Thanks a lot for the useful link and comments Alexis!
First of all, the problem occurs without doing anything else in the code (except of course loading my data from HDFS at the beginning) - so it definitely comes from the shuffling. You're right, in the current version, checkpoint files are not removed and take up some space in HDFS (this is easy to fix). But this is negligible compared to the non hdfs files which keeps growing as iterations go. So I agree with you that this must come from the shuffling operations: it seems that the shuffle files are not removed along the execution (they are only removed if I stop/kill the application), despite the use of checkpoint.
The class you mentioned is very interesting but I did not find a way to use it from pyspark. I will try to implement my own version, looking at the source code. But besides the queueing and removing of checkpoint files, I do not really see anything special there that could solve my issue.
I will continue to investigate this. Just found out I can use a command line browser to look at the webui (I cannot access the server in graphical display mode), this should help me understand what's going on. I will also try the workarounds mentioned in the link. Keep you posted.
Again, thanks a lot! Best, Aurelien Le 02/09/2015 14:15, alexis GILLAIN a écrit :
Aurélien, From what you're saying, I can think of a couple of things considering I don't know what you are doing in the rest of the code : - There is lot of non hdfs writes, it comes from the rest of your code and/or repartittion(). Repartition involve a shuffling and creation of files on disk. I would have said that the problem come from that but I just checked and checkpoint() is supposed to delete shuffle files : https://forums.databricks.com/questions/277/how-do-i-avoid-the-no-space-left-on-device-error.html (looks exactly as your problem so you could maybe try the others workarounds) Still, you may do a lot of shuffle in the rest of the code (you should see the amount of shuffle files written in the webui) and consider increasing the disk space available...if you can do that. - On the hdfs side, the class I pointed to has an update function which "automatically handles persisting and (optionally) checkpointing, as well as unpersisting and removing checkpoint files". Not sure your method for checkpointing remove previous checkpoint file. In the end, does the disk space error come from hdfs growing or local disk growing ? You should check the webui to identify which tasks spill data on disk and verify if the shuffle files are properly deleted when you checkpoint your rdd. Regards, 2015-09-01 22:48 GMT+08:00 Aurélien Bellet <aurelien.bel...@telecom-paristech.fr <mailto:aurelien.bel...@telecom-paristech.fr>>: Dear Alexis, Thanks again for your reply. After reading about checkpointing I have modified my sample code as follows: for i in range(1000): print i data2=data.repartition(50).cache() if (i+1) % 10 == 0: data2.checkpoint() data2.first() # materialize rdd data.unpersist() # unpersist previous version data=data2 The data is checkpointed every 10 iterations to a directory that I specified. While this seems to improve things a little bit, there is still a lot of writing on disk (appcache directory, shown as "non HDFS files" in Cloudera Manager) *besides* the checkpoint files (which are regular HDFS files), and the application eventually runs out of disk space. The same is true even if I checkpoint at every iteration. What am I doing wrong? Maybe some garbage collector setting? Thanks a lot for the help, Aurelien Le 24/08/2015 10:39, alexis GILLAIN a écrit : Hi Aurelien, The first code should create a new RDD in memory at each iteration (check the webui). The second code will unpersist the RDD but that's not the main problem. I think you have trouble due to long lineage as .cache() keep track of lineage for recovery. You should have a look at checkpointing : https://github.com/JerryLead/SparkInternals/blob/master/markdown/english/6-CacheAndCheckpoint.md https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/impl/PeriodicRDDCheckpointer.scala You can also have a look at the code of others iterative algorithms in mlllib for best practices. 2015-08-20 17:26 GMT+08:00 abellet <aurelien.bel...@telecom-paristech.fr <mailto:aurelien.bel...@telecom-paristech.fr> <mailto:aurelien.bel...@telecom-paristech.fr <mailto:aurelien.bel...@telecom-paristech.fr>>>: Hello, For the need of my application, I need to periodically "shuffle" the data across nodes/partitions of a reasonably-large dataset. This is an expensive operation but I only need to do it every now and then. However it seems that I am doing something wrong because as the iterations go the memory usage increases, causing the job to spill onto HDFS, which eventually gets full. I am also getting some "Lost executor" errors that I don't get if I don't repartition. Here's a basic piece of code which reproduces the problem: data = sc.textFile("ImageNet_gist_train.txt",50).map(parseLine).cache() data.count() for i in range(1000): data=data.repartition(50).persist() # below several operations are done on data What am I doing wrong? I tried the following but it doesn't solve the issue: for i in range(1000): data2=data.repartition(50).persist() data2.count() # materialize rdd data.unpersist() # unpersist previous version data=data2 Help and suggestions on this would be greatly appreciated! 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