I have a potentially interesting question for the computer scientists in the room, and the answer will help me implement an efficient pub/sub for my data.
A User can post Posts. Users can vote on Posts in the form of up, U, or down, D, which are variables used in the function s(U,D), which is the score of the Post. Suppose s(U,D) is linear in U, D.
So far, easy.
The caveat here is this: Suppose that we have introduced a real number associated with each score; score.realNumber. Suppose that we want to calculate the score of a user based on only posts whose score.realNumber lies between a specific range, like [a,b]. Suddenly it is not as easy as keeping track of a user’s score on a top-level document field, because the score is dependent on the query itself. Further, it is unfeasible to try to count something that is uncountable. If each score was associated with either Apples or Oranges, it would be easy to do.
IMAGE DESCRIPTION: http://postimg.org/image/4bdqqxyez/
- Non-reactive Aggregating using arunoda’s package. Does the job quickly, results are nonreactive…
- Using publish function this.added/changed/etc… API on server to aggregate/group and calculate user score’s on the fly, using the given subscription data. (input document:output document !== bijection, thus I am forced to group documents in the publish function.)
- Denormalization of post score information into the user documents, combined with using $elemMatch, and then using low level publish API to reduce scores on the fly using subscription data (input document:output document IS A bijection, don’t need to worry about grouping elements…)
- BAD SOLUTION: sending all post documents to client (using distributed computing principles) to do an aggregate grouping/score reduction in the browser into a null collection. I have implemented this for N<200 documents, otherwise this solution is incredibly silly at scale.
Any help on an efficient implementation would be much appreciated. See picture for better idea of what I am trying to accomplish.