- .NET Clients: other versions:
- Introduction
- Installation
- Breaking changes
- API Conventions
- Elasticsearch.Net - Low level client
- NEST - High level client
- Troubleshooting
- Search
- Query DSL
- Full text queries
- Term level queries
- Exists Query Usage
- Fuzzy Date Query Usage
- Fuzzy Numeric Query Usage
- Fuzzy Query Usage
- Ids Query Usage
- Prefix Query Usage
- Date Range Query Usage
- Long Range Query Usage
- Numeric Range Query Usage
- Term Range Query Usage
- Regexp Query Usage
- Term Query Usage
- Terms Set Query Usage
- Terms List Query Usage
- Terms Lookup Query Usage
- Terms Query Usage
- Wildcard Query Usage
- Compound queries
- Joining queries
- Geo queries
- Specialized queries
- Span queries
- NEST specific queries
- Aggregations
- Metric Aggregations
- Average Aggregation Usage
- Boxplot Aggregation Usage
- Cardinality Aggregation Usage
- Extended Stats Aggregation Usage
- Geo Bounds Aggregation Usage
- Geo Centroid Aggregation Usage
- Geo Line Aggregation Usage
- Max Aggregation Usage
- Median Absolute Deviation Aggregation Usage
- Min Aggregation Usage
- Percentile Ranks Aggregation Usage
- Percentiles Aggregation Usage
- Rate Aggregation Usage
- Scripted Metric Aggregation Usage
- Stats Aggregation Usage
- String Stats Aggregation Usage
- Sum Aggregation Usage
- T Test Aggregation Usage
- Top Hits Aggregation Usage
- Top Metrics Aggregation Usage
- Value Count Aggregation Usage
- Weighted Average Aggregation Usage
- Bucket Aggregations
- Adjacency Matrix Usage
- Auto Date Histogram Aggregation Usage
- Children Aggregation Usage
- Composite Aggregation Usage
- Date Histogram Aggregation Usage
- Date Range Aggregation Usage
- Diversified Sampler Aggregation Usage
- Filter Aggregation Usage
- Filters Aggregation Usage
- Geo Distance Aggregation Usage
- Geo Hash Grid Aggregation Usage
- Geo Tile Grid Aggregation Usage
- Global Aggregation Usage
- Histogram Aggregation Usage
- Ip Range Aggregation Usage
- Missing Aggregation Usage
- Multi Terms Aggregation Usage
- Nested Aggregation Usage
- Parent Aggregation Usage
- Range Aggregation Usage
- Rare Terms Aggregation Usage
- Reverse Nested Aggregation Usage
- Sampler Aggregation Usage
- Significant Terms Aggregation Usage
- Significant Text Aggregation Usage
- Terms Aggregation Usage
- Variable Width Histogram Usage
- Pipeline Aggregations
- Average Bucket Aggregation Usage
- Bucket Script Aggregation Usage
- Bucket Selector Aggregation Usage
- Bucket Sort Aggregation Usage
- Cumulative Cardinality Aggregation Usage
- Cumulative Sum Aggregation Usage
- Derivative Aggregation Usage
- Extended Stats Bucket Aggregation Usage
- Max Bucket Aggregation Usage
- Min Bucket Aggregation Usage
- Moving Average Ewma Aggregation Usage
- Moving Average Holt Linear Aggregation Usage
- Moving Average Holt Winters Aggregation Usage
- Moving Average Linear Aggregation Usage
- Moving Average Simple Aggregation Usage
- Moving Function Aggregation Usage
- Moving Percentiles Aggregation Usage
- Normalize Aggregation Usage
- Percentiles Bucket Aggregation Usage
- Serial Differencing Aggregation Usage
- Stats Bucket Aggregation Usage
- Sum Bucket Aggregation Usage
- Matrix Aggregations
- Metric Aggregations
Significant Terms Aggregation Usage
editSignificant Terms Aggregation Usage
editAn aggregation that returns interesting or unusual occurrences of terms in a set.
The significant_terms aggregation can be very heavy when run on large indices. Work is in progress to provide more lightweight sampling techniques. As a result, the API for this feature may change in non-backwards compatible ways
See the Elasticsearch documentation on significant terms aggregation for more detail.
Fluent DSL example
edita => a .SignificantTerms("significant_names", st => st .Field(p => p.Name) .MinimumDocumentCount(10) .MutualInformation(mi => mi .BackgroundIsSuperSet() .IncludeNegatives() ) )
Object Initializer syntax example
editnew SignificantTermsAggregation("significant_names") { Field = Field<Project>(p => p.Name), MinimumDocumentCount = 10, MutualInformation = new MutualInformationHeuristic { BackgroundIsSuperSet = true, IncludeNegatives = true } }
Example json output.
{ "significant_names": { "significant_terms": { "field": "name", "min_doc_count": 10, "mutual_information": { "background_is_superset": true, "include_negatives": true } } } }
Handling Responses
editresponse.ShouldBeValid(); var sigNames = response.Aggregations.SignificantTerms("significant_names"); sigNames.Should().NotBeNull(); sigNames.DocCount.Should().BeGreaterThan(0);
Filtering with a regular expression pattern
editUsing significant terms aggregation with filtering to include values using a regular expression pattern
Fluent DSL example
edita => a .SignificantTerms("significant_names", st => st .Field(p => p.Name) .MinimumDocumentCount(10) .MutualInformation(mi => mi .BackgroundIsSuperSet() .IncludeNegatives() ) .Include("pi*") )
Object Initializer syntax example
editnew SignificantTermsAggregation("significant_names") { Field = Field<Project>(p => p.Name), MinimumDocumentCount = 10, MutualInformation = new MutualInformationHeuristic { BackgroundIsSuperSet = true, IncludeNegatives = true }, Include = new IncludeExclude("pi*") }
Example json output.
{ "significant_names": { "significant_terms": { "field": "name", "min_doc_count": 10, "mutual_information": { "background_is_superset": true, "include_negatives": true }, "include": "pi*" } } }
Handling Responses
editresponse.ShouldBeValid(); var sigNames = response.Aggregations.SignificantTerms("significant_names"); sigNames.Should().NotBeNull(); sigNames.DocCount.Should().BeGreaterThan(0);
Filtering with exact values
editUsing significant terms aggregation with filtering to exclude specific values
Fluent DSL example
edita => a .SignificantTerms("significant_names", st => st .Field(p => p.Name) .MinimumDocumentCount(10) .MutualInformation(mi => mi .BackgroundIsSuperSet() .IncludeNegatives() ) .Exclude(new[] { "pierce" }) )
Object Initializer syntax example
editnew SignificantTermsAggregation("significant_names") { Field = Field<Project>(p => p.Name), MinimumDocumentCount = 10, MutualInformation = new MutualInformationHeuristic { BackgroundIsSuperSet = true, IncludeNegatives = true }, Exclude = new IncludeExclude(new[] { "pierce" }) }
Example json output.
{ "significant_names": { "significant_terms": { "field": "name", "min_doc_count": 10, "mutual_information": { "background_is_superset": true, "include_negatives": true }, "exclude": [ "pierce" ] } } }
Handling Responses
editresponse.ShouldBeValid(); var sigNames = response.Aggregations.SignificantTerms("significant_names"); sigNames.Should().NotBeNull(); sigNames.DocCount.Should().BeGreaterThan(0);
Numeric fields
editA significant terms aggregation on a numeric field
Fluent DSL example
edita => a .SignificantTerms("commits", st => st .Field(p => p.NumberOfContributors) )
Object Initializer syntax example
editnew SignificantTermsAggregation("commits") { Field = Field<Project, int>(p => p.NumberOfContributors) }
Example json output.
{ "commits": { "significant_terms": { "field": "numberOfContributors" } } }
Handling Responses
editresponse.ShouldBeValid(); var commits = response.Aggregations.SignificantTerms<int>("commits"); commits.Should().NotBeNull(); commits.Buckets.Should().NotBeNull(); commits.Buckets.Count.Should().BeGreaterThan(0); foreach (var item in commits.Buckets) { item.Key.Should().BeGreaterThan(0); item.DocCount.Should().BeGreaterOrEqualTo(1); }
On this page
- Fluent DSL example
- Object Initializer syntax example
- Handling Responses
- Filtering with a regular expression pattern
- Fluent DSL example
- Object Initializer syntax example
- Handling Responses
- Filtering with exact values
- Fluent DSL example
- Object Initializer syntax example
- Handling Responses
- Numeric fields
- Fluent DSL example
- Object Initializer syntax example
- Handling Responses