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Full-Text Search
editFull-Text Search
editThe searches so far have been simple: single names, filtered by age. Let’s try a more advanced, full-text search—a task that traditional databases would really struggle with.
We are going to search for all employees who enjoy rock climbing:
GET /megacorp/employee/_search { "query" : { "match" : { "about" : "rock climbing" } } }
You can see that we use the same match
query as before to search the about
field for “rock climbing”. We get back two matching documents:
{ ... "hits": { "total": 2, "max_score": 0.16273327, "hits": [ { ... "_score": 0.16273327, "_source": { "first_name": "John", "last_name": "Smith", "age": 25, "about": "I love to go rock climbing", "interests": [ "sports", "music" ] } }, { ... "_score": 0.016878016, "_source": { "first_name": "Jane", "last_name": "Smith", "age": 32, "about": "I like to collect rock albums", "interests": [ "music" ] } } ] } }
By default, Elasticsearch sorts matching results by their relevance score,
that is, by how well each document matches the query. The first and highest-scoring result is obvious: John Smith’s about
field clearly says “rock
climbing” in it.
But why did Jane Smith come back as a result? The reason her document was
returned is because the word “rock” was mentioned in her about
field.
Because only “rock” was mentioned, and not “climbing,” her _score
is
lower than John’s.
This is a good example of how Elasticsearch can search within full-text fields and return the most relevant results first. This concept of relevance is important to Elasticsearch, and is a concept that is completely foreign to traditional relational databases, in which a record either matches or it doesn’t.