Actually it's quite simple...
DStream[T] is a stream of RDD[T].
So applying count on DStream is just applying count on each RDD of this
DStream.
So at the end of count, you have a DStream[Int] containing the same number
of RDDs as before but each RDD just contains one element being the count
result for the corresponding original RDD.
For reduce, it's the same using reduce operation...
The only operations that are a bit more complex are reduceByWindow &
countByValueAndWindow which union RDD over the time window...
On Thu, Mar 20, 2014 at 9:51 AM, Sanjay Awatramani <sanjay_a...@yahoo.com>wrote:
> @TD: I do not need multiple RDDs in a DStream in every batch. On the
> contrary my logic would work fine if there is only 1 RDD. But then the
> description for functions like reduce & count (Return a new DStream of
> single-element RDDs by counting the number of elements in each RDD of the
> source DStream.) left me confused whether I should account for the fact
> that a DStream can have multiple RDDs. My streaming code processes a batch
> every hour. In the 2nd batch, i checked that the DStream contains only 1
> RDD, i.e. the 2nd batch's RDD. I verified this using sysout in foreachRDD.
> Does that mean that the DStream will always contain only 1 RDD ?
>
A DStream creates a RDD for each window corresponding to your batch
duration (maybe if there are no data in the current time window, no RDD is
created but I'm not sure about that)
So no, there is not one single RDD in a DStream, it just depends on the
batch duration and the collected data.
> Is there a way to access the RDD of the 1st batch in the 2nd batch ? The
> 1st batch may contain some records which were not relevant to the first
> batch and are to be processed in the 2nd batch. I know i can use the
> sliding window mechanism of streaming, but if i'm not using it and there is
> no way to access the previous batch's RDD, then it means that functions
> like count will always return a DStream containing only 1 RDD, am i correct
> ?
>
>
count will be executed for each RDD in the dstream as explained above.
If you want to do operations on several RDD in the same DStream, you should
try using reduceByWindow for example to "union" several RDD and perform
operations on them. But it really depends on what you want to do and I
advise you to test different approaches.
Maybe other people more skilled than me will have better answers ?
> @Pascal, yes your answer resolves my question partially, but the other
> part of the question(which i've clarified in above paragraph) still remains.
>
> Thanks for your answers !
>
> Regards,
> Sanjay
>
>
> On Thursday, 20 March 2014 1:27 PM, Pascal Voitot Dev <
> pascal.voitot....@gmail.com> wrote:
> If I may add my contribution to this discussion if I understand well
> your question...
>
> DStream is discretized stream. It discretized the data stream over windows
> of time (according to the project code I've read and paper too). so when
> you write:
>
> JavaStreamingContext stcObj = new JavaStreamingContext(confObj, new
> Duration(60 * 60 * 1000)); //1 hour
>
> It means you are discretizing over a 1h window. Each batch so each RDD of
> the dstream will collect data for 1h before going to next RDD.
> So if you want to have more RDD, you should reduce batch size/duration...
>
> Pascal
>
>
> On Thu, Mar 20, 2014 at 7:51 AM, Tathagata Das <
> tathagata.das1...@gmail.com> wrote:
>
> That is a good question. If I understand correctly, you need multiple RDDs
> from a DStream in *every batch*. Can you elaborate on why do you need
> multiple RDDs every batch?
>
> TD
>
>
> On Wed, Mar 19, 2014 at 10:20 PM, Sanjay Awatramani <sanjay_a...@yahoo.com
> > wrote:
>
> Hi,
>
> As I understand, a DStream consists of 1 or more RDDs. And foreachRDD will
> run a given func on each and every RDD inside a DStream.
>
> I created a simple program which reads log files from a folder every hour:
> JavaStreamingContext stcObj = new JavaStreamingContext(confObj, new
> Duration(60 * 60 * 1000)); //1 hour
> JavaDStream<String> obj = stcObj.textFileStream("/Users/path/to/Input");
>
> When the interval is reached, Spark reads all the files and creates one
> and only one RDD (as i verified from a sysout inside foreachRDD).
>
> The streaming doc at a lot of places gives an indication that many
> operations (e.g. flatMap) on a DStream are applied individually to a RDD
> and the resulting DStream consists of the mapped RDDs in the same number as
> the input DStream.
> ref:
> https://spark.apache.org/docs/latest/streaming-programming-guide.html#dstreams
>
> If that is the case, how can i generate a scenario where in I have
> multiple RDDs inside a DStream in my example ?
>
> Regards,
> Sanjay
>
>
>
>
>
>
