Thank you all for the responses. Basically we were dealing with file
source (not Kafka, therefore no topics involved) and dumping csv files
(about 1000 lines, 300KB per file) at a pretty high speed (10 - 15
files/second) one at a time to the stream source directory. We have a
Spark 3.0.1. cluster configured with 4 workers, each one is allocated
with 4 cores. We tried numerous options, including setting the
spark.streaming.dynamicAllocation.enabled parameter to true, and setting
the maxFilesPerTrigger to 1, but were unable to scale the
#cores*#workers >4.
What I am trying to understand is that what makes spark to allocate jobs
to more workers? Is it based on the size of the data frame, batch sizes
or trigger intervals? Looks like the Spark master scheduler doesn't
consider the number of input files waiting to be processed, only
consider the data size (i.e. the size of data frames) that has been read
or already imported, before allocating new workers. If that that case,
then Spark really missed the point and wasn't really designed for
real-time streaming applications. I could write my own stream processor
that would distribute the load based on the number of input files, given
the fact, that each batch query is atomic/independent from each other..
Thanks in advance for your comment/input.
ND
On 10/15/20 7:13 PM, muru wrote:
File streaming in SS, you can try setting "maxFilesPerTrigger" per
batch. The forEachBatch is an action, the output is written to various
sinks. Are you doing any post transformation in forEachBatch?
On Thu, Oct 15, 2020 at 1:24 PM Mich Talebzadeh
<mich.talebza...@gmail.com <mailto:mich.talebza...@gmail.com>> wrote:
Hi,
This in general depends on how many topics you want to process at
the same time and whether this is done on-premise running Spark in
cluster mode.
Have you looked at Spark GUI to see if one worker (one JVM) is
adequate for the task?
Also how these small files are read and processed. Is it the same
data microbatched? Spark streaming does not process one event at a
time which is in general I think what people call "Streaming." It
instead processes groups of events. Each group is a "MicroBatch"
that gets processed at the same time.
What parameters (BatchInterval, WindowsLength,SlidingInterval) are
you using?
Parallelism helps when you have reasonably large data and your
cores are running on different sections of data in parallel.
Roughly how much do you have in every CSV file
HTH,
Mich
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On Thu, 15 Oct 2020 at 20:02, Artemis User <arte...@dtechspace.com
<mailto:arte...@dtechspace.com>> wrote:
Thanks for the input. What I am interested is how to have
multiple
workers to read and process the small files in parallel, and
certainly
one file per worker at a time. Partitioning data frame
doesn't make
sense since the data frame is small already.
On 10/15/20 9:14 AM, Lalwani, Jayesh wrote:
> Parallelism of streaming depends on the input source. If you
are getting one small file per microbatch, then Spark will
read it in one worker. You can always repartition your data
frame after reading it to increase the parallelism.
>
> On 10/14/20, 11:26 PM, "Artemis User"
<arte...@dtechspace.com <mailto:arte...@dtechspace.com>> wrote:
>
> CAUTION: This email originated from outside of the
organization. Do not click links or open attachments unless
you can confirm the sender and know the content is safe.
>
>
>
> Hi,
>
> We have a streaming application that read microbatch
csv files and
> involves the foreachBatch call. Each microbatch can be
processed
> independently. I noticed that only one worker node is
being utilized.
> Is there anyway or any explicit method to distribute
the batch work load
> to multiple workers? I would think Spark would execute
foreachBatch
> method on different workers since each batch can be
treated as atomic?
>
> Thanks!
>
> ND
>
>
>
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