- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.10
- 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
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- License settings
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- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
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- Client JVM check
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- Early-access check
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- Starting Elasticsearch
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- 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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- Classic
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- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
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- Fingerprint
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- Length
- Limit token count
- Lowercase
- MinHash
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- Phonetic
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- Synonym
- Synonym graph
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- Unique
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- Word delimiter
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- Character filters reference
- Normalizers
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- Data streams
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- Example: Parse logs
- Enrich your data
- Processor reference
- Append
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- Bytes
- Circle
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- CSV
- Date
- Date index name
- Dissect
- Dot expander
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- Fail
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- Set
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- URL decode
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- User agent
- Aliases
- Search your data
- Collapse search results
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- Highlighting
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- Retrieve selected fields
- Search across clusters
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- Search shard routing
- Search templates
- Search with synonyms
- Sort search results
- kNN search
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- Searching with query rules
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
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- Date histogram
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- Diversified sampler
- Filter
- Filters
- Frequent item sets
- Geo-distance
- Geohash grid
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- Global
- Histogram
- IP prefix
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- Missing
- Multi Terms
- Nested
- Parent
- Random sampler
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Time series
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Change point
- Cumulative cardinality
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- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
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- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- Geospatial analysis
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- 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
- Multiple deployments writing to the same snapshot repository
- 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
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
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- Create index
- Create or update alias
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- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- 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
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
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- Create calendars
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- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
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- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
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- Update datafeeds
- Update filters
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- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
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- Machine learning trained model APIs
- Clear trained model deployment cache
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- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
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- Get trained models
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- Infer trained model
- Start trained model deployment
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- Migration APIs
- Node lifecycle APIs
- Query rules APIs
- Reload search analyzers API
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- Rollup APIs
- Script APIs
- Search APIs
- Search Application APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
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- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- 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 token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
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- OpenID Connect prepare authentication
- OpenID Connect authenticate
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- Update API key
- Bulk update API keys
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- Activate user profile
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- Enable user profile
- Get user profiles
- Suggest user profile
- Update user profile data
- Has privileges user profile
- Create Cross-Cluster API key
- Update Cross-Cluster API key
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Synonyms APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- 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
Time series data stream (TSDS)
editTime series data stream (TSDS)
editA time series data stream (TSDS) models timestamped metrics data as one or more time series.
You can use a TSDS to store metrics data more efficiently. In our benchmarks, metrics data stored in a TSDS used 70% less disk space than a regular data stream. The exact impact will vary per data set.
When to use a TSDS
editBoth a regular data stream and a TSDS can store timestamped
metrics data. Only use a TSDS if you typically add metrics data to Elasticsearch in near
real-time and @timestamp
order.
A TSDS is only intended for metrics data. For other timestamped data, such as logs or traces, use a regular data stream.
Differences from a regular data stream
editA TSDS works like a regular data stream with some key differences:
-
The matching index template for a TSDS requires a
data_stream
object with theindex.mode: time_series
option. This option enables most TSDS-related functionality. -
In addition to a
@timestamp
, each document in a TSDS must contain one or more dimension fields. The matching index template for a TSDS must contain mappings for at least onekeyword
dimension.TSDS documents also typically contain one or more metric fields.
-
Elasticsearch generates a hidden
_tsid
metadata field for each document in a TSDS. - A TSDS uses time-bound backing indices to store data from the same time period in the same backing index.
-
The matching index template for a TSDS must contain the
index.routing_path
index setting. A TSDS uses this setting to perform dimension-based routing. -
A TSDS uses internal index sorting to order
shard segments by
_tsid
and@timestamp
. -
TSDS documents only support auto-generated document
_id
values. For TSDS documents, the document_id
is a hash of the document’s dimensions and@timestamp
. A TSDS doesn’t support custom document_id
values. -
A TSDS uses synthetic
_source
, and as a result is subject to a number of restrictions.
A time series index can contain fields other than dimensions or metrics.
What is a time series?
editA time series is a sequence of observations for a specific entity. Together, these observations let you track changes to the entity over time. For example, a time series can track:
- CPU and disk usage for a computer
- The price of a stock
- Temperature and humidity readings from a weather sensor.
In a TSDS, each Elasticsearch document represents an observation, or data point, in a specific time series. Although a TSDS can contain multiple time series, a document can only belong to one time series. A time series can’t span multiple data streams.
Dimensions
editDimensions are field names and values that, in combination, identify a
document’s time series. In most cases, a dimension describes some aspect of the
entity you’re measuring. For example, documents related to the same weather
sensor may always have the same sensor_id
and location
values.
A TSDS document is uniquely identified by its time series and timestamp, both of
which are used to generate the document _id
. So, two documents with the same
dimensions and the same timestamp are considered to be duplicates. When you use
the _bulk
endpoint to add documents to a TSDS, a second document with the same
timestamp and dimensions overwrites the first. When you use the
PUT /<target>/_create/<_id>
format to add an individual document and a document
with the same _id
already exists, an error is generated.
You mark a field as a dimension using the boolean time_series_dimension
mapping parameter. The following field types support the time_series_dimension
parameter:
For a flattened field, use the time_series_dimensions
parameter to configure an array of fields as dimensions. For details refer to flattened
.
Metrics
editMetrics are fields that contain numeric measurements, as well as aggregations and/or downsampling values based off of those measurements. While not required, documents in a TSDS typically contain one or more metric fields.
Metrics differ from dimensions in that while dimensions generally remain constant, metrics are expected to change over time, even if rarely or slowly.
To mark a field as a metric, you must specify a metric type using the
time_series_metric
mapping parameter. The following field types support the
time_series_metric
parameter:
Accepted metric types vary based on the field type:
Valid values for time_series_metric
-
counter
-
A cumulative metric that only monotonically increases or resets to
0
(zero). For example, a count of errors or completed tasks.A counter field has additional semantic meaning, because it represents a cumulative counter. This works well with the
rate
aggregation, since a rate can be derived from a cumulative monotonically increasing counter. However a number of aggregations (for examplesum
) compute results that don’t make sense for a counter field, because of its cumulative nature.Only numeric and
aggregate_metric_double
fields support thecounter
metric type.
Due to the cumulative nature of counter fields, the following aggregations are supported and expected to provide meaningful results with the counter
field: rate
, histogram
, range
, min
, max
, top_metrics
and variable_width_histogram
. In order to prevent issues with existing integrations and custom dashboards, we also allow the following aggregations, even if the result might be meaningless on counters: avg
, box plot
, cardinality
, extended stats
, median absolute deviation
, percentile ranks
, percentiles
, stats
, sum
and value count
.
-
gauge
-
A metric that represents a single numeric that can arbitrarily increase or decrease. For example, a temperature or available disk space.
Only numeric and
aggregate_metric_double
fields support thegauge
metric type.
-
null
(Default) - Not a time series metric.
Time series mode
editThe matching index template for a TSDS must contain a data_stream
object with
the index_mode: time_series
option. This option ensures the TSDS creates
backing indices with an index.mode
setting of time_series
.
This setting enables most TSDS-related functionality in the backing indices.
If you convert an existing data stream to a TSDS, only backing indices created
after the conversion have an index.mode
of time_series
. You can’t
change the index.mode
of an existing backing index.
_tsid
metadata field
editWhen you add a document to a TSDS, Elasticsearch automatically generates a _tsid
metadata field for the document. The _tsid
is an object containing the
document’s dimensions. Documents in the same TSDS with the same _tsid
are part
of the same time series.
The _tsid
field is not queryable or updatable. You also can’t retrieve a
document’s _tsid
using a get document request. However, you can
use the _tsid
field in aggregations and retrieve the _tsid
value in searches
using the fields
parameter.
The format of the _tsid
field shouldn’t be relied upon. It may change
from version to version.
Time-bound indices
editIn a TSDS, each backing index, including the most recent backing index, has a
range of accepted @timestamp
values. This range is defined by the
index.time_series.start_time
and
index.time_series.end_time
index settings.
When you add a document to a TSDS, Elasticsearch adds the document to the appropriate
backing index based on its @timestamp
value. As a result, a TSDS can add
documents to any TSDS backing index that can receive writes. This applies even
if the index isn’t the most recent backing index.
Some ILM actions mark the source index as read-only, or expect the index
to not be actively written anymore in order to provide good performance. These actions are:
- Delete
- Downsample
- Force merge
- Read only
- Searchable snapshot
- Shrink
Index lifecycle management will not proceed with executing these actions until the upper time-bound
for accepting writes, represented by the index.time_series.end_time
index setting, has lapsed.
If no backing index can accept a document’s @timestamp
value, Elasticsearch rejects the
document.
Elasticsearch automatically configures index.time_series.start_time
and
index.time_series.end_time
settings as part of the index creation and rollover
process.
Look-ahead time
editUse the index.look_ahead_time
index setting to
configure how far into the future you can add documents to an index. When you
create a new write index for a TSDS, Elasticsearch calculates the index’s
index.time_series.end_time
value as:
now + index.look_ahead_time
At the time series poll interval (controlled via time_series.poll_interval
setting),
Elasticsearch checks if the write index has met the rollover criteria in its index
lifecycle policy. If not, Elasticsearch refreshes the now
value and updates the write
index’s index.time_series.end_time
to:
now + index.look_ahead_time + time_series.poll_interval
This process continues until the write index rolls over. When the index rolls
over, Elasticsearch sets a final index.time_series.end_time
value for the index. This
value borders the index.time_series.start_time
for the new write index. This
ensures the @timestamp
ranges for neighboring backing indices always border
but never overlap.
Accepted time range for adding data
editA TSDS is designed to ingest current metrics data. When the TSDS is first created the initial backing index has:
-
an
index.time_series.start_time
value set tonow - index.look_ahead_time
-
an
index.time_series.end_time
value set tonow + index.look_ahead_time
Only data that falls inside that range can be indexed.
In our TSDS example,
index.look_ahead_time
is set to three hours, so only documents with a
@timestamp
value that is within three hours previous or subsequent to the
present time are accepted for indexing.
You can use the get data stream API to check the accepted time range for writing to any TSDS.
Dimension-based routing
editWithin each TSDS backing index, Elasticsearch uses the
index.routing_path
index setting to route documents
with the same dimensions to the same shards.
When you create the matching index template for a TSDS, you must specify one or
more dimensions in the index.routing_path
setting. Each document in a TSDS
must contain one or more dimensions that match the index.routing_path
setting.
Dimensions in the index.routing_path
setting must be plain keyword
fields.
The index.routing_path
setting accepts wildcard patterns (for example dim.*
)
and can dynamically match new fields. However, Elasticsearch will reject any mapping
updates that add scripted, runtime, or non-dimension, non-keyword
fields that
match the index.routing_path
value.
TSDS documents don’t support a custom _routing
value. Similarly, you can’t
require a _routing
value in mappings for a TSDS.
Index sorting
editElasticsearch uses compression algorithms to compress repeated values. This compression works best when repeated values are stored near each other — in the same index, on the same shard, and side-by-side in the same shard segment.
Most time series data contains repeated values. Dimensions are repeated across documents in the same time series. The metric values of a time series may also change slowly over time.
Internally, each TSDS backing index uses index
sorting to order its shard segments by _tsid
and @timestamp
. This makes it
more likely that these repeated values are stored near each other for better
compression. A TSDS doesn’t support any
index.sort.*
index settings.
What’s next?
editNow that you know the basics, you’re ready to create a TSDS or convert an existing data stream to a TSDS.
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