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


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):
I recommend to read this plus a few articles on Spark best practices.

Kind regards,

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