- 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
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- Stopping Elasticsearch
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- Breaking changes
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- Aggregations changes
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- Indices changes
- Ingest changes
- Java API changes
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- Packaging changes
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- Breaking changes in 6.1
- Breaking changes in 6.0
- X-Pack Breaking Changes
- API Conventions
- Document APIs
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- Aggregations
- Metrics Aggregations
- Avg Aggregation
- Cardinality Aggregation
- Extended Stats Aggregation
- Geo Bounds Aggregation
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- Max Aggregation
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- Percentiles Aggregation
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- Scripted Metric Aggregation
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- Bucket Aggregations
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- Children Aggregation
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- Date Histogram Aggregation
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- Diversified Sampler Aggregation
- Filter Aggregation
- Filters Aggregation
- Geo Distance Aggregation
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- 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
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- 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
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- cat APIs
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- Analysis
- Anatomy of an analyzer
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- Classic Token Filter
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- Character Filters
- Modules
- Index Modules
- Ingest Node
- Pipeline Definition
- Ingest APIs
- Accessing Data in Pipelines
- Handling Failures in Pipelines
- Processors
- Append Processor
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- Monitoring Elasticsearch
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- Security APIs
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- Definitions
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- How To
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- Glossary of terms
- Release Notes
- 6.1.4 Release Notes
- 6.1.3 Release Notes
- 6.1.2 Release Notes
- 6.1.1 Release Notes
- 6.1.0 Release Notes
- 6.0.1 Release Notes
- 6.0.0 Release Notes
- 6.0.0-rc2 Release Notes
- 6.0.0-rc1 Release Notes
- 6.0.0-beta2 Release Notes
- 6.0.0-beta1 Release Notes
- 6.0.0-alpha2 Release Notes
- 6.0.0-alpha1 Release Notes
- 6.0.0-alpha1 Release Notes (Changes previously released in 5.x)
- X-Pack Release Notes
WARNING: Version 6.1 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Sampler Aggregation
editSampler Aggregation
editA filtering aggregation used to limit any sub aggregations' processing to a sample of the top-scoring documents.
Example use cases:
- Tightening the focus of analytics to high-relevance matches rather than the potentially very long tail of low-quality matches
-
Reducing the running cost of aggregations that can produce useful results using only samples e.g.
significant_terms
Example:
A query on StackOverflow data for the popular term javascript
OR the rarer term
kibana
will match many documents - most of them missing the word Kibana. To focus
the significant_terms
aggregation on top-scoring documents that are more likely to match
the most interesting parts of our query we use a sample.
POST /stackoverflow/_search?size=0 { "query": { "query_string": { "query": "tags:kibana OR tags:javascript" } }, "aggs": { "sample": { "sampler": { "shard_size": 200 }, "aggs": { "keywords": { "significant_terms": { "field": "tags", "exclude": ["kibana", "javascript"] } } } } } }
Response:
{ ... "aggregations": { "sample": { "doc_count": 200, "keywords": { "doc_count": 200, "bg_count": 650, "buckets": [ { "key": "elasticsearch", "doc_count": 150, "score": 1.078125, "bg_count": 200 }, { "key": "logstash", "doc_count": 50, "score": 0.5625, "bg_count": 50 } ] } } } }
200 documents were sampled in total. The cost of performing the nested significant_terms aggregation was therefore limited rather than unbounded. |
Without the sampler
aggregation the request query considers the full "long tail" of low-quality matches and therefore identifies
less significant terms such as jquery
and angular
rather than focusing on the more insightful Kibana-related terms.
POST /stackoverflow/_search?size=0 { "query": { "query_string": { "query": "tags:kibana OR tags:javascript" } }, "aggs": { "low_quality_keywords": { "significant_terms": { "field": "tags", "size": 3, "exclude":["kibana", "javascript"] } } } }
Response:
{ ... "aggregations": { "low_quality_keywords": { "doc_count": 600, "bg_count": 650, "buckets": [ { "key": "angular", "doc_count": 200, "score": 0.02777, "bg_count": 200 }, { "key": "jquery", "doc_count": 200, "score": 0.02777, "bg_count": 200 }, { "key": "logstash", "doc_count": 50, "score": 0.0069, "bg_count": 50 } ] } } }
shard_size
editThe shard_size
parameter limits how many top-scoring documents are collected in the sample processed on each shard.
The default value is 100.
Limitations
editCannot be nested under breadth_first
aggregations
editBeing a quality-based filter the sampler aggregation needs access to the relevance score produced for each document.
It therefore cannot be nested under a terms
aggregation which has the collect_mode
switched from the default depth_first
mode to breadth_first
as this discards scores.
In this situation an error will be thrown.