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In the same way that the match
query is the go-to query for standard
full-text search, the match_phrase
query
is the one you should reach for
when you want to find words that are near each other:
GET /my_index/my_type/_search { "query": { "match_phrase": { "title": "quick brown fox" } } }
Like the match
query, the match_phrase
query first analyzes the query
string to produce a list of terms. It then searches for all the terms, but
keeps only documents that contain all of the search terms, in the same
positions relative to each other. A query for the phrase quick fox
would not match any of our documents, because no document contains the word
quick
immediately followed by fox
.
The match_phrase
query can also be written as a match
query with type
phrase
:
"match": { "title": { "query": "quick brown fox", "type": "phrase" } }
When a string is analyzed, the analyzer returns not only a list of terms, but also the position, or order, of each term in the original string:
GET /_analyze?analyzer=standard Quick brown fox
This returns the following:
{ "tokens": [ { "token": "quick", "start_offset": 0, "end_offset": 5, "type": "<ALPHANUM>", "position": 1 }, { "token": "brown", "start_offset": 6, "end_offset": 11, "type": "<ALPHANUM>", "position": 2 }, { "token": "fox", "start_offset": 12, "end_offset": 15, "type": "<ALPHANUM>", "position": 3 } ] }
Positions can be stored in the inverted index, and position-aware queries like
the match_phrase
query can use them to match only documents that contain
all the words in exactly the order specified, with no words in-between.
For a document to be considered a match for the phrase “quick brown fox,” the following must be true:
-
quick
,brown
, andfox
must all appear in the field. -
The position of
brown
must be1
greater than the position ofquick
. -
The position of
fox
must be2
greater than the position ofquick
.
If any of these conditions is not met, the document is not considered a match.
Internally, the match_phrase
query uses the low-level span
query family to
do position-aware matching.
Span queries are term-level queries, so they have
no analysis phase; they search for the exact term specified.
Thankfully, most people never need to use the span
queries directly, as the
match_phrase
query is usually good enough. However, certain specialized
fields, like patent searches, use these low-level queries to perform very
specific, carefully constructed positional searches.