- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.2
- 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
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- License settings
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- Advanced configuration
- Important system configuration
- Bootstrap Checks
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- Client JVM check
- Use serial collector check
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- OnError and OnOutOfMemoryError checks
- Early-access check
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- Starting Elasticsearch
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- Discovery and cluster formation
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- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
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- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
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- CJK bigram
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- Classic
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- Conditional
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- Dictionary decompounder
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- Keep types
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- KStem
- Length
- Limit token count
- Lowercase
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- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
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- Remove duplicates
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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
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
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- Set
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- Aliases
- Search your data
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
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- Search across clusters
- Search multiple data streams and indices
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- Search templates
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- Aggregations
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- Filters
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- Missing
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- Terms
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- Subtleties of bucketing range fields
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- Average bucket
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- EQL
- SQL
- Overview
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- Security
- SQL REST API
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- Aggregate Functions
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- 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
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- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
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- Updating node security certificates
- User authentication
- Built-in users
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- Active Directory user authentication
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- OpenID Connect authentication
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- SAML authentication
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- JWT 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
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- Customizing roles and authorization
- Enable audit logging
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- Troubleshooting
- Some settings are not returned via the nodes settings API
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- Limitations
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- Command line tools
- elasticsearch-certgen
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- elasticsearch-croneval
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- How to
- REST APIs
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- Compact and aligned text (CAT) APIs
- cat aliases
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- Cluster APIs
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- Exists
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- Index lifecycle management APIs
- Create or update lifecycle policy
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- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
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- Add events to calendar
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- Get buckets
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
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- Machine learning trained model APIs
- Create or update trained model aliases
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- Migration APIs
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- Snapshot and restore APIs
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- SQL APIs
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- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- 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
Scripts, caching, and search speed
editScripts, caching, and search speed
editElasticsearch performs a number of optimizations to make using scripts as fast as possible. One important optimization is a script cache. The compiled script is placed in a cache so that requests that reference the script do not incur a compilation penalty.
Cache sizing is important. Your script cache should be large enough to hold all of the scripts that users need to be accessed concurrently.
If you see a large number of script cache evictions and a rising number of compilations in node stats, your cache might be too small.
All scripts are cached by default so that they only need to be recompiled
when updates occur. By default, scripts do not have a time-based expiration.
You can change this behavior by using the script.cache.expire
setting.
Use the script.cache.max_size
setting to configure the size of the cache.
The size of scripts is limited to 65,535 bytes. Set the value of script.max_size_in_bytes
to increase that soft limit. If your scripts are
really large, then consider using a
native script engine.
Improving search speed
editScripts are incredibly useful, but can’t use Elasticsearch’s index structures or related optimizations. This relationship can sometimes result in slower search speeds.
If you often use scripts to transform indexed data, you can make search faster by transforming data during ingest instead. However, that often means slower index speeds. Let’s look at a practical example to illustrate how you can increase search speed.
When running searches, it’s common to sort results by the sum of two values.
For example, consider an index named my_test_scores
that contains test score
data. This index includes two fields of type long
:
-
math_score
-
verbal_score
You can run a query with a script that adds these values together. There’s
nothing wrong with this approach, but the query will be slower because the
script valuation occurs as part of the request. The following request returns
documents where grad_year
equals 2099
, and sorts by the results by the
valuation of the script.
GET /my_test_scores/_search { "query": { "term": { "grad_year": "2099" } }, "sort": [ { "_script": { "type": "number", "script": { "source": "doc['math_score'].value + doc['verbal_score'].value" }, "order": "desc" } } ] }
If you’re searching a small index, then including the script as part of your search query can be a good solution. If you want to make search faster, you can perform this calculation during ingest and index the sum to a field instead.
First, we’ll add a new field to the index named total_score
, which will
contain sum of the math_score
and verbal_score
field values.
PUT /my_test_scores/_mapping { "properties": { "total_score": { "type": "long" } } }
Next, use an ingest pipeline containing the
script processor to calculate the sum of math_score
and
verbal_score
and index it in the total_score
field.
PUT _ingest/pipeline/my_test_scores_pipeline { "description": "Calculates the total test score", "processors": [ { "script": { "source": "ctx.total_score = (ctx.math_score + ctx.verbal_score)" } } ] }
To update existing data, use this pipeline to reindex any
documents from my_test_scores
to a new index named my_test_scores_2
.
POST /_reindex { "source": { "index": "my_test_scores" }, "dest": { "index": "my_test_scores_2", "pipeline": "my_test_scores_pipeline" } }
Continue using the pipeline to index any new documents to my_test_scores_2
.
POST /my_test_scores_2/_doc/?pipeline=my_test_scores_pipeline { "student": "kimchy", "grad_year": "2099", "math_score": 1200, "verbal_score": 800 }
These changes slow the index process, but allow for faster searches. Instead of
using a script, you can sort searches made on my_test_scores_2
using the
total_score
field. The response is near real-time! Though this process slows
ingest time, it greatly increases queries at search time.
GET /my_test_scores_2/_search { "query": { "term": { "grad_year": "2099" } }, "sort": [ { "total_score": { "order": "desc" } } ] }
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