Stats bucket aggregation
A sibling pipeline aggregation which calculates a variety of stats across all bucket of a specified metric in a sibling aggregation. The specified metric must be numeric and the sibling aggregation must be a multi-bucket aggregation.
A stats_bucket aggregation looks like this in isolation:
{
"stats_bucket": {
"buckets_path": "the_sum"
}
}
| Parameter Name | Description | Required | Default Value |
|---|---|---|---|
buckets_path |
The path to the buckets we wish to calculate stats for (see buckets_path Syntax for more details) |
Required | |
gap_policy |
The policy to apply when gaps are found in the data (see Dealing with gaps in the data for more details) | Optional | skip |
format |
DecimalFormat pattern for theoutput value. If specified, the formatted value is returned in the aggregation’svalue_as_string property |
Optional | null |
The following snippet calculates the stats for monthly sales:
POST /sales/_search
{
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"stats_monthly_sales": {
"stats_bucket": {
"buckets_path": "sales_per_month>sales"
}
}
}
}
bucket_pathsinstructs thisstats_bucketaggregation that we want the calculate stats for thesalesaggregation in thesales_per_monthdate histogram.
And the following may be the response:
{
"took": 11,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550.0
}
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
}
}
]
},
"stats_monthly_sales": {
"count": 3,
"min": 60.0,
"max": 550.0,
"avg": 328.3333333333333,
"sum": 985.0
}
}
}