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From "Dipl.-Inf. Rico Bergmann" <i...@ricobergmann.de>
Subject Re: Spark DataSets and multiple write(.) calls
Date Tue, 20 Nov 2018 08:14:01 GMT
Hi!

Thanks Vadim for your answer. But this would be like caching the
dataset, right? Or is checkpointing faster then persisting to memory or
disk?

I attach a pdf of my dataflow program. If I could compute the output of
outputs 1-5 in parallel the output of flatmap1 and groupBy could be
reused, avoiding to write to disk (at least until the grouping).

Any other ideas or proposals?

Best,

Rico.


Am 19.11.2018 um 19:12 schrieb Vadim Semenov:
> You can use checkpointing, in this case Spark will write out an rdd to
> whatever destination you specify, and then the RDD can be reused from
> the checkpointed state avoiding recomputing.
>
> On Mon, Nov 19, 2018 at 7:51 AM Dipl.-Inf. Rico Bergmann
> <info@ricobergmann.de <mailto:info@ricobergmann.de>> wrote:
>
>     Thanks for your advise. But I'm using Batch processing. Does
>     anyone have a solution for the batch processing case?
>
>     Best,
>
>     Rico.
>
>
>     Am 19.11.2018 um 09:43 schrieb Magnus Nilsson:
>>
>>
>>           Magnus Nilsson
>>
>>     	
>>     9:43 AM (0 minutes ago)
>>     	
>>     	
>>     to info
>>
>>     I had the same requirements. As far as I know the only way is to
>>     extend the foreachwriter, cache the microbatch result and write
>>     to each output.
>>
>>     https://docs.databricks.com/spark/latest/structured-streaming/foreach.html
>>
>>     Unfortunately it seems as if you have to make a new connection
>>     "per batch" instead of creating one long lasting connections for
>>     the pipeline as such. Ie you might have to implement some sort of
>>     connection pooling by yourself depending on sink. 
>>
>>     Regards,
>>
>>     Magnus
>>
>>
>>     On Mon, Nov 19, 2018 at 9:13 AM Dipl.-Inf. Rico Bergmann
>>     <info@ricobergmann.de <mailto:info@ricobergmann.de>> wrote:
>>
>>         Hi!
>>
>>         I have a SparkSQL programm, having one input and 6 ouputs
>>         (write). When
>>         executing this programm every call to write(.) executes the
>>         plan. My
>>         problem is, that I want all these writes to happen in
>>         parallel (inside
>>         one execution plan), because all writes have a common and compute
>>         intensive subpart, that can be shared by all plans. Is there a
>>         possibility to do this? (Caching is not a solution because
>>         the input
>>         dataset is way to large...)
>>
>>         Hoping for advises ...
>>
>>         Best, Rico B.
>>
>>
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