- Elasticsearch Guide: other versions:
- Getting Started
- 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
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Installing X-Pack
- Set up X-Pack
- Configuring X-Pack Java Clients
- X-Pack Settings
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- API Conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- 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
- Bucket Aggregations
- Adjacency Matrix Aggregation
- Children Aggregation
- Composite Aggregation
- Date Histogram Aggregation
- Date Range Aggregation
- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
- GeoHash grid Aggregation
- Global Aggregation
- Histogram Aggregation
- IP Range Aggregation
- Missing Aggregation
- Nested 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
- 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
- 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
- Simple Pattern Tokenizer
- Simple Pattern Split Tokenizer
- Path Hierarchy Tokenizer
- Path Hierarchy Tokenizer Examples
- Token Filters
- Standard Token Filter
- 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
- 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
- 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
- Classic Token Filter
- Apostrophe Token Filter
- Decimal Digit Token Filter
- Fingerprint Token Filter
- Minhash Token Filter
- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
- Convert Processor
- Date Processor
- Date Index Name Processor
- Fail Processor
- Foreach Processor
- Grok Processor
- Gsub Processor
- Join Processor
- JSON Processor
- KV Processor
- Lowercase Processor
- Remove Processor
- Rename Processor
- Script Processor
- Set Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- Dot Expander Processor
- URL Decode Processor
- SQL Access
- Monitor a cluster
- Rolling up historical data
- 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
- Security settings
- Auditing settings
- Getting started with security
- How security works
- User authentication
- Configuring SAML single-sign-on on the Elastic Stack
- User authorization
- Auditing security events
- Encrypting communications
- Restricting connections with IP filtering
- Cross cluster search, tribe, clients, and integrations
- Reference
- Troubleshooting
- Can’t log in after upgrading to 6.3.2
- 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 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
- X-Pack APIs
- Info API
- Explore API
- Licensing APIs
- Migration APIs
- Machine Learning APIs
- Add Events to Calendar
- Add Jobs to Calendar
- Close Jobs
- Create Calendar
- Create Datafeeds
- Create Jobs
- Delete Calendar
- Delete Datafeeds
- Delete Events from Calendar
- Delete Jobs
- Delete Jobs from Calendar
- Delete Model Snapshots
- 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 Model Snapshots
- Get Scheduled Events
- Get Records
- Open Jobs
- Post Data to Jobs
- Preview Datafeeds
- Revert Model Snapshots
- Start Datafeeds
- Stop Datafeeds
- Update Datafeeds
- Update Jobs
- Update Model Snapshots
- Rollup APIs
- Security APIs
- Authenticate API
- Change passwords API
- Clear Cache API
- Create or update role mappings API
- Clear roles cache API
- Create or update roles API
- Create or update users API
- Delete role mappings API
- Delete roles API
- Delete users API
- Disable users API
- Enable users API
- Get role mappings API
- Get roles API
- Get token API
- Get users API
- Privilege APIs
- Invalidate token API
- SSL Certificate API
- Watcher APIs
- Definitions
- Command line tools
- How To
- Testing
- Glossary of terms
- Release Highlights
- Breaking changes
- Release Notes
- Elasticsearch version 6.3.2
- Elasticsearch version 6.3.1
- Elasticsearch version 6.3.0
- Elasticsearch version 6.2.4
- Elasticsearch version 6.2.3
- Elasticsearch version 6.2.2
- Elasticsearch version 6.2.1
- Elasticsearch version 6.2.0
- Elasticsearch version 6.1.4
- Elasticsearch version 6.1.3
- Elasticsearch version 6.1.2
- Elasticsearch version 6.1.1
- Elasticsearch version 6.1.0
- Elasticsearch version 6.0.1
- Elasticsearch version 6.0.0
- Elasticsearch version 6.0.0-rc2
- Elasticsearch version 6.0.0-rc1
- Elasticsearch version 6.0.0-beta2
- Elasticsearch version 6.0.0-beta1
- Elasticsearch version 6.0.0-alpha2
- Elasticsearch version 6.0.0-alpha1
- Elasticsearch version 6.0.0-alpha1 (Changes previously released in 5.x)
Tune for disk usage
editTune for disk usage
editDisable the features you do not need
editBy default Elasticsearch indexes and adds doc values to most fields so that they
can be searched and aggregated out of the box. For instance if you have a numeric
field called foo
that you need to run histograms on but that you never need to
filter on, you can safely disable indexing on this field in your
mappings:
PUT index { "mappings": { "_doc": { "properties": { "foo": { "type": "integer", "index": false } } } } }
text
fields store normalization factors in the index in order to be
able to score documents. If you only need matching capabilities on a text
field but do not care about the produced scores, you can configure Elasticsearch
to not write norms to the index:
PUT index { "mappings": { "_doc": { "properties": { "foo": { "type": "text", "norms": false } } } } }
text
fields also store frequencies and positions in the index by
default. Frequencies are used to compute scores and positions are used to run
phrase queries. If you do not need to run phrase queries, you can tell
Elasticsearch to not index positions:
PUT index { "mappings": { "_doc": { "properties": { "foo": { "type": "text", "index_options": "freqs" } } } } }
Furthermore if you do not care about scoring either, you can configure Elasticsearch to just index matching documents for every term. You will still be able to search on this field, but phrase queries will raise errors and scoring will assume that terms appear only once in every document.
PUT index { "mappings": { "_doc": { "properties": { "foo": { "type": "text", "norms": false, "index_options": "freqs" } } } } }
Don’t use default dynamic string mappings
editThe default dynamic string mappings will index string fields
both as text
and keyword
. This is wasteful if you only
need one of them. Typically an id
field will only need to be indexed as a
keyword
while a body
field will only need to be indexed as a text
field.
This can be disabled by either configuring explicit mappings on string fields
or setting up dynamic templates that will map string fields as either text
or keyword
.
For instance, here is a template that can be used in order to only map string
fields as keyword
:
PUT index { "mappings": { "_doc": { "dynamic_templates": [ { "strings": { "match_mapping_type": "string", "mapping": { "type": "keyword" } } } ] } } }
Watch your shard size
editLarger shards are going to be more efficient at storing data. To increase the size of your shards, you can decrease the number of primary shards in an index by creating indices with less primary shards, creating less indices (e.g. by leveraging the Rollover API), or modifying an existing index using the Shrink API.
Keep in mind that large shard sizes come with drawbacks, such as long full recovery times.
Disable _all
editThe _all
field indexes the value of all fields of a
document and can use significant space. If you never need to search against all
fields at the same time, it can be disabled.
Disable _source
editThe _source
field stores the original JSON body of the document. If you don’t need access to it you can disable it. However, APIs that needs access to _source
such as update and reindex won’t work.
Use best_compression
editThe _source
and stored fields can easily take a non negligible amount of disk
space. They can be compressed more aggressively by using the best_compression
codec.
Force Merge
editIndices in Elasticsearch are stored in one or more shards. Each shard is a Lucene index and made up of one or more segments - the actual files on disk. Larger segments are more efficient for storing data.
The _forcemerge
API can be used to reduce the number of segments per shard. In many cases, the number of segments can be reduced to one per shard by setting max_num_segments=1
.
Shrink Index
editThe Shrink API allows you to reduce the number of shards in an index. Together with the Force Merge API above, this can significantly reduce the number of shards and segments of an index.
Use the smallest numeric type that is sufficient
editThe type that you pick for numeric data can have a significant impact
on disk usage. In particular, integers should be stored using an integer type
(byte
, short
, integer
or long
) and floating points should either be
stored in a scaled_float
if appropriate or in the smallest type that fits the
use-case: using float
over double
, or half_float
over float
will help
save storage.
Use index sorting to colocate similar documents
editWhen Elasticsearch stores _source
, it compresses multiple documents at once
in order to improve the overall compression ratio. For instance it is very
common that documents share the same field names, and quite common that they
share some field values, especially on fields that have a low cardinality or
a zipfian distribution.
By default documents are compressed together in the order that they are added to the index. If you enabled index sorting then instead they are compressed in sorted order. Sorting documents with similar structure, fields, and values together should improve the compression ratio.
Put fields in the same order in documents
editDue to the fact that multiple documents are compressed together into blocks,
it is more likely to find longer duplicate strings in those _source
documents
if fields always occur in the same order.
On this page
- Disable the features you do not need
- Don’t use default dynamic string mappings
- Watch your shard size
- Disable
_all
- Disable
_source
- Use
best_compression
- Force Merge
- Shrink Index
- Use the smallest numeric type that is sufficient
- Use index sorting to colocate similar documents
- Put fields in the same order in documents