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.



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.


Dr Mich Talebzadeh

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On 22 September 2016 at 18:09, Hanumath Rao Maduri <hanu....@gmail.com>

> 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?
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