A multi-value
metrics aggregation that computes stats over numeric values 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.
The stats that are returned consist of: min
, max
, sum
, count
and avg
.
Assuming the data consists of documents representing exams grades (between 0 and 100) of students
POST /exams/_search?size=0 { "aggs" : { "grades_stats" : { "stats" : { "field" : "grade" } } } }
The above aggregation computes the grades statistics over all documents. The aggregation type is stats
and the field
setting defines the numeric field of the documents the stats will be computed on. The above will return the following:
{ ... "aggregations": { "grades_stats": { "count": 2, "min": 50.0, "max": 100.0, "avg": 75.0, "sum": 150.0 } } }
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.
Computing the grades stats based on a script:
POST /exams/_search?size=0 { "aggs" : { "grades_stats" : { "stats" : { "script" : { "lang": "painless", "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" : { "grades_stats" : { "stats" : { "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 a value script to get the new stats:
POST /exams/_search?size=0 { "aggs" : { "grades_stats" : { "stats" : { "field" : "grade", "script" : { "lang": "painless", "source": "_value * params.correction", "params" : { "correction" : 1.2 } } } } } }