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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: sc.phoenixTableAsRDD number of initial partitions
Date Fri, 14 Oct 2016 07:54:34 GMT
"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



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On 14 October 2016 at 08:37, Antonio Murgia <antonio.murgia@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.murgia@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.
>>
>>
>

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