Derivative aggregation
A parent pipeline 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 (default for histogram aggregations).
A derivative aggregation looks like this in isolation:
"derivative": {
"buckets_path": "the_sum"
}
| Parameter Name | Description | Required | Default Value |
|---|---|---|---|
buckets_path |
The path to the buckets we wish to find the derivative 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 derivative of the total monthly sales:
POST /sales/_search {
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_path": "sales" 1
}
}
}
}
}
}
buckets_pathinstructs this derivative aggregation to use the output of thesalesaggregation for the derivative
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
} 1
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"sales_deriv": {
"value": -490.0 2
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2, 3
"sales": {
"value": 375.0
},
"sales_deriv": {
"value": 315.0
}
}
]
}
}
}
- No derivative for the first bucket since we need at least 2 data points to calculate the derivative
- Derivative value units are implicitly defined by the
salesaggregation and the parent histogram so in this case the units would be $/month assuming thepricefield has units of $. - The number of documents in the bucket are represented by the
doc_count
A second order derivative can be calculated by chaining the derivative pipeline aggregation onto the result of another derivative pipeline aggregation as in the following example which will calculate both the first and the second order derivative of the total monthly sales:
POST /sales/_search {
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_path": "sales"
}
},
"sales_2nd_deriv": {
"derivative": {
"buckets_path": "sales_deriv" 1
}
}
}
}
}
}
buckets_pathfor the second derivative points to the name of the first derivative
And the following may be the response:
{
"took": 50,
"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
} 1
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"sales_deriv": {
"value": -490.0
} 1
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
},
"sales_deriv": {
"value": 315.0
},
"sales_2nd_deriv": {
"value": 805.0
}
}
]
}
}
}
- 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
The derivative aggregation allows the units of the derivative values to be specified. This returns an extra field in the response normalized_value which reports the derivative value in the desired x-axis units. In the below example we calculate the derivative of the total sales per month but ask for the derivative of the sales as in the units of sales per day:
POST /sales/_search {
"size": 0,
"aggs": {
"sales_per_month": {
"date_histogram": {
"field": "date",
"calendar_interval": "month"
},
"aggs": {
"sales": {
"sum": {
"field": "price"
}
},
"sales_deriv": {
"derivative": {
"buckets_path": "sales",
"unit": "day" 1
}
}
}
}
}
}
unitspecifies what unit to use for the x-axis of the derivative calculation
And the following may be the response:
{
"took": 50,
"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
} 1
},
{
"key_as_string": "2015/02/01 00:00:00",
"key": 1422748800000,
"doc_count": 2,
"sales": {
"value": 60.0
},
"sales_deriv": {
"value": -490.0, 1
"normalized_value": -15.806451612903226 2
}
},
{
"key_as_string": "2015/03/01 00:00:00",
"key": 1425168000000,
"doc_count": 2,
"sales": {
"value": 375.0
},
"sales_deriv": {
"value": 315.0,
"normalized_value": 11.25
}
}
]
}
}
}
valueis reported in the original units of per monthnormalized_valueis reported in the desired units of per day