Scoring by term position in ElasticSearch?

Question

I\'m implementing an auto-complete index in ElasticSearch and have run into an issue with sorting/scoring. Say I have the following strings in an index:

appl         


        

Answer 1

Here's the solution I ended up with, based on Andrei's answer and expanded to support multiple search terms and additional scoring based on length of the first word in the result:

First, define the following custom analyzer (it keeps the entire string as a single token and lowercases it):

"raw_analyzer": {
    "type": "custom",
    "filter": [
        "lowercase"
    ],
    "tokenizer": "keyword"
}

Second, define your search field mapping like so (mine's named "name"):

"name": {
    "type": "string",
    "analyzer": "english",
    "fields": {
        "raw": {
            "type": "string",
            "index_analyzer": "raw_analyzer",
            "search_analyzer": "standard"
        }
    }
},
"_nameFirstWordLength": {
    "type": "long"
}

Third, when populating the index use the following logic (mine's in C#) to populate:

_nameFirstWordLength = fi.Name.Split(new[] {' '}, StringSplitOptions.RemoveEmptyEntries)[0].Length

Finally, do your search as follows:

{
   "query":{
      "bool":{
         "must":{
            "match_phrase_prefix":{
               "name":{
                  "query":"apple"
               }
            }
         },
         "should":{
            "function_score":{
               "query":{
                  "query_string":{
                     "fields":[
                        "name.raw"
                     ],
                     "query":"apple*"
                  }
               },
               "script_score":{
                  "script":"100/doc['_nameFirstWordLength'].value"
               },
               "boost_mode":"replace"
            }
         }
      }
   }
}

I'm using match_phrase_prefix so that partial matches are supported, such as "ap" matching "apple". The bool must/should with that second query_string query against name.raw gives a higher score to results whose name starts with one of the search terms (in my code I'm pre-processing the search string, just for that second query, to add a "*" after every word). Finally, wrapping that second query in a function_score script that uses the value of _nameFirstWordLength causes the results up-scored by the second query to be further sorted by the length of their first word (causing Apple to show before Applebee's, for example).