- 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
- Maximum size virtual memory check
- Max file size 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
- Stopping Elasticsearch
- Upgrade Elasticsearch
- Set up X-Pack
- Breaking changes
- Breaking changes in 6.0
- Aggregations changes
- Analysis changes
- Cat API changes
- Clients changes
- Cluster changes
- Document API changes
- Indices changes
- Ingest changes
- Java API changes
- Mapping changes
- Packaging changes
- Percolator changes
- Plugins changes
- Reindex changes
- REST changes
- Scripting changes
- Search and Query DSL changes
- Settings changes
- Stats and info changes
- Breaking changes in 6.1
- Breaking changes in 6.0
- X-Pack Breaking Changes
- API Conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- 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
- 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
- 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
- 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
- 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
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- Minhash Token Filter
- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Fail Processor
- Foreach Processor
- Grok Processor
- Gsub Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- Dot Expander Processor
- URL Decode Processor
- Monitoring Elasticsearch
- X-Pack APIs
- Info API
- Explore API
- Machine Learning APIs
- Close Jobs
- Create Datafeeds
- Create Jobs
- Delete Datafeeds
- Delete Jobs
- Delete Model Snapshots
- Flush Jobs
- Forecast Jobs
- Get Buckets
- Get Overall Buckets
- Get Categories
- Get Datafeeds
- Get Datafeed Statistics
- Get Influencers
- Get Jobs
- Get Job Statistics
- Get Model Snapshots
- Get Records
- Open Jobs
- Post Data to Jobs
- Preview Datafeeds
- Revert Model Snapshots
- Start Datafeeds
- Stop Datafeeds
- Update Datafeeds
- Update Jobs
- Update Model Snapshots
- Security APIs
- Watcher APIs
- Migration APIs
- Deprecation Info APIs
- Definitions
- X-Pack Commands
- How To
- Testing
- Glossary of terms
- Release Notes
- 6.1.4 Release Notes
- 6.1.3 Release Notes
- 6.1.2 Release Notes
- 6.1.1 Release Notes
- 6.1.0 Release Notes
- 6.0.1 Release Notes
- 6.0.0 Release Notes
- 6.0.0-rc2 Release Notes
- 6.0.0-rc1 Release Notes
- 6.0.0-beta2 Release Notes
- 6.0.0-beta1 Release Notes
- 6.0.0-alpha2 Release Notes
- 6.0.0-alpha1 Release Notes
- 6.0.0-alpha1 Release Notes (Changes previously released in 5.x)
- X-Pack Release Notes
WARNING: Version 6.1 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
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. New templates can be appended to the end of the list with the PUT mapping API. If a new template has the same name as an existing template, it will replace the old version.
match_mapping_type
editThe match_mapping_type
matches on the datatype detected by
dynamic field mapping, in other words, the datatype
that Elasticsearch thinks the field should have. Only the following datatypes
can be automatically detected: boolean
, date
, double
, long
, object
,
string
. It also accepts *
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": { "my_type": { "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/my_type/1 { "my_integer": 5, "my_string": "Some string" }
The |
|
The |
match
and unmatch
editThe match
parameter uses a pattern to match on the fieldname, 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": { "my_type": { "dynamic_templates": [ { "full_name": { "path_match": "name.*", "path_unmatch": "*.middle", "mapping": { "type": "text", "copy_to": "full_name" } } } ] } } } PUT my_index/my_type/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": { "my_type": { "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/my_type/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": { "my_type": { "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": { "my_type": { "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": { "my_type": { "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:
On this page