A multi-bucket values source based aggregation that can be applied on numeric values extracted from the documents.
It dynamically builds fixed size (a.k.a. interval) buckets over the values. For example, if the documents have a field
that holds a price (numeric), we can configure this aggregation to dynamically build buckets with interval 5
(in case of price it may represent $5). When the aggregation executes, the price field of every document will be
evaluated and will be rounded down to its closest bucket - for example, if the price is 32 and the bucket size is 5
then the rounding will yield 30 and thus the document will "fall" into the bucket that is associated withe the key 30.
To make this more formal, here is the rounding function that is used:
rem = value % interval
if (rem < 0) {
rem += interval
}
bucket_key = value - remThe following snippet "buckets" the products based on their price by interval of 50:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50
}
}
}
}And the following may be the response:
{
"aggregations": {
"prices" : {
"buckets": [
{
"key": 0,
"doc_count": 2
},
{
"key": 50,
"doc_count": 4
},
{
"key": 150,
"doc_count": 3
}
]
}
}
}The response above shows that none of the aggregated products has a price that falls within the range of [100 - 150).
By default, the response will only contain those buckets with a doc_count greater than 0. It is possible change that
and request buckets with either a higher minimum count or even 0 (in which case elasticsearch will "fill in the gaps"
and create buckets with zero documents). This can be configured using the min_doc_count setting:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"min_doc_count" : 0
}
}
}
}Response:
{
"aggregations": {
"prices" : {
"buckets": [
{
"key": 0,
"doc_count": 2
},
{
"key": 50,
"doc_count": 4
},
{
"key" : 100,
"doc_count" : 0 (1)
},
{
"key": 150,
"doc_count": 3
}
]
}
}
}-
No documents were found that belong in this bucket, yet it is still returned with zero
doc_count.
By default the returned buckets are sorted by their key ascending, though the order behaviour can be controled
using the order setting.
Ordering the buckets by their key - descending:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"order" : { "_key" : "desc" }
}
}
}
}Ordering the buckets by their doc_count - ascending:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"order" : { "_count" : "asc" }
}
}
}
}If the histogram aggregation has a direct metrics sub-aggregation, the latter can determine the order of the buckets:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"order" : { "price_stats.min" : "asc" } (1)
},
"aggs" : {
"price_stats" : { "stats" : {} } (2)
}
}
}
}-
The
{ "price_stats.min" : asc" }will sort the buckets based onminvalue of their theirprice_statssub-aggregation. -
There is no need to configure the
pricefield for theprice_statsaggregation as it will inherit it by default from its parent histogram aggregation.
It is also possible to order the buckets based on a "deeper" aggregation in the hierarchy. This is supported as long
as the aggregations path are of a single-bucket type, where the last aggregation in the path may either by a single-bucket
one or a metrics one. If it’s a single-bucket type, the order will be defined by the number of docs in the bucket (i.e. doc_count),
in case it’s a metrics one, the same rules as above apply (where the path must indicate the metric name to sort by in case of
a multi-value metrics aggregation, and in case of a single-value metrics aggregation the sort will be applied on that value).
The path must be defined in the following form:
AGG_SEPARATOR := '>' METRIC_SEPARATOR := '.' AGG_NAME := <the name of the aggregation> METRIC := <the name of the metric (in case of multi-value metrics aggregation)> PATH := <AGG_NAME>[<AGG_SEPARATOR><AGG_NAME>]*[<METRIC_SEPARATOR><METRIC>]
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"order" : { "promoted_products>rating_stats.avg" : "desc" } (1)
},
"aggs" : {
"promoted_products" : {
"filter" : { "term" : { "promoted" : true }},
"aggs" : {
"rating_stats" : { "stats" : { "field" : "rating" }}
}
}
}
}
}
}The above will sort the buckets based on the avg rating among the promoted products
It is possible to only return buckets that have a document count that is greater than or equal to a configured
limit through the min_doc_count option.
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"min_doc_count": 10
}
}
}
}The above aggregation would only return buckets that contain 10 documents or more. Default value is 1.
|
Note
|
The special value 0 can be used to add empty buckets to the response between the minimum and the maximum buckets.
Here is an example of what the response could look like:
|
{
"aggregations": {
"prices": {
"buckets": {
"0": {
"key": 0,
"doc_count": 2
},
"50": {
"key": 50,
"doc_count": 0
},
"150": {
"key": 150,
"doc_count": 3
},
"200": {
"key": 150,
"doc_count": 0
},
"250": {
"key": 150,
"doc_count": 0
},
"300": {
"key": 150,
"doc_count": 1
}
}
}
}
}By default, the buckets are returned as an ordered array. It is also possible to request the response as a hash instead keyed by the buckets keys:
{
"aggs" : {
"prices" : {
"histogram" : {
"field" : "price",
"interval" : 50,
"keyed" : true
}
}
}
}Response:
{
"aggregations": {
"prices": {
"buckets": {
"0": {
"key": 0,
"doc_count": 2
},
"50": {
"key": 50,
"doc_count": 4
},
"150": {
"key": 150,
"doc_count": 3
}
}
}
}
}