Jeff/Everett, Thanks so much! Though I am slightly confused... I implemented what I believe you were going for, after a Dofn is processed I used the write command everett mentioned followed by a call to pipeline.run, but received this issue:
2015-11-24 19:52:31,346 ERROR [main] org.apache.crunch.materialize.MaterializableIterable: Could not materialize: SeqFile(/tmp/crunch-245883570/p1) java.io.IOException: No files found to materialize at: /tmp/crunch-245883570/p1 Any ideas? --------------------------------------------------------------------------- Landon Robinson Big Data/Hadoop Engineer --------------------------------------------------------------------------- From: Jeff Quinn <[email protected]<mailto:[email protected]>> Reply-To: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>> Date: Tuesday, November 24, 2015 at 1:36 PM To: LCI <[email protected]<mailto:[email protected]>> Cc: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>> Subject: Re: Crunch Planner Hint to Not Combine Tasks Hey Landon, Happy to help! As Everett said you can even skip the "Ingest that file into a new Pcollection" step, as long as you write to a SequenceFile target, this will be implicit. As for #run vs #done, what we do is call #run in between each segment of DoFns and then #done once finally at the end. #run and #done do basically the same thing (#done calls #run), except #done cleans up the crunch temporary directories after calling #run. On Tue, Nov 24, 2015 at 10:00 AM, Robinson, Landon - Landon <[email protected]<mailto:[email protected]>> wrote: Apologies, pipeline.execute should be pipeline.done --------------------------------------------------------------------------- [cid:FC34F77D-D315-4325-B8B1-27D04C7003A9] Landon Robinson Big Data/Hadoop Engineer Lowe’s Companies Inc. | IT Business Intelligence --------------------------------------------------------------------------- From: <Robinson>, LCI <[email protected]<mailto:[email protected]>> Reply-To: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>> Date: Tuesday, November 24, 2015 at 12:55 PM To: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>>, Jeff Quinn <[email protected]<mailto:[email protected]>> Subject: Re: Crunch Planner Hint to Not Combine Tasks Jeff, Thanks for that awesome set of tips. Building on your solution, here’s some information about ours: Our program: * Starts the MR pipeline * Does some DoFn ParallelDos * Calls pipeline.execute We never leveraged pipeline.run(). Going with your suggestion, would my course of action be: * Start the MR pipeline * Do some of the dofn parallelDos * Call pipeline.write * Call pipeline.run * Ingest that file into a new Pcollection * Call another (or more) Dofns * Call pipeline.execute Does that seem in line with your recommendation, Jeff? Let me know if my logic needs adjusting… - Landon --------------------------------------------------------------------------- Landon Robinson Big Data/Hadoop Engineer --------------------------------------------------------------------------- From: Jeff Quinn <[email protected]<mailto:[email protected]>> Reply-To: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>> Date: Tuesday, November 24, 2015 at 12:00 PM To: Apache Crunch Mailing List <[email protected]<mailto:[email protected]>> Subject: Re: Crunch Planner Hint to Not Combine Tasks Hey Landon, Our team has dealt with this exact problem before. Our solution was to call PCollection#write after each DoFn or string of DoFns then call Pipeline#run, which will do an iteration of crunch planning / job submission, then use that same PCollection object for the next round of DoFns, call #write, and then #call run again, etc. #parallelDo -> #write -> #run -> repeat The importance of calling #write is that it the crunch planner will not actually do any work unless it has seen that there is at least one materialization of the data. This technique allows you to guarantee your DoFns are segmented into different MR jobs, as DoFns cannot be combined if they are already completed. Hope this helps, Jeff On Tue, Nov 24, 2015 at 6:14 AM, Ron Hashimshony <[email protected]<mailto:[email protected]>> wrote: Try set mapreduce.input.fileinputformat.split.minsize & mapreduce.input.fileinputformat.split.maxsize to a lower number from the default (usually 64 MB). If you know of a specific DoFn in which this is required, better put it there in its configure function. On Tue, Nov 24, 2015 at 3:28 PM Robinson, Landon - Landon <[email protected]<mailto:[email protected]>> wrote: Hi all, I have a Crunch job that tries to combine the last four tasks of my program into one M/R job. That’s normally not a problem, but my data starts small and grows exponentially in the most major of those DoFn tasks, resulting in spills to disk (local, not HDFS). I’ve already: * Implemented scaleFactor on the DoFn where the data will emit back more records than it consumed, which is 40.0f * Set io.sort.mb parameter to cluster setting, which is 1792 * Implemented map-side compression with snappy Data set I’m ingesting is from a previous map-reduce job, which comes out to 19 files of 10mb size (which in Crunch comes to 2 splits). 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