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Fuzzy query of information is often needed on some information management platforms, the earliest time is the fuzzy query on a certain field, but the information returned at this time will not be very accurate, because only A field or B field can be checked, and a very simple full-text search is realized in MongoDB.

Example: define a new collection

Db.news.insert ({"title": "stoneA", "content": "ttA"})

Db.news.insert ({"title": "stoneB", "content": "ttB"})

Db.news.insert ({"title": "stoneC", "content": "ttC"})

Db.news.insert ({"title": "stoneD", "content": "ttD"})

Example: create a full-text index

> db.news.createIndex ({"title": "text", "content": "text"})

{

"createdCollectionAutomatically": false

"numIndexesBefore": 1

"numIndexesAfter": 2

"ok": 1

}

Example: implement fuzzy query of data

If you want to represent a full-text search, use the "$text" judge, and if you want to query the data, use the "$search" operator:

Keyword specified by ● query: {"$search": "query keyword"}

● query multiple keywords (or relationships): {"$search": "query keywords query keywords."}

● query multiple keywords (and relations): {"$search": "\" query keywords\ "\" query keywords\ "..."}

● query multiple keywords (excluding one): {"$search": "query keywords query keywords...-troubleshoot keywords"}

Example: query individual content

> db.news.find ({"$text": {"$search": "stoneA"}})

{"_ id": ObjectId ("5992c4310184ff511bf02bbb"), "title": "stoneA", "content": "ttA"}

Example: the query contains information for "stoneA" and "stoneB"

> db.news.find ({"$text": {"$search": "stoneA stoneB"}})

{"_ id": ObjectId ("5992c4310184ff511bf02bbc"), "title": "stoneB", "content": "ttB"}

{"_ id": ObjectId ("5992c4310184ff511bf02bbb"), "title": "stoneA", "content": "ttA"}

Example: query contains both "ttC" and "ttD"

> db.news.find ({"$text": {"$search": "\" ttC\ "\" ttD\ "}})

{"_ id": ObjectId ("5992c61d0184ff511bf02bc1"), "title": "stoneC", "content": "ttC ttD ttE"}

{"_ id": ObjectId ("5992c61d0184ff511bf02bc2"), "title": "stoneD", "content": "ttC ttD ttF"}

Example: the query contains "ttE" but not "ttF"

> db.news.find ({"$text": {"$search": "ttE-ttF"}})

{"_ id": ObjectId ("5992c61d0184ff511bf02bc1"), "title": "stoneC", "content": "ttC ttD ttE"}

However, in the full-text retrieval operation, we can also use the similarity score to judge the retrieval results.

Example: score query results

> db.news.find ({"$text": {"$search": "ttC ttD ttE"}}, {"score": {"$meta": "textScore"}}) .sort ({"score": {"$meta": "textScore"}})

{"_ id": ObjectId ("5992c61d0184ff511bf02bc1"), "title": "stoneC", "content": "ttC ttD ttE", "score": 2}

{"_ id": ObjectId ("5992c61d0184ff511bf02bc2"), "title": "stoneD", "content": "ttC ttD ttF", "score": 1.3333333333333333}

Ranking according to the scored results, you can actually achieve a more accurate information search.

If there are too many fields in a collection, it is troublesome to set a full-text index for each field, which is simpler, and you can set a full-text index for all fields.

Example: set up a full-text index for all fields

> db.news.dropIndexes ()

{

"nIndexesWas": 2

"msg": "non-_id indexes dropped for collection"

"ok": 1

}

> db.news.createIndex ({"$* *": "text"})

{

"createdCollectionAutomatically": false

"numIndexesBefore": 1

"numIndexesAfter": 2

"ok": 1

}

This is the easiest way to set up a full-text index, but don't use it as much as possible and it will be slow.

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