Hi Aditya, If you cache the RDDs - like textFile.cache(), textFile1().cache() - then it will not load the data again from file system.
Once done with related operations it is recommended to uncache the RDDs to manage memory efficiently and avoid it's exhaustion. Note caching operation is with main memory and persist is to disk. Datta https://in.linkedin.com/in/datta-khot-240b544 http://www.datasherpa.io/ On Fri, Sep 23, 2016 at 10:23 AM, Aditya <aditya.calangut...@augmentiq.co.in > wrote: > Thanks for the reply. > > One more question. > How spark handles data if it does not fit in memory? The answer which I > got is that it flushes the data to disk and handle the memory issue. > Plus in below example. > val textFile = sc.textFile("/user/emp.txt") > val textFile1 = sc.textFile("/user/emp1.xt") > val join = textFile.join(textFile1) > join.saveAsTextFile("/home/output") > val count = join.count() > > When the first action is performed it loads textFile and textFile1 in > memory, performes join and save the result. > But when the second action (count) is called, it again loads textFile and > textFile1 in memory and again performs the join operation? > If it loads again what is the correct way to prevent it from loading again > again the same data? > > > On Thursday 22 September 2016 11:12 PM, Mich Talebzadeh wrote: > > Hi, > > unpersist works on storage memory not execution memory. So I do not think > you can flush it out of memory if you have not cached it using cache or > something like below in the first place. > > s.persist(org.apache.spark.storage.StorageLevel.MEMORY_ONLY) > > s.unpersist > > I believe the recent versions of Spark deploy Least Recently Used > (LRU) mechanism to flush unused data out of memory much like RBMS cache > management. I know LLDAP does that. > > HTH > > > > Dr Mich Talebzadeh > > > > LinkedIn * > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > > <http://talebzadehmich.wordpress.com>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 22 September 2016 at 18:09, Hanumath Rao Maduri <hanu....@gmail.com> > wrote: > >> Hello Aditya, >> >> After an intermediate action has been applied you might want to call >> rdd.unpersist() to let spark know that this rdd is no longer required. >> >> Thanks, >> -Hanu >> >> On Thu, Sep 22, 2016 at 7:54 AM, Aditya <aditya.calangutkar@augmentiq. >> co.in> wrote: >> >>> Hi, >>> >>> Suppose I have two RDDs >>> val textFile = sc.textFile("/user/emp.txt") >>> val textFile1 = sc.textFile("/user/emp1.xt") >>> >>> Later I perform a join operation on above two RDDs >>> val join = textFile.join(textFile1) >>> >>> And there are subsequent transformations without including textFile and >>> textFile1 further and an action to start the execution. >>> >>> When action is called, textFile and textFile1 will be loaded in memory >>> first. Later join will be performed and kept in memory. >>> My question is once join is there memory and is used for subsequent >>> execution, what happens to textFile and textFile1 RDDs. Are they still kept >>> in memory untill the full lineage graph is completed or is it destroyed >>> once its use is over? If it is kept in memory, is there any way I can >>> explicitly remove it from memory to free the memory? >>> >>> >>> >>> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>> >> > > >
