Another wild guess, if your data is stored in S3, you might be running into
an issue where the default jets3t properties limits the number of parallel
S3 connections to 4. Consider increasing the max-thread-counts from here:
http://www.jets3t.org/toolkit/configuration.html.
On Tue, Oct 21, 2014 at 10:39 AM, Andy Davidson <
a...@santacruzintegration.com> wrote:
> On a related note, how are you submitting your job?
>
> I have a simple streaming proof of concept and noticed that everything
> runs on my master. I wonder if I do not have enough load for spark to push
> tasks to the slaves.
>
> Thanks
>
> Andy
>
> From: Daniel Mahler <dmah...@gmail.com>
> Date: Monday, October 20, 2014 at 5:22 PM
> To: Nicholas Chammas <nicholas.cham...@gmail.com>
> Cc: user <user@spark.apache.org>
> Subject: Re: Getting spark to use more than 4 cores on Amazon EC2
>
> I am using globs though
>
> raw = sc.textFile("/path/to/dir/*/*")
>
> and I have tons of files so 1 file per partition should not be a problem.
>
> On Mon, Oct 20, 2014 at 7:14 PM, Nicholas Chammas <
> nicholas.cham...@gmail.com> wrote:
>
>> The biggest danger with gzipped files is this:
>>
>> >>> raw = sc.textFile("/path/to/file.gz", 8)>>> raw.getNumPartitions()1
>>
>> You think you’re telling Spark to parallelize the reads on the input, but
>> Spark cannot parallelize reads against gzipped files. So 1 gzipped file
>> gets assigned to 1 partition.
>>
>> It might be a nice user hint if Spark warned when parallelism is disabled
>> by the input format.
>>
>> Nick
>>
>>
>> On Mon, Oct 20, 2014 at 6:53 PM, Daniel Mahler <dmah...@gmail.com> wrote:
>>
>>> Hi Nicholas,
>>>
>>> Gzipping is a an impressive guess! Yes, they are.
>>> My data sets are too large to make repartitioning viable, but I could
>>> try it on a subset.
>>> I generally have many more partitions than cores.
>>> This was happenning before I started setting those configs.
>>>
>>> thanks
>>> Daniel
>>>
>>>
>>> On Mon, Oct 20, 2014 at 5:37 PM, Nicholas Chammas <
>>> nicholas.cham...@gmail.com> wrote:
>>>
>>>> Are you dealing with gzipped files by any chance? Does explicitly
>>>> repartitioning your RDD to match the number of cores in your cluster help
>>>> at all? How about if you don't specify the configs you listed and just go
>>>> with defaults all around?
>>>>
>>>> On Mon, Oct 20, 2014 at 5:22 PM, Daniel Mahler <dmah...@gmail.com>
>>>> wrote:
>>>>
>>>>> I launch the cluster using vanilla spark-ec2 scripts.
>>>>> I just specify the number of slaves and instance type
>>>>>
>>>>> On Mon, Oct 20, 2014 at 4:07 PM, Daniel Mahler <dmah...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I usually run interactively from the spark-shell.
>>>>>> My data definitely has more than enough partitions to keep all the
>>>>>> workers busy.
>>>>>> When I first launch the cluster I first do:
>>>>>>
>>>>>> +++++++++++++++++++++++++++++++++++++++++++++++++
>>>>>> cat <<EOF >>~/spark/conf/spark-defaults.conf
>>>>>> spark.serializer org.apache.spark.serializer.KryoSerializer
>>>>>> spark.rdd.compress true
>>>>>> spark.shuffle.consolidateFiles true
>>>>>> spark.akka.frameSize 20
>>>>>> EOF
>>>>>>
>>>>>> copy-dir /root/spark/conf
>>>>>> spark/sbin/stop-all.sh
>>>>>> sleep 5
>>>>>> spark/sbin/start-all.sh
>>>>>> +++++++++++++++++++++++++++++++++++++++++++++++++
>>>>>>
>>>>>> before starting the spark-shell or running any jobs.
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Mon, Oct 20, 2014 at 2:57 PM, Nicholas Chammas <
>>>>>> nicholas.cham...@gmail.com> wrote:
>>>>>>
>>>>>>> Perhaps your RDD is not partitioned enough to utilize all the cores
>>>>>>> in your system.
>>>>>>>
>>>>>>> Could you post a simple code snippet and explain what kind of
>>>>>>> parallelism you are seeing for it? And can you report on how many
>>>>>>> partitions your RDDs have?
>>>>>>>
>>>>>>> On Mon, Oct 20, 2014 at 3:53 PM, Daniel Mahler <dmah...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>>
>>>>>>>> I am launching EC2 clusters using the spark-ec2 scripts.
>>>>>>>> My understanding is that this configures spark to use the available
>>>>>>>> resources.
>>>>>>>> I can see that spark will use the available memory on larger
>>>>>>>> istance types.
>>>>>>>> However I have never seen spark running at more than 400% (using
>>>>>>>> 100% on 4 cores)
>>>>>>>> on machines with many more cores.
>>>>>>>> Am I misunderstanding the docs? Is it just that high end ec2
>>>>>>>> instances get I/O starved when running spark? It would be strange if
>>>>>>>> that
>>>>>>>> consistently produced a 400% hard limit though.
>>>>>>>>
>>>>>>>> thanks
>>>>>>>> Daniel
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
