- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.8
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Setting JVM options
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- HTTP
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging configuration
- Machine learning settings
- Monitoring settings
- Node
- Network settings
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot lifecycle management settings
- Transforms settings
- Transport
- Thread pools
- Watcher settings
- Important Elasticsearch configuration
- Important System Configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- G1GC check
- All permission check
- Discovery configuration check
- 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
- Set up X-Pack
- Configuring X-Pack Java Clients
- Plugins
- Upgrade Elasticsearch
- Index templates
- Search your data
- Query DSL
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted Avg Aggregation
- Boxplot Aggregation
- Cardinality Aggregation
- Stats Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Median Absolute Deviation Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- String Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Top Metrics Aggregation
- Value Count Aggregation
- T-Test Aggregation
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Auto-interval Date Histogram Aggregation
- Children Aggregation
- Composite aggregation
- Date histogram aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- GeoTile Grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested Aggregation
- Parent Aggregation
- Range Aggregation
- Rare Terms Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Subtleties of bucketing range fields
- Pipeline Aggregations
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Avg Bucket Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Cumulative Cardinality Aggregation
- Cumulative Sum Aggregation
- Derivative Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Moving Function Aggregation
- Serial Differencing Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Indexing aggregation results with transforms
- Metrics Aggregations
- Scripting
- Mapping
- 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 modules
- Ingest node
- ILM: Manage the index lifecycle
- Monitor a cluster
- Frozen indices
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure a cluster
- Overview
- Configuring security
- User authentication
- Built-in users
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Granting access to Stack Management features
- Security privileges
- Document level security
- Field level security
- Granting privileges for indices and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enabling audit logging
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, clients, and integrations
- Tutorial: Getting started with security
- Tutorial: Encrypting communications
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Alerting on cluster and index events
- Command line tools
- How To
- Glossary of terms
- REST APIs
- API conventions
- cat APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- 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 shards
- cat segments
- 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
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Nodes reload secure settings
- Nodes stats
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Cross-cluster replication APIs
- Document APIs
- Enrich APIs
- Explore API
- Index APIs
- Add index alias
- Analyze
- Clear cache
- Clone index
- Close index
- Create index
- Delete index
- Delete index alias
- Delete component template
- Delete index template
- Flush
- Force merge
- Freeze index
- Get component template
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists
- Open index
- Put index template
- Put index template (legacy)
- Put component template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Index lifecycle management API
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Find file structure
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Revert model snapshots
- Set upgrade mode
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filter
- Update jobs
- Update model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create inference trained model
- Delete data frame analytics jobs
- Delete inference trained model
- Evaluate data frame analytics
- Explain data frame analytics API
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Get inference trained model
- Get inference trained model stats
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Migration APIs
- Reload search analyzers
- Rollup APIs
- Search APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get token
- Get users
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect authenticate API
- OpenID Connect logout API
- SAML prepare authentication API
- SAML authenticate API
- SAML logout API
- SAML invalidate API
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management API
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Breaking changes
- Release notes
- Elasticsearch version 7.8.1
- Elasticsearch version 7.8.0
- Elasticsearch version 7.7.1
- Elasticsearch version 7.7.0
- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
Voting configurations
editVoting configurations
editEach Elasticsearch cluster has a voting configuration, which is the set of master-eligible nodes whose responses are counted when making decisions such as electing a new master or committing a new cluster state. Decisions are made only after a majority (more than half) of the nodes in the voting configuration respond.
Usually the voting configuration is the same as the set of all the master-eligible nodes that are currently in the cluster. However, there are some situations in which they may be different.
To ensure the cluster remains available, you must not stop half or more of the nodes in the voting configuration at the same time. As long as more than half of the voting nodes are available, the cluster can work normally. For example, if there are three or four master-eligible nodes, the cluster can tolerate one unavailable node. If there are two or fewer master-eligible nodes, they must all remain available.
After a node joins or leaves the cluster, Elasticsearch reacts by automatically making corresponding changes to the voting configuration in order to ensure that the cluster is as resilient as possible. It is important to wait for this adjustment to complete before you remove more nodes from the cluster. For more information, see Adding and removing nodes.
The current voting configuration is stored in the cluster state so you can inspect its current contents as follows:
GET /_cluster/state?filter_path=metadata.cluster_coordination.last_committed_config
The current voting configuration is not necessarily the same as the set of all available master-eligible nodes in the cluster. Altering the voting configuration involves taking a vote, so it takes some time to adjust the configuration as nodes join or leave the cluster. Also, there are situations where the most resilient configuration includes unavailable nodes or does not include some available nodes. In these situations, the voting configuration differs from the set of available master-eligible nodes in the cluster.
Larger voting configurations are usually more resilient, so Elasticsearch normally prefers to add master-eligible nodes to the voting configuration after they join the cluster. Similarly, if a node in the voting configuration leaves the cluster and there is another master-eligible node in the cluster that is not in the voting configuration then it is preferable to swap these two nodes over. The size of the voting configuration is thus unchanged but its resilience increases.
It is not so straightforward to automatically remove nodes from the voting
configuration after they have left the cluster. Different strategies have
different benefits and drawbacks, so the right choice depends on how the cluster
will be used. You can control whether the voting configuration automatically
shrinks by using the
cluster.auto_shrink_voting_configuration
setting.
If cluster.auto_shrink_voting_configuration
is set to true
(which is
the default and recommended value) and there are at least three master-eligible
nodes in the cluster, Elasticsearch remains capable of processing cluster state
updates as long as all but one of its master-eligible nodes are healthy.
There are situations in which Elasticsearch might tolerate the loss of multiple
nodes, but this is not guaranteed under all sequences of failures. If the
cluster.auto_shrink_voting_configuration
setting is false
, you must remove
departed nodes from the voting configuration manually. Use the
voting exclusions API to achieve the desired level
of resilience.
No matter how it is configured, Elasticsearch will not suffer from a
"split-brain" inconsistency. The cluster.auto_shrink_voting_configuration
setting affects only its availability in the event of the failure of some of its
nodes and the administrative tasks that must be performed as nodes join and
leave the cluster.
Even numbers of master-eligible nodes
editThere should normally be an odd number of master-eligible nodes in a cluster. If there is an even number, Elasticsearch leaves one of them out of the voting configuration to ensure that it has an odd size. This omission does not decrease the failure-tolerance of the cluster. In fact, improves it slightly: if the cluster suffers from a network partition that divides it into two equally-sized halves then one of the halves will contain a majority of the voting configuration and will be able to keep operating. If all of the votes from master-eligible nodes were counted, neither side would contain a strict majority of the nodes and so the cluster would not be able to make any progress.
For instance if there are four master-eligible nodes in the cluster and the voting configuration contained all of them, any quorum-based decision would require votes from at least three of them. This situation means that the cluster can tolerate the loss of only a single master-eligible node. If this cluster were split into two equal halves, neither half would contain three master-eligible nodes and the cluster would not be able to make any progress. If the voting configuration contains only three of the four master-eligible nodes, however, the cluster is still only fully tolerant to the loss of one node, but quorum-based decisions require votes from two of the three voting nodes. In the event of an even split, one half will contain two of the three voting nodes so that half will remain available.
Setting the initial voting configuration
editWhen a brand-new cluster starts up for the first time, it must elect its first master node. To do this election, it needs to know the set of master-eligible nodes whose votes should count. This initial voting configuration is known as the bootstrap configuration and is set in the cluster bootstrapping process.
It is important that the bootstrap configuration identifies exactly which nodes should vote in the first election. It is not sufficient to configure each node with an expectation of how many nodes there should be in the cluster. It is also important to note that the bootstrap configuration must come from outside the cluster: there is no safe way for the cluster to determine the bootstrap configuration correctly on its own.
If the bootstrap configuration is not set correctly, when you start a brand-new cluster there is a risk that you will accidentally form two separate clusters instead of one. This situation can lead to data loss: you might start using both clusters before you notice that anything has gone wrong and it is impossible to merge them together later.
To illustrate the problem with configuring each node to expect a certain cluster size, imagine starting up a three-node cluster in which each node knows that it is going to be part of a three-node cluster. A majority of three nodes is two, so normally the first two nodes to discover each other form a cluster and the third node joins them a short time later. However, imagine that four nodes were erroneously started instead of three. In this case, there are enough nodes to form two separate clusters. Of course if each node is started manually then it’s unlikely that too many nodes are started. If you’re using an automated orchestrator, however, it’s certainly possible to get into this situation-- particularly if the orchestrator is not resilient to failures such as network partitions.
The initial quorum is only required the very first time a whole cluster starts up. New nodes joining an established cluster can safely obtain all the information they need from the elected master. Nodes that have previously been part of a cluster will have stored to disk all the information that is required when they restart.