- Elasticsearch Guide: other versions:
- Getting Started
- Setup Elasticsearch
- Breaking changes
- Breaking changes in 5.1
- Breaking changes in 5.0
- Search and Query DSL changes
- Mapping changes
- Percolator changes
- Suggester changes
- Index APIs changes
- Document API changes
- Settings changes
- Allocation changes
- HTTP changes
- REST API changes
- CAT API changes
- Java API changes
- Packaging
- Plugin changes
- Filesystem related changes
- Path to data on disk
- Aggregation changes
- Script related 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
- Children 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
- 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
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Shadow replica indices
- 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
- Tokenizers
- Token Filters
- Standard Token Filter
- ASCII Folding 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
- 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
- Compound Word Token Filter
- 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
- Lowercase Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- Dot Expander Processor
- How To
- Testing
- Glossary of terms
- Release Notes
- 5.1.2 Release Notes
- 5.1.1 Release Notes
- 5.1.0 Release Notes
- 5.0.2 Release Notes
- 5.0.1 Release Notes
- 5.0.0 Combined Release Notes
- 5.0.0 GA Release Notes
- 5.0.0-rc1 Release Notes
- 5.0.0-beta1 Release Notes
- 5.0.0-alpha5 Release Notes
- 5.0.0-alpha4 Release Notes
- 5.0.0-alpha3 Release Notes
- 5.0.0-alpha2 Release Notes
- 5.0.0-alpha1 Release Notes
- 5.0.0-alpha1 Release Notes (Changes previously released in 2.x)
WARNING: Version 5.1 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.
Mapping
editMapping
editMapping is the process of defining how a document, and the fields it contains, are stored and indexed. For instance, use mappings to define:
- which string fields should be treated as full text fields.
- which fields contain numbers, dates, or geolocations.
-
whether the values of all fields in the document should be
indexed into the catch-all
_all
field. - the format of date values.
- custom rules to control the mapping for dynamically added fields.
Mapping Types
editEach index has one or more mapping types, which are used to divide the
documents in an index into logical groups. User documents might be stored in a
user
type, and blog posts in a blogpost
type.
Each mapping type has:
- Meta-fields
-
Meta-fields are used to customize how a document’s metadata associated is
treated. Examples of meta-fields include the document’s
_index
,_type
,_id
, and_source
fields. - Fields or properties
-
Each mapping type contains a list of fields or
properties
pertinent to that type. Auser
type might containtitle
,name
, andage
fields, while ablogpost
type might containtitle
,body
,user_id
andcreated
fields. Fields with the same name in different mapping types in the same index must have the same mapping.
Field datatypes
editEach field has a data type
which can be:
It is often useful to index the same field in different ways for different
purposes. For instance, a string
field could be indexed as
a text
field for full-text search, and as a keyword
field for
sorting or aggregations. Alternatively, you could index a string field with
the standard
analyzer, the
english
analyzer, and the
french
analyzer.
This is the purpose of multi-fields. Most datatypes support multi-fields
via the fields
parameter.
Settings to prevent mappings explosion
editThe following settings allow you to limit the number of field mappings that can be created manually or dynamically, in order to prevent bad documents from causing a mapping explosion:
-
index.mapping.total_fields.limit
-
The maximum number of fields in an index. The default value is
1000
. -
index.mapping.depth.limit
-
The maximum depth for a field, which is measured as the number of inner
objects. For instance, if all fields are defined at the root object level,
then the depth is
1
. If there is one object mapping, then the depth is2
, etc. The default is20
. -
index.mapping.nested_fields.limit
-
The maximum number of
nested
fields in an index, defaults to50
. Indexing 1 document with 100 nested fields actually indexes 101 documents as each nested document is indexed as a separate hidden document.
Dynamic mapping
editFields and mapping types do not need to be defined before being used. Thanks
to dynamic mapping, new mapping types and new field names will be added
automatically, just by indexing a document. New fields can be added both to
the top-level mapping type, and to inner object
and
nested
fields.
The dynamic mapping rules can be configured to customise the mapping that is used for new types and new fields.
Explicit mappings
editYou know more about your data than Elasticsearch can guess, so while dynamic mapping can be useful to get started, at some point you will want to specify your own explicit mappings.
You can create mapping types and field mappings when you create an index, and you can add mapping types and fields to an existing index with the PUT mapping API.
Updating existing mappings
editOther than where documented, existing type and field mappings cannot be updated. Changing the mapping would mean invalidating already indexed documents. Instead, you should create a new index with the correct mappings and reindex your data into that index.
Fields are shared across mapping types
editMapping types are used to group fields, but the fields in each mapping type are not independent of each other. Fields with:
- the same name
- in the same index
- in different mapping types
- map to the same field internally,
- and must have the same mapping.
If a title
field exists in both the user
and blogpost
mapping types, the
title
fields must have exactly the same mapping in each type. The only
exceptions to this rule are the copy_to
, dynamic
, enabled
,
ignore_above
, include_in_all
, and properties
parameters, which may
have different settings per field.
Usually, fields with the same name also contain the same type of data, so
having the same mapping is not a problem. When conflicts do arise, these can
be solved by choosing more descriptive names, such as user_title
and
blog_title
.
Example mapping
editA mapping for the example described above could be specified when creating the index, as follows:
PUT my_index { "mappings": { "user": { "_all": { "enabled": false }, "properties": { "title": { "type": "text" }, "name": { "type": "text" }, "age": { "type": "integer" } } }, "blogpost": { "_all": { "enabled": false }, "properties": { "title": { "type": "text" }, "body": { "type": "text" }, "user_id": { "type": "keyword" }, "created": { "type": "date", "format": "strict_date_optional_time||epoch_millis" } } } } }
Create an index called |
|
Add mapping types called |
|
Disable the |
|
Specify fields or properties in each mapping type. |
|
Specify the data |
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