mathias kluba commented on KYLIN-3138:

I also have that issue with cube with large amount of dimensions (ex: 300).

I know we can optimize the cube 
([http://kylin.apache.org/docs21/howto/howto_optimize_cubes.html)] but it 
requires to think upfront. 

It would be nice to build the 1st layer only, and aggregate "on the fly" during 
query time for a missing cuboid.


> cuboids on-demand build
> -----------------------
>                 Key: KYLIN-3138
>                 URL: https://issues.apache.org/jira/browse/KYLIN-3138
>             Project: Kylin
>          Issue Type: New Feature
>          Components: Job Engine, Query Engine, Spark Engine
>    Affects Versions: v2.2.0, v2.3.0
>            Reporter: Ruslan Dautkhanov
>            Assignee: Shaofeng SHI
>            Priority: Critical
> We just started using Kylin and quite like it so far.
> Although some of the datasets we have are quite wide to even consider for 
> OLAP cubing.
> Unless those cuboids will be built on-demand.
> I know some commercial non-open source products do this successfully. 
> This idea is to build a cuboid only when a user actually needs it. 
> So for example, our BI dashboards does a certain rollup, so then a SQL
> query hits Kylin backend. Kylin realizes it hasn't built that particular 
> cuboid just yet,
> so immediately starts building it. Users has to wait a bit longer first time
> it request that combination of dimensions. But all other requests or requests 
> of other users will be fast from that point on.
> Kylin (or any other OLAP solution) wouldn't be feasible to use on very wide 
> datasets 
> unless this on-demand functionality is implemented. For example, some 
> datasets we have have 100-200 dimensions. And we don't know up front rollups 
> users would want to do.
> Suggesting to have a new dimension build rule "lazy / on-demand". All 
> previous rules apply. This new rule type would mean, a cuboid for a 
> particular set of dimensions wouldn't be built up-front if it's marked as 
> "lazy / on-demand". 
> Thoughts / ideas?

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