- Elasticsearch Guide: other versions:
- What’s new in 8.17
- Elasticsearch basics
- Quick starts
- Set up Elasticsearch
- Run Elasticsearch locally
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Data stream lifecycle settings
- Field data cache settings
- Local gateway settings
- Health Diagnostic settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- Inference settings
- License settings
- Machine learning settings
- Monitoring settings
- Node settings
- Networking
- Node query cache settings
- Path settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Set JVM options
- 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
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Dynamic mapping
- Explicit mapping
- Runtime fields
- Field data types
- Aggregate metric
- Alias
- Arrays
- Binary
- Boolean
- Completion
- Date
- Date nanoseconds
- Dense vector
- Flattened
- Geopoint
- Geoshape
- Histogram
- IP
- Join
- Keyword
- Nested
- Numeric
- Object
- Pass-through object
- Percolator
- Point
- Range
- Rank feature
- Rank features
- Rank Vectors
- Search-as-you-type
- Semantic text
- Shape
- Sparse vector
- Text
- Token count
- Unsigned long
- Version
- Metadata fields
- Mapping parameters
analyzer
coerce
copy_to
doc_values
dynamic
eager_global_ordinals
enabled
format
ignore_above
index.mapping.ignore_above
ignore_malformed
index
index_options
index_phrases
index_prefixes
meta
fields
normalizer
norms
null_value
position_increment_gap
properties
search_analyzer
similarity
store
subobjects
term_vector
- Mapping limit settings
- Removal of mapping types
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Attachment
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- Geo-grid
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- IP Location
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Redact
- Registered domain
- Remove
- Rename
- Reroute
- Script
- Set
- Set security user
- Sort
- Split
- Terminate
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Ingest pipelines in Search
- Aliases
- Search your data
- Re-ranking
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Frequent item sets
- Geo-distance
- Geohash grid
- Geohex grid
- Geotile grid
- Global
- Histogram
- IP prefix
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Random sampler
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Time series
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Change point
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- Connectors
- EQL
- ES|QL
- SQL
- 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
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Tutorial: Customize built-in policies
- Tutorial: Automate rollover
- Index management in Kibana
- Overview
- Concepts
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Data tiers
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- 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
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams 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
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- 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
- Watcher
- Cross-cluster replication
- Data store architecture
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Prevalidate node removal
- Nodes reload secure settings
- Nodes stats
- Cluster Info
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Create or update desired nodes
- Get desired nodes
- Delete desired nodes
- Get desired balance
- Reset desired balance
- Cross-cluster replication APIs
- Connector APIs
- Create connector
- Delete connector
- Get connector
- List connectors
- Update connector API key id
- Update connector configuration
- Update connector index name
- Update connector features
- Update connector filtering
- Update connector name and description
- Update connector pipeline
- Update connector scheduling
- Update connector service type
- Create connector sync job
- Cancel connector sync job
- Delete connector sync job
- Get connector sync job
- List connector sync jobs
- Check in a connector
- Update connector error
- Update connector last sync stats
- Update connector status
- Check in connector sync job
- Claim connector sync job
- Set connector sync job error
- Set connector sync job stats
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- ES|QL APIs
- Features APIs
- Fleet APIs
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Resolve cluster
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Inference APIs
- Delete inference API
- Get inference API
- Perform inference API
- Chat completion inference API
- Create inference API
- Stream inference API
- Update inference API
- AlibabaCloud AI Search inference service
- Amazon Bedrock inference service
- Anthropic inference service
- Azure AI studio inference service
- Azure OpenAI inference service
- Cohere inference service
- Elasticsearch inference service
- ELSER inference service
- Google AI Studio inference service
- Google Vertex AI inference service
- HuggingFace inference service
- JinaAI inference service
- Mistral inference service
- OpenAI inference service
- Watsonx inference service
- Info API
- Ingest APIs
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Clear trained model deployment cache
- Create or update trained model aliases
- Create part of a trained model
- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
- Delete trained models
- Get trained models
- Get trained models stats
- Infer trained model
- Start trained model deployment
- Stop trained model deployment
- Update trained model deployment
- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Root API
- Script APIs
- Search APIs
- Search Application APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Bulk create or update roles API
- Bulk delete roles API
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Query Role
- Get service accounts
- Get service account credentials
- Get Security settings
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- Query User
- Update API key
- Update Security settings
- Bulk update API keys
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Activate user profile
- Disable user profile
- Enable user profile
- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Text structure APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- Optimizations
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Troubleshooting broken repositories
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- Troubleshooting an unbalanced cluster
- Capture diagnostics
- Migration guide
- Release notes
- Elasticsearch version 8.18.0
- Elasticsearch version 8.17.0
- Elasticsearch version 8.16.2
- Elasticsearch version 8.16.1
- Elasticsearch version 8.16.0
- Elasticsearch version 8.15.5
- Elasticsearch version 8.15.4
- Elasticsearch version 8.15.3
- Elasticsearch version 8.15.2
- Elasticsearch version 8.15.1
- Elasticsearch version 8.15.0
- Elasticsearch version 8.14.3
- Elasticsearch version 8.14.2
- Elasticsearch version 8.14.1
- Elasticsearch version 8.14.0
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
- Elasticsearch version 8.11.0
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- Elasticsearch version 8.2.3
- Elasticsearch version 8.2.2
- Elasticsearch version 8.2.1
- Elasticsearch version 8.2.0
- Elasticsearch version 8.1.3
- Elasticsearch version 8.1.2
- Elasticsearch version 8.1.1
- Elasticsearch version 8.1.0
- Elasticsearch version 8.0.1
- Elasticsearch version 8.0.0
- Elasticsearch version 8.0.0-rc2
- Elasticsearch version 8.0.0-rc1
- Elasticsearch version 8.0.0-beta1
- Elasticsearch version 8.0.0-alpha2
- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Node roles
editNode roles
editAny time that you start an instance of Elasticsearch, you are starting a node. A collection of connected nodes is called a cluster. If you are running a single node of Elasticsearch, then you have a cluster of one node. All nodes know about all the other nodes in the cluster and can forward client requests to the appropriate node.
Each node performs one or more roles. Roles control the behavior of the node in the cluster.
Set node roles
editYou define a node’s roles by setting node.roles
in elasticsearch.yml
. If you set node.roles
, the node is only assigned the roles you specify. If you don’t set node.roles
, the node is assigned the following roles:
-
master
-
data
-
data_content
-
data_hot
-
data_warm
-
data_cold
-
data_frozen
-
ingest
-
ml
-
remote_cluster_client
-
transform
If you set node.roles
, ensure you specify every node role your cluster needs.
Every cluster requires the following node roles:
-
master
-
data_content
anddata_hot
OR
data
Some Elastic Stack features also require specific node roles:
-
Cross-cluster search and cross-cluster replication require the
remote_cluster_client
role. -
Stack Monitoring and ingest pipelines require the
ingest
role. -
Fleet, the Elastic Security app, and transforms require the
transform
role. Theremote_cluster_client
role is also required to use cross-cluster search with these features. -
Machine learning features, such as anomaly detection, require the
ml
role.
As the cluster grows and in particular if you have large machine learning jobs or continuous transforms, consider separating dedicated master-eligible nodes from dedicated data nodes, machine learning nodes, and transform nodes.
Change the role of a node
editEach data node maintains the following data on disk:
- the shard data for every shard allocated to that node,
- the index metadata corresponding with every shard allocated to that node, and
- the cluster-wide metadata, such as settings and index templates.
Similarly, each master-eligible node maintains the following data on disk:
- the index metadata for every index in the cluster, and
- the cluster-wide metadata, such as settings and index templates.
Each node checks the contents of its data path at startup. If it discovers
unexpected data then it will refuse to start. This is to avoid importing
unwanted dangling indices which can lead
to a red cluster health. To be more precise, nodes without the data
role will
refuse to start if they find any shard data on disk at startup, and nodes
without both the master
and data
roles will refuse to start if they have any
index metadata on disk at startup.
It is possible to change the roles of a node by adjusting its
elasticsearch.yml
file and restarting it. This is known as repurposing a
node. In order to satisfy the checks for unexpected data described above, you
must perform some extra steps to prepare a node for repurposing when starting
the node without the data
or master
roles.
-
If you want to repurpose a data node by removing the
data
role then you should first use an allocation filter to safely migrate all the shard data onto other nodes in the cluster. -
If you want to repurpose a node to have neither the
data
normaster
roles then it is simplest to start a brand-new node with an empty data path and the desired roles. You may find it safest to use an allocation filter to migrate the shard data elsewhere in the cluster first.
If it is not possible to follow these extra steps then you may be able to use
the elasticsearch-node repurpose
tool to delete any
excess data that prevents a node from starting.
Available node roles
editThe following is a list of the roles that a node can perform in a cluster. A node can have one or more roles.
-
Master-eligible node (
master
): A node that is eligible to be elected as the master node, which controls the cluster. -
Data node (
data
,data_content
,data_hot
,data_warm
,data_cold
,data_frozen
): A node that has one of several data roles. Data nodes hold data and perform data related operations such as CRUD, search, and aggregations. You might use multiple data roles in a cluster so you can implement data tiers. -
Ingest node (
ingest
): Ingest nodes are able to apply an ingest pipeline to a document in order to transform and enrich the document before indexing. With a heavy ingest load, it makes sense to use dedicated ingest nodes and to not include theingest
role from nodes that have themaster
ordata
roles. -
Remote-eligible node (
remote_cluster_client
): A node that is eligible to act as a remote client. -
Machine learning node (
ml
): A node that can run machine learning features. If you want to use machine learning features, there must be at least one machine learning node in your cluster. For more information, see Machine learning settings and Machine learning in the Elastic Stack. -
Transform node (
transform
): A node that can perform transforms. If you want to use transforms, there must be at least one transform node in your cluster. For more information, see Transforms settings and Transforming data.
Requests like search requests or bulk-indexing requests may involve data held on different data nodes. A search request, for example, is executed in two phases which are coordinated by the node which receives the client request — the coordinating node.
In the scatter phase, the coordinating node forwards the request to the data nodes which hold the data. Each data node executes the request locally and returns its results to the coordinating node. In the gather phase, the coordinating node reduces each data node’s results into a single global result set.
Every node is implicitly a coordinating node. This means that a node that has
an explicit empty list of roles in the node.roles
setting will only act as a coordinating
node, which cannot be disabled. As a result, such a node needs to have enough
memory and CPU in order to deal with the gather phase.
Master-eligible node
editThe master node is responsible for lightweight cluster-wide actions such as creating or deleting an index, tracking which nodes are part of the cluster, and deciding which shards to allocate to which nodes. It is important for cluster health to have a stable master node.
Any master-eligible node that is not a voting-only node may be elected to become the master node by the master election process.
Master nodes must have a path.data
directory whose contents
persist across restarts, just like data nodes, because this is where the
cluster metadata is stored. The cluster metadata describes how to read the data
stored on the data nodes, so if it is lost then the data stored on the data
nodes cannot be read.
Dedicated master-eligible node
editIt is important for the health of the cluster that the elected master node has
the resources it needs to fulfill its responsibilities. If the elected master
node is overloaded with other tasks then the cluster will not operate well. The
most reliable way to avoid overloading the master with other tasks is to
configure all the master-eligible nodes to be dedicated master-eligible nodes
which only have the master
role, allowing them to focus on managing the
cluster. Master-eligible nodes will still also behave as
coordinating nodes that route requests from clients to
the other nodes in the cluster, but you should not use dedicated master nodes
for this purpose.
A small or lightly-loaded cluster may operate well if its master-eligible nodes have other roles and responsibilities, but once your cluster comprises more than a handful of nodes it usually makes sense to use dedicated master-eligible nodes.
To create a dedicated master-eligible node, set:
node.roles: [ master ]
Voting-only master-eligible node
editA voting-only master-eligible node is a node that participates in master elections but which will not act as the cluster’s elected master node. In particular, a voting-only node can serve as a tiebreaker in elections.
It may seem confusing to use the term "master-eligible" to describe a voting-only node since such a node is not actually eligible to become the master at all. This terminology is an unfortunate consequence of history: master-eligible nodes are those nodes that participate in elections and perform certain tasks during cluster state publications, and voting-only nodes have the same responsibilities even if they can never become the elected master.
To configure a master-eligible node as a voting-only node, include master
and
voting_only
in the list of roles. For example to create a voting-only data
node:
node.roles: [ data, master, voting_only ]
Only nodes with the master
role can be marked as having the
voting_only
role.
High availability (HA) clusters require at least three master-eligible nodes, at least two of which are not voting-only nodes. Such a cluster will be able to elect a master node even if one of the nodes fails.
Voting-only master-eligible nodes may also fill other roles in your cluster. For instance, a node may be both a data node and a voting-only master-eligible node. A dedicated voting-only master-eligible nodes is a voting-only master-eligible node that fills no other roles in the cluster. To create a dedicated voting-only master-eligible node, set:
node.roles: [ master, voting_only ]
Since dedicated voting-only nodes never act as the cluster’s elected master, they may require less heap and a less powerful CPU than the true master nodes. However all master-eligible nodes, including voting-only nodes, are on the critical path for publishing cluster state updates. Cluster state updates are usually independent of performance-critical workloads such as indexing or searches, but they are involved in management activities such as index creation and rollover, mapping updates, and recovery after a failure. The performance characteristics of these activities are a function of the speed of the storage on each master-eligible node, as well as the reliability and latency of the network interconnections between the elected master node and the other nodes in the cluster. You must therefore ensure that the storage and networking available to the nodes in your cluster are good enough to meet your performance goals.
Data nodes
editData nodes hold the shards that contain the documents you have indexed. Data nodes handle data related operations like CRUD, search, and aggregations. These operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more data nodes if they are overloaded.
The main benefit of having dedicated data nodes is the separation of the master and data roles.
In a multi-tier deployment architecture, you use specialized data roles to
assign data nodes to specific tiers: data_content
,data_hot
, data_warm
,
data_cold
, or data_frozen
. A node can belong to multiple tiers.
If you want to include a node in all tiers, or if your cluster does not use multiple tiers, then you can use the generic data
role.
Cluster shard limits prevent creation of more than 1000 non-frozen shards per node, and 3000 frozen shards per dedicated frozen node. Make sure you have enough nodes of each type in your cluster to handle the number of shards you need.
If you assign a node to a specific tier using a specialized data role, then you shouldn’t also assign it the generic data
role. The generic data
role takes precedence over specialized data roles.
Generic data node
editGeneric data nodes are included in all content tiers. A node with a generic data
role can fill any of the specialized data node roles.
To create a dedicated generic data node, set:
node.roles: [ data ]
Content data node
editContent data nodes are part of the content tier. Data stored in the content tier is generally a collection of items such as a product catalog or article archive. Unlike time series data, the value of the content remains relatively constant over time, so it doesn’t make sense to move it to a tier with different performance characteristics as it ages. Content data typically has long data retention requirements, and you want to be able to retrieve items quickly regardless of how old they are.
Content tier nodes are usually optimized for query performance—they prioritize processing power over IO throughput so they can process complex searches and aggregations and return results quickly. While they are also responsible for indexing, content data is generally not ingested at as high a rate as time series data such as logs and metrics. From a resiliency perspective the indices in this tier should be configured to use one or more replicas.
The content tier is required and is often deployed within the same node grouping as the hot tier. System indices and other indices that aren’t part of a data stream are automatically allocated to the content tier.
To create a dedicated content node, set:
node.roles: [ data_content ]
Hot data node
editHot data nodes are part of the hot tier. The hot tier is the Elasticsearch entry point for time series data and holds your most-recent, most-frequently-searched time series data. Nodes in the hot tier need to be fast for both reads and writes, which requires more hardware resources and faster storage (SSDs). For resiliency, indices in the hot tier should be configured to use one or more replicas.
The hot tier is required. New indices that are part of a data stream are automatically allocated to the hot tier.
To create a dedicated hot node, set:
node.roles: [ data_hot ]
Warm data node
editWarm data nodes are part of the warm tier. Time series data can move to the warm tier once it is being queried less frequently than the recently-indexed data in the hot tier. The warm tier typically holds data from recent weeks. Updates are still allowed, but likely infrequent. Nodes in the warm tier generally don’t need to be as fast as those in the hot tier. For resiliency, indices in the warm tier should be configured to use one or more replicas.
To create a dedicated warm node, set:
node.roles: [ data_warm ]
Cold data node
editCold data nodes are part of the cold tier. When you no longer need to search time series data regularly, it can move from the warm tier to the cold tier. While still searchable, this tier is typically optimized for lower storage costs rather than search speed.
For better storage savings, you can keep fully mounted indices of searchable snapshots on the cold tier. Unlike regular indices, these fully mounted indices don’t require replicas for reliability. In the event of a failure, they can recover data from the underlying snapshot instead. This potentially halves the local storage needed for the data. A snapshot repository is required to use fully mounted indices in the cold tier. Fully mounted indices are read-only.
Alternatively, you can use the cold tier to store regular indices with replicas instead of using searchable snapshots. This lets you store older data on less expensive hardware but doesn’t reduce required disk space compared to the warm tier.
To create a dedicated cold node, set:
node.roles: [ data_cold ]
Frozen data node
editFrozen data nodes are part of the frozen tier. Once data is no longer being queried, or being queried rarely, it may move from the cold tier to the frozen tier where it stays for the rest of its life.
The frozen tier requires a snapshot repository. The frozen tier uses partially mounted indices to store and load data from a snapshot repository. This reduces local storage and operating costs while still letting you search frozen data. Because Elasticsearch must sometimes fetch frozen data from the snapshot repository, searches on the frozen tier are typically slower than on the cold tier.
To create a dedicated frozen node, set:
node.roles: [ data_frozen ]
Ingest node
editIngest nodes can execute pre-processing pipelines, composed of one or more ingest processors. Depending on the type of operations performed by the ingest processors and the required resources, it may make sense to have dedicated ingest nodes, that will only perform this specific task.
To create a dedicated ingest node, set:
node.roles: [ ingest ]
Coordinating only node
editIf you take away the ability to be able to handle master duties, to hold data, and pre-process documents, then you are left with a coordinating node that can only route requests, handle the search reduce phase, and distribute bulk indexing. Essentially, coordinating only nodes behave as smart load balancers.
Coordinating only nodes can benefit large clusters by offloading the coordinating node role from data and master-eligible nodes. They join the cluster and receive the full cluster state, like every other node, and they use the cluster state to route requests directly to the appropriate place(s).
Adding too many coordinating only nodes to a cluster can increase the burden on the entire cluster because the elected master node must await acknowledgement of cluster state updates from every node! The benefit of coordinating only nodes should not be overstated — data nodes can happily serve the same purpose.
To create a dedicated coordinating node, set:
node.roles: [ ]
Remote-eligible node
editA remote-eligible node acts as a cross-cluster client and connects to remote clusters. Once connected, you can search remote clusters using cross-cluster search. You can also sync data between clusters using cross-cluster replication.
node.roles: [ remote_cluster_client ]
Machine learning node
editMachine learning nodes run jobs and handle machine learning API requests. For more information, see Machine learning settings.
To create a dedicated machine learning node, set:
node.roles: [ ml, remote_cluster_client]
The remote_cluster_client
role is optional but strongly recommended.
Otherwise, cross-cluster search fails when used in machine learning jobs or datafeeds. If you use cross-cluster search in
your anomaly detection jobs, the remote_cluster_client
role is also required on all
master-eligible nodes. Otherwise, the datafeed cannot start. See Remote-eligible node.
Transform node
editTransform nodes run transforms and handle transform API requests. For more information, see Transforms settings.
To create a dedicated transform node, set:
node.roles: [ transform, remote_cluster_client ]
The remote_cluster_client
role is optional but strongly recommended.
Otherwise, cross-cluster search fails when used in transforms. See Remote-eligible node.
On this page
- Set node roles
- Change the role of a node
- Available node roles
- Master-eligible node
- Dedicated master-eligible node
- Voting-only master-eligible node
- Data nodes
- Generic data node
- Content data node
- Hot data node
- Warm data node
- Cold data node
- Frozen data node
- Ingest node
- Coordinating only node
- Remote-eligible node
- Machine learning node
- Transform node
ElasticON events are back!
Learn about the Elastic Search AI Platform from the experts at our live events.
Register now