The only other thing to keep in mind is that window duration and slide
duration have to be multiples of batch duration, IDK if you made that fully
clear
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Difference-among-batchDuration-windowDuration-slideDurati
Would it be a reasonable use case of spark streaming to have a very large
window size (lets say on the scale of weeks). In this particular case the
reduce function would be invertible so that would aid in efficiency. I
assume that having a larger batch size since the window is so large would
also l
I think you probably want to use `AvroSequenceFileOutputFormat` with
`newAPIHadoopFile`. I'm not even sure that in hadoop you would use
SequenceFileInput format to read an avro sequence file
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/NullPointerExceptio
Unfortunately for reasons I won't go into my options for what I can use are
limited, it was more of a curiosity to see if spark could handle a use case
like this since the functionality I wanted fit perfectly into the
reduceByKeyAndWindow frame of thinking. Anyway thanks for answering.
--
View t
So I think I may end up using hourglass
(https://engineering.linkedin.com/datafu/datafus-hourglass-incremental-data-processing-hadoop)
a hadoop framework for incremental data processing, it would be very cool if
spark (not streaming ) could support something like this
--
View this message in co
use mapPartitions to get the equivalent functionality to hadoop
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/Keep-state-inside-map-function-tp10968p10969.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
I'm not sure that this will work but it makes sense to me. Basically you
write the functionality in a static block in a class and broadcast that
class. Not sure what your use case is but I need to load a native library
and want to avoid running the init in mapPartitions if it's not necessary
(just