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From "Aditya" <aditya.calangut...@augmentiq.co.in>
Subject Re: Spark RDD and Memory
Date Fri, 23 Sep 2016 06:54:46 GMT
Hi Datta,

Thanks for the reply.

If I havent cached any rdd and the data that is being loaded into memory 
after performing some operations exceeds the memory, how it is handled 
by spark.
Is previosly loaded rdds removed from memory to make it free for 
subsequent steps in DAG?

I am running into an issue where my DAG is very long and all the data 
does not fits into memory and at some point all my executors gets lost.

On Friday 23 September 2016 12:15 PM, Aditya wrote:
>
> Hi Datta,
>
> Thanks for the reply.
>
> If I havent cached any rdd and the data that is being loaded into 
> memory after performing some operations exceeds the memory, how it is 
> handled by spark.
> Is previosly loaded rdds removed from memory to make it free for 
> subsequent steps in DAG?
>
> I am running into an issue where my DAG is very long and all the data 
> does not fits into memory and at some point all my executors gets lost.
>
>
> On Friday 23 September 2016 12:02 PM, Datta Khot wrote:
>> 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.calangutkar@augmentiq.co.in 
>> <mailto:aditya.calangutkar@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/
>>>
>>>     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.ncr@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
>>>         <mailto: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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>>>
>>>
>>>
>>
>>
>>
>




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