Elasticsearch - How to boost score by the results of an aggregation?

Question

My use case is as follows: Execute a search against Products and boost the score by its salesRank relative to the other documents in the results. The top 10% sellers should be boosted by a factor of 1.5 and the top 25-10% should be boosted by a factor of 1.25. The percentiles are calculated on the results of the query, not the entire data set. This is feature is being used for on-the-fly instant results as the user types, so single character queries would still return results.

So for example, if I search for "Widget" and get back 100 results, the top 10 sellers returned will get boosted by 1.5 and the top 10-25 will get boosted by 1.25.

I immediately thought of using the percentiles aggregation feature to calculate the 75th and 90th percentiles of the result set.

POST /catalog/product/_search?_source_include=name,salesRank
{
  "query": {
    "match_phrase_prefix": {
      "name": "N"
    }
  },
  "aggs": {
    "sales_rank_percentiles": {
      "percentiles": {
        "field" : "salesRank",
        "percents" : [75, 90]
      }
    }
  }
}

This gets me the following:

{
   "hits": {
      "total": 142,
      "max_score": 1.6653868,
      "hits": [
         {
            "_score": 1.6653868,
            "_source": {
               "name": "nylon",
               "salesRank": 46
            }
         },
         {
            "_score": 1.6643861,
            "_source": {
               "name": "neon",
               "salesRank": 358
            }
         },
         ..... <SNIP> .....
      ]
   },
   "aggregations": {
      "sales_rank_percentiles": {
         "values": {
            "75.0": 83.25,
            "90.0": 304
         }
      }
   }
}

So great, that gives me the results and the percentiles. But I would like to boost "neon" above "nylon" because it's a top 10% seller in the results (note: in our system, the salesRank value is descending in precedence, higher value = more sales). The text relevancy is very low since only one character was supplied, so sales rank should have a big effect.

It seems that a function core query could be used here, but all of the examples in the documentation uses doc[] to use values from the document. There aren't any for using other information from the top-level of the response, e.g. "aggs" {}. I would basically like to boost a document if its sales rank falls within the 100-90th and 89th-75th percentiles, by 1.5 and 1.25 respectively.

Is this something Elasticsearch supports or am I going to have to roll my own with a custom script or plugin? Or try a different approach entirely? My preference would be to pre-calculate percentiles, index them, and do a constant score boost, but stakeholder prefers the run-time calculation.

I'm using Elasticsearch 1.2.0.

Answer 1

What if you keep sellers as a parent document and periodically updates their stars (and some boosting factor), say, via some worker. Then you match products using has_parent query, and use a combination of score mode, custom score query to match top products from top sellers?