Hi, My source checkpoint is actually the file list. But it's not trivially small as I may have hundreds of thousand of files, with long filenames. My sink checkpoint is a smaller hdfs file list with current size.
-------- Message d'origine -------- De : Ken Krugler <kkrugler_li...@transpac.com> Date : ven., mars 01, 2019 7:05 PM +0100 A : "LINZ, Arnaud" <al...@bouyguestelecom.fr> CC : zhijiang <wangzhijiang...@aliyun.com>, user <user@flink.apache.org> Objet : Re: Checkpoints and catch-up burst (heavy back pressure) Hi Arnaud, 1. What’s your checkpoint configuration? Wondering if you’re writing to HDFS, and thus the load you’re putting on it while catching up & checkpointing is too high. If so, then you could monitor the TotalLoad metric (FSNamesystem) in your source, and throttle back the emitting of file paths when this (empirically) gets too high. 2. I’m wondering what all you are checkpointing, and why. E.g. if this is just an ETL-ish workflow to pull files, parse them, and write out (transformed) results, then you could in theory just checkpoint which files have been processed. This means catching up after a failure could take more time, but your checkpoint size will be trivially small. — Ken On Mar 1, 2019, at 5:04 AM, LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>> wrote: Hi, I think I should go into more details to explain my use case. I have one non parallel source (parallelism = 1) that list binary files in a HDFS directory. DataSet emitted by the source is a data set of file names, not file content. These filenames are rebalanced, and sent to workers (parallelism = 15) that will use a flatmapper that open the file, read it, decode it, and send records (forward mode) to the sinks (with a few 1-to-1 mapping in-between). So the flatmap operation is a time-consuming one as the files are more than 200Mb large each; the flatmapper will emit millions of record to the sink given one source record (filename). The rebalancing, occurring at the file name level, does not use much I/O and I cannot use one-to-one mode at that point if I want some parallelims since I have only one source. I did not put file decoding directly in the sources because I have no good way to distribute files to sources without a controller (input directory is unique, filenames are random and cannot be “attributed” to one particular source instance easily). Alternatively, I could have used a dispatcher daemon separated from the streaming app that distribute files to various directories, each directory being associated with a flink source instance, and put the file reading & decoding directly in the source, but that seemed more complex to code and exploit than the filename source. Would it have been better from the checkpointing perspective? About the ungraceful source sleep(), is there a way, programmatically, to know the “load” of the app, or to determine if checkpointing takes too much time, so that I can do it only on purpose? Thanks, Arnaud De : zhijiang <wangzhijiang...@aliyun.com<mailto:wangzhijiang...@aliyun.com>> Envoyé : vendredi 1 mars 2019 04:59 À : user <user@flink.apache.org<mailto:user@flink.apache.org>>; LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>> Objet : Re: Checkpoints and catch-up burst (heavy back pressure) Hi Arnaud, Thanks for the further feedbacks! For option1: 40min still does not makes sense, which indicates it might take more time to finish checkpoint in your case. I also experienced some scenarios of catching up data to take several hours to finish one checkpoint. If the current checkpoint expires because of timeout, the next new triggered checkpoint might still be failed for timeout. So it seems better to wait the current checkpoint until finishes, not expires it, unless we can not bear this long time for some reasons such as wondering failover to restore more data during this time. For option2: The default network setting should be make sense. The lower values might cause performance regression and the higher values would increase the inflighing buffers and checkpoint delay more seriously. For option3: If the resource is limited, it is still not working on your side. It is an option and might work in your case for sleeping some time in source as you mentioned, although it seems not a graceful way. I think there are no data skew in your case to cause backpressure, because you used the rebalance mode as mentioned. Another option might use the forward mode which would be better than rebalance mode if possible in your case. Because the source and downstream task is one-to-one in forward mode, so the total flighting buffers are 2+2+8 for one single downstream task before barrier. If in rebalance mode, the total flighting buffer would be (a*2+a*2+8) for one single downstream task (`a` is the parallelism of source vertex), because it is all-to-all connection. The barrier alignment takes more time in rebalance mode than forward mode. Best, Zhijiang ------------------------------------------------------------------ From:LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>> Send Time:2019年3月1日(星期五) 00:46 To:zhijiang <wangzhijiang...@aliyun.com<mailto:wangzhijiang...@aliyun.com>>; user <user@flink.apache.org<mailto:user@flink.apache.org>> Subject:RE: Checkpoints and catch-up burst (heavy back pressure) Update : Option 1 does not work. It still fails at the end of the timeout, no matter its value. Should I implement a “bandwidth” management system by using artificial Thread.sleep in the source depending on the back pressure ? De : LINZ, Arnaud Envoyé : jeudi 28 février 2019 15:47 À : 'zhijiang' <wangzhijiang...@aliyun.com<mailto:wangzhijiang...@aliyun.com>>; user <user@flink.apache.org<mailto:user@flink.apache.org>> Objet : RE: Checkpoints and catch-up burst (heavy back pressure) Hi Zhihiang, Thanks for your feedback. * I’ll try option 1 ; time out is 4min for now, I’ll switch it to 40min and will let you know. Setting it higher than 40 min does not make much sense since after 40 min the pending output is already quite large. * Option 3 won’t work ; I already take too many ressources, and as my source is more or less a hdfs directory listing, it will always be far faster than any mapper that reads the file and emits records based on its content or sink that store the transformed data, unless I put “sleeps” in it (but is this really a good idea?) * Option 2: taskmanager.network.memory.buffers-per-channel and taskmanager.network.memory.buffers-per-gate are currently unset in my configuration (so to their default of 2 and 8), but for this streaming app I have very few exchanges between nodes (just a rebalance after the source that emit file names, everything else is local to the node). Should I adjust their values nonetheless ? To higher or lower values ? Best, Arnaud De : zhijiang <wangzhijiang...@aliyun.com<mailto:wangzhijiang...@aliyun.com>> Envoyé : jeudi 28 février 2019 10:58 À : user <user@flink.apache.org<mailto:user@flink.apache.org>>; LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>> Objet : Re: Checkpoints and catch-up burst (heavy back pressure) Hi Arnaud, I think there are two key points. First the checkpoint barrier might be emitted delay from source under high backpressure for synchronizing lock. Second the barrier has to be queued in flighting data buffers, so the downstream task has to process all the buffers before barriers to trigger checkpoint and this would take some time under back pressure. There has three ways to work around: 1. Increase the checkpoint timeout avoid expire in short time. 2. Decrease the setting of network buffers to decrease the amount of flighting buffers before barrier, you can check the config of "taskmanager.network.memory.buffers-per-channel" and "taskmanager.network.memory.buffers-per-gate". 3. Adjust the parallelism such as increasing it for sink vertex in order to process source data faster, to avoid backpressure in some extent. You could check which way is suitable for your scenario and may have a try. Best, Zhijiang ------------------------------------------------------------------ From:LINZ, Arnaud <al...@bouyguestelecom.fr<mailto:al...@bouyguestelecom.fr>> Send Time:2019年2月28日(星期四) 17:28 To:user <user@flink.apache.org<mailto:user@flink.apache.org>> Subject:Checkpoints and catch-up burst (heavy back pressure) Hello, I have a simple streaming app that get data from a source and store it to HDFS using a sink similar to the bucketing file sink. Checkpointing mode is “exactly once”. Everything is fine on a “normal” course as the sink is faster than the source; but when we stop the application for a while and then restart it, we have a catch-up burst to get all the messages emitted in the meanwhile. During this burst, the source is faster than the sink, and all checkpoints fail (time out) until the source has been totally caught up. This is annoying because the sink does not “commit” the data before a successful checkpoint is made, and so the app release all the “catch up” data as a atomic block that can be huge if the streaming app was stopped for a while, adding an unwanted stress to all the following hive treatments that use the data provided in micro batches and to the Hadoop cluster. How should I handle the situation? Is there something special to do to get checkpoints even during heavy load? The problem does not seem to be new, but I was unable to find any practical solution in the documentation. Best regards, Arnaud ________________________________ L'intégrité de ce message n'étant pas assurée sur internet, la société expéditrice ne peut être tenue responsable de son contenu ni de ses pièces jointes. Toute utilisation ou diffusion non autorisée est interdite. Si vous n'êtes pas destinataire de ce message, merci de le détruire et d'avertir l'expéditeur. The integrity of this message cannot be guaranteed on the Internet. The company that sent this message cannot therefore be held liable for its content nor attachments. Any unauthorized use or dissemination is prohibited. If you are not the intended recipient of this message, then please delete it and notify the sender. -------------------------- Ken Krugler +1 530-210-6378 http://www.scaleunlimited.com Custom big data solutions & training Flink, Solr, Hadoop, Cascading & Cassandra
