A single-value
metrics aggregation that computes the average of numeric values that are extracted from the aggregated documents. These values can be extracted either from specific numeric fields in the documents, or be generated by a provided script.
Assuming the data consists of documents representing exams grades (between 0 and 100) of students we can average their scores with:
POST /exams/_search?size=0 { "aggs" : { "avg_grade" : { "avg" : { "field" : "grade" } } } }
The above aggregation computes the average grade over all documents. The aggregation type is avg
and the field
setting defines the numeric field of the documents the average will be computed on. The above will return the following:
{ ... "aggregations": { "avg_grade": { "value": 75.0 } } }
The name of the aggregation (avg_grade
above) also serves as the key by which the aggregation result can be retrieved from the returned response.
Computing the average grade based on a script:
POST /exams/_search?size=0 { "aggs" : { "avg_grade" : { "avg" : { "script" : { "source" : "doc.grade.value" } } } } }
This will interpret the script
parameter as an inline
script with the painless
script language and no script parameters. To use a stored script use the following syntax:
POST /exams/_search?size=0 { "aggs" : { "avg_grade" : { "avg" : { "script" : { "id": "my_script", "params": { "field": "grade" } } } } } }
It turned out that the exam was way above the level of the students and a grade correction needs to be applied. We can use value script to get the new average:
POST /exams/_search?size=0 { "aggs" : { "avg_corrected_grade" : { "avg" : { "field" : "grade", "script" : { "lang": "painless", "source": "_value * params.correction", "params" : { "correction" : 1.2 } } } } } }