- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.0
- Quick start
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- 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
- Index lifecycle management settings
- Index management settings
- Index recovery settings
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- License settings
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- Thread pools
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- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
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- 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
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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
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- 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
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
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- JSON
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- Lowercase
- Network direction
- Pipeline
- Registered domain
- Remove
- Rename
- Script
- Set
- Set security user
- Sort
- Split
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- 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
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
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- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Geo-distance
- Geohash grid
- Geotile grid
- Global
- Histogram
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Range
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- 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
- 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
- EQL
- 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
- Overview
- Concepts
- Automate rollover
- Tutorial: Customize built-in policies
- 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
- 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
- Configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- 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 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
- 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 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
- 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
- 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
- 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 deployment
- Start trained model deployment
- Stop trained model deployment
- Migration APIs
- Node lifecycle APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search 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
- 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
- Get service accounts
- Get service account credentials
- 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
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Dependencies and versions
Grokking grok
editGrokking grok
editGrok is a regular expression dialect that supports reusable aliased expressions. Grok works really well with syslog logs, Apache and other webserver logs, mysql logs, and generally any log format that is written for humans and not computer consumption.
Grok sits on top of the Oniguruma regular expression library, so any regular expressions are valid in grok. Grok uses this regular expression language to allow naming existing patterns and combining them into more complex patterns that match your fields.
Grok patterns
editThe Elastic Stack ships with numerous predefined grok patterns that simplify working with grok. The syntax for reusing grok patterns takes one of the following forms:
|
|
|
-
SYNTAX
-
The name of the pattern that will match your text. For example,
NUMBER
andIP
are both patterns that are provided within the default patterns set. TheNUMBER
pattern matches data like3.44
, and theIP
pattern matches data like55.3.244.1
. -
ID
-
The identifier you give to the piece of text being matched. For example,
3.44
could be the duration of an event, so you might call itduration
. The string55.3.244.1
might identify theclient
making a request. -
TYPE
-
The data type you want to cast your named field.
int
,long
,double
,float
andboolean
are supported types.
For example, let’s say you have message data that looks like this:
3.44 55.3.244.1
The first value is a number, followed by what appears to be an IP address. You can match this text by using the following grok expression:
%{NUMBER:duration} %{IP:client}
Migrating to Elastic Common Schema (ECS)
editTo ease migration to the Elastic Common Schema (ECS), a new set of ECS-compliant patterns is available in addition to the existing patterns. The new ECS pattern definitions capture event field names that are compliant with the schema.
The ECS pattern set has all of the pattern definitions from the legacy set, and
is a drop-in replacement. Use the
ecs-compatability
setting to switch modes.
New features and enhancements will be added to the ECS-compliant files. The legacy patterns may still receive bug fixes which are backwards compatible.
Use grok patterns in Painless scripts
editYou can incorporate predefined grok patterns into Painless scripts to extract data. To test your script, use either the field contexts of the Painless execute API or create a runtime field that includes the script. Runtime fields offer greater flexibility and accept multiple documents, but the Painless execute API is a great option if you don’t have write access on a cluster where you’re testing a script.
If you need help building grok patterns to match your data, use the Grok Debugger tool in Kibana.
For example, if you’re working with Apache log data, you can use the
%{COMMONAPACHELOG}
syntax, which understands the structure of Apache logs. A
sample document might look like this:
"timestamp":"2020-04-30T14:30:17-05:00","message":"40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"
To extract the IP address from the message
field, you can write a Painless
script that incorporates the %{COMMONAPACHELOG}
syntax. You can test this
script using the ip
field context of the Painless execute API, but let’s use a runtime field
instead.
Based on the sample document, index the @timestamp
and message
fields. To
remain flexible, use wildcard
as the field type for message
:
PUT /my-index/ { "mappings": { "properties": { "@timestamp": { "format": "strict_date_optional_time||epoch_second", "type": "date" }, "message": { "type": "wildcard" } } } }
Next, use the bulk API to index some log data into
my-index
.
POST /my-index/_bulk?refresh {"index":{}} {"timestamp":"2020-04-30T14:30:17-05:00","message":"40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"} {"index":{}} {"timestamp":"2020-04-30T14:30:53-05:00","message":"232.0.0.0 - - [30/Apr/2020:14:30:53 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"} {"index":{}} {"timestamp":"2020-04-30T14:31:12-05:00","message":"26.1.0.0 - - [30/Apr/2020:14:31:12 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"} {"index":{}} {"timestamp":"2020-04-30T14:31:19-05:00","message":"247.37.0.0 - - [30/Apr/2020:14:31:19 -0500] \"GET /french/splash_inet.html HTTP/1.0\" 200 3781"} {"index":{}} {"timestamp":"2020-04-30T14:31:22-05:00","message":"247.37.0.0 - - [30/Apr/2020:14:31:22 -0500] \"GET /images/hm_nbg.jpg HTTP/1.0\" 304 0"} {"index":{}} {"timestamp":"2020-04-30T14:31:27-05:00","message":"252.0.0.0 - - [30/Apr/2020:14:31:27 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"} {"index":{}} {"timestamp":"2020-04-30T14:31:28-05:00","message":"not a valid apache log"}
Incorporate grok patterns and scripts in runtime fields
editNow you can define a runtime field in the mappings that includes your Painless
script and grok pattern. If the pattern matches, the script emits the value of
the matching IP address. If the pattern doesn’t match (clientip != null
), the
script just returns the field value without crashing.
PUT my-index/_mappings { "runtime": { "http.clientip": { "type": "ip", "script": """ String clientip=grok('%{COMMONAPACHELOG}').extract(doc["message"].value)?.clientip; if (clientip != null) emit(clientip); """ } } }
Alternatively, you can define the same runtime field but in the context of a
search request. The runtime definition and the script are exactly the same as
the one defined previously in the index mapping. Just copy that definition into
the search request under the runtime_mappings
section and include a query
that matches on the runtime field. This query returns the same results as if
you defined a search query for the http.clientip
runtime field in your index mappings, but only in the context of this specific
search:
GET my-index/_search { "runtime_mappings": { "http.clientip": { "type": "ip", "script": """ String clientip=grok('%{COMMONAPACHELOG}').extract(doc["message"].value)?.clientip; if (clientip != null) emit(clientip); """ } }, "query": { "match": { "http.clientip": "40.135.0.0" } }, "fields" : ["http.clientip"] }
Return calculated results
editUsing the http.clientip
runtime field, you can define a simple query to run a
search for a specific IP address and return all related fields. The fields
parameter on the _search
API works for all fields,
even those that weren’t sent as part of the original _source
:
GET my-index/_search { "query": { "match": { "http.clientip": "40.135.0.0" } }, "fields" : ["http.clientip"] }
The response includes the specific IP address indicated in your search query.
The grok pattern within the Painless script extracted this value from the
message
field at runtime.
{ "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "my-index", "_id" : "1iN2a3kBw4xTzEDqyYE0", "_score" : 1.0, "_source" : { "timestamp" : "2020-04-30T14:30:17-05:00", "message" : "40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736" }, "fields" : { "http.clientip" : [ "40.135.0.0" ] } } ] } }
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