- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.10
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Setting JVM options
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- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
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- Starting Elasticsearch
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- Overview
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- Built-in analyzer reference
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- Synonym graph
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- EQL
- SQL access
- Overview
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- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Manage Filebeat time-based indices
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
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- Set up a cluster for high availability
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- Secure a cluster
- Overview
- Configuring security
- User authentication
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- Realm chains
- Active Directory user authentication
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- LDAP user authentication
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- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
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- Security privileges
- Document level security
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- Granting privileges for data streams and index aliases
- Mapping users and groups to roles
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- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enabling audit logging
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
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- Limitations
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- How To
- Glossary of terms
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- Compact and aligned text (CAT) APIs
- cat aliases
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- Cluster APIs
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- Add index alias
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- Create data frame analytics jobs
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- Migration APIs
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- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.10.2
- Elasticsearch version 7.10.1
- Elasticsearch version 7.10.0
- Elasticsearch version 7.9.3
- Elasticsearch version 7.9.2
- Elasticsearch version 7.9.1
- Elasticsearch version 7.9.0
- Elasticsearch version 7.8.1
- Elasticsearch version 7.8.0
- Elasticsearch version 7.7.1
- Elasticsearch version 7.7.0
- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
- Dependencies and versions
Match query
editMatch query
editReturns documents that match a provided text, number, date or boolean value. The provided text is analyzed before matching.
The match
query is the standard query for performing a full-text search,
including options for fuzzy matching.
Example request
editGET /_search { "query": { "match": { "message": { "query": "this is a test" } } } }
Top-level parameters for match
edit-
<field>
- (Required, object) Field you wish to search.
Parameters for <field>
edit-
query
-
(Required) Text, number, boolean value or date you wish to find in the provided
<field>
.The
match
query analyzes any provided text before performing a search. This means thematch
query can searchtext
fields for analyzed tokens rather than an exact term. -
analyzer
-
(Optional, string) Analyzer used to convert the text in the
query
value into tokens. Defaults to the index-time analyzer mapped for the<field>
. If no analyzer is mapped, the index’s default analyzer is used. -
auto_generate_synonyms_phrase_query
-
(Optional, Boolean) If
true
, match phrase queries are automatically created for multi-term synonyms. Defaults totrue
.See Use synonyms with match query for an example.
-
fuzziness
- (Optional, string) Maximum edit distance allowed for matching. See Fuzziness for valid values and more information. See Fuzziness in the match query for an example.
-
max_expansions
-
(Optional, integer) Maximum number of terms to which the query will
expand. Defaults to
50
. -
prefix_length
-
(Optional, integer) Number of beginning characters left unchanged for fuzzy
matching. Defaults to
0
. -
fuzzy_transpositions
-
(Optional, Boolean) If
true
, edits for fuzzy matching include transpositions of two adjacent characters (ab → ba). Defaults totrue
. -
fuzzy_rewrite
-
(Optional, string) Method used to rewrite the query. See the
rewrite
parameter for valid values and more information.If the
fuzziness
parameter is not0
, thematch
query uses afuzzy_rewrite
method oftop_terms_blended_freqs_${max_expansions}
by default. -
lenient
-
(Optional, Boolean) If
true
, format-based errors, such as providing a textquery
value for a numeric field, are ignored. Defaults tofalse
. -
operator
-
(Optional, string) Boolean logic used to interpret text in the
query
value. Valid values are:-
OR
(Default) -
For example, a
query
value ofcapital of Hungary
is interpreted ascapital OR of OR Hungary
. -
AND
-
For example, a
query
value ofcapital of Hungary
is interpreted ascapital AND of AND Hungary
.
-
-
minimum_should_match
-
(Optional, string) Minimum number of clauses that must match for a document to be returned. See the
minimum_should_match
parameter for valid values and more information. -
zero_terms_query
-
(Optional, string) Indicates whether no documents are returned if the
analyzer
removes all tokens, such as when using astop
filter. Valid values are:-
none
(Default) -
No documents are returned if the
analyzer
removes all tokens. -
all
-
Returns all documents, similar to a
match_all
query.
See Zero terms query for an example.
-
Notes
editShort request example
editYou can simplify the match query syntax by combining the <field>
and query
parameters. For example:
GET /_search { "query": { "match": { "message": "this is a test" } } }
How the match query works
editThe match
query is of type boolean
. It means that the text
provided is analyzed and the analysis process constructs a boolean query
from the provided text. The operator
parameter can be set to or
or and
to control the boolean clauses (defaults to or
). The minimum number of
optional should
clauses to match can be set using the
minimum_should_match
parameter.
Here is an example with the operator
parameter:
GET /_search { "query": { "match": { "message": { "query": "this is a test", "operator": "and" } } } }
The analyzer
can be set to control which analyzer will perform the
analysis process on the text. It defaults to the field explicit mapping
definition, or the default search analyzer.
The lenient
parameter can be set to true
to ignore exceptions caused by
data-type mismatches, such as trying to query a numeric field with a text
query string. Defaults to false
.
Fuzziness in the match query
editfuzziness
allows fuzzy matching based on the type of field being queried.
See Fuzziness for allowed settings.
The prefix_length
and
max_expansions
can be set in this case to control the fuzzy process.
If the fuzzy option is set the query will use top_terms_blended_freqs_${max_expansions}
as its rewrite
method the fuzzy_rewrite
parameter allows to control how the query will get
rewritten.
Fuzzy transpositions (ab
→ ba
) are allowed by default but can be disabled
by setting fuzzy_transpositions
to false
.
Fuzzy matching is not applied to terms with synonyms or in cases where the analysis process produces multiple tokens at the same position. Under the hood these terms are expanded to a special synonym query that blends term frequencies, which does not support fuzzy expansion.
GET /_search { "query": { "match": { "message": { "query": "this is a testt", "fuzziness": "AUTO" } } } }
Zero terms query
editIf the analyzer used removes all tokens in a query like a stop
filter
does, the default behavior is to match no documents at all. In order to
change that the zero_terms_query
option can be used, which accepts
none
(default) and all
which corresponds to a match_all
query.
GET /_search { "query": { "match": { "message": { "query": "to be or not to be", "operator": "and", "zero_terms_query": "all" } } } }
Cutoff frequency
editDeprecated in 7.3.0.
This option can be omitted as the Match can skip blocks of documents efficiently, without any configuration, provided that the total number of hits is not tracked.
The match query supports a cutoff_frequency
that allows
specifying an absolute or relative document frequency where high
frequency terms are moved into an optional subquery and are only scored
if one of the low frequency (below the cutoff) terms in the case of an
or
operator or all of the low frequency terms in the case of an and
operator match.
This query allows handling stopwords
dynamically at runtime, is domain
independent and doesn’t require a stopword file. It prevents scoring /
iterating high frequency terms and only takes the terms into account if a
more significant / lower frequency term matches a document. Yet, if all
of the query terms are above the given cutoff_frequency
the query is
automatically transformed into a pure conjunction (and
) query to
ensure fast execution.
The cutoff_frequency
can either be relative to the total number of
documents if in the range from 0 (inclusive) to 1 (exclusive) or absolute if greater or equal to
1.0
.
Here is an example showing a query composed of stopwords exclusively:
GET /_search { "query": { "match": { "message": { "query": "to be or not to be", "cutoff_frequency": 0.001 } } } }
The cutoff_frequency
option operates on a per-shard-level. This means
that when trying it out on test indexes with low document numbers you
should follow the advice in Relevance is broken.
Synonyms
editThe match
query supports multi-terms synonym expansion with the synonym_graph token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms.
For example, the following synonym: "ny, new york"
would produce:
(ny OR ("new york"))
It is also possible to match multi terms synonyms with conjunctions instead:
GET /_search { "query": { "match" : { "message": { "query" : "ny city", "auto_generate_synonyms_phrase_query" : false } } } }
The example above creates a boolean query:
(ny OR (new AND york)) city
that matches documents with the term ny
or the conjunction new AND york
.
By default the parameter auto_generate_synonyms_phrase_query
is set to true
.
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