Extended stats aggregation
editExtended stats aggregation
editA multi-value
metrics aggregation that computes stats over numeric values extracted from the aggregated documents.
The extended_stats
aggregations is an extended version of the stats
aggregation, where additional metrics are added such as sum_of_squares
, variance
, std_deviation
and std_deviation_bounds
.
Assuming the data consists of documents representing exams grades (between 0 and 100) of students
response = client.search( index: 'exams', body: { size: 0, aggregations: { grades_stats: { extended_stats: { field: 'grade' } } } } ) puts response
GET /exams/_search { "size": 0, "aggs": { "grades_stats": { "extended_stats": { "field": "grade" } } } }
The above aggregation computes the grades statistics over all documents. The aggregation type is extended_stats
and the field
setting defines the numeric field of the documents the stats will be computed on. The above will return the following:
The std_deviation
and variance
are calculated as population metrics so they are always the same as std_deviation_population
and variance_population
respectively.
{ ... "aggregations": { "grades_stats": { "count": 2, "min": 50.0, "max": 100.0, "avg": 75.0, "sum": 150.0, "sum_of_squares": 12500.0, "variance": 625.0, "variance_population": 625.0, "variance_sampling": 1250.0, "std_deviation": 25.0, "std_deviation_population": 25.0, "std_deviation_sampling": 35.35533905932738, "std_deviation_bounds": { "upper": 125.0, "lower": 25.0, "upper_population": 125.0, "lower_population": 25.0, "upper_sampling": 145.71067811865476, "lower_sampling": 4.289321881345245 } } } }
The name of the aggregation (grades_stats
above) also serves as the key by which the aggregation result can be retrieved from the returned response.
Standard Deviation Bounds
editBy default, the extended_stats
metric will return an object called std_deviation_bounds
, which provides an interval of plus/minus two standard
deviations from the mean. This can be a useful way to visualize variance of your data. If you want a different boundary, for example
three standard deviations, you can set sigma
in the request:
response = client.search( index: 'exams', body: { size: 0, aggregations: { grades_stats: { extended_stats: { field: 'grade', sigma: 3 } } } } ) puts response
GET /exams/_search { "size": 0, "aggs": { "grades_stats": { "extended_stats": { "field": "grade", "sigma": 3 } } } }
sigma
can be any non-negative double, meaning you can request non-integer values such as 1.5
. A value of 0
is valid, but will simply
return the average for both upper
and lower
bounds.
The upper
and lower
bounds are calculated as population metrics so they are always the same as upper_population
and
lower_population
respectively.
Standard Deviation and Bounds require normality
The standard deviation and its bounds are displayed by default, but they are not always applicable to all data-sets. Your data must be normally distributed for the metrics to make sense. The statistics behind standard deviations assumes normally distributed data, so if your data is skewed heavily left or right, the value returned will be misleading.
Script
editIf you need to aggregate on a value that isn’t indexed, use a runtime field. Say the we found out that the grades we’ve been working on were for an exam that was above the level of the students and we want to "correct" it:
response = client.search( index: 'exams', body: { size: 0, runtime_mappings: { "grade.corrected": { type: 'double', script: { source: "emit(Math.min(100, doc['grade'].value * params.correction))", params: { correction: 1.2 } } } }, aggregations: { grades_stats: { extended_stats: { field: 'grade.corrected' } } } } ) puts response
GET /exams/_search { "size": 0, "runtime_mappings": { "grade.corrected": { "type": "double", "script": { "source": "emit(Math.min(100, doc['grade'].value * params.correction))", "params": { "correction": 1.2 } } } }, "aggs": { "grades_stats": { "extended_stats": { "field": "grade.corrected" } } } }
Missing value
editThe missing
parameter defines how documents that are missing a value should be treated.
By default they will be ignored but it is also possible to treat them as if they
had a value.
response = client.search( index: 'exams', body: { size: 0, aggregations: { grades_stats: { extended_stats: { field: 'grade', missing: 0 } } } } ) puts response