- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.7
- 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 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
- SQL access 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
- 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
- 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
- Char Group Tokenizer
- Classic Tokenizer
- Edge n-gram tokenizer
- Keyword Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- N-gram tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Pattern Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Standard Tokenizer
- Thai Tokenizer
- UAX URL Email Tokenizer
- Whitespace Tokenizer
- Token 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
- Pipeline Definition
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Enrich your data
- Processors
- Append Processor
- Bytes Processor
- Circle Processor
- Convert Processor
- CSV Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Enrich Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip Processor
- Inference Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Pipeline Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Set Security User Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- URL Decode Processor
- User Agent processor
- 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
- 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 index template
- Flush
- Force merge
- Freeze index
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists
- Open index
- Put index template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Index lifecycle management API
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
- Create datafeeds
- Create filter
- Delete calendar
- Delete datafeeds
- Delete events from calendar
- Delete filter
- Delete forecast
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- 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 7.7.1
- 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
Exporters
editExporters
editMetricbeat is the recommended method for collecting and shipping monitoring data to a monitoring cluster.
If you have previously configured legacy collection methods, you should migrate to using Metricbeat collection methods. Use either Metricbeat collection or legacy collection methods; do not use both.
Learn more about Collecting monitoring data with Metricbeat.
The purpose of exporters is to take data collected from any Elastic Stack source and route it to the monitoring cluster. It is possible to configure more than one exporter, but the general and default setup is to use a single exporter.
There are two types of exporters in Elasticsearch:
-
local
- The default exporter used by Elasticsearch monitoring features. This exporter routes data back into the same cluster. See Local exporters.
-
http
- The preferred exporter, which you can use to route data into any supported Elasticsearch cluster accessible via HTTP. Production environments should always use a separate monitoring cluster. See HTTP exporters.
Both exporters serve the same purpose: to set up the monitoring cluster and route monitoring data. However, they perform these tasks in very different ways. Even though things happen differently, both exporters are capable of sending all of the same data.
Exporters are configurable at both the node and cluster level. Cluster-wide
settings, which are updated with the
_cluster/settings
API, take precedence over
settings in the elasticsearch.yml
file on each node. When you update an
exporter, it is completely replaced by the updated version of the exporter.
It is critical that all nodes share the same setup. Otherwise, monitoring data might be routed in different ways or to different places.
When the exporters route monitoring data into the monitoring cluster, they use
_bulk
indexing for optimal performance. All monitoring data is forwarded in
bulk to all enabled exporters on the same node. From there, the exporters
serialize the monitoring data and send a bulk request to the monitoring cluster.
There is no queuing—in memory or persisted to disk—so any failure during the
export results in the loss of that batch of monitoring data. This design limits
the impact on Elasticsearch and the assumption is that the next pass will succeed.
Routing monitoring data involves indexing it into the appropriate monitoring
indices. Once the data is indexed, it exists in a monitoring index that, by
default, is named with a daily index pattern. For Elasticsearch monitoring data, this is
an index that matches .monitoring-es-6-*
. From there, the data lives inside
the monitoring cluster and must be curated or cleaned up as necessary. If you do
not curate the monitoring data, it eventually fills up the nodes and the cluster
might fail due to lack of disk space.
You are strongly recommended to manage the curation of indices and particularly the monitoring indices. To do so, you can take advantage of the cleaner service or Elastic Curator.
There is also a disk watermark (known as the flood stage watermark), which protects clusters from running out of disk space. When this feature is triggered, it makes all indices (including monitoring indices) read-only until the issue is fixed and a user manually makes the index writeable again. While an active monitoring index is read-only, it will naturally fail to write (index) new data and will continuously log errors that indicate the write failure. For more information, see Disk-based shard allocation settings.
Default exporters
editIf a node or cluster does not explicitly define an exporter, the following default exporter is used:
The exporter name uniquely defines the exporter, but it is otherwise unused.
When you specify your own exporters, you do not need to explicitly overwrite
or reference |
If another exporter is already defined, the default exporter is not created. When you define a new exporter, if the default exporter exists, it is automatically removed.
Exporter templates and ingest pipelines
editBefore exporters can route monitoring data, they must set up certain Elasticsearch resources. These resources include templates and ingest pipelines. The following table lists the templates that are required before an exporter can route monitoring data:
Template | Purpose |
---|---|
|
All cluster alerts for monitoring data. |
|
All Beats monitoring data. |
|
All Elasticsearch monitoring data. |
|
All Kibana monitoring data. |
|
All Logstash monitoring data. |
The templates are ordinary Elasticsearch templates that control the default settings and mappings for the monitoring indices.
By default, monitoring indices are created daily (for example,
.monitoring-es-6-2017.08.26
). You can change the default date suffix for
monitoring indices with the index.name.time_format
setting. You can use this
setting to control how frequently monitoring indices are created by a specific
http
exporter. You cannot use this setting with local
exporters. For more
information, see HTTP Exporter Settings.
Some users create their own templates that match all index patterns,
which therefore impact the monitoring indices that get created. It is critical
that you do not disable _source
storage for the monitoring indices. If you do,
Kibana monitoring features do not work and you cannot visualize monitoring data
for your cluster.
The following table lists the ingest pipelines that are required before an exporter can route monitoring data:
Pipeline | Purpose |
---|---|
|
Upgrades X-Pack monitoring data coming from X-Pack 5.0 - 5.4 to be compatible with the format used in 5.5 monitoring features. |
|
A placeholder pipeline that is empty. |
Exporters handle the setup of these resources before ever sending data. If resource setup fails (for example, due to security permissions), no data is sent and warnings are logged.
Empty pipelines are evaluated on the coordinating node during indexing and they are ignored without any extra effort. This inherently makes them a safe, no-op operation.
For monitoring clusters that have disabled node.ingest
on all nodes, it is
possible to disable the use of the ingest pipeline feature. However, doing so
blocks its purpose, which is to upgrade older monitoring data as our mappings
improve over time. Beginning in 6.0, the ingest pipeline feature is a
requirement on the monitoring cluster; you must have node.ingest
enabled on at
least one node.
Once any node running 5.5 or later has set up the templates and ingest
pipeline on a monitoring cluster, you must use Kibana 5.5 or later to view all
subsequent data on the monitoring cluster. The easiest way to determine
whether this update has occurred is by checking for the presence of indices
matching .monitoring-es-6-*
(or more concretely the existence of the
new pipeline). Versions prior to 5.5 used .monitoring-es-2-*
.
Each resource that is created by an exporter has a version
field,
which is used to determine whether the resource should be replaced. The version
field value represents the latest version of monitoring features that changed the
resource. If a resource is edited by someone or something external to the
monitoring features, those changes are lost the next time an automatic update
occurs.