- 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
Understanding groups
editUnderstanding groups
editThis functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
To preserve flexibility, Rollup Jobs are defined based on how future queries may need to use the data. Traditionally, systems force
the admin to make decisions about what metrics to rollup and on what interval. E.g. The average of cpu_time
on an hourly basis. This
is limiting; if, at a future date, the admin wishes to see the average of cpu_time
on an hourly basis and partitioned by `host_name`,
they are out of luck.
Of course, the admin can decide to rollup the [hour, host]
tuple on an hourly basis, but as the number of grouping keys grows, so do the
number of tuples the admin needs to configure. Furthermore, these [hours, host]
tuples are only useful for hourly rollups… daily, weekly,
or monthly rollups all require new configurations.
Rather than force the admin to decide ahead of time which individual tuples should be rolled up, Elasticsearch’s Rollup jobs are configured based on which groups are potentially useful to future queries. For example, this configuration:
"groups" : { "date_histogram": { "field": "timestamp", "fixed_interval": "1h", "delay": "7d" }, "terms": { "fields": ["hostname", "datacenter"] }, "histogram": { "fields": ["load", "net_in", "net_out"], "interval": 5 } }
Allows date_histogram
's to be used on the "timestamp"
field, terms
aggregations to be used on the "hostname"
and "datacenter"
fields, and histograms
to be used on any of "load"
, "net_in"
, "net_out"
fields.
Importantly, these aggs/fields can be used in any combination. This aggregation:
"aggs" : { "hourly": { "date_histogram": { "field": "timestamp", "fixed_interval": "1h" }, "aggs": { "host_names": { "terms": { "field": "hostname" } } } } }
is just as valid as this aggregation:
"aggs" : { "hourly": { "date_histogram": { "field": "timestamp", "fixed_interval": "1h" }, "aggs": { "data_center": { "terms": { "field": "datacenter" } }, "aggs": { "host_names": { "terms": { "field": "hostname" } }, "aggs": { "load_values": { "histogram": { "field": "load", "interval": 5 } } } } } } }
You’ll notice that the second aggregation is not only substantially larger, it also swapped the position of the terms aggregation on
"hostname"
, illustrating how the order of aggregations does not matter to rollups. Similarly, while the date_histogram
is required
for rolling up data, it isn’t required while querying (although often used). For example, this is a valid aggregation for
Rollup Search to execute:
"aggs" : { "host_names": { "terms": { "field": "hostname" } } }
Ultimately, when configuring groups
for a job, think in terms of how you might wish to partition data in a query at a future date…
then include those in the config. Because Rollup Search allows any order or combination of the grouped fields, you just need to decide
if a field is useful for aggregating later, and how you might wish to use it (terms, histogram, etc).
Calendar vs fixed time intervals
editEach rollup-job must have a date histogram group with a defined interval. Elasticsearch
understands both
calendar and fixed time intervals. Fixed time
intervals are fairly easy to understand; 60s
means sixty seconds. But what
does 1M
mean? One month of time depends on which month we are talking about,
some months are longer or shorter than others. This is an example of calendar
time and the duration of that unit depends on context. Calendar units are also
affected by leap-seconds, leap-years, etc.
This is important because the buckets generated by rollup are in either calendar or fixed intervals and this limits how you can query them later. See Requests must be multiples of the config.
We recommend sticking with fixed time intervals, since they are easier to understand and are more flexible at query time. It will introduce some drift in your data during leap-events and you will have to think about months in a fixed quantity (30 days) instead of the actual calendar length. However, it is often easier than dealing with calendar units at query time.
Multiples of units are always "fixed". For example, 2h
is always the fixed
quantity 7200
seconds. Single units can be fixed or calendar depending on the
unit:
Unit | Calendar | Fixed |
---|---|---|
millisecond |
NA |
|
second |
NA |
|
minute |
|
|
hour |
|
|
day |
|
|
week |
|
NA |
month |
|
NA |
quarter |
|
NA |
year |
|
NA |
For some units where there are both fixed and calendar, you may need to express
the quantity in terms of the next smaller unit. For example, if you want a fixed
day (not a calendar day), you should specify 24h
instead of 1d
. Similarly,
if you want fixed hours, specify 60m
instead of 1h
. This is because the
single quantity entails calendar time, and limits you to querying by calendar
time in the future.
Grouping limitations with heterogeneous indices
editThere was previously a limitation in how Rollup could handle indices that had heterogeneous mappings (multiple, unrelated/non-overlapping
mappings). The recommendation at the time was to configure a separate job per data "type". For example, you might configure a separate
job for each Beats module that you had enabled (one for process
, another for filesystem
, etc).
This recommendation was driven by internal implementation details that caused document counts to be potentially incorrect if a single "merged" job was used.
This limitation has since been alleviated. As of 6.4.0, it is now considered best practice to combine all rollup configurations into a single job.
As an example, if your index has two types of documents:
{ "timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a" }
and
{ "timestamp": 1516729294000, "price": 123, "title": "Foo" }
the best practice is to combine them into a single rollup job which covers both of these document types, like this:
PUT _rollup/job/combined { "index_pattern": "data-*", "rollup_index": "data_rollup", "cron": "*/30 * * * * ?", "page_size" :1000, "groups" : { "date_histogram": { "field": "timestamp", "fixed_interval": "1h", "delay": "7d" }, "terms": { "fields": ["node", "title"] } }, "metrics": [ { "field": "temperature", "metrics": ["min", "max", "sum"] }, { "field": "price", "metrics": ["avg"] } ] }
Doc counts and overlapping jobs
editThere was previously an issue with document counts on "overlapping" job configurations, driven by the same internal implementation detail. If there were two Rollup jobs saving to the same index, where one job is a "subset" of another job, it was possible that document counts could be incorrect for certain aggregation arrangements.
This issue has also since been eliminated in 6.4.0.
On this page