- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.10
- 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
- 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 modules
- 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 templates
- Data streams
- Ingest node
- Search your data
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Geo-distance
- Geohash grid
- Geotile grid
- Global
- Histogram
- IP range
- Missing
- Nested
- Parent
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Bucket aggregations
- EQL
- 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
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Manage Filebeat time-based indices
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Resolve lifecycle policy execution errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- 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 data streams and index 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
- Watch for cluster and index events
- Command line tools
- How To
- Glossary of terms
- REST APIs
- API conventions
- Compact and aligned text (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 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
- 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
- Data stream APIs
- Document APIs
- Enrich APIs
- Graph 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
- Delete index template (legacy)
- 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 (legacy)
- Open index
- Put index template
- Put index template (legacy)
- Put component template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Simulate index
- Simulate template
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Resolve index
- List dangling indices
- Import dangling index
- Delete dangling index
- Index lifecycle management APIs
- Ingest APIs
- Info API
- Licensing 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
- 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 filters
- Update jobs
- Update model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create trained models
- Update data frame analytics jobs
- Delete data frame analytics jobs
- Delete trained models
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Get trained models
- Get trained models stats
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Migration APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Search APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key 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
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 7.10.2
- Elasticsearch version 7.10.1
- Elasticsearch version 7.10.0
- Elasticsearch version 7.9.3
- Elasticsearch version 7.9.2
- Elasticsearch version 7.9.1
- Elasticsearch version 7.9.0
- 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
- Dependencies and versions
Collectors
editCollectors
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.
Collectors, as their name implies, collect things. Each collector runs once for each collection interval to obtain data from the public APIs in Elasticsearch and X-Pack that it chooses to monitor. When the data collection is finished, the data is handed in bulk to the exporters to be sent to the monitoring clusters. Regardless of the number of exporters, each collector only runs once per collection interval.
There is only one collector per data type gathered. In other words, for any monitoring document that is created, it comes from a single collector rather than being merged from multiple collectors. The Elasticsearch monitoring features currently have a few collectors because the goal is to minimize overlap between them for optimal performance.
Each collector can create zero or more monitoring documents. For example,
the index_stats
collector collects all index statistics at the same time to
avoid many unnecessary calls.
Collector | Data Types | Description |
---|---|---|
Cluster Stats |
|
Gathers details about the cluster state, including parts of the actual cluster
state (for example |
Index Stats |
|
Gathers details about the indices in the cluster, both in summary and
individually. This creates many documents that represent parts of the index
statistics output (for example, |
Index Recovery |
|
Gathers details about index recovery in the cluster. Index recovery represents the assignment of shards at the cluster level. If an index is not recovered, it is not usable. This also corresponds to shard restoration via snapshots. This information only needs to be collected once, so it is collected on the elected master node. The most common failure for this collector relates to an extreme number of shards — and therefore time to gather them — resulting in timeouts. This creates a single document that contains all recoveries by default, which can be quite large, but it gives the most accurate picture of recovery in the production cluster. |
Shards |
|
Gathers details about all allocated shards for all indices, particularly including what node the shard is allocated to. This information only needs to be collected once, so it is collected on the elected master node. The collector uses the local cluster state to get the routing table without any network timeout issues unlike most other collectors. Each shard is represented by a separate monitoring document. |
Jobs |
|
Gathers details about all machine learning job statistics (for example, |
Node Stats |
|
Gathers details about the running node, such as memory utilization and CPU
usage (for example, |
The Elasticsearch monitoring features use a single threaded scheduler to run the
collection of Elasticsearch monitoring data by all of the appropriate collectors on each
node. This scheduler is managed locally by each node and its interval is
controlled by specifying the xpack.monitoring.collection.interval
, which
defaults to 10 seconds (10s
), at either the node or cluster level.
Fundamentally, each collector works on the same principle. Per collection interval, each collector is checked to see whether it should run and then the appropriate collectors run. The failure of an individual collector does not impact any other collector.
Once collection has completed, all of the monitoring data is passed to the exporters to route the monitoring data to the monitoring clusters.
If gaps exist in the monitoring charts in Kibana, it is typically because either a collector failed or the monitoring cluster did not receive the data (for example, it was being restarted). In the event that a collector fails, a logged error should exist on the node that attempted to perform the collection.
Collection is currently done serially, rather than in parallel, to avoid extra overhead on the elected master node. The downside to this approach is that collectors might observe a different version of the cluster state within the same collection period. In practice, this does not make a significant difference and running the collectors in parallel would not prevent such a possibility.
For more information about the configuration options for the collectors, see Monitoring collection settings.
Collecting data from across the Elastic Stack
editElasticsearch monitoring features also receive monitoring data from other parts of the Elastic Stack. In this way, it serves as an unscheduled monitoring data collector for the stack.
By default, data collection is disabled. Elasticsearch monitoring data is not
collected and all monitoring data from other sources such as Kibana, Beats, and
Logstash is ignored. You must set xpack.monitoring.collection.enabled
to true
to enable the collection of monitoring data. See Monitoring settings.
Once data is received, it is forwarded to the exporters to be routed to the monitoring cluster like all monitoring data.
Because this stack-level "collector" lives outside of the collection
interval of Elasticsearch monitoring features, it is not impacted by the
xpack.monitoring.collection.interval
setting. Therefore, data is passed to the
exporters whenever it is received. This behavior can result in indices for Kibana,
Logstash, or Beats being created somewhat unexpectedly.
While the monitoring data is collected and processed, some production cluster metadata is added to incoming documents. This metadata enables Kibana to link the monitoring data to the appropriate cluster. If this linkage is unimportant to the infrastructure that you’re monitoring, it might be simpler to configure Logstash and Beats to report monitoring data directly to the monitoring cluster. This scenario also prevents the production cluster from adding extra overhead related to monitoring data, which can be very useful when there are a large number of Logstash nodes or Beats.
For more information about typical monitoring architectures, see How it works.
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