[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression

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[jira] [Comment Edited] (SOLR-13047) Add facet2D Streaming Expression

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    [ https://issues.apache.org/jira/browse/SOLR-13047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16818790#comment-16818790 ]

Nazerke Seidan edited comment on SOLR-13047 at 4/16/19 8:56 AM:
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Regarding the implementation details, are the math expressions limited to metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we need count(*)  as a parameter in the facet2D? Obviously, we are returning 300 diseases containing 10 symptoms for each disease.  


was (Author: snazerke):
Regarding the implementation details, are the math expressions limited to metrics such as count(*), sum(col), max(col), min(col) and avg(col)? Why do we need count(*)  as a parameter in the facet2D? Obviously, we are returning 300 diseases containing 10 symptoms for each disease.  

> Add facet2D Streaming Expression
> --------------------------------
>
>                 Key: SOLR-13047
>                 URL: https://issues.apache.org/jira/browse/SOLR-13047
>             Project: Solr
>          Issue Type: New Feature
>      Security Level: Public(Default Security Level. Issues are Public)
>            Reporter: Joel Bernstein
>            Assignee: Joel Bernstein
>            Priority: Major
>
> The current facet expression is a generic tool for creating multi-dimension aggregations. The *facet2D* Streaming Expression has semantics specific for 2 dimensional facets which are designed to be *pivoted* into a matrix and operated on by *Math Expressions*. 
> facet2D will use the json facet API under the covers. 
> Proposed syntax:
> {code:java}
> facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10", count(*)){code}
> The example above will return tuples containing the top 300 diseases and the top ten symptoms for each disease. 
> Using math expression the tuples can be *pivoted* into a matrix where the rows of the matrix are the diseases, the columns of the matrix are the symptoms and the cells in the matrix contain the counts. This matrix can then be *clustered* to find clusters of *diseases* that are correlated by *symptoms*. 
> {code:java}
> let(a=facet2D(medrecords, q=*:*, x=diseases, y=symptoms, dimensions="300, 10", count(*)),
>     b=pivot(a, diseases, symptoms, count(*)),
>     c=kmeans(b, 10)){code}
>  
> *Implementation Note:*
> The implementation plan for this ticket is to create a new stream called Facet2DStream. The FacetStream code is a good starting point for the new implementation and can be adapted for the Facet2D parameters. Similar tests to the FacetStream can be added to StreamExpressionTest
>  



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