Percentiles bucket aggregationedit
A sibling pipeline aggregation which calculates percentiles 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.
Syntaxedit
A percentiles_bucket aggregation looks like this in isolation:
{
"percentiles_bucket": {
"buckets_path": "the_sum"
}
}
Table 76. percentiles_bucket Parameters
| Parameter Name | Description | Required | Default Value |
|---|---|---|---|
|
The path to the buckets we wish to find the percentiles for (see |
Required |
|
|
The policy to apply when gaps are found in the data (see Dealing with gaps in the data for more details) |
Optional |
|
|
DecimalFormat pattern for the
output value. If specified, the formatted value is returned in the aggregation’s
|
Optional |
|
|
The list of percentiles to calculate |
Optional |
|
|
Flag which returns the range as an hash instead of an array of key-value pairs |
Optional |
|
The following snippet calculates the percentiles for the total monthly sales buckets:
POST /sales/_search
{
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
}
}
},
"percentiles_monthly_sales": {
"percentiles_bucket": {
"buckets_path": "sales_per_month>sales",
"percents": [ 25.0, 50.0, 75.0 ]
}
}
}
}
|
|
|
|
|
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
}
}
]
},
"percentiles_monthly_sales": {
"values" : {
"25.0": 375.0,
"50.0": 375.0,
"75.0": 550.0
}
}
}
}
Percentiles_bucket implementationedit
The Percentile Bucket returns the nearest input data point that is not greater than the requested percentile; it does not interpolate between data points.
The percentiles are calculated exactly and is not an approximation (unlike the Percentiles Metric). This means
the implementation maintains an in-memory, sorted list of your data to compute the percentiles, before discarding the
data. You may run into memory pressure issues if you attempt to calculate percentiles over many millions of
data-points in a single percentiles_bucket.