- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 7.7
- 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 recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging configuration
- Machine learning settings
- Monitoring settings
- Node
- Network settings
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot lifecycle management settings
- SQL access 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
- Search your data
- Query DSL
- 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
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted Avg Aggregation
- Boxplot Aggregation
- Cardinality Aggregation
- Stats Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
- Geo Centroid Aggregation
- Max Aggregation
- Min Aggregation
- Median Absolute Deviation Aggregation
- Percentiles Aggregation
- Percentile Ranks Aggregation
- Scripted Metric Aggregation
- String Stats Aggregation
- Sum Aggregation
- Top Hits Aggregation
- Top Metrics Aggregation
- Value Count 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
- Rare Terms Aggregation
- Reverse nested Aggregation
- Sampler Aggregation
- Significant Terms Aggregation
- Significant Text Aggregation
- Terms Aggregation
- Subtleties of bucketing range fields
- Pipeline Aggregations
- Bucket Script Aggregation
- Bucket Selector Aggregation
- Bucket Sort Aggregation
- Avg Bucket Aggregation
- Max Bucket Aggregation
- Min Bucket Aggregation
- Sum Bucket Aggregation
- Cumulative Cardinality Aggregation
- Cumulative Sum Aggregation
- Derivative Aggregation
- Percentiles Bucket Aggregation
- Moving Average Aggregation
- Moving Function Aggregation
- Serial Differencing Aggregation
- Stats Bucket Aggregation
- Extended Stats Bucket Aggregation
- Matrix Aggregations
- Caching heavy aggregations
- Returning only aggregation results
- Aggregation Metadata
- Returning the type of the aggregation
- Indexing aggregation results with transforms
- Metrics Aggregations
- Scripting
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Char Group Tokenizer
- Classic Tokenizer
- Edge n-gram tokenizer
- Keyword Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- N-gram tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Pattern Tokenizer
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Standard Tokenizer
- Thai Tokenizer
- UAX URL Email Tokenizer
- Whitespace Tokenizer
- 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 modules
- Ingest node
- Pipeline Definition
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Enrich your data
- Processors
- Append Processor
- Bytes Processor
- Circle Processor
- Convert Processor
- CSV Processor
- Date Processor
- Date Index Name Processor
- Dissect Processor
- Dot Expander Processor
- Drop Processor
- Enrich Processor
- Fail Processor
- Foreach Processor
- GeoIP Processor
- Grok Processor
- Gsub Processor
- HTML Strip Processor
- Inference 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
- ILM: Manage the index lifecycle
- 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
- Security privileges
- Document level security
- Field level security
- Granting privileges for indices 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
- 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
- Alerting on cluster and index events
- Command line tools
- How To
- Glossary of terms
- REST APIs
- API conventions
- 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 shards
- cat segments
- 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
- Document APIs
- Enrich APIs
- Explore API
- Index APIs
- Add index alias
- Analyze
- Clear cache
- Clone index
- Close index
- Create index
- Delete index
- Delete index alias
- Delete index template
- Flush
- Force merge
- Freeze index
- Get field mapping
- Get index
- Get index alias
- Get index settings
- Get index template
- Get mapping
- Index alias exists
- Index exists
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists
- Open index
- Put index template
- Put mapping
- Refresh
- Rollover index
- Shrink index
- Split index
- Synced flush
- Type exists
- Unfreeze index
- Update index alias
- Update index settings
- Index lifecycle management API
- Ingest APIs
- Info API
- Licensing APIs
- Machine learning anomaly detection 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
- 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 filter
- Update jobs
- Update model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Create inference trained model
- Delete data frame analytics jobs
- Delete inference trained model
- Evaluate data frame analytics
- Explain data frame analytics API
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Get inference trained model
- Get inference trained model stats
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Migration APIs
- Reload search analyzers
- Rollup APIs
- Search 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
- 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
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect authenticate API
- OpenID Connect logout API
- SAML prepare authentication API
- SAML authenticate API
- SAML logout API
- SAML invalidate API
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management API
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Breaking changes
- Release notes
- 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
Data in: documents and indices
editData in: documents and indices
editElasticsearch is a distributed document store. Instead of storing information as rows of columnar data, Elasticsearch stores complex data structures that have been serialized as JSON documents. When you have multiple Elasticsearch nodes in a cluster, stored documents are distributed across the cluster and can be accessed immediately from any node.
When a document is stored, it is indexed and fully searchable in near real-time--within 1 second. Elasticsearch uses a data structure called an inverted index that supports very fast full-text searches. An inverted index lists every unique word that appears in any document and identifies all of the documents each word occurs in.
An index can be thought of as an optimized collection of documents and each document is a collection of fields, which are the key-value pairs that contain your data. By default, Elasticsearch indexes all data in every field and each indexed field has a dedicated, optimized data structure. For example, text fields are stored in inverted indices, and numeric and geo fields are stored in BKD trees. The ability to use the per-field data structures to assemble and return search results is what makes Elasticsearch so fast.
Elasticsearch also has the ability to be schema-less, which means that documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document. When dynamic mapping is enabled, Elasticsearch automatically detects and adds new fields to the index. This default behavior makes it easy to index and explore your data—just start indexing documents and Elasticsearch will detect and map booleans, floating point and integer values, dates, and strings to the appropriate Elasticsearch datatypes.
Ultimately, however, you know more about your data and how you want to use it than Elasticsearch can. You can define rules to control dynamic mapping and explicitly define mappings to take full control of how fields are stored and indexed.
Defining your own mappings enables you to:
- Distinguish between full-text string fields and exact value string fields
- Perform language-specific text analysis
- Optimize fields for partial matching
- Use custom date formats
-
Use data types such as
geo_point
andgeo_shape
that cannot be automatically detected
It’s often useful to index the same field in different ways for different purposes. For example, you might want to index a string field as both a text field for full-text search and as a keyword field for sorting or aggregating your data. Or, you might choose to use more than one language analyzer to process the contents of a string field that contains user input.
The analysis chain that is applied to a full-text field during indexing is also used at search time. When you query a full-text field, the query text undergoes the same analysis before the terms are looked up in the index.