In our web application we use a denormalized data mart in SQL Server for geo-based user project content.
Users have 1..*projects, 1..*geo areas. Content is stored (in the data mart) with UserID, ProjectID, text values for geo areas, title and description (both free text search indexed):
UserID, ProjectID, Geo, Title, Description, Timestamp
Now wanting to move this over to ElasticSearch, what would be a good data modeling approach?
Simply for the data mart, I was thinking of just serializing the data object (currently using .Net and EntityFramework) to give me the JSON representation and stuffing that into ES. Is this a good approach (also requires least re-work)?
With regards to modeling the entire application, I have seen examples where an ES type would be organized by, say Users, so the model may look something like this:
User User ID, Name, etc... ProfileSettings Setting1, Setting2, etc... Geographies GeoID, GeoName Projects ProjectID, ProjectName ProjectContent Key (UserID:ProjectID:ProjectContentID), GeoName, Title, Description, Timestamp
So this looks like the whole web application could run off of one index/type. A bit scary, no? I'm just trying to wrap my head around creating a denormalized data model in ES for a web app.
I would like to use Kibana and other analysis tools in the future, and have read about data modeling limitations like not using parent/child types.
What is would a good ElasticSearch data model look like for something like this?
Another way of asking would be, how would one model a live web application using ElasticSearch, and/or would it be better to store user configs and profiles in a separate RDBMS?