- Elasticsearch Guide: other versions:
- 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
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- Set JVM options
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- Bootstrap Checks
- Heap size check
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- Client JVM check
- Use serial collector check
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- Early-access check
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- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
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- Discovery and cluster formation
- Add and remove nodes in your cluster
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- Remote clusters
- Plugins
- Search your data
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- Index modules
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- Aliases
- Mapping
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- Alias
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- Binary
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- 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
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- Classic
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- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
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- Keep types
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- Keyword marker
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- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
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- Remove duplicates
- Reverse
- Shingle
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- Synonym
- Synonym graph
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- Word delimiter
- Word delimiter graph
- Character filters reference
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- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Attachment
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- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
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- Fail
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- IP Location
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- User agent
- Ingest pipelines in Search
- Connectors
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- 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
- Roll up or transform your data
- Query DSL
- EQL
- ES|QL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- 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
- 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
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- 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
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- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- Watcher
- Monitor a cluster
- 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
- Set up a cluster for high availability
- Optimizations
- Autoscaling
- Snapshot and restore
- 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
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- 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
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- 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
- 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
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- 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
- 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
- Upgrade Elasticsearch
- Migration guide
- Release notes
- Dependencies and versions
Miscellaneous cluster settings
editMiscellaneous cluster settings
editCluster name setting
editA node can only join a cluster when it shares its cluster.name
with all the
other nodes in the cluster. The default name is elasticsearch
, but you should
change it to an appropriate name that describes the purpose of the cluster.
cluster.name: logging-prod
Do not reuse the same cluster names in different environments. Otherwise, nodes might join the wrong cluster.
Changing the name of a cluster requires a full cluster restart.
Metadata
editAn entire cluster may be set to read-only with the following setting:
-
cluster.blocks.read_only
-
(Dynamic)
Make the whole cluster read only (indices do not accept write
operations), metadata is not allowed to be modified (create or delete
indices). Defaults to
false
. -
cluster.blocks.read_only_allow_delete
-
(Dynamic)
Identical to
cluster.blocks.read_only
but allows to delete indices to free up resources. Defaults tofalse
.
Don’t rely on this setting to prevent changes to your cluster. Any user with access to the cluster-update-settings API can make the cluster read-write again.
Cluster shard limits
editThere is a limit on the number of shards in a cluster, based on the number of nodes in the cluster. This is intended to prevent a runaway process from creating too many shards which can harm performance and in extreme cases may destabilize your cluster.
These limits are intended as a safety net to protect against runaway shard creation and are not a sizing recommendation. The exact number of shards your cluster can safely support depends on your hardware configuration and workload, and may be smaller than the default limits.
We do not recommend increasing these limits beyond the defaults. Clusters with more shards may appear to run well in normal operation, but may take a very long time to recover from temporary disruptions such as a network partition or an unexpected node restart, and may encounter problems when performing maintenance activities such as a rolling restart or upgrade.
If an operation, such as creating a new index, restoring a snapshot of an index, or opening a closed index would lead to the number of shards in the cluster going over this limit, the operation will fail with an error indicating the shard limit. To resolve this, either scale out your cluster by adding nodes, or delete some indices to bring the number of shards below the limit.
If a cluster is already over the limit, perhaps due to changes in node membership or setting changes, all operations that create or open indices will fail.
The cluster shard limit defaults to 1000 shards per non-frozen data node for normal (non-frozen) indices and 3000 shards per frozen data node for frozen indices. Both primary and replica shards of all open indices count toward the limit, including unassigned shards. For example, an open index with 5 primary shards and 2 replicas counts as 15 shards. Closed indices do not contribute to the shard count.
You can dynamically adjust the cluster shard limit with the following setting:
-
cluster.max_shards_per_node
-
(Dynamic) Limits the total number of primary and replica shards for the cluster. Elasticsearch calculates the limit as follows:
cluster.max_shards_per_node * number of non-frozen data nodes
Shards for closed indices do not count toward this limit. Defaults to
1000
. A cluster with no data nodes is unlimited.Elasticsearch rejects any request that creates more shards than this limit allows. For example, a cluster with a
cluster.max_shards_per_node
setting of100
and three data nodes has a shard limit of 300. If the cluster already contains 296 shards, Elasticsearch rejects any request that adds five or more shards to the cluster.Note that if
cluster.max_shards_per_node
is set to a higher value than the default, the limits for mmap count and open file descriptors might also require adjustment.Notice that frozen shards have their own independent limit.
-
cluster.max_shards_per_node.frozen
-
(Dynamic) Limits the total number of primary and replica frozen shards for the cluster. Elasticsearch calculates the limit as follows:
cluster.max_shards_per_node.frozen * number of frozen data nodes
Shards for closed indices do not count toward this limit. Defaults to
3000
. A cluster with no frozen data nodes is unlimited.Elasticsearch rejects any request that creates more frozen shards than this limit allows. For example, a cluster with a
cluster.max_shards_per_node.frozen
setting of100
and three frozen data nodes has a frozen shard limit of 300. If the cluster already contains 296 shards, Elasticsearch rejects any request that adds five or more frozen shards to the cluster.
These limits only apply to actions which create shards and do not limit
the number of shards assigned to each node. To limit the number of shards
assigned to each node, use the
cluster.routing.allocation.total_shards_per_node
setting.
User-defined cluster metadata
editUser-defined metadata can be stored and retrieved using the Cluster Settings API.
This can be used to store arbitrary, infrequently-changing data about the cluster
without the need to create an index to store it. This data may be stored using
any key prefixed with cluster.metadata.
. For example, to store the email
address of the administrator of a cluster under the key cluster.metadata.administrator
,
issue this request:
resp = client.cluster.put_settings( persistent={ "cluster.metadata.administrator": "sysadmin@example.com" }, ) print(resp)
response = client.cluster.put_settings( body: { persistent: { 'cluster.metadata.administrator' => 'sysadmin@example.com' } } ) puts response
const response = await client.cluster.putSettings({ persistent: { "cluster.metadata.administrator": "sysadmin@example.com", }, }); console.log(response);
PUT /_cluster/settings { "persistent": { "cluster.metadata.administrator": "sysadmin@example.com" } }
User-defined cluster metadata is not intended to store sensitive or confidential information. Any information stored in user-defined cluster metadata will be viewable by anyone with access to the Cluster Get Settings API, and is recorded in the Elasticsearch logs.
Index tombstones
editThe cluster state maintains index tombstones to explicitly denote indices that have been deleted. The number of tombstones maintained in the cluster state is controlled by the following setting:
-
cluster.indices.tombstones.size
-
(Static)
Index tombstones prevent nodes that are not part of the cluster when a delete
occurs from joining the cluster and reimporting the index as though the delete
was never issued. To keep the cluster state from growing huge we only keep the
last
cluster.indices.tombstones.size
deletes, which defaults to 500. You can increase it if you expect nodes to be absent from the cluster and miss more than 500 deletes. We think that is rare, thus the default. Tombstones don’t take up much space, but we also think that a number like 50,000 is probably too big.
If Elasticsearch encounters index data that is absent from the current cluster
state, those indices are considered to be dangling. For example,
this can happen if you delete more than
cluster.indices.tombstones.size
indices while an Elasticsearch node is offline.
You can use the Dangling indices API to manage this situation.
Logger
editThe settings which control logging can be updated dynamically with the
logger.
prefix. For instance, to increase the logging level of the
indices.recovery
module to DEBUG
, issue this request:
resp = client.cluster.put_settings( persistent={ "logger.org.elasticsearch.indices.recovery": "DEBUG" }, ) print(resp)
response = client.cluster.put_settings( body: { persistent: { 'logger.org.elasticsearch.indices.recovery' => 'DEBUG' } } ) puts response
const response = await client.cluster.putSettings({ persistent: { "logger.org.elasticsearch.indices.recovery": "DEBUG", }, }); console.log(response);
PUT /_cluster/settings { "persistent": { "logger.org.elasticsearch.indices.recovery": "DEBUG" } }
Persistent tasks allocation
editPlugins can create a kind of tasks called persistent tasks. Those tasks are usually long-lived tasks and are stored in the cluster state, allowing the tasks to be revived after a full cluster restart.
Every time a persistent task is created, the master node takes care of assigning the task to a node of the cluster, and the assigned node will then pick up the task and execute it locally. The process of assigning persistent tasks to nodes is controlled by the following settings:
-
cluster.persistent_tasks.allocation.enable
-
(Dynamic) Enable or disable allocation for persistent tasks:
-
all
- (default) Allows persistent tasks to be assigned to nodes -
none
- No allocations are allowed for any type of persistent task
This setting does not affect the persistent tasks that are already being executed. Only newly created persistent tasks, or tasks that must be reassigned (after a node left the cluster, for example), are impacted by this setting.
-
-
cluster.persistent_tasks.allocation.recheck_interval
- (Dynamic) The master node will automatically check whether persistent tasks need to be assigned when the cluster state changes significantly. However, there may be other factors, such as memory usage, that affect whether persistent tasks can be assigned to nodes but do not cause the cluster state to change. This setting controls how often assignment checks are performed to react to these factors. The default is 30 seconds. The minimum permitted value is 10 seconds.
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