"I do know how Spark in general works, and how it stores data in memory etc. It's been almost 2 years that I work on it. So I'm definetely not collecting the whole rdd in memory ;)"
Spark doc is a good start. To see how spark memory is utilised look at Spark UI on <HOST>:4040 by default under storage tab. It will tell you what is stored. Spark uses execution memory for result set on operation (RDD + DF) and storage memory for anything cached with cache() or persist(). You can verify all this in Spark UI. HTH Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 14 October 2016 at 08:37, Antonio Murgia <antonio.mur...@eng.it> wrote: > Hi Constantin, > > thank you for your reply. I do know how Spark in general works, and how it > stores data in memory etc. It's been almost 2 years that I work on it. So > I'm definetely not collecting the whole rdd in memory ;) > > Our "mantainance use case" is the following: > > Copying the whole content of a table to another table applying a simple > transformation (e.g. aggregating some columns). We tried with an Upsert > from select, but we ran into some memory issue from the phoenix side. > > Do you have any suggestion to perform something like that? > > Thank you in advance > > #A.M. > > On 10/14/2016 08:10 AM, Ciureanu Constantin wrote: > > Hi Antonio, > Reading the whole table is not a good use-case for Phoenix / HBase or any > DB. > You should never ever store the whole content read from DB / disk into > memory, that's definitely wrong. > Spark doesn't do that by itself, no matter what "they" told you that it's > going to do in order to be faster bla bla. Review your algorithm and see > what's to improve, After all, I hope you just use collect() so the OOM is > on the driver (that's easier to fix, :p by not using it). > Back to the OOM: After reading an RDD you can shuffle yourself / > repartition in any number of partitions easily (but that sends data through > network so it's expensive): > repartition(numPartitions) > http://spark.apache.org/docs/latest/programming-guide.html > I recommend to read this plus a few articles on Spark best practices. > > Kind regards, > Constantin > > În Joi, 13 oct. 2016, 18:16 Antonio Murgia, <antonio.mur...@eng.it> a > scris: > >> Hello everyone, >> >> I'm trying to read data from a Phoenix Table using apache Spark. I >> actually use the suggested method: sc.phoenixTableAsRDD without issuing >> any query (e.g. reading the whole table) and I noticed that the number >> of partitions that spark creates is equal to the number of >> regionServers. Is there a way to use a custom number of regions? >> >> The problem we actually face is that if a region is bigger than the >> available memory of the spark executor, it goes in OOM. Being able to >> tune the number of regions, we might use a higher number of partitions >> reducing the memory footprint of the processing (and also slowing it >> down, i know :( ). >> >> Thank you in advance >> >> #A.M. >> >> >
