- 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
- 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
- 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
- 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 8.17.1
- 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
Cross-cluster replication
editCross-cluster replication
editWith cross-cluster replication, you can replicate indices across clusters to:
- Continue handling search requests in the event of a datacenter outage
- Prevent search volume from impacting indexing throughput
- Reduce search latency by processing search requests in geo-proximity to the user
Cross-cluster replication uses an active-passive model. You index to a leader index, and the data is replicated to one or more read-only follower indices. Before you can add a follower index to a cluster, you must configure the remote cluster that contains the leader index.
When the leader index receives writes, the follower indices pull changes from the leader index on the remote cluster. You can manually create follower indices, or configure auto-follow patterns to automatically create follower indices for new time series indices.
You configure cross-cluster replication clusters in a uni-directional or bi-directional setup:
- In a uni-directional configuration, one cluster contains only leader indices, and the other cluster contains only follower indices.
- In a bi-directional configuration, each cluster contains both leader and follower indices.
In a uni-directional configuration, the cluster containing follower indices must be running the same or newer version of Elasticsearch as the remote cluster. If newer, the versions must also be compatible as outlined in the following matrix.
Version compatibility matrix
Local cluster |
|||||||||
Remote cluster |
5.0–5.5 |
5.6 |
6.0–6.6 |
6.7 |
6.8 |
7.0 |
7.1–7.16 |
7.17 |
8.0–8.17 |
5.0–5.5 |
|||||||||
5.6 |
|||||||||
6.0–6.6 |
|||||||||
6.7 |
|||||||||
6.8 |
|||||||||
7.0 |
|||||||||
7.1–7.16 |
|||||||||
7.17 |
|||||||||
8.0–8.17 |
Multi-cluster architectures
editUse cross-cluster replication to construct several multi-cluster architectures within the Elastic Stack:
- Disaster recovery in case a primary cluster fails, with a secondary cluster serving as a hot backup
- Data locality to maintain multiple copies of the dataset close to the application servers (and users), and reduce costly latency
- Centralized reporting for minimizing network traffic and latency in querying multiple geo-distributed Elasticsearch clusters, or for preventing search load from interfering with indexing by offloading search to a secondary cluster
Watch the cross-cluster replication webinar to learn more about the following use cases. Then, set up cross-cluster replication on your local machine and work through the demo from the webinar.
In all of these use cases, you must
configure security independently on every
cluster. The security configuration is not replicated when configuring cross-cluster replication for
disaster recovery. To ensure that the Elasticsearch security
feature state is backed up,
take snapshots regularly. You can then restore
the native users, roles, and tokens from your security configuration.
Disaster recovery and high availability
editDisaster recovery provides your mission-critical applications with the tolerance to withstand datacenter or region outages. This use case is the most common deployment of cross-cluster replication. You can configure clusters in different architectures to support disaster recovery and high availability:
Single disaster recovery datacenter
editIn this configuration, data is replicated from the production datacenter to the disaster recovery datacenter. Because the follower indices replicate the leader index, your application can use the disaster recovery datacenter if the production datacenter is unavailable.
Multiple disaster recovery datacenters
editYou can replicate data from one datacenter to multiple datacenters. This configuration provides both disaster recovery and high availability, ensuring that data is replicated in two datacenters if the primary datacenter is down or unavailable.
In the following diagram, data from Datacenter A is replicated to Datacenter B and Datacenter C, which both have a read-only copy of the leader index from Datacenter A.
Chained replication
editYou can replicate data across multiple datacenters to form a replication chain. In the following diagram, Datacenter A contains the leader index. Datacenter B replicates data from Datacenter A, and Datacenter C replicates from the follower indices in Datacenter B. The connection between these datacenters forms a chained replication pattern.
Bi-directional replication
editIn a bi-directional replication setup, all clusters have access to view all data, and all clusters have an index to write to without manually implementing failover. Applications can write to the local index within each datacenter, and read across multiple indices for a global view of all information.
This configuration requires no manual intervention when a cluster or datacenter is unavailable. In the following diagram, if Datacenter A is unavailable, you can continue using Datacenter B without manual failover. When Datacenter A comes online, replication resumes between the clusters.
This configuration is particularly useful for index-only workloads, where no updates to document values occur. In this configuration, documents indexed by Elasticsearch are immutable. Clients are located in each datacenter alongside the Elasticsearch cluster, and do not communicate with clusters in different datacenters.
Data locality
editBringing data closer to your users or application server can reduce latency and response time. This methodology also applies when replicating data in Elasticsearch. For example, you can replicate a product catalog or reference dataset to 20 or more datacenters around the world to minimize the distance between the data and the application server.
In the following diagram, data is replicated from one datacenter to three additional datacenters, each in their own region. The central datacenter contains the leader index, and the additional datacenters contain follower indices that replicate data in that particular region. This configuration puts data closer to the application accessing it.
Centralized reporting
editUsing a centralized reporting cluster is useful when querying across a large network is inefficient. In this configuration, you replicate data from many smaller clusters to the centralized reporting cluster.
For example, a large global bank might have 100 Elasticsearch clusters around the world that are distributed across different regions for each bank branch. Using cross-cluster replication, the bank can replicate events from all 100 banks to a central cluster to analyze and aggregate events locally for reporting. Rather than maintaining a mirrored cluster, the bank can use cross-cluster replication to replicate specific indices.
In the following diagram, data from three datacenters in different regions is replicated to a centralized reporting cluster. This configuration enables you to copy data from regional hubs to a central cluster, where you can run all reports locally.
Replication mechanics
editAlthough you set up cross-cluster replication at the index level, Elasticsearch achieves replication at the shard level. When a follower index is created, each shard in that index pulls changes from its corresponding shard in the leader index, which means that a follower index has the same number of shards as its leader index. All operations on the leader are replicated by the follower, such as operations to create, update, or delete a document. These requests can be served from any copy of the leader shard (primary or replica).
When a follower shard sends a read request, the leader shard responds with any new operations, limited by the read parameters that you establish when configuring the follower index. If no new operations are available, the leader shard waits up to the configured timeout for new operations. If the timeout elapses, the leader shard responds to the follower shard that there are no new operations. The follower shard updates shard statistics and immediately sends another read request to the leader shard. This communication model ensures that network connections between the remote cluster and the local cluster are continually in use, avoiding forceful termination by an external source such as a firewall.
If a read request fails, the cause of the failure is inspected. If the cause of the failure is deemed to be recoverable (such as a network failure), the follower shard enters into a retry loop. Otherwise, the follower shard pauses until you resume it.
Processing updates
editYou can’t manually modify a follower index’s mappings or aliases. To make changes, you must update the leader index. Because they are read-only, follower indices reject writes in all configurations.
Although changes to aliases on the leader index are replicated to follower
indices, write indices are ignored. Follower indices can’t accept direct writes,
so if any leader aliases have is_write_index
set to true
, that value is
forced to false
.
For example, you index a document named doc_1
in Datacenter A, which
replicates to Datacenter B. If a client connects to Datacenter B and attempts
to update doc_1
, the request fails. To update doc_1
, the client must
connect to Datacenter A and update the document in the leader index.
When a follower shard receives operations from the leader shard, it places those operations in a write buffer. The follower shard submits bulk write requests using operations from the write buffer. If the write buffer exceeds its configured limits, no additional read requests are sent. This configuration provides a back-pressure against read requests, allowing the follower shard to resume sending read requests when the write buffer is no longer full.
To manage how operations are replicated from the leader index, you can configure settings when creating the follower index.
Changes in the index mapping on the leader index are replicated to the follower index as soon as possible. This behavior is true for index settings as well, except for some settings that are local to the leader index. For example, changing the number of replicas on the leader index is not replicated by the follower index, so that setting might not be retrieved.
If you apply a non-dynamic settings change to the leader index that is needed by the follower index, the follower index closes itself, applies the settings update, and then re-opens itself. The follower index is unavailable for reads and cannot replicate writes during this cycle.
Initializing followers using remote recovery
editWhen you create a follower index, you cannot use it until it is fully initialized. The remote recovery process builds a new copy of a shard on a follower node by copying data from the primary shard in the leader cluster.
Elasticsearch uses this remote recovery process to bootstrap a follower index using the data from the leader index. This process provides the follower with a copy of the current state of the leader index, even if a complete history of changes is not available on the leader due to Lucene segment merging.
Remote recovery is a network intensive process that transfers all of the Lucene segment files from the leader cluster to the follower cluster. The follower requests that a recovery session be initiated on the primary shard in the leader cluster. The follower then requests file chunks concurrently from the leader. By default, the process concurrently requests five 1MB file chunks. This default behavior is designed to support leader and follower clusters with high network latency between them.
You can modify dynamic remote recovery settings to rate-limit the transmitted data and manage the resources consumed by remote recoveries.
Use the recovery API on the cluster containing the follower
index to obtain information about an in-progress remote recovery. Because Elasticsearch
implements remote recoveries using the
snapshot and restore infrastructure, running remote
recoveries are labelled as type snapshot
in the recovery API.
Replicating a leader requires soft deletes
editCross-cluster replication works by replaying the history of individual write operations that were performed on the shards of the leader index. Elasticsearch needs to retain the history of these operations on the leader shards so that they can be pulled by the follower shard tasks. The underlying mechanism used to retain these operations is soft deletes.
A soft delete occurs whenever an existing document is deleted or updated. By retaining these soft deletes up to configurable limits, the history of operations can be retained on the leader shards and made available to the follower shard tasks as it replays the history of operations.
The index.soft_deletes.retention_lease.period
setting defines the maximum time to retain a shard history retention lease
before it is considered expired. This setting determines how long the cluster
containing your follower index can be offline, which is 12 hours by default. If
a shard copy recovers after its retention lease expires, but the missing
operations are still available on the leader index, then Elasticsearch will establish a
new lease and copy the missing operations. However Elasticsearch does not guarantee to
retain unleased operations, so it is also possible that some of the missing
operations have been discarded by the leader and are now completely
unavailable. If this happens then the follower cannot recover automatically so
you must recreate it.
Soft deletes must be enabled for indices that you want to use as leader indices. Soft deletes are enabled by default on new indices created on or after Elasticsearch 7.0.0.
Cross-cluster replication cannot be used on existing indices created using Elasticsearch 7.0.0 or earlier, where soft deletes are disabled. You must reindex your data into a new index with soft deletes enabled.
Use cross-cluster replication
editThis following sections provide more information about how to configure and use cross-cluster replication:
Cross-cluster replication limitations
editCross-cluster replication is designed to replicate user-generated indices only, and doesn’t currently replicate any of the following:
If you want to replicate any of this data, you must replicate it to a remote cluster manually.
Data for searchable snapshot indices is stored in the snapshot repository. Cross-cluster replication won’t replicate these indices completely, even though they’re either partially or fully-cached on the Elasticsearch nodes. To achieve searchable snapshots in a remote cluster, configure snapshot repositories on the remote cluster and use the same index lifecycle management policy from the local cluster to move data into the cold or frozen tiers on the remote cluster.
On this page
- Multi-cluster architectures
- Disaster recovery and high availability
- Single disaster recovery datacenter
- Multiple disaster recovery datacenters
- Chained replication
- Bi-directional replication
- Data locality
- Centralized reporting
- Replication mechanics
- Processing updates
- Initializing followers using remote recovery
- Replicating a leader requires soft deletes
- Use cross-cluster replication
- Cross-cluster replication limitations