How to search for a part of a word with ElasticSearch

Question

I'm using nGram, too. I use standard tokenizer and nGram just as a filter. Here is my setup:

{
  "index": {
    "index": "my_idx",
    "type": "my_type",
    "analysis": {
      "index_analyzer": {
        "my_index_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "mynGram"
          ]
        }
      },
      "search_analyzer": {
        "my_search_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "standard",
            "lowercase",
            "mynGram"
          ]
        }
      },
      "filter": {
        "mynGram": {
          "type": "nGram",
          "min_gram": 2,
          "max_gram": 50
        }
      }
    }
  }
}

Let's you find word parts up to 50 letters. Adjust the max_gram as you need. In german words can get really big, so I set it to a high value.

Searching with leading and trailing wildcards is going to be extremely slow on a large index. If you want to be able to search by word prefix, remove leading wildcard. If you really need to find a substring in a middle of a word, you would be better of using ngram tokenizer.

I think there's no need to change any mapping. Try to use query_string, it's perfect. All scenarios will work with default standard analyzer:

We have data:

{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}

Scenario 1:

{"query": {
    "query_string" : {"default_field" : "name", "query" : "*Doe*"}
} }

Response:

{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}

Scenario 2:

{"query": {
    "query_string" : {"default_field" : "name", "query" : "*Jan*"}
} }

Response:

{"_id" : "1","name" : "John Doeman","function" : "Janitor"}

Scenario 3:

{"query": {
    "query_string" : {"default_field" : "name", "query" : "*oh* *oe*"}
} }

Response:

{"_id" : "1","name" : "John Doeman","function" : "Janitor"}
{"_id" : "2","name" : "Jane Doewoman","function" : "Teacher"}

EDIT - Same implementation with spring data elastic search https://stackoverflow.com/a/43579948/2357869

One more explanation how query_string is better than others https://stackoverflow.com/a/43321606/2357869

without changing your index mappings you could do a simple prefix query that will do partial searches like you are hoping for

ie.

{
  "query": { 
    "prefix" : { "name" : "Doe" }
  }
}

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

Try the solution with is described here: Exact Substring Searches in ElasticSearch

{
    "mappings": {
        "my_type": {
            "index_analyzer":"index_ngram",
            "search_analyzer":"search_ngram"
        }
    },
    "settings": {
        "analysis": {
            "filter": {
                "ngram_filter": {
                    "type": "ngram",
                    "min_gram": 3,
                    "max_gram": 8
                }
            },
            "analyzer": {
                "index_ngram": {
                    "type": "custom",
                    "tokenizer": "keyword",
                    "filter": [ "ngram_filter", "lowercase" ]
                },
                "search_ngram": {
                    "type": "custom",
                    "tokenizer": "keyword",
                    "filter": "lowercase"
                }
            }
        }
    }
}

To solve the disk usage problem and the too-long search term problem short 8 characters long ngrams are used (configured with: "max_gram": 8). To search for terms with more than 8 characters, turn your search into a boolean AND query looking for every distinct 8-character substring in that string. For example, if a user searched for large yard (a 10-character string), the search would be:

"arge ya AND arge yar AND rge yard.

While there are a lot of answers which focuses on solving the issue at hand but don't talk much about the various trade-off which someone needs to make before choosing a particular answer. So let me try to add a few more details on this perspective.

Partial search is now a day a very common and important feature and if not implemented properly can lead to poor user experience and bad performance, so first know your application function and non-function requirement related to this feature which I talked about in my this detailed SO answer.

Now there are various approaches, like query time, index time, completion suggester and search as you type data-types added in recent version of elasticsarch.

Now people who quickly want to just implement a solution can use below end to end working solution.

Index mapping

{
  "settings": {
    "analysis": {
      "filter": {
        "autocomplete_filter": {
          "type": "ngram",
          "min_gram": 1,
          "max_gram": 10
        }
      },
      "analyzer": {
        "autocomplete": { 
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "autocomplete_filter"
          ]
        }
      }
    },
    "index.max_ngram_diff" : 10
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "autocomplete", 
        "search_analyzer": "standard" 
      }
    }
  }
}

Index given sample docs

{
  "title" : "John Doeman"
  
}

{
  "title" : "Jane Doewoman"
  
}

{
  "title" : "Jimmy Jackal"
  
}

And search query

{
    "query": {
        "match": {
            "title": "Doe"
        }
    }
}

which returns expected search results

 "hits": [
            {
                "_index": "6467067",
                "_type": "_doc",
                "_id": "1",
                "_score": 0.76718915,
                "_source": {
                    "title": "John Doeman"
                }
            },
            {
                "_index": "6467067",
                "_type": "_doc",
                "_id": "2",
                "_score": 0.76718915,
                "_source": {
                    "title": "Jane Doewoman"
                }
            }
        ]