,
It's a standalone cluster. The submit commands are:
== CLIENT
spark-submit --class com.fake.Test \
--deploy-mode client --master spark://fake.com:7077 \
fake.jar <arguments>
== CLUSTER
spark-submit --class com.fake.Test \
--deploy-mode cluster --master spark://fake.com:7077 \
s3n://fake.jar <arguments>
And they are both occupying all available slots. (8 * 2 machine = 16 slots).
ᐧ
On Thu, Jan 1, 2015 at 12:21 AM, Tathagata Das <tathagata.das1...@gmail.com>
wrote:
> 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 <eshi...@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 <eshi...@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 <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
>>>>
>>>
>>>
>>
>
