The mistake that you are doing is that you are using the term query on keyword field and both of them are not analyzed, which means they try to find the exact same search string in inverted index.
What you should be doing is: define a `text` field which you anyway will have if you have not defined your mapping. I am also assuming the same as in your query you mentioned `.keyword` which gets created automatically if you don't define mapping.
Now you can just use below [match query][1] which is analyzed and uses [standard analyzer][2] which splits the token on whitespace, so `Anthropology` `250` and `230` will be generated for your 2 sample docs.
**Simple and efficient query which brings both the docs**
{
"query": {
"match" : {
"name" : "Anthropology 230"
}
}
}
**And search result**
"hits": [
{
"_index": "matchterm",
"_type": "_doc",
"_id": "1",
"_score": 0.8754687,
"_source": {
"name": "Anthropology 230"
}
},
{
"_index": "matchterm",
"_type": "_doc",
"_id": "2",
"_score": 0.18232156,
"_source": {
"name": "Anthropology 250"
}
}
]
The reason why above query matched both docs is that it created two tokens `anthropology` and `230` and matches `anthropology` in both of the documents.
You should definitely read about the [analysis process][3] and can also try [analyze API][4] to see the tokens generated for any text.
**Analyze API output for your text**
POST http://{{hostname}}:{{port}}/{{index-name}}/_analyze
{
"analyzer": "standard",
"text": "Anthropology 250"
}
{
"tokens": [
{
"token": "anthropology",
"start_offset": 0,
"end_offset": 12,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "250",
"start_offset": 13,
"end_offset": 16,
"type": "<NUM>",
"position": 1
}
]
}
[1]:
[To see links please register here]
[2]:
[To see links please register here]
[3]:
[To see links please register here]
[4]:
[To see links please register here]
]
