- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- 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
- Median Absolute Deviation Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram 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
- Parent 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
- Scripting
- 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
- Parsing synonym files
- 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
- Dot Expander Processor
- Drop Processor
- Fail Processor
- Foreach Processor
- GeoIP 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
- User Agent processor
- Managing the index lifecycle
- SQL Access
- Monitor a cluster
- Rolling up historical data
- Frozen indices
- 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
- Token-based authentication services
- 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 6.8.23
- 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
- Glossary of terms
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management 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
- Delete expired data
- 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
- Set upgrade mode
- 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 API keys
- 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 API key information
- Get application privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- SSL certificate
- Unfreeze index
- Watcher APIs
- Definitions
- Release Highlights
- Breaking changes
- Release Notes
- Elasticsearch version 6.8.23
- Elasticsearch version 6.8.22
- Elasticsearch version 6.8.21
- Elasticsearch version 6.8.20
- Elasticsearch version 6.8.19
- Elasticsearch version 6.8.18
- Elasticsearch version 6.8.17
- Elasticsearch version 6.8.16
- Elasticsearch version 6.8.15
- Elasticsearch version 6.8.14
- Elasticsearch version 6.8.13
- Elasticsearch version 6.8.12
- Elasticsearch version 6.8.11
- Elasticsearch version 6.8.10
- Elasticsearch version 6.8.9
- Elasticsearch version 6.8.8
- Elasticsearch version 6.8.7
- Elasticsearch version 6.8.6
- Elasticsearch version 6.8.5
- Elasticsearch version 6.8.4
- Elasticsearch version 6.8.3
- Elasticsearch version 6.8.2
- Elasticsearch version 6.8.1
- Elasticsearch version 6.8.0
- Elasticsearch version 6.7.2
- Elasticsearch version 6.7.1
- Elasticsearch version 6.7.0
- Elasticsearch version 6.6.2
- Elasticsearch version 6.6.1
- Elasticsearch version 6.6.0
- 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)
NOTE: You are looking at documentation for an older release. For the latest information, see the current release documentation.
eager_global_ordinals
editeager_global_ordinals
editWhat are global ordinals?
editTo support aggregations and other operations that require looking up field
values on a per-document basis, Elasticsearch uses a data structure called
doc values. Term-based field types such as keyword
store
their doc values using an ordinal mapping for a more compact representation.
This mapping works by assigning each term an incremental integer or ordinal
based on its lexicographic order. The field’s doc values store only the
ordinals for each document instead of the original terms, with a separate
lookup structure to convert between ordinals and terms.
When used during aggregations, ordinals can greatly improve performance. As an
example, the terms
aggregation relies only on ordinals to collect documents
into buckets at the shard-level, then converts the ordinals back to their
original term values when combining results across shards.
Each index segment defines its own ordinal mapping, but aggregations collect data across an entire shard. So to be able to use ordinals for shard-level operations like aggregations, Elasticsearch creates a unified mapping called global ordinals. The global ordinal mapping is built on top of segment ordinals, and works by maintaining a map from global ordinal to the local ordinal for each segment.
Global ordinals are used if a search contains any of the following components:
-
Certain bucket aggregations on
keyword
,ip
, andflattened
fields. This includesterms
aggregations as mentioned above, as well ascomposite
,diversified_sampler
, andsignificant_terms
. -
Bucket aggregations on
text
fields that requirefielddata
to be enabled. -
Operations on parent and child documents from a
join
field, includinghas_child
queries andparent
aggregations.
The global ordinal mapping is an on-heap data structure. When measuring
memory usage, Elasticsearch counts the memory from global ordinals as
fielddata. Global ordinals memory is included in the
fielddata circuit breaker, and is returned
under fielddata
in the node stats response.
Loading global ordinals
editThe global ordinal mapping must be built before ordinals can be used during a search. By default, the mapping is loaded during search on the first time that global ordinals are needed. This is is the right approach if you are optimizing for indexing speed, but if search performance is a priority, it’s recommended to eagerly load global ordinals eagerly on fields that will be used in aggregations:
PUT my_index/_mapping/_doc { "properties": { "tags": { "type": "keyword", "eager_global_ordinals": true } } }
When eager_global_ordinals
is enabled, global ordinals are built when a shard
is refreshed — Elasticsearch always loads them before
exposing changes to the content of the index. This shifts the cost of building
global ordinals from search to index-time. Elasticsearch will also eagerly
build global ordinals when creating a new copy of a shard, as can occur when
increasing the number of replicas or relocating a shard onto a new node.
Eager loading can be disabled at any time by updating the eager_global_ordinals
setting:
PUT my_index/_mapping/_doc { "properties": { "tags": { "type": "keyword", "eager_global_ordinals": false } } }
On a frozen index, global ordinals are discarded
after each search and rebuilt again when they’re requested. This means that
eager_global_ordinals
should not be used on frozen indices: it would
cause global ordinals to be reloaded on every search. Instead, the index should
be force-merged to a single segment before being frozen. This avoids building
global ordinals altogether (more details can be found in the next section).
Avoiding global ordinal loading
editUsually, global ordinals do not present a large overhead in terms of their loading time and memory usage. However, loading global ordinals can be expensive on indices with large shards, or if the fields contain a large number of unique term values. Because global ordinals provide a unified mapping for all segments on the shard, they also need to be rebuilt entirely when a new segment becomes visible.
In some cases it is possible to avoid global ordinal loading altogether:
-
The
terms
,sampler
, andsignificant_terms
aggregations support a parameterexecution_hint
that helps control how buckets are collected. It defaults toglobal_ordinals
, but can be set tomap
to instead use the term values directly. - If a shard has been force-merged down to a single segment, then its segment ordinals are already global to the shard. In this case, Elasticsearch does not need to build a global ordinal mapping and there is no additional overhead from using global ordinals. Note that for performance reasons you should only force-merge an index to which you will never write to again.