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From Saiph Kappa <saiph.ka...@gmail.com>
Subject Re: throughput in the web console?
Date Tue, 03 Mar 2015 02:47:34 GMT
I performed repartitioning and everything went fine with respect to the
number of CPU cores being used (and respective times). However, I noticed
something very strange: inside a map operation I was doing a very simple
calculation and always using the same dataset (small enough to be entirely
processed in the same batch); then I iterated the RDDs and calculated the
mean, "foreachRDD(rdd => println("MEAN: " + rdd.mean()))". I noticed that
for different numbers of partitions (for instance, 4 and 8), the result of
the mean is different. Why does this happen?

On Thu, Feb 26, 2015 at 7:03 PM, Tathagata Das <tdas@databricks.com> wrote:

> If you have one receiver, and you are doing only map-like operaitons then
> the process will primarily happen on one machine. To use all the machines,
> either receiver in parallel with multiple receivers, or spread out the
> computation by explicitly repartitioning the received streams
> (DStream.repartition) with sufficient partitions to load balance across
> more machines.
>
> TD
>
> On Thu, Feb 26, 2015 at 9:52 AM, Saiph Kappa <saiph.kappa@gmail.com>
> wrote:
>
>> One more question: while processing the exact same batch I noticed that
>> giving more CPUs to the worker does not decrease the duration of the batch.
>> I tried this with 4 and 8 CPUs. Though, I noticed that giving only 1 CPU
>> the duration increased, but apart from that the values were pretty similar,
>> whether I was using 4 or 6 or 8 CPUs.
>>
>> On Thu, Feb 26, 2015 at 5:35 PM, Saiph Kappa <saiph.kappa@gmail.com>
>> wrote:
>>
>>> By setting spark.eventLog.enabled to true it is possible to see the
>>> application UI after the application has finished its execution, however
>>> the Streaming tab is no longer visible.
>>>
>>> For measuring the duration of batches in the code I am doing something
>>> like this:
>>> «wordCharValues.foreachRDD(rdd => {
>>>             val startTick = System.currentTimeMillis()
>>>             val result = rdd.take(1)
>>>             val timeDiff = System.currentTimeMillis() - startTick»
>>>
>>> But my quesiton is: is it possible to see the rate/throughput
>>> (records/sec) when I have a stream to process log files that appear in a
>>> folder?
>>>
>>>
>>>
>>> On Thu, Feb 26, 2015 at 1:36 AM, Tathagata Das <tdas@databricks.com>
>>> wrote:
>>>
>>>> Yes. # tuples processed in a batch = sum of all the tuples received by
>>>> all the receivers.
>>>>
>>>> In screen shot, there was a batch with 69.9K records, and there was a
>>>> batch which took 1 s 473 ms. These two batches can be the same, can be
>>>> different batches.
>>>>
>>>> TD
>>>>
>>>> On Wed, Feb 25, 2015 at 10:11 AM, Josh J <joshjdevl@gmail.com> wrote:
>>>>
>>>>> If I'm using the kafka receiver, can I assume the number of records
>>>>> processed in the batch is the sum of the number of records processed
by the
>>>>> kafka receiver?
>>>>>
>>>>> So in the screen shot attached the max rate of tuples processed in a
>>>>> batch is 42.7K + 27.2K = 69.9K tuples processed in a batch with a max
>>>>> processing time of 1 second 473 ms?
>>>>>
>>>>> On Wed, Feb 25, 2015 at 8:48 AM, Akhil Das <akhil@sigmoidanalytics.com
>>>>> > wrote:
>>>>>
>>>>>> By throughput you mean Number of events processed etc?
>>>>>>
>>>>>> [image: Inline image 1]
>>>>>>
>>>>>> Streaming tab already have these statistics.
>>>>>>
>>>>>>
>>>>>>
>>>>>> Thanks
>>>>>> Best Regards
>>>>>>
>>>>>> On Wed, Feb 25, 2015 at 9:59 PM, Josh J <joshjdevl@gmail.com>
wrote:
>>>>>>
>>>>>>>
>>>>>>> On Wed, Feb 25, 2015 at 7:54 AM, Akhil Das <
>>>>>>> akhil@sigmoidanalytics.com> wrote:
>>>>>>>
>>>>>>>> For SparkStreaming applications, there is already a tab called
>>>>>>>> "Streaming" which displays the basic statistics.
>>>>>>>
>>>>>>>
>>>>>>> Would I just need to extend this tab to add the throughput?
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> ---------------------------------------------------------------------
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>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>
>>>>
>>>>
>>>
>>
>

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