- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- 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
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- API conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Value Count Aggregation
- Median Absolute Deviation 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
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Pipeline Aggregations
- Avg Bucket Aggregation
- Derivative Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Moving Function Aggregation
- Cumulative Sum Aggregation
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Serial Differencing Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Metrics Aggregations
- Indices APIs
- Create Index
- Delete Index
- Get Index
- Indices Exists
- Open / Close Index API
- Shrink Index
- Split Index
- Rollover Index
- Put Mapping
- Get Mapping
- Get Field Mapping
- Types Exists
- Index Aliases
- Update Indices Settings
- Get Settings
- Analyze
- Index Templates
- Indices Stats
- Indices Segments
- Indices Recovery
- Indices Shard Stores
- Clear Cache
- Flush
- Refresh
- Force Merge
- cat APIs
- Cluster APIs
- Query DSL
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Standard Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- Whitespace Tokenizer
- UAX URL Email Tokenizer
- Classic Tokenizer
- Thai Tokenizer
- NGram Tokenizer
- Edge NGram Tokenizer
- Keyword Tokenizer
- Pattern Tokenizer
- Char Group Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Token Filters
- ASCII Folding Token Filter
- Flatten Graph Token Filter
- Length Token Filter
- Lowercase Token Filter
- Uppercase Token Filter
- NGram Token Filter
- Edge NGram Token Filter
- Porter Stem Token Filter
- Shingle Token Filter
- Stop Token Filter
- Word Delimiter Token Filter
- Word Delimiter Graph Token Filter
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- Predicate Token Filter Script
- Stemmer Token Filter
- Stemmer Override Token Filter
- Keyword Marker Token Filter
- Keyword Repeat Token Filter
- KStem Token Filter
- Snowball Token Filter
- Phonetic Token Filter
- Synonym Token Filter
- Parsing synonym files
- Synonym Graph Token Filter
- Compound Word Token Filters
- Reverse Token Filter
- Elision Token Filter
- Truncate Token Filter
- Unique Token Filter
- Pattern Capture Token Filter
- Pattern Replace Token Filter
- Trim Token Filter
- Limit Token Count Token Filter
- Hunspell Token Filter
- Common Grams Token Filter
- Normalization Token Filter
- CJK Width Token Filter
- CJK Bigram Token Filter
- Delimited Payload Token Filter
- Keep Words Token Filter
- Keep Types Token Filter
- Exclude mode settings example
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- MinHash Token Filter
- Remove Duplicates Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Bytes Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip 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
- Managing the index lifecycle
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- SQL CLI
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- Functions and Operators
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- Aggregate Functions
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- Date/Time and Interval Functions and Operators
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- Mathematical Functions
- String Functions
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- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Monitor a cluster
- Frozen indices
- Set up a cluster for high availability
- Roll up or transform your data
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management API
- Licensing APIs
- Machine learning APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendar
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- Create filter
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- Delete datafeeds
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- Find file structure
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- Stop datafeeds
- Update datafeeds
- Update filter
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- Migration APIs
- Rollup 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
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete users
- Disable users
- Enable users
- Get API key information
- Get application 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
- SSL certificate
- Transform APIs
- Unfreeze index
- Watcher APIs
- Definitions
- Secure a cluster
- Overview
- Configuring security
- Encrypting communications in Elasticsearch
- Encrypting communications in an Elasticsearch Docker Container
- Enabling cipher suites for stronger encryption
- Separating node-to-node and client traffic
- Configuring an Active Directory realm
- Configuring a file realm
- Configuring an LDAP realm
- Configuring a native realm
- Configuring a PKI realm
- Configuring a SAML realm
- Configuring a Kerberos realm
- Security files
- FIPS 140-2
- How security works
- 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
- Auditing security events
- 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
- Testing
- Glossary of terms
- Release highlights
- Breaking changes
- Release notes
- 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
Configuring a Kerberos realm
editConfiguring a Kerberos realm
editKerberos is used to protect services and uses a ticket-based authentication protocol to authenticate users. You can configure Elasticsearch to use the Kerberos V5 authentication protocol, which is an industry standard protocol, to authenticate users. In this scenario, clients must present Kerberos tickets for authentication.
In Kerberos, users authenticate with an authentication service and later with a ticket granting service to generate a TGT (ticket-granting ticket). This ticket is then presented to the service for authentication. Refer to your Kerberos installation documentation for more information about obtaining TGT. Elasticsearch clients must first obtain a TGT then initiate the process of authenticating with Elasticsearch.
For a summary of Kerberos terminology, see Kerberos authentication.
Before you begin
edit-
Deploy Kerberos.
You must have the Kerberos infrastructure set up in your environment.
Kerberos requires a lot of external services to function properly, such as time synchronization between all machines and working forward and reverse DNS mappings in your domain. Refer to your Kerberos documentation for more details.
These instructions do not cover setting up and configuring your Kerberos deployment. Where examples are provided, they pertain to an MIT Kerberos V5 deployment. For more information, see MIT Kerberos documentation
-
Configure Java GSS.
Elasticsearch uses Java GSS framework support for Kerberos authentication. To support Kerberos authentication, Elasticsearch needs the following files:
-
krb5.conf
, a Kerberos configuration file -
A
keytab
file that contains credentials for the Elasticsearch service principal
The configuration requirements depend on your Kerberos setup. Refer to your Kerberos documentation to configure the
krb5.conf
file.For more information on Java GSS, see Java GSS Kerberos requirements
-
Create a Kerberos realm
editTo configure a Kerberos realm in Elasticsearch:
-
Configure the JVM to find the Kerberos configuration file.
Elasticsearch uses Java GSS and JAAS Krb5LoginModule to support Kerberos authentication using a Simple and Protected GSSAPI Negotiation Mechanism (SPNEGO) mechanism. The Kerberos configuration file (
krb5.conf
) provides information such as the default realm, the Key Distribution Center (KDC), and other configuration details required for Kerberos authentication. When the JVM needs some configuration properties, it tries to find those values by locating and loading this file. The JVM system property to configure the file path isjava.security.krb5.conf
. To configure JVM system properties see Setting JVM options. If this system property is not specified, Java tries to locate the file based on the conventions.It is recommended that this system property be configured for Elasticsearch. The method for setting this property depends on your Kerberos infrastructure. Refer to your Kerberos documentation for more details.
For more information, see krb5.conf
-
Create a keytab for the Elasticsearch node.
A keytab is a file that stores pairs of principals and encryption keys. Elasticsearch uses the keys from the keytab to decrypt the tickets presented by the user. You must create a keytab for Elasticsearch by using the tools provided by your Kerberos implementation. For example, some tools that create keytabs are
ktpass.exe
on Windows andkadmin
for MIT Kerberos. -
Put the keytab file in the Elasticsearch configuration directory.
Make sure that this keytab file has read permissions. This file contains credentials, therefore you must take appropriate measures to protect it.
Elasticsearch uses Kerberos on the HTTP network layer, therefore there must be a keytab file for the HTTP service principal on every Elasticsearch node. The service principal name must have the format
HTTP/es.domain.local@ES.DOMAIN.LOCAL
. The keytab files are unique for each node since they include the hostname. An Elasticsearch node can act as any principal a client requests as long as that principal and its credentials are found in the configured keytab. -
Create a Kerberos realm.
To enable Kerberos authentication in Elasticsearch, you must add a Kerberos realm in the realm chain.
You can configure only one Kerberos realm on Elasticsearch nodes.
To configure a Kerberos realm, there are a few mandatory realm settings and other optional settings that you need to configure in the
elasticsearch.yml
configuration file. Add a realm configuration under thexpack.security.authc.realms.kerberos
namespace.The most common configuration for a Kerberos realm is as follows:
xpack.security.authc.realms.kerberos.kerb1: order: 3 keytab.path: es.keytab remove_realm_name: false
The
username
is extracted from the ticket presented by user and usually has the formatusername@REALM
. Thisusername
is used for mapping roles to the user. If realm settingremove_realm_name
is set totrue
, the realm part (@REALM
) is removed. The resultingusername
is used for role mapping.For detailed information of available realm settings, see Kerberos realm settings.
- Restart Elasticsearch
-
Map Kerberos users to roles.
The
kerberos
realm enables you to map Kerberos users to roles. You can configure these role mappings by using the role-mapping API. You identify users by theirusername
field.The following example uses the role mapping API to map
user@REALM
to the rolesmonitoring
anduser
:POST /_security/role_mapping/kerbrolemapping { "roles" : [ "monitoring_user" ], "enabled": true, "rules" : { "field" : { "username" : "user@REALM" } } }
In case you want to support Kerberos cross realm authentication you may need to map roles based on the Kerberos realm name. For such scenarios following are the additional user metadata available for role mapping: -
kerberos_realm
will be set to Kerberos realm name. -kerberos_user_principal_name
will be set to user principal name from the Kerberos ticket.For more information, see Mapping users and groups to roles.
The Kerberos realm supports authorization realms as an alternative to role mapping.
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