phoenix-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Antonio Murgia <>
Subject Re: sc.phoenixTableAsRDD number of initial partitions
Date Fri, 14 Oct 2016 09:12:23 GMT
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.


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.
> Dr Mich Talebzadeh
> LinkedIn 
> /
> *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 14 October 2016 at 08:37, Antonio Murgia < 
> <>> 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)
>>     <>
>>     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,
>>     < <>> 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.

View raw message