hdfs 1.0.4 but we primarily use Cassandra + Spark (calliope). I tested it
with both
 Are you using it with HDFS? What version of Hadoop? 1.0.4?
Ognen

On 3/10/14, 8:49 PM, abhinav chowdary wrote:

for any one who is interested to know about job server from Ooyala.. we
started using it recently and been working great so far..
On Feb 25, 2014 9:23 PM, "Ognen Duzlevski" <og...@nengoiksvelzud.com> wrote:

>  In that case, I must have misunderstood the following (from
> http://spark.incubator.apache.org/docs/0.8.1/job-scheduling.html).
> Apologies. Ognen
>
> "Inside a given Spark application (SparkContext instance), multiple
> parallel jobs can run simultaneously if they were submitted from separate
> threads. By "job", in this section, we mean a Spark action (e.g. save,
> collect) and any tasks that need to run to evaluate that action. Spark's
> scheduler is fully thread-safe and supports this use case to enable
> applications that serve multiple requests (e.g. queries for multiple
> users).
>
> By default, Spark's scheduler runs jobs in FIFO fashion. Each job is
> divided into "stages" (e.g. map and reduce phases), and the first job gets
> priority on all available resources while its stages have tasks to launch,
> then the second job gets priority, etc. If the jobs at the head of the
> queue don't need to use the whole cluster, later jobs can start to run
> right away, but if the jobs at the head of the queue are large, then later
> jobs may be delayed significantly.
>
> Starting in Spark 0.8, it is also possible to configure fair sharing
> between jobs. Under fair sharing, Spark assigns tasks between jobs in a
> "round robin" fashion, so that all jobs get a roughly equal share of
> cluster resources. This means that short jobs submitted while a long job is
> running can start receiving resources right away and still get good
> response times, without waiting for the long job to finish. This mode is
> best for multi-user settings.
>
> To enable the fair scheduler, simply set the spark.scheduler.mode to FAIR 
> before
> creating a SparkContext:"
> On 2/25/14, 12:30 PM, Mayur Rustagi wrote:
>
> fair scheduler merely reorders tasks .. I think he is looking to run
> multiple pieces of code on a single context on demand from customers...if
> the code & order is decided then fair scheduler will ensure that all tasks
> get equal cluster time :)
>
>
>
>  Mayur Rustagi
> Ph: +919632149971
> h <https://twitter.com/mayur_rustagi>ttp://www.sigmoidanalytics.com
>  https://twitter.com/mayur_rustagi
>
>
>
> On Tue, Feb 25, 2014 at 10:24 AM, Ognen Duzlevski <
> og...@nengoiksvelzud.com> wrote:
>
>>  Doesn't the fair scheduler solve this?
>> Ognen
>>
>>
>> On 2/25/14, 12:08 PM, abhinav chowdary wrote:
>>
>> Sorry for not being clear earlier
>> how do you want to pass the operations to the spark context?
>> this is partly what i am looking for . How to access the active spark
>> context and possible ways to pass operations
>>
>>  Thanks
>>
>>
>>
>>  On Tue, Feb 25, 2014 at 10:02 AM, Mayur Rustagi <mayur.rust...@gmail.com
>> > wrote:
>>
>>> how do you want to pass the operations to the spark context?
>>>
>>>
>>>  Mayur Rustagi
>>> Ph: +919632149971
>>> h <https://twitter.com/mayur_rustagi>ttp://www.sigmoidanalytics.com
>>>  https://twitter.com/mayur_rustagi
>>>
>>>
>>>
>>> On Tue, Feb 25, 2014 at 9:59 AM, abhinav chowdary <
>>> abhinav.chowd...@gmail.com> wrote:
>>>
>>>> Hi,
>>>>        I am looking for ways to share the sparkContext, meaning i need
>>>> to be able to perform multiple operations on the same spark context.
>>>>
>>>>  Below is code of a simple app i am testing
>>>>
>>>>   def main(args: Array[String]) {
>>>>     println("Welcome to example application!")
>>>>
>>>>      val sc = new SparkContext("spark://10.128.228.142:7077", "Simple
>>>> App")
>>>>
>>>>      println("Spark context created!")
>>>>
>>>>      println("Creating RDD!")
>>>>
>>>>  Now once this context is created i want to access  this to submit
>>>> multiple jobs/operations
>>>>
>>>>  Any help is much appreciated
>>>>
>>>>  Thanks
>>>>
>>>>
>>>>
>>>>
>>>
>>
>>
>>  --
>> Warm Regards
>> Abhinav Chowdary
>>
>>
>>
>
>
-- 
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