=== Most Important Queries and Filters
While Elasticsearch comes with many queries and filters, you will use
just a few frequently. We discuss them in much greater
detail in <
==== term Filter
The term filter is used to filter by((("filters", "important")))((("term filter"))) exact values, be they numbers, dates,
Booleans, or not_analyzed exact-value string fields:
{ "term": { "age": 26 }} { "term": { "date": "2014-09-01" }} { "term": { "public": true }}
// SENSE: 054_Query_DSL/70_Term_filter.json
==== terms Filter
The terms filter is((("terms filter"))) the same as the term filter, but allows you
to specify multiple values to match. If the field contains any of
the specified values, the document matches:
// SENSE: 054_Query_DSL/70_Terms_filter.json
==== range Filter
The range filter allows you to find((("range filters"))) numbers or dates that fall into
a specified range:
{ "range": { "age": { "gte": 20, "lt": 30 } }
// SENSE: 054_Query_DSL/70_Range_filter.json
The operators that it accepts are as follows:
gt::
Greater than
gte::
Greater than or equal to
lt::
Less than
lte::
Less than or equal to
==== exists and missing Filters
The exists and missing filters are ((("exists filter")))((("missing filter")))used to find documents in which the
specified field either has one or more values (exists) or doesn't have any
values (missing). It is similar in nature to IS_NULL (missing) and NOT
IS_NULL (exists)in SQL:
{ "exists": { "field": "title" }
// SENSE: 054_Query_DSL/70_Exists_filter.json
These filters are frequently used to apply a condition only if a field is present, and to apply a different condition if it is missing.
==== bool Filter
The bool filter is used ((("bool filter")))((("must clause", "in bool filters")))((("must_not clause", "in bool filters")))((("should clause", "in bool filters")))to combine multiple filter clauses using
Boolean logic. ((("bool filter", "must, must_not, and should clauses"))) It accepts three parameters:
must::
These clauses must match, like and.
must_not::
These clauses must not match, like not.
should::
At least one of these clauses must match, like or.
Each of these parameters can accept a single filter clause or an array of filter clauses:
{ "bool": { "must": { "term": { "folder": "inbox" }}, "must_not": { "term": { "tag": "spam" }}, "should": [ { "term": { "starred": true }}, { "term": { "unread": true }} ] }
// SENSE: 054_Query_DSL/70_Bool_filter.json
==== match_all Query
The match_all query simply((("match_all query")))((("queries", "important"))) matches all documents. It is the default
query that is used if no query has been specified:
// SENSE: 054_Query_DSL/70_Match_all_query.json
This query is frequently used in combination with a filter--for instance, to
retrieve all emails in the inbox folder. All documents are considered to be
equally relevant, so they all receive a neutral _score of 1.
==== match Query
The match query should be the standard((("match query"))) query that you reach for whenever
you want to query for a full-text or exact value in almost any field.
If you run a match query against a full-text field, it will analyze
the query string by using the correct analyzer for that field before executing
the search:
// SENSE: 054_Query_DSL/70_Match_query.json
If you use it on a field containing an exact value, ((("exact values", "searching for, match queries and")))such as a number, a date,
a Boolean, or a not_analyzed string field, then it will search for that
exact value:
{ "match": { "age": 26 }} { "match": { "date": "2014-09-01" }} { "match": { "public": true }}
// SENSE: 054_Query_DSL/70_Match_query.json
TIP: For exact-value searches, you probably want to use a filter instead of a query, as a filter will be cached.
Unlike the query-string search that we showed in <match
query does not use a query syntax like +user_id:2 +tweet:search. It just
looks for the words that are specified. This means that it is safe to expose
to your users via a search field; you control what fields they can query, and
it is not prone to throwing syntax errors.
==== multi_match Query
The multi_match query allows((("multi_match queries"))) to run the same match query on multiple
fields:
{ "multi_match": { "query": "full text search", "fields": [ "title", "body" ] }
// SENSE: 054_Query_DSL/70_Multi_match_query.json
==== bool Query
The bool query, like the bool filter,((("bool query"))) is used to combine multiple
query clauses. However, there are some differences. Remember that while
filters give binary yes/no answers, queries calculate a relevance score
instead. The bool query combines the _score from each must or
should clause that matches.((("bool query", "must, must_not, and should clauses")))((("should clause", "in bool queries")))((("must_not clause", "in bool queries")))((("must clause", "in bool queries"))) This query accepts the following parameters:
must::
Clauses that must match for the document to be included.
must_not::
Clauses that must not match for the document to be included.
should::
If these clauses match, they increase the _score;
otherwise, they have no effect. They are simply used to refine
the relevance score for each document.
The following query finds documents whose title field matches
the query string how to make millions and that are not marked
as spam. If any documents are starred or are from 2014 onward,
they will rank higher than they would have otherwise. Documents that
match both conditions will rank even higher:
{ "bool": { "must": { "match": { "title": "how to make millions" }}, "must_not": { "match": { "tag": "spam" }}, "should": [ { "match": { "tag": "starred" }}, { "range": { "date": { "gte": "2014-01-01" }}} ] }
// SENSE: 054_Query_DSL/70_Bool_query.json
TIP: If there are no must clauses, at least one should clause has to
match. However, if there is at least one must clause, no should clauses
are required to match.