We have the same issue. We are finding that we cannot express the data flow in
a natural way because of unnecessary spilling. Instead, we're making our own
operators which combine multiple steps together and essentially hide it from
flink OR sometimes we even have to read an input dataset once per flow to avoid
spilling. The performance improvements are dramatic but it's kind of reducing
flink to a thread scheduler rather than a data flow engine because we basically
cannot express the flow to flink. This worries us because if we let others
write flink code using our infra, we'll be spending all our time collapsing
their flows into much simpler but less intuititve flows to prevent flink from
spilling.
This also means higher level APIs such as the table API or Beam are off the
table because they prevent us optimizing in this manner.
We already have prior implementations of the logic we are implementing in flink
and as a result, we know it's much less efficient than the prior
implementations which is giving us pause for rolling it out more broadly, we're
afraid of the flink tax in effect from a performance point of view as well as
from a usability point of view given naïve flows are not performant without
significant collapsing.
For example, we see spilling here:
Dataset -> Map > Filter -> Map -> Output
We're trying to combine the Map ->Output into the filter operation now to write
the records which are not passed through to an output file during the Filter.
Or in this case
Dataset -> Map -> [FilterT -> CoGroup > ;FilterF] > Map -> Output
Rewriting as
Dataset -> Map -> FilterT -> CoGroup > Map -> Output
Dataset -> Map -> FilterF -> Map -> Output
That is two separate flows is multiples faster. That is, reading the file twice
rather than once.
This is all pretty unintuitive and makes using Flink pretty difficult for us
never mind our users. Writing the flink dataflows in a naïve way is fast but
getting it to run with acceptable efficiency results in obscure workarounds and
collapsing and takes the bulk of the time for us which is a shame and the main
reason, we don't want to push it out for general use yet.
It seems like it badly needs a flow rewriter which is capable of rewriting a
naïve flow to use operators or restructured flows automatically. We're doing it
by hand right now but there has to be a better way.
It's a shame really, it's so close.
Billy
-----Original Message-----
From: Urs Schoenenberger [mailto:urs.schoenenber...@tngtech.com]
Sent: Tuesday, September 05, 2017 6:30 AM
To: user
Subject: DataSet: partitionByHash without materializing/spilling the entire
partition?
Hi all,
we have a DataSet pipeline which reads CSV input data and then
essentially does a combinable GroupReduce via first(n).
In our first iteration (readCsvFile -> groupBy(0) -> sortGroup(0) ->
first(n)), we got a jobgraph like this:
source --[Forward]--> combine --[Hash Partition on 0, Sort]--> reduce
This works, but we found the combine phase to be inefficient because not
enough combinable elements fit into a sorter. My idea was to
pre-partition the DataSet to increase the chance of combinable elements
(readCsvFile -> partitionBy(0) -> groupBy(0) -> sortGroup(0) -> first(n)).
To my surprise, I found that this changed the job graph to
source --[Hash Partition on 0]--> partition(noop) --[Forward]--> combine
--[Hash Partition on 0, Sort]--> reduce
while materializing and spilling the entire partitions at the
partition(noop)-Operator!
Is there any way I can partition the data on the way from source to
combine without spilling? That is, can I get a job graph that looks like
source --[Hash Partition on 0]--> combine --[Hash Partition on 0,
Sort]--> reduce
instead?
Thanks,
Urs
--
Urs Schönenberger - urs.schoenenber...@tngtech.com
TNG Technology Consulting GmbH, Betastr. 13a, 85774 Unterföhring
Geschäftsführer: Henrik Klagges, Dr. Robert Dahlke, Gerhard Müller
Sitz: Unterföhring * Amtsgericht München * HRB 135082
