- Elasticsearch Guide: other versions:
- Elasticsearch introduction
- Getting started with Elasticsearch
- 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
- Discovery configuration check
- Starting Elasticsearch
- Stopping Elasticsearch
- Adding nodes to your cluster
- Set up X-Pack
- Configuring X-Pack Java Clients
- Bootstrap Checks for X-Pack
- Upgrade Elasticsearch
- API conventions
- Document APIs
- Search APIs
- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Weighted 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
- Median Absolute Deviation 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
- 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
- Moving Function 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
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- Types Exists
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- Update Indices Settings
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- Analyze
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- Clear Cache
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- cat APIs
- Cluster APIs
- Query DSL
- Scripting
- Mapping
- Analysis
- Anatomy of an analyzer
- Testing analyzers
- Analyzers
- Normalizers
- Tokenizers
- Standard Tokenizer
- Letter Tokenizer
- Lowercase Tokenizer
- Whitespace Tokenizer
- UAX URL Email Tokenizer
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- Thai Tokenizer
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- Path Hierarchy Tokenizer Examples
- Token Filters
- ASCII Folding Token Filter
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- Parsing synonym files
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- CJK Width Token Filter
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- Exclude mode settings example
- Classic Token Filter
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- Remove Duplicates Token Filter
- Character Filters
- Modules
- Index modules
- Ingest node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Conditional Execution in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
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- Script Processor
- Set Processor
- Set Security User Processor
- Split Processor
- Sort Processor
- Trim Processor
- Uppercase Processor
- URL Decode Processor
- User Agent processor
- Managing the index lifecycle
- Getting started with index lifecycle management
- Policy phases and actions
- Set up index lifecycle management policy
- Using policies to manage index rollover
- Update policy
- Index lifecycle error handling
- Restoring snapshots of managed indices
- Start and stop index lifecycle management
- Using ILM with existing indices
- SQL access
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
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- Aggregate Functions
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- Mathematical Functions
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- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Monitor a cluster
- Frozen indices
- Set up a cluster for high availability
- Roll up or transform your data
- X-Pack APIs
- Info API
- Cross-cluster replication APIs
- Explore API
- Freeze index
- Index lifecycle management API
- Licensing APIs
- Machine learning APIs
- Add events to calendar
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- Migration APIs
- Rollup APIs
- Security APIs
- Authenticate
- Change passwords
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- Get roles
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- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect Prepare Authentication API
- OpenID Connect Authenticate API
- OpenID Connect Logout API
- SSL certificate
- Transform APIs
- Unfreeze index
- Watcher APIs
- Definitions
- 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
- Configuring a Kerberos realm
- Security files
- FIPS 140-2
- How security works
- 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
- Auditing security events
- 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
- Testing
- Glossary of terms
- Release highlights
- Breaking changes
- Release notes
- 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)
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.