Ndtyjky

Suppose there is the following mapping with Edge NGram Tokenizer:

{

  "settings": {

    "analysis": {

      "analyzer": {

        "autocomplete_analyzer": {

          "tokenizer": "autocomplete_tokenizer",

          "filter": [

            "standard"

          ]

        },

        "autocomplete_search": {

          "tokenizer": "whitespace"

        }

      },

      "tokenizer": {

        "autocomplete_tokenizer": {

          "type": "edge_ngram",

          "min_gram": 1,

          "max_gram": 10,

          "token_chars": [

            "letter",

            "symbol"

          ]

        }

      }

    }

  },

  "mappings": {

    "tag": {

      "properties": {

        "id": {

          "type": "long"

        },

        "name": {

          "type": "text",

          "analyzer": "autocomplete_analyzer",

          "search_analyzer": "autocomplete_search"

        }

      }

    }

  }

}

And the following documents are indexed:

POST /tag/tag/_bulk

{"index":{}}

{"name" : "HITS FIND SOME"}

{"index":{}}

{"name" : "TRENDING HI"}

{"index":{}}

{"name" : "HITS OTHER"}

Then searching

{

  "query": {

    "match": {

      "name": {

        "query": "HI"

      }

    }

  }

}

yields all with the same score, or TRENDING - HI with a score higher than one of the others.

How can it be configured, to show with a higher score the entries that actually start with the searcher n-gram? In this case, HITS FIND SOME and HITS OTHER to have a higher score than TRENDING HI; at the same time TRENDING HI should be in the results.

Highlighter is also used, so the given solution shouldn't mess it up.

The highlighter used in query is:

 "highlight": {

    "pre_tags": [

      "<"

    ],

    "post_tags": [

      ">"

    ],

    "fields": {

      "name": {}

    }

  }

Using this with match_phrase_prefix messes up the highlighting, yielding <H><I><T><S> FIND SOME when searching only for H.

In this particular case you could add a match_phrase_prefix term to your query, which does prefix match on the last term in the text:

{

  "query": {

    "bool": {

      "should": [

        {

          "match": {

            "name": "HI"

          }

        },

        {

          "match_phrase_prefix": {

            "name": "HI"

          }

        }

      ]

    }

  }

}

The match term will match on all three results, but the match_phrase_prefix won't match on TRENDING HI. As a result, you'll get all three items in the results, but TRENDING HI will appear with a lower score.

Quoting the docs:

The match_phrase_prefix query is a poor-man’s autocomplete[...] For better solutions for search-as-you-type see the completion suggester and Index-Time Search-as-You-Type.

On a side note, if you're introducing that bool query, you'll probably want to look at the minimum_should_match option, depending on the results you want.

You must understand how elasticsearch/lucene analyzes your data and calculate the search score.

1. Analyze API

https://www.elastic.co/guide/en/elasticsearch/reference/current/_testing_analyzers.html this will show you what elasticsearch will store, in your case:

T / TR / TRE /.... TRENDING / / H / HI

2. Score

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html

The bool query is often used to build complex query where you need a particular use case. Use must to filter document, then should to score. A common use case is to use different analyzers on a same field (by using the keyword fields in the mapping, you can analyze a same field differently).

3. dont mess highlight

According the doc: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-highlighting.html#specify-highlight-query

You can add an extra query:

{

  "query": {

    "bool": {

            "must" : [

                        {

          "match": {

            "name": "HI"

          }

        }

            ],

      "should": [

        {

          "prefix": {

            "name": "HI"

          }

        }

      ]

    }

  },

     "highlight": {

    "pre_tags": [

      "<"

    ],

    "post_tags": [

      ">"

    ],

    "fields": {

      "name": {

                "highlight_query": {

                        "match": {

            "name": "HI"

          }

                }

            }

    }

  }

}