- 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
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- IP Range Aggregation
- Missing Aggregation
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- Parent Aggregation
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- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
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- 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
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- Indices Exists
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- Put Mapping
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- cat APIs
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- Analysis
- Anatomy of an analyzer
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- Analyzers
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- Path Hierarchy Tokenizer Examples
- Token Filters
- ASCII Folding Token Filter
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- Parsing synonym files
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- Compound Word Token Filters
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- Exclude mode settings example
- Classic Token Filter
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- 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
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- 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
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- LIKE and RLIKE Operators
- Aggregate Functions
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- 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
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- Migration APIs
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- Authenticate
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- 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
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- 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
Script score query
editScript score query
editUses a script to provide a custom score for returned documents.
The script_score
query is useful if, for example, a scoring function is expensive and you only need to calculate the score of a filtered set of documents.
Example request
editThe following script_score
query assigns each returned document a score equal to the likes
field value divided by 10
.
GET /_search { "query" : { "script_score" : { "query" : { "match": { "message": "elasticsearch" } }, "script" : { "source" : "doc['likes'].value / 10 " } } } }
Top-level parameters for script_score
edit-
query
- (Required, query object) Query used to return documents.
-
script
-
(Required, script object) Script used to compute the score of documents returned by the
query
.Final relevance scores from the
script_score
query cannot be negative. To support certain search optimizations, Lucene requires scores be positive or0
. -
min_score
- (Optional, float) Documents with a relevance score lower than this floating point number are excluded from the search results.
Notes
editUse relevance scores in a script
editWithin a script, you can
access
the _score
variable which represents the current relevance score of a
document.
Predefined functions
editYou can use any of the available painless
functions in your script
. You can also use the following predefined functions
to customize scoring:
We suggest using these predefined functions instead of writing your own. These functions take advantage of efficiencies from Elasticsearch' internal mechanisms.
Saturation
editsaturation(value,k) = value/(k + value)
"script" : { "source" : "saturation(doc['likes'].value, 1)" }
Sigmoid
editsigmoid(value, k, a) = value^a/ (k^a + value^a)
"script" : { "source" : "sigmoid(doc['likes'].value, 2, 1)" }
Random score function
editrandom_score
function generates scores that are uniformly distributed
from 0 up to but not including 1.
randomScore
function has the following syntax:
randomScore(<seed>, <fieldName>)
.
It has a required parameter - seed
as an integer value,
and an optional parameter - fieldName
as a string value.
"script" : { "source" : "randomScore(100, '_seq_no')" }
If the fieldName
parameter is omitted, the internal Lucene
document ids will be used as a source of randomness. This is very efficient,
but unfortunately not reproducible since documents might be renumbered
by merges.
"script" : { "source" : "randomScore(100)" }
Note that documents that are within the same shard and have the
same value for field will get the same score, so it is usually desirable
to use a field that has unique values for all documents across a shard.
A good default choice might be to use the _seq_no
field, whose only drawback is that scores will change if the document is
updated since update operations also update the value of the _seq_no
field.
Decay functions for numeric fields
editYou can read more about decay functions here.
-
double decayNumericLinear(double origin, double scale, double offset, double decay, double docValue)
-
double decayNumericExp(double origin, double scale, double offset, double decay, double docValue)
-
double decayNumericGauss(double origin, double scale, double offset, double decay, double docValue)
Decay functions for geo fields
edit-
double decayGeoLinear(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)
-
double decayGeoExp(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)
-
double decayGeoGauss(String originStr, String scaleStr, String offsetStr, double decay, GeoPoint docValue)
"script" : { "source" : "decayGeoExp(params.origin, params.scale, params.offset, params.decay, doc['location'].value)", "params": { "origin": "40, -70.12", "scale": "200km", "offset": "0km", "decay" : 0.2 } }
Decay functions for date fields
edit-
double decayDateLinear(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)
-
double decayDateExp(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)
-
double decayDateGauss(String originStr, String scaleStr, String offsetStr, double decay, JodaCompatibleZonedDateTime docValueDate)
"script" : { "source" : "decayDateGauss(params.origin, params.scale, params.offset, params.decay, doc['date'].value)", "params": { "origin": "2008-01-01T01:00:00Z", "scale": "1h", "offset" : "0", "decay" : 0.5 } }
Decay functions on dates are limited to dates in the default format
and default time zone. Also calculations with now
are not supported.
Faster alternatives
editThe script_score
query calculates the score for
every matching document, or hit. There are faster alternative query types that
can efficiently skip non-competitive hits:
-
If you want to boost documents on some static fields, use the
rank_feature
query. -
If you want to boost documents closer to a date or geographic point, use the
distance_feature
query.
Transition from the function score query
editWe are deprecating the function_score
query. We recommend using the script_score
query instead.
You can implement the following functions from the function_score
query using
the script_score
query:
script_score
editWhat you used in script_score
of the Function Score query, you
can copy into the Script Score query. No changes here.
weight
editweight
function can be implemented in the Script Score query through
the following script:
"script" : { "source" : "params.weight * _score", "params": { "weight": 2 } }
random_score
editUse randomScore
function
as described in random score function.
field_value_factor
editfield_value_factor
function can be easily implemented through script:
"script" : { "source" : "Math.log10(doc['field'].value * params.factor)", params" : { "factor" : 5 } }
For checking if a document has a missing value, you can use
doc['field'].size() == 0
. For example, this script will use
a value 1
if a document doesn’t have a field field
:
"script" : { "source" : "Math.log10((doc['field'].size() == 0 ? 1 : doc['field'].value()) * params.factor)", params" : { "factor" : 5 } }
This table lists how field_value_factor
modifiers can be implemented
through a script:
Modifier | Implementation in Script Score |
---|---|
|
- |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
decay
functions
editThe script_score
query has equivalent decay functions
that can be used in script.
On this page
- Example request
- Top-level parameters for
script_score
- Notes
- Use relevance scores in a script
- Predefined functions
- Saturation
- Sigmoid
- Random score function
- Decay functions for numeric fields
- Decay functions for geo fields
- Decay functions for date fields
- Faster alternatives
- Transition from the function score query
script_score
weight
random_score
field_value_factor
decay
functions