- 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
- Multiplexer Token Filter
- Conditional Token Filter
- 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
- 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
- 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
- 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
- Find file structure
- Flush jobs
- Forecast jobs
- Get calendars
- Get buckets
- Get overall buckets
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get machine learning info
- Get model snapshots
- 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
- 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
Glossary of terms
editGlossary of terms
edit- analysis
-
Analysis is the process of converting full text to terms. Depending on which analyzer is used, these phrases:
FOO BAR
,Foo-Bar
,foo,bar
will probably all result in the termsfoo
andbar
. These terms are what is actually stored in the index.A full text query (not a term query) for
FoO:bAR
will also be analyzed to the termsfoo
,bar
and will thus match the terms stored in the index.It is this process of analysis (both at index time and at search time) that allows Elasticsearch to perform full text queries.
- cluster
- A cluster consists of one or more nodes which share the same cluster name. Each cluster has a single master node which is chosen automatically by the cluster and which can be replaced if the current master node fails.
- cross-cluster replication (CCR)
- The cross-cluster replication feature enables you to replicate indices in remote clusters to your local cluster. For more information, see Cross-cluster replication.
- cross-cluster search (CCS)
- The cross-cluster search feature enables any node to act as a federated client across multiple clusters. See Cross-cluster search.
- document
-
A document is a JSON document which is stored in Elasticsearch. It is like a row in a table in a relational database. Each document is stored in an index and has a type and an id.
A document is a JSON object (also known in other languages as a hash / hashmap / associative array) which contains zero or more fields, or key-value pairs.
The original JSON document that is indexed will be stored in the
_source
field, which is returned by default when getting or searching for a document. - field
-
A document contains a list of fields, or key-value pairs. The value can be a simple (scalar) value (eg a string, integer, date), or a nested structure like an array or an object. A field is similar to a column in a table in a relational database.
The mapping for each field has a field type (not to be confused with document type) which indicates the type of data that can be stored in that field, eg
integer
,string
,object
. The mapping also allows you to define (amongst other things) how the value for a field should be analyzed. - filter
- A filter is a non-scoring query, meaning that it does not score documents. It is only concerned about answering the question - "Does this document match?". The answer is always a simple, binary yes or no. This kind of query is said to be made in a filter context, hence it is called a filter. Filters are simple checks for set inclusion or exclusion. In most cases, the goal of filtering is to reduce the number of documents that have to be examined.
- follower index
- Follower indices are the target indices for cross-cluster replication. They exist in your local cluster and replicate leader indices.
- id
-
The ID of a document identifies a document. The
index/id
of a document must be unique. If no ID is provided, then it will be auto-generated. (also see routing) - index
-
An index is like a table in a relational database. It has a mapping which contains a type, which contains the fields in the index.
An index is a logical namespace which maps to one or more primary shards and can have zero or more replica shards.
- leader index
- Leader indices are the source indices for cross-cluster replication. They exist on remote clusters and are replicated to follower indices.
- mapping
-
A mapping is like a schema definition in a relational database. Each index has a mapping, which defines a type, plus a number of index-wide settings.
A mapping can either be defined explicitly, or it will be generated automatically when a document is indexed.
- node
-
A node is a running instance of Elasticsearch which belongs to a cluster. Multiple nodes can be started on a single server for testing purposes, but usually you should have one node per server.
At startup, a node will use unicast to discover an existing cluster with the same cluster name and will try to join that cluster.
- primary shard
-
Each document is stored in a single primary shard. When you index a document, it is indexed first on the primary shard, then on all replicas of the primary shard.
By default, an index has one primary shard. You can specify more primary shards to scale the number of documents that your index can handle.
You cannot change the number of primary shards in an index, once the index is created. However, an index can be split into a new index using the split API.
See also routing
- query
-
A request for information from Elasticsearch. You can think of a query as a question, written in a way Elasticsearch understands. A search consists of one or more queries combined.
There are two types of queries: scoring queries and filters. For more information about query types, see Query and filter context.
- recovery
-
The process of syncing a shard copy from a source shard. Upon completion, the recovery process makes the shard copy available for queries.
Recovery automatically occurs anytime a shard moves to a different node in the same cluster, including:
- Node startup
- Node failure
- Index shard replication
- Snapshot restoration
- replica shard
-
Each primary shard can have zero or more replicas. A replica is a copy of the primary shard, and has two purposes:
- increase failover: a replica shard can be promoted to a primary shard if the primary fails
-
increase performance: get and search requests can be handled by primary or replica shards.
By default, each primary shard has one replica, but the number of replicas can be changed dynamically on an existing index. A replica shard will never be started on the same node as its primary shard.
- routing
-
When you index a document, it is stored on a single primary shard. That shard is chosen by hashing the
routing
value. By default, therouting
value is derived from the ID of the document or, if the document has a specified parent document, from the ID of the parent document (to ensure that child and parent documents are stored on the same shard).This value can be overridden by specifying a
routing
value at index time, or a routing field in the mapping. - shard
-
A shard is a single Lucene instance. It is a low-level “worker” unit which is managed automatically by Elasticsearch. An index is a logical namespace which points to primary and replica shards.
Other than defining the number of primary and replica shards that an index should have, you never need to refer to shards directly. Instead, your code should deal only with an index.
Elasticsearch distributes shards amongst all nodes in the cluster, and can move shards automatically from one node to another in the case of node failure, or the addition of new nodes.
- source field
-
By default, the JSON document that you index will be stored in the
_source
field and will be returned by all get and search requests. This allows you access to the original object directly from search results, rather than requiring a second step to retrieve the object from an ID. - term
-
A term is an exact value that is indexed in Elasticsearch. The terms
foo
,Foo
,FOO
are NOT equivalent. Terms (i.e. exact values) can be searched for using term queries. - text
-
Text (or full text) is ordinary unstructured text, such as this paragraph. By default, text will be analyzed into terms, which is what is actually stored in the index.
Text fields need to be analyzed at index time in order to be searchable as full text, and keywords in full text queries must be analyzed at search time to produce (and search for) the same terms that were generated at index time.
- type
-
A type used to represent the type of document, e.g. an
email
, auser
, or atweet
. Types are deprecated and are in the process of being removed. See Removal of mapping types.