- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.7
- Set up Elasticsearch
- Installing Elasticsearch
- Run Elasticsearch locally
- 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
- Field data cache settings
- Health Diagnostic settings
- 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
- Networking
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Advanced 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
- 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
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- Keep types
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- Keyword marker
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- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
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- Phonetic
- Porter stem
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- Remove duplicates
- Reverse
- Shingle
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- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
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- 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
- Join
- JSON
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- Lowercase
- Network direction
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- Redact
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- Set
- Set security user
- Sort
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- Uppercase
- URL decode
- URI parts
- User agent
- Aliases
- Search your data
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
- Near real-time search
- Paginate search results
- Retrieve inner hits
- Retrieve selected fields
- Search across clusters
- Search multiple data streams and indices
- Search shard routing
- Search templates
- Sort search results
- kNN search
- Query DSL
- Aggregations
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- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
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- 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
- 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
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- Derivative
- Extended stats bucket
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- Max bucket
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- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
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- Bucket aggregations
- Geospatial analysis
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
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- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- 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
- 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
- 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
- How to
- 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
- Multiple deployments writing to the same snapshot repository
- Addressing repeated snapshot policy failures
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling 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
- 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
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- 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
- 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
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
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- Create filters
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- Delete events from calendar
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- Delete forecasts
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- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
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- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
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- Open jobs
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- Start datafeeds
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- Update filters
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
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- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
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- Update data frame analytics jobs
- Machine learning trained model APIs
- Clear trained model deployment cache
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- Get trained models
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- Start trained model deployment
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- Migration APIs
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- Searchable snapshots APIs
- Security APIs
- Authenticate
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- Clear cache
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- Clear API key cache
- Clear service account token caches
- Create API keys
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- Create or update role mappings
- Create or update roles
- 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
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- Get service account credentials
- Get token
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- Get users
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- OpenID Connect prepare authentication
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- Has privileges user profile
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
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- Usage API
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- Definitions
- Migration guide
- Release notes
- 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
Understanding groups
editUnderstanding groups
editThis functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
For version 8.5 and above we recommend downsampling over rollups as a way to reduce your storage costs for time series data.
To preserve flexibility, Rollup Jobs are defined based on how future queries may need to use the data. Traditionally, systems force
the admin to make decisions about what metrics to rollup and on what interval. E.g. The average of cpu_time
on an hourly basis. This
is limiting; if, in the future, the admin wishes to see the average of cpu_time
on an hourly basis and partitioned by host_name
,
they are out of luck.
Of course, the admin can decide to rollup the [hour, host]
tuple on an hourly basis, but as the number of grouping keys grows, so do the
number of tuples the admin needs to configure. Furthermore, these [hours, host]
tuples are only useful for hourly rollups… daily, weekly,
or monthly rollups all require new configurations.
Rather than force the admin to decide ahead of time which individual tuples should be rolled up, Elasticsearch’s Rollup jobs are configured based on which groups are potentially useful to future queries. For example, this configuration:
"groups" : { "date_histogram": { "field": "timestamp", "fixed_interval": "1h", "delay": "7d" }, "terms": { "fields": ["hostname", "datacenter"] }, "histogram": { "fields": ["load", "net_in", "net_out"], "interval": 5 } }
Allows date_histogram
to be used on the "timestamp"
field, terms
aggregations to be used on the "hostname"
and "datacenter"
fields, and histograms
to be used on any of "load"
, "net_in"
, "net_out"
fields.
Importantly, these aggs/fields can be used in any combination. This aggregation:
"aggs" : { "hourly": { "date_histogram": { "field": "timestamp", "fixed_interval": "1h" }, "aggs": { "host_names": { "terms": { "field": "hostname" } } } } }
is just as valid as this aggregation:
"aggs" : { "hourly": { "date_histogram": { "field": "timestamp", "fixed_interval": "1h" }, "aggs": { "data_center": { "terms": { "field": "datacenter" } }, "aggs": { "host_names": { "terms": { "field": "hostname" } }, "aggs": { "load_values": { "histogram": { "field": "load", "interval": 5 } } } } } } }
You’ll notice that the second aggregation is not only substantially larger, it also swapped the position of the terms aggregation on
"hostname"
, illustrating how the order of aggregations does not matter to rollups. Similarly, while the date_histogram
is required
for rolling up data, it isn’t required while querying (although often used). For example, this is a valid aggregation for
Rollup Search to execute:
"aggs" : { "host_names": { "terms": { "field": "hostname" } } }
Ultimately, when configuring groups
for a job, think in terms of how you might wish to partition data in a query at a future date…
then include those in the config. Because Rollup Search allows any order or combination of the grouped fields, you just need to decide
if a field is useful for aggregating later, and how you might wish to use it (terms, histogram, etc).
Calendar vs fixed time intervals
editEach rollup-job must have a date histogram group with a defined interval. Elasticsearch
understands both
calendar and fixed time intervals. Fixed time
intervals are fairly easy to understand; 60s
means sixty seconds. But what
does 1M
mean? One month of time depends on which month we are talking about,
some months are longer or shorter than others. This is an example of calendar
time and the duration of that unit depends on context. Calendar units are also
affected by leap-seconds, leap-years, etc.
This is important because the buckets generated by rollup are in either calendar or fixed intervals and this limits how you can query them later. See Requests must be multiples of the config.
We recommend sticking with fixed time intervals, since they are easier to understand and are more flexible at query time. It will introduce some drift in your data during leap-events and you will have to think about months in a fixed quantity (30 days) instead of the actual calendar length. However, it is often easier than dealing with calendar units at query time.
Multiples of units are always "fixed". For example, 2h
is always the fixed
quantity 7200
seconds. Single units can be fixed or calendar depending on the
unit:
Unit | Calendar | Fixed |
---|---|---|
millisecond |
NA |
|
second |
NA |
|
minute |
|
|
hour |
|
|
day |
|
|
week |
|
NA |
month |
|
NA |
quarter |
|
NA |
year |
|
NA |
For some units where there are both fixed and calendar, you may need to express
the quantity in terms of the next smaller unit. For example, if you want a fixed
day (not a calendar day), you should specify 24h
instead of 1d
. Similarly,
if you want fixed hours, specify 60m
instead of 1h
. This is because the
single quantity entails calendar time, and limits you to querying by calendar
time in the future.
Grouping limitations with heterogeneous indices
editThere was previously a limitation in how Rollup could handle indices that had heterogeneous mappings (multiple, unrelated/non-overlapping
mappings). The recommendation at the time was to configure a separate job per data "type". For example, you might configure a separate
job for each Beats module that you had enabled (one for process
, another for filesystem
, etc).
This recommendation was driven by internal implementation details that caused document counts to be potentially incorrect if a single "merged" job was used.
This limitation has since been alleviated. As of 6.4.0, it is now considered best practice to combine all rollup configurations into a single job.
As an example, if your index has two types of documents:
{ "timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a" }
and
{ "timestamp": 1516729294000, "price": 123, "title": "Foo" }
the best practice is to combine them into a single rollup job which covers both of these document types, like this:
PUT _rollup/job/combined { "index_pattern": "data-*", "rollup_index": "data_rollup", "cron": "*/30 * * * * ?", "page_size": 1000, "groups": { "date_histogram": { "field": "timestamp", "fixed_interval": "1h", "delay": "7d" }, "terms": { "fields": [ "node", "title" ] } }, "metrics": [ { "field": "temperature", "metrics": [ "min", "max", "sum" ] }, { "field": "price", "metrics": [ "avg" ] } ] }
Doc counts and overlapping jobs
editThere was previously an issue with document counts on "overlapping" job configurations, driven by the same internal implementation detail. If there were two Rollup jobs saving to the same index, where one job is a "subset" of another job, it was possible that document counts could be incorrect for certain aggregation arrangements.
This issue has also since been eliminated in 6.4.0.
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