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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: How can we connect RDD from previous job to next job
Date Mon, 29 Aug 2016 09:08:05 GMT
Can someone correct me on this:


   1. Jobs run and finish independently of each other. There is no
   correlation between job 1 and job2
   2. If job 2 depends on job1 output, then a persistent storage like
   Parquet file on HDFS can be used to save the outcome of job 1 and job2 can
   start from reading this file. That is the only pipeline.

I read some article on Spark server in Bloomberg. Has anyone looked at
this? I did not make much sense out of it

http://www.slideshare.net/JenAman/spark-at-bloomberg-dynamically-composable-analytics

HTH


Dr Mich Talebzadeh



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On 29 August 2016 at 09:43, Sean Owen <sowen@cloudera.com> wrote:

> If you mean to persist data in an RDD, then you should do just that --
> persist the RDD to durable storage so it can be read later by any
> other app. Checkpointing is not a way to store RDDs, but a specific
> way to recover the same application in some cases. Parquet has been
> supported for a long while, yes. It's the most common binary format.
> You could also literally store the serialized form of your objects.
>
> On Mon, Aug 29, 2016 at 9:27 AM, Sachin Mittal <sjmittal@gmail.com> wrote:
> > I understood the approach.
> > Does spark 1.6 support Parquet format, I mean saving and loading from
> > Parquet file.
> >
> > Also if I use checkpoint, what I understand is that RDD location on
> > filesystem is not removed when job is over. So I can read that RDD in
> next
> > job.
> > Is that one of the usecase of checkpoint. Basically does my current
> problem
> > can be solved using checkpoint.
> >
> > Also which option would be better, store the output of RDD to a
> persistent
> > storage, or store the new RDD of that ouput itself using checkpoint.
> >
> > Thanks
> > Sachin
> >
> >
> >
> >
> > On Mon, Aug 29, 2016 at 1:39 PM, Sean Owen <sowen@cloudera.com> wrote:
> >>
> >> You just save the data in the RDD in whatever form you want to
> >> whatever persistent storage you want, and then re-read it from another
> >> job. This could be Parquet format on HDFS for example. Parquet is just
> >> a common file format. There is no need to keep the job running just to
> >> keep an RDD alive.
> >>
> >> On Mon, Aug 29, 2016 at 5:30 AM, Sachin Mittal <sjmittal@gmail.com>
> wrote:
> >> > Hi,
> >> > I would need some thoughts or inputs or any starting point to achieve
> >> > following scenario.
> >> > I submit a job using spark-submit with a certain set of parameters.
> >> >
> >> > It reads data from a source, does some processing on RDDs and
> generates
> >> > some
> >> > output and completes.
> >> >
> >> > Then I submit same job again with next set of parameters.
> >> > It should also read data from a source do same processing and at the
> >> > same
> >> > time read data from the result generated by previous job and merge the
> >> > two
> >> > and again store the results.
> >> >
> >> > This process goes on and on.
> >> >
> >> > So I need to store RDD or output of RDD into some storage of previous
> >> > job to
> >> > make it available to next job.
> >> >
> >> > What are my options.
> >> > 1. Use checkpoint
> >> > Can I use checkpoint on the final stage of RDD and then load the same
> >> > RDD
> >> > again by specifying checkpoint path in next job. Is checkpoint right
> for
> >> > this kind of situation.
> >> >
> >> > 2. Save output of previous job into some json file and then create a
> >> > data
> >> > frame of that in next job.
> >> > Have I got this right, is this option better than option 1.
> >> >
> >> > 3. I have heard a lot about paquet files. However I don't know how it
> >> > integrates with spark.
> >> > Can I use that here as intermediate storage.
> >> > Is this available in spark 1.6?
> >> >
> >> > Any other thoughts or idea.
> >> >
> >> > Thanks
> >> > Sachin
> >> >
> >> >
> >> >
> >> >
> >
> >
>
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