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