I looked into Wait.on() but doc say it waits untill window is completely done, so it is not quite fir for my case, as my lateness can be a day or two and I would like to compact and publish hourly data sooner.
What i am thinking of is write triggers under different location than target. I will have lot's of EARLY fires for main data and than some LATE fires. What I would like to do is observe all EARLY fires for window (ideally in ON_TIME event time) in one group and move those files to target dir by merging them. Observed LATE fires would be just moved immediately because that is not much and does not hurt to keep them fragmented now. The question is if it make sense and can be done with Beam? FileIO returns WriteFilesResult where I can call `getPerDestinationOutputFilenames()` which returns me a collection of KV with key being destination and value being a file which was written. I tried to window it again with different triggers (no early trigger) and groupBy key, but so far, no luck as it never yield a collection of files in which were emitted as EARLY in first window. On Fri, Jul 20, 2018 at 9:06 PM Raghu Angadi <[email protected]> wrote: > On Fri, Jul 20, 2018 at 2:58 AM Jozef Vilcek <[email protected]> > wrote: > >> Hm, that is interesting idea to make the write composite and merge files >> later. Do not know Beam well yet. >> I will look into it and learn about Wait.on() transform (wonder how it >> will work with late fires). Thanks! >> >> But keeps me thinking... >> Does it make sense to have support from SDK? >> Is my use case that uncommon? Not fit for Beam? How does others out there >> does similar thing? >> > > SDK does allow it. Looks like you are running into scaling and memory > limits with amount of state stored in large windows. This is something that > will improve. I am not familiar enough with Flink runner to comment on > specifics. I was mainly thinking of a work around. > > Raghu. > > >> >> On Thu, Jul 19, 2018 at 11:21 PM Raghu Angadi <[email protected]> wrote: >> >>> One option (but requires more code): Write to smaller files with >>> frequent triggers to directory_X and once the window properly closes, copy >>> all the files to a single file in your own DoFn. This is certainly more >>> code on your part, but might be worth it. You can use Wait.on() transoform >>> to run your finalizer DoFn right after the window that writes smaller files >>> closes. >>> >>> >>> On Thu, Jul 19, 2018 at 2:43 AM Jozef Vilcek <[email protected]> >>> wrote: >>> >>>> Hey, >>>> >>>> I am looking for the advice. >>>> >>>> I am trying to do a stream processing with Beam on Flink runtime. >>>> Reading data from Kafka, doing some processing with it which is not >>>> important here and in the same time want to store consumed data to history >>>> storage for archive and reprocessing, which is HDFS. >>>> >>>> Now, the part of writing batches to HDFS is giving me hard time. >>>> Logically, I want to do: >>>> >>>> fileIO = FileIO.writeDynamic() >>>> .by(destinationFn) >>>> .via(AvroIO.sink(avroClass)) >>>> .to(path) >>>> .withNaming(namingFn) >>>> .withTempDirectory(tmp) >>>> .withNumShards(shards) >>>> >>>> data >>>> .withFixedWindow(1H, afterWatermarkTrigger, discardFiredPanes) >>>> .saveTo(fileIO) >>>> >>>> >>>> This write generates in Flink execution graph 3 operators, which I do >>>> not full understand yet. >>>> >>>> Now, the problem is, that I am not able to run this at scale. >>>> >>>> If I want to write big enough files to not to have lots of files on >>>> HDFS, I keep running into the OOM. With Flink, I use rocksdb state backend >>>> and I was warned about this JIRA which is probably related to my OOM >>>> https://issues.apache.org/jira/browse/FLINK-8297 >>>> Therefore, I need to trigger more often and small batches which leads >>>> to too many files on HDFS. >>>> >>>> Question here is, if there is some path I do not see how to make this >>>> work ( write bulks of data to HDFS of my choosing without running to memory >>>> troubles ). Also, keeping whole window data which is designated for write >>>> to output to filesystem in state involves more IO. >>>> >>>> Thanks for any thoughts and guidelines, >>>> Jozef >>>> >>>>
