- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.17
- Quick start
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging
- Machine learning settings
- Monitoring settings
- Node
- Networking
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Advanced 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
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Set up X-Pack
- Configuring X-Pack Java Clients
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Registered domain
- Remove
- Rename
- Script
- Set
- Set security user
- Sort
- Split
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Aliases
- Search your data
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Geo-distance
- Geohash grid
- Geotile grid
- Global
- Histogram
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving average
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- EQL
- SQL
- 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
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Customize built-in ILM policies
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Configuring security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- 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
- Built-in roles
- Defining roles
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- 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
- Watcher
- Command line tools
- How to
- REST APIs
- API conventions
- Autoscaling APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Nodes reload secure settings
- Nodes stats
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Freeze index
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Find file structure
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Migration APIs
- Node lifecycle APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get service accounts
- Get service account credentials
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.17.27
- Elasticsearch version 7.17.26
- Elasticsearch version 7.17.25
- Elasticsearch version 7.17.24
- Elasticsearch version 7.17.23
- Elasticsearch version 7.17.22
- Elasticsearch version 7.17.21
- Elasticsearch version 7.17.20
- Elasticsearch version 7.17.19
- Elasticsearch version 7.17.18
- Elasticsearch version 7.17.17
- Elasticsearch version 7.17.16
- Elasticsearch version 7.17.15
- Elasticsearch version 7.17.14
- Elasticsearch version 7.17.13
- Elasticsearch version 7.17.12
- Elasticsearch version 7.17.11
- Elasticsearch version 7.17.10
- Elasticsearch version 7.17.9
- Elasticsearch version 7.17.8
- Elasticsearch version 7.17.7
- Elasticsearch version 7.17.6
- Elasticsearch version 7.17.5
- Elasticsearch version 7.17.4
- Elasticsearch version 7.17.3
- Elasticsearch version 7.17.2
- Elasticsearch version 7.17.1
- Elasticsearch version 7.17.0
- Elasticsearch version 7.16.3
- Elasticsearch version 7.16.2
- Elasticsearch version 7.16.1
- Elasticsearch version 7.16.0
- Elasticsearch version 7.15.2
- Elasticsearch version 7.15.1
- Elasticsearch version 7.15.0
- Elasticsearch version 7.14.2
- Elasticsearch version 7.14.1
- Elasticsearch version 7.14.0
- Elasticsearch version 7.13.4
- Elasticsearch version 7.13.3
- Elasticsearch version 7.13.2
- Elasticsearch version 7.13.1
- Elasticsearch version 7.13.0
- Elasticsearch version 7.12.1
- Elasticsearch version 7.12.0
- Elasticsearch version 7.11.2
- Elasticsearch version 7.11.1
- Elasticsearch version 7.11.0
- 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
Retrieve selected fields from a search
editRetrieve selected fields from a search
editBy default, each hit in the search response includes the document
_source
, which is the entire JSON object that was
provided when indexing the document. There are two recommended methods to
retrieve selected fields from a search query:
-
Use the
fields
option to extract the values of fields present in the index mapping -
Use the
_source
option if you need to access the original data that was passed at index time
You can use both of these methods, though the fields
option is preferred
because it consults both the document data and index mappings. In some
instances, you might want to use other methods of
retrieving data.
The fields
option
editTo retrieve specific fields in the search response, use the fields
parameter.
Because it consults the index mappings, the fields
parameter provides several
advantages over referencing the _source
directly. Specifically, the fields
parameter:
- Returns each value in a standardized way that matches its mapping type
- Accepts multi-fields and field aliases
- Formats dates and spatial data types
- Retrieves runtime field values
- Returns fields calculated by a script at index time
Other mapping options are also respected, including
ignore_above
, ignore_malformed
, and
null_value
.
The fields
option returns values in the way that matches how Elasticsearch indexes
them. For standard fields, this means that the fields
option looks in
_source
to find the values, then parses and formats them using the mappings.
Retrieve specific fields
editThe following search request uses the fields
parameter to retrieve values
for the user.id
field, all fields starting with http.response.
, and the
@timestamp
field.
Using object notation, you can pass a format
argument to
customize the format of returned date or geospatial values.
POST my-index-000001/_search { "query": { "match": { "user.id": "kimchy" } }, "fields": [ "user.id", "http.response.*", { "field": "@timestamp", "format": "epoch_millis" } ], "_source": false }
Both full field names and wildcard patterns are accepted. |
|
Use the |
By default, document metadata fields like _id
or _index
are not
returned when the requested fields
option uses wildcard patterns like *
.
However, when explicitly requested using the field name, the _id
, _routing
,
_ignored
, _index
and _version
metadata fields can be retrieved.
Response always returns an array
editThe fields
response always returns an array of values for each field,
even when there is a single value in the _source
. This is because Elasticsearch has
no dedicated array type, and any field could contain multiple values. The
fields
parameter also does not guarantee that array values are returned in
a specific order. See the mapping documentation on arrays for more
background.
The response includes values as a flat list in the fields
section for each
hit. Because the fields
parameter doesn’t fetch entire objects, only leaf
fields are returned.
{ "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "my-index-000001", "_id" : "0", "_score" : 1.0, "_type" : "_doc", "fields" : { "user.id" : [ "kimchy" ], "@timestamp" : [ "4098435132000" ], "http.response.bytes": [ 1070000 ], "http.response.status_code": [ 200 ] } } ] } }
Retrieve nested fields
editDetails
The fields
response for nested
fields is slightly different from that
of regular object fields. While leaf values inside regular object
fields are
returned as a flat list, values inside nested
fields are grouped to maintain the
independence of each object inside the original nested array.
For each entry inside a nested field array, values are again returned as a flat list
unless there are other nested
fields inside the parent nested object, in which case
the same procedure is repeated again for the deeper nested fields.
Given the following mapping where user
is a nested field, after indexing
the following document and retrieving all fields under the user
field:
PUT my-index-000001 { "mappings": { "properties": { "group" : { "type" : "keyword" }, "user": { "type": "nested", "properties": { "first" : { "type" : "keyword" }, "last" : { "type" : "keyword" } } } } } } PUT my-index-000001/_doc/1?refresh=true { "group" : "fans", "user" : [ { "first" : "John", "last" : "Smith" }, { "first" : "Alice", "last" : "White" } ] } POST my-index-000001/_search { "fields": ["*"], "_source": false }
The response will group first
and last
name instead of
returning them as a flat list.
{ "took": 2, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 1, "relation": "eq" }, "max_score": 1.0, "hits": [{ "_index": "my-index-000001", "_id": "1", "_score": 1.0, "_type": "_doc", "fields": { "group" : ["fans"], "user": [{ "first": ["John"], "last": ["Smith"] }, { "first": ["Alice"], "last": ["White"] } ] } }] } }
Nested fields will be grouped by their nested paths, no matter the pattern used
to retrieve them. For example, if you query only for the user.first
field from
the previous example:
POST my-index-000001/_search { "fields": ["user.first"], "_source": false }
The response returns only the user’s first name, but still maintains the
structure of the nested user
array:
{ "took": 2, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 1, "relation": "eq" }, "max_score": 1.0, "hits": [{ "_index": "my-index-000001", "_id": "1", "_score": 1.0, "_type": "_doc", "fields": { "user": [{ "first": ["John"] }, { "first": ["Alice"] } ] } }] } }
However, when the fields
pattern targets the nested user
field directly, no
values will be returned because the pattern doesn’t match any leaf fields.
Retrieve unmapped fields
editDetails
By default, the fields
parameter returns only values of mapped fields.
However, Elasticsearch allows storing fields in _source
that are unmapped, such as
setting dynamic field mapping to false
or by using
an object field with enabled: false
. These options disable parsing and
indexing of the object content.
To retrieve unmapped fields in an object from _source
, use the
include_unmapped
option in the fields
section:
PUT my-index-000001 { "mappings": { "enabled": false } } PUT my-index-000001/_doc/1?refresh=true { "user_id": "kimchy", "session_data": { "object": { "some_field": "some_value" } } } POST my-index-000001/_search { "fields": [ "user_id", { "field": "session_data.object.*", "include_unmapped" : true } ], "_source": false }
The response will contain field results under the session_data.object.*
path,
even if the fields are unmapped. The user_id
field is also unmapped, but it
won’t be included in the response because include_unmapped
isn’t set to
true
for that field pattern.
{ "took" : 2, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "my-index-000001", "_id" : "1", "_score" : 1.0, "_type" : "_doc", "fields" : { "session_data.object.some_field": [ "some_value" ] } } ] } }
Ignored field values
editDetails
The fields
section of the response only returns values that were valid when indexed.
If your search request asks for values from a field that ignored certain values
because they were malformed or too large these values are returned
separately in an ignored_field_values
section.
In this example we index a document that has a value which is ignored and not added to the index so is shown separately in search results:
PUT my-index-000001 { "mappings": { "properties": { "my-small" : { "type" : "keyword", "ignore_above": 2 }, "my-large" : { "type" : "keyword" } } } } PUT my-index-000001/_doc/1?refresh=true { "my-small": ["ok", "bad"], "my-large": "ok content" } POST my-index-000001/_search { "fields": ["my-*"], "_source": false }
This field has a size restriction |
|
This document field has a value that exceeds the size restriction so is ignored and not indexed |
The response will contain ignored field values under the ignored_field_values
path.
These values are retrieved from the document’s original JSON source and are raw so will
not be formatted or treated in any way, unlike the successfully indexed fields which are
returned in the fields
section.
{ "took" : 2, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "my-index-000001", "_type" : "_doc", "_id" : "1", "_score" : 1.0, "_ignored" : [ "my-small"], "fields" : { "my-large": [ "ok content" ], "my-small": [ "ok" ] }, "ignored_field_values" : { "my-small": [ "bad" ] } } ] } }
The _source
option
editYou can use the _source
parameter to select what fields of the source are
returned. This is called source filtering.
The following search API request sets the _source
request body parameter to
false
. The document source is not included in the response.
GET /_search { "_source": false, "query": { "match": { "user.id": "kimchy" } } }
To return only a subset of source fields, specify a wildcard (*
) pattern in
the _source
parameter. The following search API request returns the source for
only the obj
field and its properties.
GET /_search { "_source": "obj.*", "query": { "match": { "user.id": "kimchy" } } }
You can also specify an array of wildcard patterns in the _source
field. The
following search API request returns the source for only the obj1
and
obj2
fields and their properties.
GET /_search { "_source": [ "obj1.*", "obj2.*" ], "query": { "match": { "user.id": "kimchy" } } }
For finer control, you can specify an object containing arrays of includes
and
excludes
patterns in the _source
parameter.
If the includes
property is specified, only source fields that match one of
its patterns are returned. You can exclude fields from this subset using the
excludes
property.
If the includes
property is not specified, the entire document source is
returned, excluding any fields that match a pattern in the excludes
property.
The following search API request returns the source for only the obj1
and
obj2
fields and their properties, excluding any child description
fields.
GET /_search { "_source": { "includes": [ "obj1.*", "obj2.*" ], "excludes": [ "*.description" ] }, "query": { "term": { "user.id": "kimchy" } } }
Other methods of retrieving data
editA document’s _source
is stored as a single field in Lucene. This structure
means that the whole _source
object must be loaded and parsed even if you’re
only requesting part of it. To avoid this limitation, you can try other options
for loading fields:
-
Use the
docvalue_fields
parameter to get values for selected fields. This can be a good choice when returning a fairly small number of fields that support doc values, such as keywords and dates. -
Use the
stored_fields
parameter to get the values for specific stored fields (fields that use thestore
mapping option).
Elasticsearch always attempts to load values from _source
. This behavior has the same
implications of source filtering where Elasticsearch needs to load and parse the entire
_source
to retrieve just one field.
Doc value fields
editYou can use the docvalue_fields
parameter to return
doc values for one or more fields in the search response.
Doc values store the same values as the _source
but in an on-disk,
column-based structure that’s optimized for sorting and aggregations. Since each
field is stored separately, Elasticsearch only reads the field values that were requested
and can avoid loading the whole document _source
.
Doc values are stored for supported fields by default. However, doc values are
not supported for text
or
text_annotated
fields.
The following search request uses the docvalue_fields
parameter to retrieve
doc values for the user.id
field, all fields starting with http.response.
, and the
@timestamp
field:
GET my-index-000001/_search { "query": { "match": { "user.id": "kimchy" } }, "docvalue_fields": [ "user.id", "http.response.*", { "field": "date", "format": "epoch_millis" } ] }
Both full field names and wildcard patterns are accepted. |
|
Using object notation, you can pass a |
You cannot use the docvalue_fields
parameter to retrieve doc values for
nested objects. If you specify a nested object, the search returns an empty
array ([ ]
) for the field. To access nested fields, use the
inner_hits
parameter’s docvalue_fields
property.
Stored fields
editIt’s also possible to store an individual field’s values by using the
store
mapping option. You can use the
stored_fields
parameter to include these stored values in the search response.
The stored_fields
parameter is for fields that are explicitly marked as
stored in the mapping, which is off by default and generally not recommended.
Use source filtering instead to select
subsets of the original source document to be returned.
Allows to selectively load specific stored fields for each document represented by a search hit.
GET /_search { "stored_fields" : ["user", "postDate"], "query" : { "term" : { "user" : "kimchy" } } }
*
can be used to load all stored fields from the document.
An empty array will cause only the _id
and _type
for each hit to be
returned, for example:
GET /_search { "stored_fields" : [], "query" : { "term" : { "user" : "kimchy" } } }
If the requested fields are not stored (store
mapping set to false
), they will be ignored.
Stored field values fetched from the document itself are always returned as an array. On the contrary, metadata fields like _routing
are never returned as an array.
Also only leaf fields can be returned via the stored_fields
option. If an object field is specified, it will be ignored.
On its own, stored_fields
cannot be used to load fields in nested
objects — if a field contains a nested object in its path, then no data will
be returned for that stored field. To access nested fields, stored_fields
must be used within an inner_hits
block.
Disable stored fields
editTo disable the stored fields (and metadata fields) entirely use: _none_
:
GET /_search { "stored_fields": "_none_", "query" : { "term" : { "user" : "kimchy" } } }
Script fields
editYou can use the script_fields
parameter to retrieve a script
evaluation (based on different fields) for each hit. For example:
GET /_search { "query": { "match_all": {} }, "script_fields": { "test1": { "script": { "lang": "painless", "source": "doc['price'].value * 2" } }, "test2": { "script": { "lang": "painless", "source": "doc['price'].value * params.factor", "params": { "factor": 2.0 } } } } }
Script fields can work on fields that are not stored (price
in
the above case), and allow to return custom values to be returned (the
evaluated value of the script).
Script fields can also access the actual _source
document and
extract specific elements to be returned from it by using params['_source']
.
Here is an example:
GET /_search { "query": { "match_all": {} }, "script_fields": { "test1": { "script": "params['_source']['message']" } } }
Note the _source
keyword here to navigate the json-like model.
It’s important to understand the difference between
doc['my_field'].value
and params['_source']['my_field']
. The first,
using the doc keyword, will cause the terms for that field to be loaded to
memory (cached), which will result in faster execution, but more memory
consumption. Also, the doc[...]
notation only allows for simple valued
fields (you can’t return a json object from it) and makes sense only for
non-analyzed or single term based fields. However, using doc
is
still the recommended way to access values from the document, if at all
possible, because _source
must be loaded and parsed every time it’s used.
Using _source
is very slow.
On this page
ElasticON events are back!
Learn about the Elastic Search AI Platform from the experts at our live events.
Register now