- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.13
- Quick start
- Set up Elasticsearch
- Installing Elasticsearch
- Run Elasticsearch locally
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Miscellaneous cluster settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- Health Diagnostic settings
- Index lifecycle management settings
- Data stream lifecycle settings
- Index management settings
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- License settings
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- Nodes
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- Transforms settings
- Thread pools
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- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
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- 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
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- Early-access check
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- Bootstrap Checks for X-Pack
- Starting Elasticsearch
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- Discovery and cluster formation
- Add and remove nodes in your cluster
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- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
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- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
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- Phonetic
- Porter stem
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- Synonym
- Synonym graph
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- Word delimiter
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- Character filters reference
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- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
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- Bytes
- Circle
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- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
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- Fail
- Fingerprint
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- Grok
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- Inference
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- Set
- Set security user
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- User agent
- Ingest pipelines in Search
- Aliases
- Search your data
- Query DSL
- Aggregations
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- Diversified sampler
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- Global
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- Significant terms
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- Terms
- Time series
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- Subtleties of bucketing range fields
- Metrics aggregations
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- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
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- Change point
- Cumulative cardinality
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- Extended stats bucket
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- Max bucket
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- Moving function
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- Percentiles bucket
- Serial differencing
- Stats bucket
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- Bucket aggregations
- Geospatial analysis
- EQL
- ES|QL
- SQL
- 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
- 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
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled automatically
- Manually configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- User profiles
- Realms
- Realm chains
- Security domains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- JWT authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Looking up users without authentication
- 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
- Role restriction
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams 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
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- 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
- Watcher
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- How to
- Troubleshooting
- Fix common cluster issues
- Diagnose unassigned shards
- Add a missing tier to the system
- Allow Elasticsearch to allocate the data in the system
- Allow Elasticsearch to allocate the index
- Indices mix index allocation filters with data tiers node roles to move through data tiers
- Not enough nodes to allocate all shard replicas
- Total number of shards for an index on a single node exceeded
- Total number of shards per node has been reached
- Troubleshooting corruption
- Fix data nodes out of disk
- Fix master nodes out of disk
- Fix other role nodes out of disk
- Start index lifecycle management
- Start Snapshot Lifecycle Management
- Restore from snapshot
- Troubleshooting broken repositories
- Addressing repeated snapshot policy failures
- Troubleshooting an unstable cluster
- Troubleshooting discovery
- Troubleshooting monitoring
- Troubleshooting transforms
- Troubleshooting Watcher
- Troubleshooting searches
- Troubleshooting shards capacity health issues
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Behavioral Analytics APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat component templates
- 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 segments
- cat shards
- 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
- Health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Prevalidate node removal
- Nodes reload secure settings
- Nodes stats
- Cluster Info
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Create or update desired nodes
- Get desired nodes
- Delete desired nodes
- Get desired balance
- Reset desired balance
- Cross-cluster replication APIs
- Connector APIs
- Cancel connector sync job
- Check in a connector
- Check in connector sync job
- Create connector
- Create connector sync job
- Delete connector
- Delete connector sync job
- Get connector
- Get connector sync job
- List connectors
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- Set connector sync job error
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- Update connector API key id
- Update connector configuration
- Update connector error
- Update connector filtering
- Update connector index name
- Update connector last sync stats
- Update connector name and description
- Update connector pipeline
- Update connector scheduling
- Update connector service type
- Update connector status
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- ES|QL APIs
- Features APIs
- Fleet APIs
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
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- Create index
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- Delete component template
- Delete dangling index
- Delete alias
- Delete index
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- Exists
- Field usage stats
- Flush
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- Get alias
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- Get index
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- Get mapping
- Import dangling index
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- Index template exists (legacy)
- List dangling indices
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- Resolve cluster
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- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Inference APIs
- Info API
- Ingest APIs
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
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- Estimate model memory
- Flush jobs
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- Get buckets
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- Get categories
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- Get datafeed statistics
- Get influencers
- Get jobs
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- Get model snapshot upgrade statistics
- Get overall buckets
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- Start datafeeds
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
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- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
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- Machine learning trained model APIs
- Clear trained model deployment cache
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- Create part of a trained model
- Create trained models
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- Delete trained model aliases
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- Get trained models
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- Start trained model deployment
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- Update trained model deployment
- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Root API
- Script APIs
- Search APIs
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- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
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- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
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- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
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- Delete roles
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- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get service accounts
- Get service account credentials
- Get Security settings
- Get token
- Get user privileges
- Get users
- Grant API keys
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- Invalidate API key
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- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Text structure APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- Elasticsearch version 8.13.4
- Elasticsearch version 8.13.3
- Elasticsearch version 8.13.2
- Elasticsearch version 8.13.1
- Elasticsearch version 8.13.0
- Elasticsearch version 8.12.2
- Elasticsearch version 8.12.1
- Elasticsearch version 8.12.0
- Elasticsearch version 8.11.4
- Elasticsearch version 8.11.3
- Elasticsearch version 8.11.2
- Elasticsearch version 8.11.1
- Elasticsearch version 8.11.0
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
- Elasticsearch version 8.3.0
- 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
Point in time API
editPoint in time API
editA search request by default executes against the most recent visible data of the target indices, which is called point in time. Elasticsearch pit (point in time) is a lightweight view into the state of the data as it existed when initiated. In some cases, it’s preferred to perform multiple search requests using the same point in time. For example, if refreshes happen between search_after requests, then the results of those requests might not be consistent as changes happening between searches are only visible to the more recent point in time.
Prerequisites
edit-
If the Elasticsearch security features are enabled, you must have the
read
index privilege for the target data stream, index, or alias.To search a point in time (PIT) for an alias, you must have the
read
index privilege for the alias’s data streams or indices.
Request body
edit-
index_filter
-
(Optional, query object Allows to filter indices if the provided
query rewrites to
match_none
on every shard.
Examples
editA point in time must be opened explicitly before being used in search requests. The
keep_alive parameter tells Elasticsearch how long it should keep a point in time alive,
e.g. ?keep_alive=5m
.
response = client.open_point_in_time( index: 'my-index-000001', keep_alive: '1m' ) puts response
POST /my-index-000001/_pit?keep_alive=1m
The result from the above request includes a id
, which should
be passed to the id
of the pit
parameter of a search request.
POST /_search { "size": 100, "query": { "match" : { "title" : "elasticsearch" } }, "pit": { "id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==", "keep_alive": "1m" } }
A search request with the |
|
Just like regular searches, you can use |
|
The |
|
The |
The open point in time request and each subsequent search request can
return different id
; thus always use the most recently received id
for the
next search request.
Keeping point in time alive
editThe keep_alive
parameter, which is passed to a open point in time request and
search request, extends the time to live of the corresponding point in time.
The value (e.g. 1m
, see Time units) does not need to be long enough to
process all data — it just needs to be long enough for the next request.
Normally, the background merge process optimizes the index by merging together smaller segments to create new, bigger segments. Once the smaller segments are no longer needed they are deleted. However, open point-in-times prevent the old segments from being deleted since they are still in use.
Keeping older segments alive means that more disk space and file handles are needed. Ensure that you have configured your nodes to have ample free file handles. See File Descriptors.
Additionally, if a segment contains deleted or updated documents then the point in time must keep track of whether each document in the segment was live at the time of the initial search request. Ensure that your nodes have sufficient heap space if you have many open point-in-times on an index that is subject to ongoing deletes or updates. Note that a point-in-time doesn’t prevent its associated indices from being deleted.
You can check how many point-in-times (i.e, search contexts) are open with the nodes stats API:
$params = [ 'metric' => 'indices', 'index_metric' => 'search', ]; $response = $client->nodes()->stats($params);
response = client.nodes.stats( metric: 'indices', index_metric: 'search' ) puts response
res, err := es.Nodes.Stats( es.Nodes.Stats.WithMetric([]string{"indices"}...), es.Nodes.Stats.WithIndexMetric([]string{"search"}...), ) fmt.Println(res, err)
const response = await client.nodes.stats({ metric: 'indices', index_metric: 'search' }) console.log(response)
GET /_nodes/stats/indices/search
Close point in time API
editPoint-in-time is automatically closed when its keep_alive
has
been elapsed. However keeping point-in-times has a cost, as discussed in the
previous section. Point-in-times should be closed
as soon as they are no longer used in search requests.
response = client.close_point_in_time( body: { id: '46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==' } ) puts response
DELETE /_pit { "id" : "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==" }
The API returns the following response:
If true, all search contexts associated with the point-in-time id are successfully closed |
|
The number of search contexts have been successfully closed |
Search slicing
editWhen paging through a large number of documents, it can be helpful to split the search into multiple slices to consume them independently:
GET /_search { "slice": { "id": 0, "max": 2 }, "query": { "match": { "message": "foo" } }, "pit": { "id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==" } } GET /_search { "slice": { "id": 1, "max": 2 }, "pit": { "id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIAAAAAAAAAAAwBYgACBXV1aWQyAAAFdXVpZDEAAQltYXRjaF9hbGw_gAAAAA==" }, "query": { "match": { "message": "foo" } } }
The result from the first request returns documents belonging to the first slice (id: 0) and the result from the second request returns documents in the second slice. Since the maximum number of slices is set to 2 the union of the results of the two requests is equivalent to the results of a point-in-time search without slicing. By default the splitting is done first on the shards, then locally on each shard. The local splitting partitions the shard into contiguous ranges based on Lucene document IDs.
For instance if the number of shards is equal to 2 and the user requested 4 slices then the slices 0 and 2 are assigned to the first shard and the slices 1 and 3 are assigned to the second shard.
The same point-in-time ID should be used for all slices. If different PIT IDs are used, then slices can overlap and miss documents. This is because the splitting criterion is based on Lucene document IDs, which are not stable across changes to the index.
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