- 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.
fielddata
editfielddata
editMost fields are indexed by default, which makes them searchable. Sorting, aggregations, and accessing field values in scripts, however, requires a different access pattern from search.
Search needs to answer the question "Which documents contain this term?", while sorting and aggregations need to answer a different question: "What is the value of this field for this document?".
Most fields can use index-time, on-disk doc_values
for this
data access pattern, but text
fields do not support doc_values
.
Instead, text
fields use a query-time in-memory data structure called
fielddata
. This data structure is built on demand the first time that a
field is used for aggregations, sorting, or in a script. It is built by
reading the entire inverted index for each segment from disk, inverting the
term ↔︎ document relationship, and storing the result in memory, in the JVM
heap.
Fielddata is disabled on text
fields by default
editFielddata can consume a lot of heap space, especially when loading high
cardinality text
fields. Once fielddata has been loaded into the heap, it
remains there for the lifetime of the segment. Also, loading fielddata is an
expensive process which can cause users to experience latency hits. This is
why fielddata is disabled by default.
If you try to sort, aggregate, or access values from a script on a text
field, you will see this exception:
Fielddata is disabled on text fields by default. Set
fielddata=true
on [your_field_name
] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory.
Before enabling fielddata
editBefore you enable fielddata, consider why you are using a text
field for
aggregations, sorting, or in a script. It usually doesn’t make sense to do
so.
A text field is analyzed before indexing so that a value like
New York
can be found by searching for new
or for york
. A terms
aggregation on this field will return a new
bucket and a york
bucket, when
you probably want a single bucket called New York
.
Instead, you should have a text
field for full text searches, and an
unanalyzed keyword
field with doc_values
enabled for aggregations, as follows:
Enabling fielddata on text
fields
editYou can enable fielddata on an existing text
field using the
PUT mapping API as follows:
PUT my_index/_mapping/my_type { "properties": { "my_field": { "type": "text", "fielddata": true } } }
The mapping that you specify for |
The fielddata.*
parameter must have the same settings for fields of the
same name in the same index. Its value can be updated on existing fields
using the PUT mapping API.
fielddata_frequency_filter
editFielddata filtering can be used to reduce the number of terms loaded into memory, and thus reduce memory usage. Terms can be filtered by frequency:
The frequency filter allows you to only load terms whose document frequency falls
between a min
and max
value, which can be expressed an absolute
number (when the number is bigger than 1.0) or as a percentage
(eg 0.01
is 1%
and 1.0
is 100%
). Frequency is calculated
per segment. Percentages are based on the number of docs which have a
value for the field, as opposed to all docs in the segment.
Small segments can be excluded completely by specifying the minimum
number of docs that the segment should contain with min_segment_size
:
PUT my_index { "mappings": { "my_type": { "properties": { "tag": { "type": "text", "fielddata": true, "fielddata_frequency_filter": { "min": 0.001, "max": 0.1, "min_segment_size": 500 } } } } } }
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