Rate aggregationedit
A rate
metrics aggregation can be used only inside a date_histogram
and calculates a rate of documents or a field in each
date_histogram
bucket. The field values can be generated by a provided script or extracted from specific numeric or
histogram fields in the documents.
Syntaxedit
A rate
aggregation looks like this in isolation:
{ "rate": { "unit": "month", "field": "requests" } }
The following request will group all sales records into monthly bucket and than convert the number of sales transaction in each bucket into per annual sales rate.
GET sales/_search { "size": 0, "aggs": { "by_date": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "my_rate": { "rate": { "unit": "year" } } } } } }
The response will return the annual rate of transaction in each bucket. Since there are 12 months per year, the annual rate will be automatically calculated by multiplying monthly rate by 12.
{ ... "aggregations" : { "by_date" : { "buckets" : [ { "key_as_string" : "2015/01/01 00:00:00", "key" : 1420070400000, "doc_count" : 3, "my_rate" : { "value" : 36.0 } }, { "key_as_string" : "2015/02/01 00:00:00", "key" : 1422748800000, "doc_count" : 2, "my_rate" : { "value" : 24.0 } }, { "key_as_string" : "2015/03/01 00:00:00", "key" : 1425168000000, "doc_count" : 2, "my_rate" : { "value" : 24.0 } } ] } } }
Instead of counting the number of documents, it is also possible to calculate a sum of all values of the fields in the documents in each bucket or the number of values in each bucket. The following request will group all sales records into monthly bucket and than calculate the total monthly sales and convert them into average daily sales.
GET sales/_search { "size": 0, "aggs": { "by_date": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "avg_price": { "rate": { "field": "price", "unit": "day" } } } } } }
The response will contain the average daily sale prices for each month.
{ ... "aggregations" : { "by_date" : { "buckets" : [ { "key_as_string" : "2015/01/01 00:00:00", "key" : 1420070400000, "doc_count" : 3, "avg_price" : { "value" : 17.741935483870968 } }, { "key_as_string" : "2015/02/01 00:00:00", "key" : 1422748800000, "doc_count" : 2, "avg_price" : { "value" : 2.142857142857143 } }, { "key_as_string" : "2015/03/01 00:00:00", "key" : 1425168000000, "doc_count" : 2, "avg_price" : { "value" : 12.096774193548388 } } ] } } }
By adding the mode
parameter with the value value_count
, we can change the calculation from sum
to the number of values of the field:
GET sales/_search { "size": 0, "aggs": { "by_date": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "avg_number_of_sales_per_year": { "rate": { "field": "price", "unit": "year", "mode": "value_count" } } } } } }
Histogram is grouped by month. 

Calculate number of all sale prices 

Convert to annual counts 

Changing the mode to value count 
The response will contain the average daily sale prices for each month.
{ ... "aggregations" : { "by_date" : { "buckets" : [ { "key_as_string" : "2015/01/01 00:00:00", "key" : 1420070400000, "doc_count" : 3, "avg_number_of_sales_per_year" : { "value" : 36.0 } }, { "key_as_string" : "2015/02/01 00:00:00", "key" : 1422748800000, "doc_count" : 2, "avg_number_of_sales_per_year" : { "value" : 24.0 } }, { "key_as_string" : "2015/03/01 00:00:00", "key" : 1425168000000, "doc_count" : 2, "avg_number_of_sales_per_year" : { "value" : 24.0 } } ] } } }
By default sum
mode is used.

"mode": "sum"
 calculate the sum of all values field

"mode": "value_count"
 use the number of values in the field
The mode
parameter can only be used with fields and scripts.
Relationship between bucket sizes and rateedit
The rate
aggregation supports all rate that can be used calendar_intervals parameter of date_histogram
aggregation. The specified rate should compatible with the date_histogram
aggregation interval, i.e. it should be possible to
convert the bucket size into the rate. By default the interval of the date_histogram
is used.

"rate": "second"
 compatible with all intervals

"rate": "minute"
 compatible with all intervals

"rate": "hour"
 compatible with all intervals

"rate": "day"
 compatible with all intervals

"rate": "week"
 compatible with all intervals

"rate": "month"

compatible with only with
month
,quarter
andyear
calendar intervals 
"rate": "quarter"

compatible with only with
month
,quarter
andyear
calendar intervals 
"rate": "year"

compatible with only with
month
,quarter
andyear
calendar intervals
There is also an additional limitations if the date histogram is not a direct parent of the rate histogram. In this case both rate interval
and histogram interval have to be in the same group: [second
, ` minute`, hour
, day
, week
] or [month
, quarter
, year
]. For
example, if the date histogram is month
based, only rate intervals of month
, quarter
or year
are supported. If the date histogram
is day
based, only second
, ` minute`, hour
, day
, and week
rate intervals are supported.
Scriptedit
The rate
aggregation also supports scripting. For example, if we need to adjust out prices before calculating rates, we could use
a script to recalculate them onthefly:
GET sales/_search { "size": 0, "aggs": { "by_date": { "date_histogram": { "field": "date", "calendar_interval": "month" }, "aggs": { "avg_price": { "rate": { "script": { "lang": "painless", "source": "doc['price'].value * params.adjustment", "params": { "adjustment": 0.9 } } } } } } } }
The 

Scripting supports parameterized input just like any other script. 
{ ... "aggregations" : { "by_date" : { "buckets" : [ { "key_as_string" : "2015/01/01 00:00:00", "key" : 1420070400000, "doc_count" : 3, "avg_price" : { "value" : 495.0 } }, { "key_as_string" : "2015/02/01 00:00:00", "key" : 1422748800000, "doc_count" : 2, "avg_price" : { "value" : 54.0 } }, { "key_as_string" : "2015/03/01 00:00:00", "key" : 1425168000000, "doc_count" : 2, "avg_price" : { "value" : 337.5 } } ] } } }