- 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
- Discovery configuration check
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Full-cluster restart and rolling restart
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- 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
- GeoTile Grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Parent Aggregation
- Range Aggregation
- Rare Terms Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Subtleties of bucketing range fields
- 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
- Cumulative Cardinality 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
- Query DSL
- Search across clusters
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Char Group Tokenizer
- Classic Tokenizer
- Edge n-gram tokenizer
- Keyword Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- N-gram tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Pattern Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Standard Tokenizer
- Thai Tokenizer
- UAX URL Email Tokenizer
- Whitespace Tokenizer
- Token Filters
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten Graph Token Filter
- Hunspell Token Filter
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Length Token Filter
- Limit Token Count Token Filter
- Lowercase Token Filter
- MinHash Token Filter
- Multiplexer Token Filter
- N-gram
- Normalization Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Phonetic Token Filter
- Porter Stem Token Filter
- Predicate Token Filter Script
- Remove Duplicates Token Filter
- Reverse Token Filter
- Shingle Token Filter
- Snowball Token Filter
- Stemmer Token Filter
- Stemmer Override Token Filter
- Stop Token Filter
- Synonym Token Filter
- Synonym Graph Token Filter
- Trim Token Filter
- Truncate Token Filter
- Unique Token Filter
- Uppercase Token Filter
- Word Delimiter Token Filter
- Word Delimiter Graph Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Bytes Processor
- Circle 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
- HTML Strip 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
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- Getting started with snapshot lifecycle management
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Monitor a cluster
- Frozen indices
- Roll up or transform your data
- Set up a cluster for high availability
- Secure a cluster
- Overview
- Configuring security
- 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
- OpenID Connect 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
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Security privileges
- Document level security
- Field level security
- Granting privileges for indices and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enabling audit logging
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- 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
- REST APIs
- API conventions
- cat APIs
- Cluster APIs
- Cross-cluster replication APIs
- Document APIs
- Explore API
- Index APIs
- Add index alias
- Analyze
- Clear cache
- Clone index
- Close index
- Create index
- Delete index
- Delete index alias
- Delete index template
- Flush
- Force merge
- Freeze index
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists
- Open index
- Put index template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Index lifecycle management API
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- 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 buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get overall buckets
- 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
- Machine learning data frame analytics APIs
- Migration APIs
- Reload search analyzers
- Rollup APIs
- Search 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
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect authenticate API
- OpenID Connect logout API
- SSL certificate
- Snapshot lifecycle management API
- Transform APIs
- Watcher APIs
- Definitions
- Release highlights
- Breaking changes
- Release notes
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
Shard allocation awareness
editShard allocation awareness
editYou can use custom node attributes as awareness attributes to enable Elasticsearch to take your physical hardware configuration into account when allocating shards. If Elasticsearch knows which nodes are on the same physical server, in the same rack, or in the same zone, it can distribute the primary shard and its replica shards to minimise the risk of losing all shard copies in the event of a failure.
When shard allocation awareness is enabled with the
cluster.routing.allocation.awareness.attributes
setting, shards are only
allocated to nodes that have values set for the specified awareness
attributes. If you use multiple awareness attributes, Elasticsearch considers
each attribute separately when allocating shards.
The allocation awareness settings can be configured in
elasticsearch.yml
and updated dynamically with the
cluster-update-settings API.
Elasticsearch prefers using shards in the same location (with the same awareness attribute values) to process search or GET requests. Using local shards is usually faster than crossing rack or zone boundaries.
The number of attribute values determines how many shard copies are allocated in each location. If the number of nodes in each location is unbalanced and there are a lot of replicas, replica shards might be left unassigned.
Enabling shard allocation awareness
editTo enable shard allocation awareness:
-
Specify the location of each node with a custom node attribute. For example, if you want Elasticsearch to distribute shards across different racks, you might set an awareness attribute called
rack_id
in each node’selasticsearch.yml
config file.node.attr.rack_id: rack_one
You can also set custom attributes when you start a node:
`./bin/elasticsearch -Enode.attr.rack_id=rack_one`
-
Tell Elasticsearch to take one or more awareness attributes into account when allocating shards by setting
cluster.routing.allocation.awareness.attributes
in every master-eligible node’selasticsearch.yml
config file.You can also use the cluster-update-settings API to set or update a cluster’s awareness attributes.
With this example configuration, if you start two nodes with
node.attr.rack_id
set to rack_one
and create an index with 5 primary
shards and 1 replica of each primary, all primaries and replicas are
allocated across the two nodes.
If you add two nodes with node.attr.rack_id
set to rack_two
,
Elasticsearch moves shards to the new nodes, ensuring (if possible)
that no two copies of the same shard are in the same rack.
If rack_two
fails and takes down both its nodes, by default Elasticsearch
allocates the lost shard copies to nodes in rack_one
. To prevent multiple
copies of a particular shard from being allocated in the same location, you can
enable forced awareness.
Forced awareness
editBy default, if one location fails, Elasticsearch assigns all of the missing replica shards to the remaining locations. While you might have sufficient resources across all locations to host your primary and replica shards, a single location might be unable to host ALL of the shards.
To prevent a single location from being overloaded in the event of a failure,
you can set cluster.routing.allocation.awareness.force
so no replicas are
allocated until nodes are available in another location.
For example, if you have an awareness attribute called zone
and configure nodes
in zone1
and zone2
, you can use forced awareness to prevent Elasticsearch
from allocating replicas if only one zone is available:
cluster.routing.allocation.awareness.attributes: zone cluster.routing.allocation.awareness.force.zone.values: zone1,zone2
With this example configuration, if you start two nodes with node.attr.zone
set
to zone1
and create an index with 5 shards and 1 replica, Elasticsearch creates
the index and allocates the 5 primary shards but no replicas. Replicas are
only allocated once nodes with node.attr.zone
set to zone2
are available.