On Fri, Jun 19, 2015 at 5:15 PM, Tathagata Das <t...@databricks.com> wrote:
> Also, can you find from the spark UI the break up of the stages in each
> batch's jobs, and find which stage is taking more time after a while?
>
Sure, will try to debug/troubleshoot. Are there enhancements to this
specific API between 1.3 and 1.4 that can substantially change it's
behaviour?
> On Fri, Jun 19, 2015 at 4:51 PM, Cody Koeninger <c...@koeninger.org>
> wrote:
>
>> when you say your old version was
>>
>> k = createStream .....
>>
>> were you manually creating multiple receivers? Because otherwise you're
>> only using one receiver on one executor...
>>
>
Yes, sorry, the earlier/stable version was more like:
kInStreams = (1 to n).map{_ => KafkaUtils.createStream ............ // n
being the number of kafka partitions, 1 receiver per partition
val k = ssc.union(kInStreams)
val dataout = k.map(x=>myFunc(x._2,someParams))
dataout.foreachRDD ( rdd => rdd.foreachPartition(rec => {
myOutputFunc.write(rec) })
Thanks,
Tim
>
>> If that's the case I'd try direct stream without the repartitioning.
>>
>>
>> On Fri, Jun 19, 2015 at 6:43 PM, Tim Smith <secs...@gmail.com> wrote:
>>
>>> Essentially, I went from:
>>> k = createStream .....
>>> val dataout = k.map(x=>myFunc(x._2,someParams))
>>> dataout.foreachRDD ( rdd => rdd.foreachPartition(rec => {
>>> myOutputFunc.write(rec) })
>>>
>>> To:
>>> kIn = createDirectStream .....
>>> k = kIn.repartition(numberOfExecutors) //since #kafka partitions <
>>> #spark-executors
>>> val dataout = k.map(x=>myFunc(x._2,someParams))
>>> dataout.foreachRDD ( rdd => rdd.foreachPartition(rec => {
>>> myOutputFunc.write(rec) })
>>>
>>> With the new API, the app starts up and works fine for a while but I
>>> guess starts to deteriorate after a while. With the existing API
>>> "createStream", the app does deteriorate but over a much longer period,
>>> hours vs days.
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Fri, Jun 19, 2015 at 1:40 PM, Tathagata Das <t...@databricks.com>
>>> wrote:
>>>
>>>> Yes, please tell us what operation are you using.
>>>>
>>>> TD
>>>>
>>>> On Fri, Jun 19, 2015 at 11:42 AM, Cody Koeninger <c...@koeninger.org>
>>>> wrote:
>>>>
>>>>> Is there any more info you can provide / relevant code?
>>>>>
>>>>> On Fri, Jun 19, 2015 at 1:23 PM, Tim Smith <secs...@gmail.com> wrote:
>>>>>
>>>>>> Update on performance of the new API: the new code using the
>>>>>> createDirectStream API ran overnight and when I checked the app state in
>>>>>> the morning, there were massive scheduling delays :(
>>>>>>
>>>>>> Not sure why and haven't investigated a whole lot. For now, switched
>>>>>> back to the createStream API build of my app. Yes, for the record, this
>>>>>> is
>>>>>> with CDH 5.4.1 and Spark 1.3.
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Thu, Jun 18, 2015 at 7:05 PM, Tim Smith <secs...@gmail.com> wrote:
>>>>>>
>>>>>>> Thanks for the super-fast response, TD :)
>>>>>>>
>>>>>>> I will now go bug my hadoop vendor to upgrade from 1.3 to 1.4.
>>>>>>> Cloudera, are you listening? :D
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Jun 18, 2015 at 7:02 PM, Tathagata Das <
>>>>>>> tathagata.das1...@gmail.com> wrote:
>>>>>>>
>>>>>>>> Are you using Spark 1.3.x ? That explains. This issue has been
>>>>>>>> fixed in Spark 1.4.0. Bonus you get a fancy new streaming UI with more
>>>>>>>> awesome stats. :)
>>>>>>>>
>>>>>>>> On Thu, Jun 18, 2015 at 7:01 PM, Tim Smith <secs...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>>
>>>>>>>>> I just switched from "createStream" to the "createDirectStream"
>>>>>>>>> API for kafka and while things otherwise seem happy, the first thing I
>>>>>>>>> noticed is that stream/receiver stats are gone from the Spark UI :(
>>>>>>>>> Those
>>>>>>>>> stats were very handy for keeping an eye on health of the app.
>>>>>>>>>
>>>>>>>>> What's the best way to re-create those in the Spark UI? Maintain
>>>>>>>>> Accumulators? Would be really nice to get back receiver-like stats
>>>>>>>>> even
>>>>>>>>> though I understand that "createDirectStream" is a receiver-less
>>>>>>>>> design.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> Tim
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
