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From Bill Jay <bill.jaypeter...@gmail.com>
Subject Re: Lifecycle of RDD in spark-streaming
Date Thu, 27 Nov 2014 01:07:26 GMT
Just add one more point. If Spark streaming knows when the RDD will not be
used any more, I believe Spark will not try to retrieve data it will not
use any more. However, in practice, I often encounter the error of "cannot
compute split". Based on my understanding, this is  because Spark cleared
out data that will be used again. In my case, the data volume is much
smaller (30M/s, the batch size is 60 seconds) than the memory (20G each
executor). If Spark will only keep RDD that are in use, I expect that this
error may not happen.

Bill

On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das <tathagata.das1565@gmail.com>
wrote:

> Let me further clarify Lalit's point on when RDDs generated by
> DStreams are destroyed, and hopefully that will answer your original
> questions.
>
> 1.  How spark (streaming) guarantees that all the actions are taken on
> each input rdd/batch.
> This is isnt hard! By the time you call streamingContext.start(), you
> have already set up the output operations (foreachRDD, saveAs***Files,
> etc.) that you want to do with the DStream. There are RDD actions
> inside the DStream output oeprations that need to be done every batch
> interval. So all the systems does is this - after every batch
> interval, put all the output operations (that will call RDD actions)
> in a job queue, and then keep executing stuff in the queue. If there
> is any failure in running the jobs, the streaming context will stop.
>
> 2.  How does spark determines that the life-cycle of a rdd is
> complete. Is there any chance that a RDD will be cleaned out of ram
> before all actions are taken on them?
> Spark Streaming knows when the all the processing related to batch T
> has been completed. And also it keeps track of how much time of the
> previous RDDs does it need to remember and keep around in the cache
> based on what DStream operations have been done. For example, if you
> are using a window 1 minute, the system knows that it needs to keep
> around at least last 1 minute data in the memory. Accordingly, it
> cleans up the input data (actively unpersisted), and cached RDD
> (simply dereferenced from DStream metadata, and then Spark unpersists
> them as the RDD object gets GarbageCollected by the JVM).
>
> TD
>
>
>
> On Wed, Nov 26, 2014 at 10:10 AM, tian zhang
> <tzhang101@yahoo.com.invalid> wrote:
> > I have found this paper seems to answer most of questions about life
> > duration.
> >
> https://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf
> >
> > Tian
> >
> >
> > On Tuesday, November 25, 2014 4:02 AM, Mukesh Jha <
> me.mukesh.jha@gmail.com>
> > wrote:
> >
> >
> > Hey Experts,
> >
> > I wanted to understand in detail about the lifecycle of rdd(s) in a
> > streaming app.
> >
> > From my current understanding
> > - rdd gets created out of the realtime input stream.
> > - Transform(s) functions are applied in a lazy fashion on the RDD to
> > transform into another rdd(s).
> > - Actions are taken on the final transformed rdds to get the data out of
> the
> > system.
> >
> > Also rdd(s) are stored in the clusters RAM (disc if configured so) and
> are
> > cleaned in LRU fashion.
> >
> > So I have the following questions on the same.
> > - How spark (streaming) guarantees that all the actions are taken on each
> > input rdd/batch.
> > - How does spark determines that the life-cycle of a rdd is complete. Is
> > there any chance that a RDD will be cleaned out of ram before all actions
> > are taken on them?
> >
> > Thanks in advance for all your help. Also, I'm relatively new to scala &
> > spark so pardon me in case these are naive questions/assumptions.
> >
> > --
> > Thanks & Regards,
> > Mukesh Jha
> >
> >
>
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