- Elasticsearch Guide: other versions:
- Getting Started
- 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
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Installing X-Pack
- Set up X-Pack
- Configuring X-Pack Java Clients
- X-Pack Settings
- 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
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Intervals
- Children Aggregation
- Composite Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested 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
- 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
- Standard Token Filter
- 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
- 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
- Drop Processor
- Dot Expander Processor
- Fail Processor
- Foreach Processor
- Grok Processor
- Gsub 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
- SQL Access
- Monitor a cluster
- Rolling up historical data
- Set up a cluster for high availability
- 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
- FIPS 140-2
- Security settings
- Security files
- Auditing settings
- How security works
- User authentication
- Built-in users
- Internal users
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user 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
- User authorization
- Auditing security events
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, tribe, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Can’t log in after upgrading to 6.5.4
- 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
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Licensing APIs
- Migration APIs
- Machine learning APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create calendar
- Create datafeeds
- Create filter
- Create jobs
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- 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
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filter
- Update jobs
- Update model snapshots
- Rollup APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- 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 application privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate token
- SSL certificate
- Watcher APIs
- Definitions
- Release Highlights
- Breaking changes
- Release Notes
- Elasticsearch version 6.5.4
- Elasticsearch version 6.5.3
- Elasticsearch version 6.5.2
- Elasticsearch version 6.5.1
- Elasticsearch version 6.5.0
- Elasticsearch version 6.4.3
- Elasticsearch version 6.4.2
- Elasticsearch version 6.4.1
- Elasticsearch version 6.4.0
- Elasticsearch version 6.3.2
- Elasticsearch version 6.3.1
- Elasticsearch version 6.3.0
- Elasticsearch version 6.2.4
- Elasticsearch version 6.2.3
- Elasticsearch version 6.2.2
- Elasticsearch version 6.2.1
- Elasticsearch version 6.2.0
- Elasticsearch version 6.1.4
- Elasticsearch version 6.1.3
- Elasticsearch version 6.1.2
- Elasticsearch version 6.1.1
- Elasticsearch version 6.1.0
- Elasticsearch version 6.0.1
- Elasticsearch version 6.0.0
- Elasticsearch version 6.0.0-rc2
- Elasticsearch version 6.0.0-rc1
- Elasticsearch version 6.0.0-beta2
- Elasticsearch version 6.0.0-beta1
- Elasticsearch version 6.0.0-alpha2
- Elasticsearch version 6.0.0-alpha1
- Elasticsearch version 6.0.0-alpha1 (Changes previously released in 5.x)
Dynamic templates
editDynamic templates
editDynamic templates allow you to define custom mappings that can be applied to dynamically added fields based on:
-
the datatype detected by Elasticsearch, with
match_mapping_type
. -
the name of the field, with
match
andunmatch
ormatch_pattern
. -
the full dotted path to the field, with
path_match
andpath_unmatch
.
The original field name {name}
and the detected datatype
{dynamic_type
} template variables can be used in
the mapping specification as placeholders.
Dynamic field mappings are only added when a field contains a
concrete value — not null
or an empty array. This means that if the
null_value
option is used in a dynamic_template
, it will only be applied
after the first document with a concrete value for the field has been
indexed.
Dynamic templates are specified as an array of named objects:
"dynamic_templates": [ { "my_template_name": { ... match conditions ... "mapping": { ... } } }, ... ]
The template name can be any string value. |
|
The match conditions can include any of : |
|
The mapping that the matched field should use. |
Templates are processed in order — the first matching template wins. When putting new dynamic templates through the put mapping API, all existing templates are overwritten. This allows for dynamic templates to be reordered or deleted after they were initially added.
match_mapping_type
editThe match_mapping_type
is the datatype detected by the json parser. Since
JSON doesn’t allow to distinguish a long
from an integer
or a double
from
a float
, it will always choose the wider datatype, ie. long
for integers
and double
for floating-point numbers.
The following datatypes may be automatically detected:
-
boolean
whentrue
orfalse
are encountered. -
date
when date detection is enabled and a string is found that matches any of the configured date formats. -
double
for numbers with a decimal part. -
long
for numbers without a decimal part. -
object
for objects, also called hashes. -
string
for character strings.
*
may also be used in order to match all datatypes.
For example, if we wanted to map all integer fields as integer
instead of
long
, and all string
fields as both text
and keyword
, we
could use the following template:
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "integers": { "match_mapping_type": "long", "mapping": { "type": "integer" } } }, { "strings": { "match_mapping_type": "string", "mapping": { "type": "text", "fields": { "raw": { "type": "keyword", "ignore_above": 256 } } } } } ] } } } PUT my_index/_doc/1 { "my_integer": 5, "my_string": "Some string" }
The |
|
The |
match
and unmatch
editThe match
parameter uses a pattern to match on the field name, while
unmatch
uses a pattern to exclude fields matched by match
.
The following example matches all string
fields whose name starts with
long_
(except for those which end with _text
) and maps them as long
fields:
match_pattern
editThe match_pattern
parameter adjusts the behavior of the match
parameter
such that it supports full Java regular expression matching on the field name
instead of simple wildcards, for instance:
"match_pattern": "regex", "match": "^profit_\d+$"
path_match
and path_unmatch
editThe path_match
and path_unmatch
parameters work in the same way as match
and unmatch
, but operate on the full dotted path to the field, not just the
final name, e.g. some_object.*.some_field
.
This example copies the values of any fields in the name
object to the
top-level full_name
field, except for the middle
field:
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "full_name": { "path_match": "name.*", "path_unmatch": "*.middle", "mapping": { "type": "text", "copy_to": "full_name" } } } ] } } } PUT my_index/_doc/1 { "name": { "first": "Alice", "middle": "Mary", "last": "White" } }
{name}
and {dynamic_type}
editThe {name}
and {dynamic_type}
placeholders are replaced in the mapping
with the field name and detected dynamic type. The following example sets all
string fields to use an analyzer
with the same name as the
field, and disables doc_values
for all non-string fields:
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "named_analyzers": { "match_mapping_type": "string", "match": "*", "mapping": { "type": "text", "analyzer": "{name}" } } }, { "no_doc_values": { "match_mapping_type":"*", "mapping": { "type": "{dynamic_type}", "doc_values": false } } } ] } } } PUT my_index/_doc/1 { "english": "Some English text", "count": 5 }
Template examples
editHere are some examples of potentially useful dynamic templates:
Structured search
editBy default Elasticsearch will map string fields as a text
field with a sub
keyword
field. However if you are only indexing structured content and not
interested in full text search, you can make Elasticsearch map your fields
only as `keyword`s. Note that this means that in order to search those fields,
you will have to search on the exact same value that was indexed.
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "strings_as_keywords": { "match_mapping_type": "string", "mapping": { "type": "keyword" } } } ] } } }
text
-only mappings for strings
editOn the contrary to the previous example, if the only thing that you care about on your string fields is full-text search, and if you don’t plan on running aggregations, sorting or exact search on your string fields, you could tell Elasticsearch to map it only as a text field (which was the default behaviour before 5.0):
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "strings_as_text": { "match_mapping_type": "string", "mapping": { "type": "text" } } } ] } } }
Disabled norms
editNorms are index-time scoring factors. If you do not care about scoring, which would be the case for instance if you never sort documents by score, you could disable the storage of these scoring factors in the index and save some space.
PUT my_index { "mappings": { "_doc": { "dynamic_templates": [ { "strings_as_keywords": { "match_mapping_type": "string", "mapping": { "type": "text", "norms": false, "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } } } } ] } } }
The sub keyword
field appears in this template to be consistent with the
default rules of dynamic mappings. Of course if you do not need them because
you don’t need to perform exact search or aggregate on this field, you could
remove it as described in the previous section.
Time-series
editWhen doing time series analysis with Elasticsearch, it is common to have many numeric fields that you will often aggregate on but never filter on. In such a case, you could disable indexing on those fields to save disk space and also maybe gain some indexing speed:
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