honeymoose
/
OpenSearch
mirror of https://github.com/honeymoose/OpenSearch.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Documentation for the max_bucket reducer

This commit is contained in:
Colin Goodheart-Smithe 2015-04-17 11:34:01 +01:00
parent be647a89d3
commit bd28c9c44e
1 changed files with 19 additions and 129 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

148
docs/reference/search/aggregations/reducer/max-bucket-aggregation.asciidoc
View File

@ -1,16 +1,17 @@
[[search-aggregations-reducer-max-bucket-aggregation]]
=== Max Bucket Aggregation
A parent reducer aggregation which calculates the derivative of a specified metric in a parent histogram (or date_histogram)
aggregation. The specified metric must be numeric and the enclosing histogram must have `min_doc_count` set to `0`.
A sibling reducer aggregation which identifies the bucket(s) with the maximum value of a specified metric in a sibing aggregation
and outputs both the value and the key(s) of the bucket(s). The specified metric must be numeric and the sibling aggregation must
be a multi-bucket aggregation.
The following snippet calculates the derivative of the total monthly `sales`:
The following snippet calculates the maximum of the total monthly `sales`:
[source,js]
--------------------------------------------------
{
"aggs" : {
"sales" : {
"sales_per_month" : {
"date_histogram" : {
"field" : "date",
"interval" : "month"
@ -20,19 +21,20 @@ The following snippet calculates the derivative of the total monthly `sales`:
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_paths": "sales" <1>
}
}
}
},
"max_monthly_sales": {
"max_bucket": {
"buckets_paths": "sales_per_month>sales" <1>
}
}
}
}
--------------------------------------------------
<1> `bucket_paths` instructs this derivative aggregation to use the output of the `sales` aggregation for the derivative
<1> `bucket_paths` instructs this max_bucket aggregation that we want the maximum value of the `sales` aggregation in the
"sales_per_month` date histogram.
And the following may be the response:
@ -40,7 +42,7 @@ And the following may be the response:
--------------------------------------------------
{
"aggregations": {
"sales": {
"sales_per_month": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
@ -48,7 +50,7 @@ And the following may be the response:
"doc_count": 3,
"sales": {
"value": 550
} <1>
}
},
{
"key_as_string": "2015/02/01 00:00:00",
@ -56,9 +58,6 @@ And the following may be the response:
"doc_count": 2,
"sales": {
"value": 60
},
"sales_deriv": {
"value": -490 <2>
}
},
{
@ -67,126 +66,17 @@ And the following may be the response:
"doc_count": 2,
"sales": {
"value": 375
},
"sales_deriv": {
"value": 315
}
}
]
},
"max_monthly_sales": {
"keys": ["2015/01/01 00:00:00"], <1>
"value": 550
}
}
}
--------------------------------------------------
<1> No derivative for the first bucket since we need at least 2 data points to calculate the derivative
<2> Derivative value units are implicitly defined by the `sales` aggregation and the parent histogram so in this case the units
would be $/month assuming the `price` field has units of $.
==== Second Order Derivative
A second order derivative can be calculated by chaining the derivative reducer aggregation onto the result of another derivative
reducer aggregation as in the following example which will calculate both the first and the second order derivative of the total
monthly sales:
[source,js]
--------------------------------------------------
{
"aggs" : {
"sales" : {
"date_histogram" : {
"field" : "date",
"interval" : "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_paths": "sales"
}
},
"sales_2nd_deriv": {
"derivative": {
"buckets_paths": "sales_deriv" <1>
}
}
}
}
}
}
--------------------------------------------------
<1> `bucket_paths` for the second derivative points to the name of the first derivative
And the following may be the response:
[source,js]
--------------------------------------------------
{
"aggregations": {
"sales": {
"buckets": [
{
"key_as_string": "2015/01/01 00:00:00",
"key": 1420070400000,
"doc_count": 3,
"sales": {
"value": 550
} <1>
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60
},
"sales_deriv": {
"value": -490
} <1>
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375
},
"sales_deriv": {
"value": 315
},
"sales_2nd_deriv": {
"value": 805
}
}
]
}
}
}
--------------------------------------------------
<1> No second derivative for the first two buckets since we need at least 2 data points from the first derivative to calculate the
second derivative
==== Dealing with gaps in the data
There are a couple of reasons why the data output by the enclosing histogram may have gaps:
* There are no documents matching the query for some buckets
* The data for a metric is missing in all of the documents falling into a bucket (this is most likely with either a small interval
on the enclosing histogram or with a query matching only a small number of documents)
Where there is no data available in a bucket for a given metric it presents a problem for calculating the derivative value for both
the current bucket and the next bucket. In the derivative reducer aggregation has a `gap policy` parameter to define what the behavior
should be when a gap in the data is found. There are currently two options for controlling the gap policy:
_ignore_::
This option will not produce a derivative value for any buckets where the value in the current or previous bucket is
missing
_insert_zeros_::
This option will assume the missing value is `0` and calculate the derivative with the value `0`.
<1> `keys` is an array of strings since the maximum value may be present in multiple buckets