- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.15
- 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
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- License settings
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- Node
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- Snapshot lifecycle management settings
- Transforms settings
- Thread pools
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- 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
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- 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
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- Classic
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- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
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- Keep types
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- 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
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- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
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- 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
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- Fail
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- Network direction
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- Registered domain
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- Set
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- Sort
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- URL decode
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- User agent
- Aliases
- Search your data
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Children
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- Filter
- Filters
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- Missing
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- Parent
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- Terms
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- Subtleties of bucketing range fields
- Metrics aggregations
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- Average bucket
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- Bucket count K-S test
- Bucket correlation
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- Cumulative cardinality
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- Derivative
- Extended stats bucket
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- Max bucket
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- Moving function
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- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
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- Bucket aggregations
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
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- Functions and Operators
- Comparison Operators
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- 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
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- cat count
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- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
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- Cluster state
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- Cross-cluster replication APIs
- Data stream APIs
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- Alias exists
- Aliases
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- Exists
- Field usage stats
- Flush
- Force merge
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- Get alias
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- Get index
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- Get index template (legacy)
- Get mapping
- Import dangling index
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- List dangling indices
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- Index lifecycle management APIs
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- Info API
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- Logstash APIs
- Machine learning anomaly detection APIs
- Add events to calendar
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create or update trained model aliases
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- Update data frame analytics jobs
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- Delete trained models
- Delete trained model aliases
- Evaluate data frame analytics
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- Get data frame analytics jobs stats
- Get trained models
- Get trained models stats
- Preview data frame analytics
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- Migration APIs
- Node lifecycle APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search APIs
- Searchable snapshots APIs
- Security APIs
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- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- 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
Flattened field type
editFlattened field type
editBy default, each subfield in an object is mapped and indexed separately. If the names or types of the subfields are not known in advance, then they are mapped dynamically.
The flattened
type provides an alternative approach, where the entire
object is mapped as a single field. Given an object, the flattened
mapping will parse out its leaf values and index them into one field as
keywords. The object’s contents can then be searched through simple queries
and aggregations.
This data type can be useful for indexing objects with a large or unknown number of unique keys. Only one field mapping is created for the whole JSON object, which can help prevent a mappings explosion from having too many distinct field mappings.
On the other hand, flattened object fields present a trade-off in terms of search functionality. Only basic queries are allowed, with no support for numeric range queries or highlighting. Further information on the limitations can be found in the Supported operations section.
The flattened
mapping type should not be used for indexing all
document content, as it treats all values as keywords and does not provide full
search functionality. The default approach, where each subfield has its own
entry in the mappings, works well in the majority of cases.
A flattened object field can be created as follows:
PUT bug_reports { "mappings": { "properties": { "title": { "type": "text" }, "labels": { "type": "flattened" } } } } POST bug_reports/_doc/1 { "title": "Results are not sorted correctly.", "labels": { "priority": "urgent", "release": ["v1.2.5", "v1.3.0"], "timestamp": { "created": 1541458026, "closed": 1541457010 } } }
During indexing, tokens are created for each leaf value in the JSON object. The values are indexed as string keywords, without analysis or special handling for numbers or dates.
Querying the top-level flattened
field searches all leaf values in the
object:
POST bug_reports/_search { "query": { "term": {"labels": "urgent"} } }
To query on a specific key in the flattened object, object dot notation is used:
POST bug_reports/_search { "query": { "term": {"labels.release": "v1.3.0"} } }
Supported operations
editBecause of the similarities in the way values are indexed, flattened
fields share much of the same mapping and search functionality as
keyword
fields.
Currently, flattened object fields can be used with the following query types:
-
term
,terms
, andterms_set
-
prefix
-
range
-
match
andmulti_match
-
query_string
andsimple_query_string
-
exists
When querying, it is not possible to refer to field keys using wildcards, as in
{ "term": {"labels.time*": 1541457010}}
. Note that all queries, including
range
, treat the values as string keywords. Highlighting is not supported on
flattened
fields.
It is possible to sort on a flattened object field, as well as perform simple
keyword-style aggregations such as terms
. As with queries, there is no
special support for numerics — all values in the JSON object are treated as
keywords. When sorting, this implies that values are compared
lexicographically.
Flattened object fields currently cannot be stored. It is not possible to
specify the store
parameter in the mapping.
Retrieving flattened fields
editField values and concrete subfields can be retrieved using the
fields parameter. content. Since the flattened
field maps an
entire object with potentially many subfields as a single field, the response contains
the unaltered structure from _source
.
Single subfields, however, can be fetched by specifying them explicitly in the request. This only works for concrete paths, but not using wildcards:
PUT my-index-000001 { "mappings": { "properties": { "flattened_field": { "type": "flattened" } } } } PUT my-index-000001/_doc/1?refresh=true { "flattened_field" : { "subfield" : "value" } } POST my-index-000001/_search { "fields": ["flattened_field.subfield"], "_source": false }
{ "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, "fields": { "flattened_field.subfield" : [ "value" ] } }] } }
You can also use a Painless script to retrieve
values from sub-fields of flattened fields. Instead of including
doc['<field_name>'].value
in your Painless script, use
doc['<field_name>.<sub-field_name>'].value
. For example, if you have a
flattened field called label
with a release
sub-field, your Painless script
would be doc['labels.release'].value
.
For example, let’s say your mapping contains two fields, one of which is of the
flattened
type:
PUT my-index-000001 { "mappings": { "properties": { "title": { "type": "text" }, "labels": { "type": "flattened" } } } }
Index a few documents containing your mapped fields. The labels
field has
three sub-fields:
POST /my-index-000001/_bulk?refresh {"index":{}} {"title":"Something really urgent","labels":{"priority":"urgent","release":["v1.2.5","v1.3.0"],"timestamp":{"created":1541458026,"closed":1541457010}}} {"index":{}} {"title":"Somewhat less urgent","labels":{"priority":"high","release":["v1.3.0"],"timestamp":{"created":1541458026,"closed":1541457010}}} {"index":{}} {"title":"Not urgent","labels":{"priority":"low","release":["v1.2.0"],"timestamp":{"created":1541458026,"closed":1541457010}}}
Because labels
is a flattened
field type, the entire object is mapped as a
single field. To retrieve values from this sub-field in a Painless script, use
the doc['<field_name>.<sub-field_name>'].value
format.
"script": { "source": """ if (doc['labels.release'].value.equals('v1.3.0')) {emit(doc['labels.release'].value)} else{emit('Version mismatch')} """
Parameters for flattened object fields
editThe following mapping parameters are accepted:
Mapping field-level query time boosting. Accepts a floating point number,
defaults to |
|
|
The maximum allowed depth of the flattened object field, in terms of nested
inner objects. If a flattened object field exceeds this limit, then an
error will be thrown. Defaults to |
Should the field be stored on disk in a column-stride fashion, so that it
can later be used for sorting, aggregations, or scripting? Accepts |
|
Should global ordinals be loaded eagerly on refresh? Accepts |
|
Leaf values longer than this limit will not be indexed. By default, there is no limit and all values will be indexed. Note that this limit applies to the leaf values within the flattened object field, and not the length of the entire field. |
|
Determines if the field should be searchable. Accepts |
|
What information should be stored in the index for scoring purposes.
Defaults to |
|
A string value which is substituted for any explicit |
|
Which scoring algorithm or similarity should be used. Defaults
to |
|
|
Whether full text queries should split the input on
whitespace when building a query for this field. Accepts |