I know spark doc is really comprehensive, I read it a lot of times in
the last 2 years, I know how to check how Spark uses its memory and how
to tweak it (e.g. using more memory for caching or not). I'll try asking
to not use any memory to cache the rdd, since I'm not caching at all.
Please don't reply with general spark knowledge, because I kinda know
how spark works.
Thank you in advance.
#A.M.
On 10/14/2016 09:54 AM, Mich Talebzadeh wrote:
"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/
http://talebzadehmich.wordpress.com
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On 14 October 2016 at 08:37, Antonio Murgia <antonio.mur...@eng.it
<mailto: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
<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 <mailto: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.
