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From Shashi Vishwakarma <shashi.vish...@gmail.com>
Subject Re: Spark Streaming with Nifi
Date Thu, 08 Jun 2017 10:50:03 GMT
Hi Andrew,

Regarding creating spark provenance event,

*"let's call it a Spark Provenance Event -- in this you can populate as
much data as you have and write that to a similar data store."*

Is there any way I can write my spark provenance event to Nifi provenance
store with some EventId ?

I have ReportingTask which sends event to another application but it relies
on Nifi provenance store. I am thinking  that spark job will emit
provenance event which will be written in Nifi provenance store and
reporting task will send that to another application.

Apologies if my use case is still unclear.

Thanks
Shashi



On Thu, Jun 8, 2017 at 3:23 AM, Andrew Psaltis <psaltis.andrew@gmail.com>
wrote:

> Hi Shashi,
> Regarding your upgrade question, I may have confused things. When emitting
> a "provenance" event from your Spark Streaming job, this will not be the
> same exact event as that emitted from NiFi. I was referencing the code in
> the previous email to give insight into the details NiFi does provide. In
> your Spark application you will not have all of the information to populate
> a NiFi Provenance event. Therefore, for your Spark code you can come up
> with a new event, let's call it a Spark Provenance Event -- in this you can
> populate as much data as you have and write that to a similar data store.
> For example you would want a timestamp, the component can be Spark and any
> other data you need to emit. Basically, you will be combing the NiFi
> provenance data with your customer spark provenance data to create a
> complete picture.
>
> As far as the lineage goes, again your Spark streaming code will be
> executing outside of NiFi and you will have to write this into some other
> store, perhaps to Atlas and then you can have the lineage for both NiFi and
> Spark. This [1] is an example NiFi reporting tasks that sends lineage data
> to Atlas, you could extend this concept to work with Spark as well.
>
> Hopefully this helps clarify some things, sorry if my previous email was
> not completely clear.
>
> Thanks
> Andrew
>
> On Wed, Jun 7, 2017 at 4:31 PM, Shashi Vishwakarma <
> shashi.vish123@gmail.com> wrote:
>
>> Thanks a lot Andrew. This is something I was looking for.
>>
>> I have two question at point keeping in mind I have generate provenance
>> event.
>>
>> 1. How will I manage upgrade ? If I generate custom provenance and Nifi
>> community made significant changes in Nifi provenance structure ?
>>
>> 2. How do I get lineage information ?
>>
>> Thanks
>> Shashi
>>
>>
>> On Tue, Jun 6, 2017 at 7:38 PM, Andrew Psaltis <psaltis.andrew@gmail.com>
>> wrote:
>>
>>> Hi Shashi,
>>> Your assumption is correct -- you would want to send a "provenance"
>>> event from your Spark job, you can see the structure of the provenance
>>> events in NiFi here [1]
>>>
>>> Regarding the flow, if you are waiting on the Spark Streaming code to
>>> compute some value before you continue you can construct it perhaps this
>>> way:
>>>
>>>
>>> [image: Inline image 1]
>>>
>>> Hopefully that helps to clarify it a little. In essence if you are
>>> waiting on results form the Spark Streaming computation before continuing
>>> you would use Kafka for the output results from Spark Streaming and then
>>> consume that in NiFi and carry on with your processing.
>>>
>>> [1] https://github.com/apache/nifi/blob/master/nifi-nar-bund
>>> les/nifi-framework-bundle/nifi-framework/nifi-client-dto/src
>>> /main/java/org/apache/nifi/web/api/dto/provenance/Provenance
>>> EventDTO.java
>>>
>>> Thanks,
>>> Andrew
>>>
>>> On Mon, Jun 5, 2017 at 9:46 AM, Shashi Vishwakarma <
>>> shashi.vish123@gmail.com> wrote:
>>>
>>>> Hi Andrew,
>>>>
>>>> I am trying to understand here bit more in detail. Essentially I will
>>>> have to write some custom code in my spark streaming job and construct
>>>> provenance event and send it to some store like Hbase,PubSub system to be
>>>> consumed by others.
>>>>
>>>> Is that correct ?
>>>>
>>>> If yes how do I execute other processor which are present in pipeline ?
>>>>
>>>> Ex
>>>>
>>>> Nifi --> Kakfa -- > Spark Streaming --> Processor 1 --> Processor
2
>>>>
>>>> Thanks
>>>> Shashi
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Mon, Jun 5, 2017 at 12:36 AM, Andrew Psaltis <
>>>> psaltis.andrew@gmail.com> wrote:
>>>>
>>>>> Hi Shashi,
>>>>> Thanks for the explanation.  I have a better understanding of what you
>>>>> are trying to accomplish. Although Spark streaming is micro-batch, you
>>>>> would not want to keep launching jobs for each batch.   Think of it as
the
>>>>> Spark scheduler having a while loop in which it executes your job then
>>>>> sleeps for X amount of time based on the interval you configure.
>>>>>
>>>>> Perhaps a better way would be to do the following:
>>>>> 1. Use the S2S ProvenanceReportingTask to send provenance information
>>>>> from your NiFi instance to a second instance or cluster.
>>>>> 2. In the second NiFi instance/cluster ( the one receiving the
>>>>> provenance data) you write the data into say HBase or Solr or system
X.
>>>>> 3. In your Spark streaming job you right into the same data store a
>>>>> "provenance" event -- obviously this will not have all the fields that
a
>>>>> true NiFi provenance record does, but you can come close.
>>>>>
>>>>> With this then once you would then have all provenance data in an
>>>>> external system that you can query to understand the whole system.
>>>>>
>>>>> Thanks,
>>>>> Andrew
>>>>>
>>>>> P.S. sorry if this is choppy or not well formed, on mobile.
>>>>>
>>>>> On Sun, Jun 4, 2017 at 17:46 Shashi Vishwakarma <
>>>>> shashi.vish123@gmail.com> wrote:
>>>>>
>>>>>> Thanks Andrew.
>>>>>>
>>>>>> I agree that decoupling component is good solution from long term
>>>>>> perspective. My current data pipeline in Nifi is designed for batch
>>>>>> processing which I am trying to convert into streaming model.
>>>>>>
>>>>>> One of the processor in data pipeline invokes Spark job , once job
>>>>>> finished control  is returned to Nifi processor in turn which generates
>>>>>> provenance event for job. This provenance event is important for
us.
>>>>>>
>>>>>> Keeping batch model architecture in mind, I want to designed spark
>>>>>> streaming based model in which Nifi Spark streaming processor will
process
>>>>>> micro batch and job status will be returned to Nifi with provenance
event.
>>>>>> Then I can capture that provenance data for my reports.
>>>>>>
>>>>>> Essentially I will be using Nifi for capturing provenance event where
>>>>>> actual processing will be done by Spark streaming job.
>>>>>>
>>>>>> Do you see this approach logical ?
>>>>>>
>>>>>> Thanks
>>>>>> Shashi
>>>>>>
>>>>>>
>>>>>> On Sun, Jun 4, 2017 at 3:10 PM, Andrew Psaltis <
>>>>>> psaltis.andrew@gmail.com> wrote:
>>>>>>
>>>>>>> Hi Shashi,
>>>>>>> I'm sure there is a way to make this work. However, my first
>>>>>>> question is why you would want to? By design a Spark Streaming
application
>>>>>>> should always be running and consuming data from some source,
hence the
>>>>>>> notion of streaming. Tying Spark Streaming to NiFi would ultimately
result
>>>>>>> in a more coupled and fragile architecture. Perhaps a different
way to
>>>>>>> think about it would be to set things up like this:
>>>>>>>
>>>>>>> NiFi --> Kafka <-- Spark Streaming
>>>>>>>
>>>>>>> With this you can do what you are doing today -- using NiFi to
>>>>>>> ingest, transform, make routing decisions, and feed data into
Kafka. In
>>>>>>> essence you would be using NiFi to do all the preparation of
the data for
>>>>>>> Spark Streaming. Kafka would serve the purpose of a buffer between
NiFi and
>>>>>>> Spark Streaming. Finally, Spark Streaming would ingest data from
Kafka and
>>>>>>> do what it is designed for -- stream processing. Having a decoupled
>>>>>>> architecture like this also allows you to manage each tier separately,
thus
>>>>>>> you can tune, scale, develop, and deploy all separately.
>>>>>>>
>>>>>>> I know I did not directly answer your question on how to make
it
>>>>>>> work. But, hopefully this helps provide an approach that will
be a better
>>>>>>> long term solution. There may be something I am missing in your
initial
>>>>>>> questions.
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Andrew
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Sat, Jun 3, 2017 at 10:43 PM, Shashi Vishwakarma <
>>>>>>> shashi.vish123@gmail.com> wrote:
>>>>>>>
>>>>>>>> Hi
>>>>>>>>
>>>>>>>> I am looking for way where I can make use of spark streaming
in
>>>>>>>> Nifi. I see couple of post where SiteToSite tcp connection
is used for
>>>>>>>> spark streaming application but I thinking it will be good
If I can launch
>>>>>>>> Spark streaming from Nifi custom processor.
>>>>>>>>
>>>>>>>> PublishKafka will publish message into Kafka followed by
Nifi Spark
>>>>>>>> streaming processor will read from Kafka Topic.
>>>>>>>>
>>>>>>>> I can launch Spark streaming application from custom Nifi
processor
>>>>>>>> using Spark Streaming launcher API but biggest challenge
is that it will
>>>>>>>> create spark streaming context for each flow file which can
be costly
>>>>>>>> operation.
>>>>>>>>
>>>>>>>> Does any one suggest storing spark streaming context  in
controller
>>>>>>>> service ? or any better approach for running spark streaming
application
>>>>>>>> with Nifi ?
>>>>>>>>
>>>>>>>> Thanks and Regards,
>>>>>>>> Shashi
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Thanks,
>>>>>>> Andrew
>>>>>>>
>>>>>>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>>>>>>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>>>>>>> twiiter: @itmdata
>>>>>>> <http://twitter.com/intent/user?screen_name=itmdata>
>>>>>>>
>>>>>>
>>>>>> --
>>>>> Thanks,
>>>>> Andrew
>>>>>
>>>>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>>>>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>>>>> twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Thanks,
>>> Andrew
>>>
>>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>>> twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
>>>
>>
>>
>
>
> --
> Thanks,
> Andrew
>
> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
> twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
>

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