Hi Guillaume,

You may want to avoid the final join by using CombineFns.compose()


On Tue, Mar 13, 2018 at 9:07 PM Guillaume Balaine <igos...@gmail.com> wrote:

> Hello Beamers,
> I have been a Beam advocate for a while now, and am trying to use it for
> batch jobs as well as streaming jobs.
> I am trying to prove that it can be as fast as Spark for simple use cases.
> Currently, I have a Spark job that processes a sum + count over a TB of
> parquet files that runs in roughly 90 min.
> Using the same resources (on EMR or on Mesos) I can't even come close to
> that.
> My job with Scio/Beam and Flink processes sums roughly 20 Billion rows of
> parquet in 3h with 144 parallelism and 1TB of Ram (although I suspect many
> operators are idle, so I should probably use less parallelism with the same
> amount of cores).
> I also implemented an identical version in pure Java because I am unsure
> whether or not the Kryo encoded tuples are properly managed by the Flink
> memory optimizations. And am also testing it on Spark and Apex.
> My point is, is there anyway to optimize this simple process :
> HadoopFileIO (using parquet and specific avro coders to improve perf over
> Generic) ->
> Map to KV of (field1 str, field2 str, field3 str) (value double, 1)
> ordered by most discriminating to least -> Combine.perKey(Sum)
> Or value and then join Sum and Count with a TupledPCollection
> -> AvroIO.Write
> The equivalent Spark Job does a group by key, and then a sum.
> Are there some tricks I am missing here ?
> Thanks in advance for your help.

Reply via email to