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Significant Terms Aggregation Usageedit
An 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 exampleedit
s => s
.Aggregations(a => a
.SignificantTerms("significant_names", st => st
.Field(p => p.Name)
.MinimumDocumentCountAsLong(10)
.MutualInformation(mi => mi
.BackgroundIsSuperSet()
.IncludeNegatives()
)
)
)
Object Initializer syntax exampleedit
new SearchRequest<Project>
{
Aggregations = new SignificantTermsAggregation("significant_names")
{
Field = Field<Project>(p => p.Name),
MinimumDocumentCountAsLong = 10,
MutualInformation = new MutualInformationHeuristic
{
BackgroundIsSuperSet = true,
IncludeNegatives = true
}
}
}
Example json output.
{
"aggs": {
"significant_names": {
"significant_terms": {
"field": "name",
"min_doc_count": 10,
"mutual_information": {
"background_is_superset": true,
"include_negatives": true
}
}
}
}
}
Handling Responsesedit
response.ShouldBeValid();
var sigNames = response.Aggs.SignificantTerms("significant_names");
sigNames.Should().NotBeNull();
sigNames.DocCount.Should().BeGreaterThan(0);
Filtering with a regular expression patternedit
Using significant terms aggregation with filtering to include values using a regular expression pattern
Fluent DSL exampleedit
s => s
.Aggregations(a => a
.SignificantTerms("significant_names", st => st
.Field(p => p.Name)
.MinimumDocumentCountAsLong(10)
.MutualInformation(mi => mi
.BackgroundIsSuperSet()
.IncludeNegatives()
)
.Include("pi*")
)
)
Object Initializer syntax exampleedit
new SearchRequest<Project>
{
Aggregations = new SignificantTermsAggregation("significant_names")
{
Field = Field<Project>(p => p.Name),
MinimumDocumentCountAsLong = 10,
MutualInformation = new MutualInformationHeuristic
{
BackgroundIsSuperSet = true,
IncludeNegatives = true
},
IncludeTerms = new SignificantTermsIncludeExclude("pi*")
}
}
Example json output.
{
"aggs": {
"significant_names": {
"significant_terms": {
"field": "name",
"min_doc_count": 10,
"mutual_information": {
"background_is_superset": true,
"include_negatives": true
},
"include": "pi*"
}
}
}
}
Handling Responsesedit
response.ShouldBeValid();
var sigNames = response.Aggs.SignificantTerms("significant_names");
sigNames.Should().NotBeNull();
sigNames.DocCount.Should().BeGreaterThan(0);
Filtering with exact valuesedit
Using significant terms aggregation with filtering to exclude specific values
Fluent DSL exampleedit
s => s
.Aggregations(a => a
.SignificantTerms("significant_names", st => st
.Field(p => p.Name)
.MinimumDocumentCountAsLong(10)
.MutualInformation(mi => mi
.BackgroundIsSuperSet()
.IncludeNegatives()
)
.Exclude(new[] { "pierce" })
)
)
Object Initializer syntax exampleedit
new SearchRequest<Project>
{
Aggregations = new SignificantTermsAggregation("significant_names")
{
Field = Field<Project>(p => p.Name),
MinimumDocumentCountAsLong = 10,
MutualInformation = new MutualInformationHeuristic
{
BackgroundIsSuperSet = true,
IncludeNegatives = true
},
ExcludeTerms = new SignificantTermsIncludeExclude(new[] { "pierce" })
}
}
Example json output.
{
"aggs": {
"significant_names": {
"significant_terms": {
"field": "name",
"min_doc_count": 10,
"mutual_information": {
"background_is_superset": true,
"include_negatives": true
},
"exclude": [
"pierce"
]
}
}
}
}
Handling Responsesedit
response.ShouldBeValid();
var sigNames = response.Aggs.SignificantTerms("significant_names");
sigNames.Should().NotBeNull();
sigNames.DocCount.Should().BeGreaterThan(0);