Hi,
this is interesting, can you please share the code for this and if
possible the source schema and it will be great if you could kindly share a
sample file.
Regards,
Gourav Sengupta
On Tue, Nov 20, 2018 at 9:50 AM Michael Shtelma wrote:
>
> You can also cache the data frame on disk, if
You can also cache the data frame on disk, if it does not fit into memory.
An alternative would be to write out data frame as parquet and then read
it, you can check if in this case the whole pipeline works faster as with
the standard cache.
Best,
Michael
On Tue, Nov 20, 2018 at 9:14 AM
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,
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 <
i...@ricobergmann.de> wrote:
> Thanks for your
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
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
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
