I'm not sure what the 'official' elasticsearch view on this is but, to me, 
from day 1, elasticsearch has had the capability to do everything that OLAP 
cubes can do, in a lightweight, agile way.  Creating dimensions in cubes is 
the same as pre-indexing calculated fields in the index 

e.g. week: 6, ,Month: 2, Quarter: 1, year: 2013, decade: second, century: 
21st etc. are effectively dimensions calculated from a single date fact: 
7th February 2013

The aggregations framework adds an immense amount of power: flexible 
aggregations on top of fast search and powerful sorting capabilities, is a 
pretty amazing package for business analytics,  without any of the hype and 
expense typically associated with OLAP and Business Intelligence.

I guess time will tell on the performance front, but I'm quite optimistic, 
in the end aggregations and facets are just big in-memory map-reduce jobs - 
if you pre-calculate a lot of the dimensions you are interested in, rather 
than relying on scripts, you should get pretty decent performance.

-David.

On Monday, 20 January 2014 15:17:49 UTC, Roy Jacobs wrote:
>
> I am interested in using the new aggregations support to implement 
> something similar to an OLAP cube.
>
> Let's say I have a big bunch of documents that represent orders. On those 
> documents I want to calculate a bunch of metrics (using the "metric" 
> aggregation) based on various fields. Stuff like "# of items". Then, I want 
> to group this (using the "bucket" aggregation) based on brand, for 
> instance. All of this is multi-tenant as well, so I need to filter out a 
> whole lot of irrelevant data for every query.
>
> The amount of documents is quite high (hundreds of millions) so I was 
> wondering if aggregations have any form of caching or precalculation, or if 
> they have to traverse the entire index every time I do a query. This could 
> also be quite prohibitive memory-wise.
>
> Has anyone been using aggregations in this manner?
>
> Roy
>

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