- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- G1GC check
- All permission check
- Discovery configuration check
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- API conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Value Count Aggregation
- Median Absolute Deviation Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Children Aggregation
- Composite Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- GeoTile Grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Parent Aggregation
- Range Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Moving Function Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Split Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- cat APIs
- Cluster APIs
- Query DSL
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Standard Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- Whitespace Tokenizer
- UAX URL Email Tokenizer
- Classic Tokenizer
- Thai Tokenizer
- NGram Tokenizer
- Edge NGram Tokenizer
- Keyword Tokenizer
- Pattern Tokenizer
- Char Group Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Token Filters
- ASCII Folding Token Filter
- Flatten Graph Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Word Delimiter Graph Token Filter
- Multiplexer Token Filter
- Conditional Token Filter
- Predicate Token Filter Script
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Parsing synonym files
- Synonym Graph Token Filter
- Compound Word Token Filters
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Exclude mode settings example
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- MinHash Token Filter
- Remove Duplicates Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Bytes Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Pipeline Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Set Security User Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- URL Decode Processor
- User Agent processor
- Managing the index lifecycle
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Monitor a cluster
- Frozen indices
- Set up a cluster for high availability
- Roll up or transform your data
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management API
- Licensing APIs
- Machine learning APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Find file structure
- Flush jobs
- Forecast jobs
- Get calendars
- Get buckets
- Get overall buckets
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filter
- Update jobs
- Update model snapshots
- Migration APIs
- Rollup APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect Authenticate API
- OpenID Connect Logout API
- SSL certificate
- Transform APIs
- Unfreeze index
- Watcher APIs
- Definitions
- Secure a cluster
- Overview
- Configuring security
- Encrypting communications in Elasticsearch
- Encrypting communications in an Elasticsearch Docker Container
- Enabling cipher suites for stronger encryption
- Separating node-to-node and client traffic
- Configuring an Active Directory realm
- Configuring a file realm
- Configuring an LDAP realm
- Configuring a native realm
- Configuring a PKI realm
- Configuring a SAML realm
- Configuring a Kerberos realm
- Security files
- FIPS 140-2
- How security works
- User authentication
- Built-in users
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- 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
- Auditing security events
- 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
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Alerting on cluster and index events
- Command line tools
- How To
- Testing
- Glossary of terms
- Release highlights
- Breaking changes
- Release notes
- 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
Multi-match query
editMulti-match query
editThe multi_match
query builds on the match
query
to allow multi-field queries:
GET /_search { "query": { "multi_match" : { "query": "this is a test", "fields": [ "subject", "message" ] } } }
fields
and per-field boosting
editFields can be specified with wildcards, eg:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "fields": [ "title", "*_name" ] } } }
Individual fields can be boosted with the caret (^
) notation:
GET /_search { "query": { "multi_match" : { "query" : "this is a test", "fields" : [ "subject^3", "message" ] } } }
If no fields
are provided, the multi_match
query defaults to the index.query.default_field
index settings, which in turn defaults to *
. *
extracts all fields in the mapping that
are eligible to term queries and filters the metadata fields. All extracted fields are then
combined to build a query.
There is a limit on the number of fields that can be queried
at once. It is defined by the indices.query.bool.max_clause_count
Search Settings
which defaults to 1024.
Types of multi_match
query:
editThe way the multi_match
query is executed internally depends on the type
parameter, which can be set to:
|
(default) Finds documents which match any field, but
uses the |
|
Finds documents which match any field and combines
the |
|
Treats fields with the same |
|
Runs a |
|
Runs a |
|
Creates a |
best_fields
editThe best_fields
type is most useful when you are searching for multiple
words best found in the same field. For instance “brown fox” in a single
field is more meaningful than “brown” in one field and “fox” in the other.
The best_fields
type generates a match
query for
each field and wraps them in a dis_max
query, to
find the single best matching field. For instance, this query:
GET /_search { "query": { "multi_match" : { "query": "brown fox", "type": "best_fields", "fields": [ "subject", "message" ], "tie_breaker": 0.3 } } }
would be executed as:
GET /_search { "query": { "dis_max": { "queries": [ { "match": { "subject": "brown fox" }}, { "match": { "message": "brown fox" }} ], "tie_breaker": 0.3 } } }
Normally the best_fields
type uses the score of the single best matching
field, but if tie_breaker
is specified, then it calculates the score as
follows:
- the score from the best matching field
-
plus
tie_breaker * _score
for all other matching fields
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
fuzziness
, lenient
, prefix_length
, max_expansions
, rewrite
, zero_terms_query
,
cutoff_frequency
, auto_generate_synonyms_phrase_query
and fuzzy_transpositions
,
as explained in match query.
operator
and minimum_should_match
The best_fields
and most_fields
types are field-centric — they generate
a match
query per field. This means that the operator
and
minimum_should_match
parameters are applied to each field individually,
which is probably not what you want.
Take this query for example:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "best_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }
This query is executed as:
(+first_name:will +first_name:smith) | (+last_name:will +last_name:smith)
In other words, all terms must be present in a single field for a document to match.
See cross_fields
for a better solution.
most_fields
editThe most_fields
type is most useful when querying multiple fields that
contain the same text analyzed in different ways. For instance, the main
field may contain synonyms, stemming and terms without diacritics. A second
field may contain the original terms, and a third field might contain
shingles. By combining scores from all three fields we can match as many
documents as possible with the main field, but use the second and third fields
to push the most similar results to the top of the list.
This query:
GET /_search { "query": { "multi_match" : { "query": "quick brown fox", "type": "most_fields", "fields": [ "title", "title.original", "title.shingles" ] } } }
would be executed as:
GET /_search { "query": { "bool": { "should": [ { "match": { "title": "quick brown fox" }}, { "match": { "title.original": "quick brown fox" }}, { "match": { "title.shingles": "quick brown fox" }} ] } } }
The score from each match
clause is added together, then divided by the
number of match
clauses.
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
fuzziness
, lenient
, prefix_length
, max_expansions
, rewrite
, zero_terms_query
and cutoff_frequency
, as explained in match query, but
see operator
and minimum_should_match
.
phrase
and phrase_prefix
editThe phrase
and phrase_prefix
types behave just like best_fields
,
but they use a match_phrase
or match_phrase_prefix
query instead of a
match
query.
This query:
GET /_search { "query": { "multi_match" : { "query": "quick brown f", "type": "phrase_prefix", "fields": [ "subject", "message" ] } } }
would be executed as:
GET /_search { "query": { "dis_max": { "queries": [ { "match_phrase_prefix": { "subject": "quick brown f" }}, { "match_phrase_prefix": { "message": "quick brown f" }} ] } } }
Also, accepts analyzer
, boost
, lenient
and zero_terms_query
as explained
in Match, as well as slop
which is explained in Match phrase.
Type phrase_prefix
additionally accepts max_expansions
.
cross_fields
editThe cross_fields
type is particularly useful with structured documents where
multiple fields should match. For instance, when querying the first_name
and last_name
fields for “Will Smith”, the best match is likely to have
“Will” in one field and “Smith” in the other.
One way of dealing with these types of queries is simply to index the
first_name
and last_name
fields into a single full_name
field. Of
course, this can only be done at index time.
The cross_field
type tries to solve these problems at query time by taking a
term-centric approach. It first analyzes the query string into individual
terms, then looks for each term in any of the fields, as though they were one
big field.
A query like:
GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }
is executed as:
+(first_name:will last_name:will) +(first_name:smith last_name:smith)
In other words, all terms must be present in at least one field for a
document to match. (Compare this to
the logic used for best_fields
and most_fields
.)
That solves one of the two problems. The problem of differing term frequencies is solved by blending the term frequencies for all fields in order to even out the differences.
In practice, first_name:smith
will be treated as though it has the same
frequencies as last_name:smith
, plus one. This will make matches on
first_name
and last_name
have comparable scores, with a tiny advantage
for last_name
since it is the most likely field that contains smith
.
Note that cross_fields
is usually only useful on short string fields
that all have a boost
of 1
. Otherwise boosts, term freqs and length
normalization contribute to the score in such a way that the blending of term
statistics is not meaningful anymore.
If you run the above query through the Validate API, it returns this explanation:
+blended("will", fields: [first_name, last_name]) +blended("smith", fields: [first_name, last_name])
Also, accepts analyzer
, boost
, operator
, minimum_should_match
,
lenient
, zero_terms_query
and cutoff_frequency
, as explained in
match query.
cross_field
and analysis
editThe cross_field
type can only work in term-centric mode on fields that have
the same analyzer. Fields with the same analyzer are grouped together as in
the example above. If there are multiple groups, they are combined with a
bool
query.
For instance, if we have a first
and last
field which have
the same analyzer, plus a first.edge
and last.edge
which
both use an edge_ngram
analyzer, this query:
GET /_search { "query": { "multi_match" : { "query": "Jon", "type": "cross_fields", "fields": [ "first", "first.edge", "last", "last.edge" ] } } }
would be executed as:
blended("jon", fields: [first, last]) | ( blended("j", fields: [first.edge, last.edge]) blended("jo", fields: [first.edge, last.edge]) blended("jon", fields: [first.edge, last.edge]) )
In other words, first
and last
would be grouped together and
treated as a single field, and first.edge
and last.edge
would be
grouped together and treated as a single field.
Having multiple groups is fine, but when combined with operator
or
minimum_should_match
, it can suffer from the same problem
as most_fields
or best_fields
.
You can easily rewrite this query yourself as two separate cross_fields
queries combined with a bool
query, and apply the minimum_should_match
parameter to just one of them:
GET /_search { "query": { "bool": { "should": [ { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first", "last" ], "minimum_should_match": "50%" } }, { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "*.edge" ] } } ] } } }
You can force all fields into the same group by specifying the analyzer
parameter in the query.
GET /_search { "query": { "multi_match" : { "query": "Jon", "type": "cross_fields", "analyzer": "standard", "fields": [ "first", "last", "*.edge" ] } } }
which will be executed as:
blended("will", fields: [first, first.edge, last.edge, last]) blended("smith", fields: [first, first.edge, last.edge, last])
tie_breaker
editBy default, each per-term blended
query will use the best score returned by
any field in a group, then these scores are added together to give the final
score. The tie_breaker
parameter can change the default behaviour of the
per-term blended
queries. It accepts:
|
Take the single best score out of (eg) |
|
Add together the scores for (eg) |
|
Take the single best score plus |
bool_prefix
editThe bool_prefix
type’s scoring behaves like most_fields
, but using a
match_bool_prefix
query instead of a
match
query.
GET /_search { "query": { "multi_match" : { "query": "quick brown f", "type": "bool_prefix", "fields": [ "subject", "message" ] } } }
The analyzer
, boost
, operator
, minimum_should_match
, lenient
,
zero_terms_query
, and auto_generate_synonyms_phrase_query
parameters as
explained in match query are supported. The
fuzziness
, prefix_length
, max_expansions
, rewrite
, and
fuzzy_transpositions
parameters are supported for the terms that are used to
construct term queries, but do not have an effect on the prefix query
constructed from the final term.
The slop
and cutoff_frequency
parameters are not supported by this query
type.
On this page