Wilcard search or partial matching in Elastic search

Question

I am trying to provide the search to end user with type as they go which is is more like sqlserver. I was able to implement ES query for the given sql scenario:

Answer 1

The most efficient solution involves leveraging an ngram tokenizer in order to tokenize portions of your name field. For instance, if you have a name like peter tomson, the ngram tokenizer will tokenize and index it like this:

• pe
• pet
• pete
• peter
• peter t
• peter to
• peter tom
• peter toms
• peter tomso
• eter tomson
• ter tomson
• er tomson
• r tomson
• tomson
• tomson
• omson
• mson
• son
• on

So, when this has been indexed, searching for any of those tokens will retrieve your document with peter thomson in it.

Let's create the index:

PUT likequery
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_ngram_analyzer": {
          "tokenizer": "my_ngram_tokenizer"
        }
      },
      "tokenizer": {
        "my_ngram_tokenizer": {
          "type": "nGram",
          "min_gram": "2",
          "max_gram": "15"
        }
      }
    }
  },
  "mappings": {
    "typename": {
      "properties": {
        "name": {
          "type": "string",
          "fields": {
            "search": {
              "type": "string",
              "analyzer": "my_ngram_analyzer"
            }
          }
        },
        "type": {
          "type": "string",
          "index": "not_analyzed"
        }
      }
    }
  }
}

You'll then be able to search like this with a simple and very efficient term query:

POST likequery/_search
{
  "query": {
    "bool": {
      "should": [
        {
          "term": {
            "name.search": "peter tom"
          }
        }
      ],
      "must_not": [
        {
          "match": {
            "type": "xyz"
          }
        },
        {
          "match": {
            "type": "abc"
          }
        }
      ]
    }
  }
}