spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Best way to calculate intermediate column statistics
Date Wed, 24 Aug 2016 16:52:17 GMT
Hi Richard,

What is the business use case for such statistics?

HTH

Dr Mich Talebzadeh



LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
<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 24 August 2016 at 16:01, Bedrytski Aliaksandr <spark@bedryt.ski> wrote:

> Hi Richard,
>
> these intermediate statistics should be calculated from the result of the
> calculation or during the aggregation?
> If they can be derived from the resulting dataframe, why not to cache
> (persist) that result just after the calculation?
> Then you may aggregate statistics from the cached dataframe.
> This way it won't hit performance too much.
>
> Regards
> --
>   Bedrytski Aliaksandr
>   spark@bedryt.ski
>
>
>
> On Wed, Aug 24, 2016, at 16:42, Richard Siebeling wrote:
>
> Hi,
>
> what is the best way to calculate intermediate column statistics like the
> number of empty values and the number of distinct values each column in a
> dataset when aggregating of filtering data next to the actual result of the
> aggregate or the filtered data?
>
> We are developing an application in which the user can slice-and-dice
> through the data and we would like to, next to the actual resulting data,
> get column statistics of each column in the resulting dataset. We prefer to
> calculate the column statistics on the same pass over the data as the
> actual aggregation or filtering, is that possible?
>
> We could sacrifice a little bit of performance (but not too much), that's
> why we prefer one pass...
>
> Is this possible in the standard Spark or would this mean modifying the
> source a little bit and recompiling? Is that feasible / wise to do?
>
> thanks in advance,
> Richard
>
>
>
>
>
>

Mime
View raw message