## Sum aggregationedit

A `single-value`

metrics aggregation that sums up numeric values that are extracted from the aggregated documents.
These values can be extracted either from specific numeric or histogram fields.

Assuming the data consists of documents representing sales records we can sum the sale price of all hats with:

response = client.search( index: 'sales', size: 0, body: { query: { constant_score: { filter: { match: { type: 'hat' } } } }, aggregations: { hat_prices: { sum: { field: 'price' } } } } ) puts response

POST /sales/_search?size=0 { "query": { "constant_score": { "filter": { "match": { "type": "hat" } } } }, "aggs": { "hat_prices": { "sum": { "field": "price" } } } }

Resulting in:

{ ... "aggregations": { "hat_prices": { "value": 450.0 } } }

The name of the aggregation (`hat_prices`

above) also serves as the key by which the aggregation result can be retrieved from the returned response.

### Scriptedit

If you need to get the `sum`

for something more complex than a single
field, run the aggregation on a runtime field.

response = client.search( index: 'sales', size: 0, body: { runtime_mappings: { "price.weighted": { type: 'double', script: "\n double price = doc['price'].value;\n if (doc['promoted'].value) {\n price *= 0.8;\n }\n emit(price);\n " } }, query: { constant_score: { filter: { match: { type: 'hat' } } } }, aggregations: { hat_prices: { sum: { field: 'price.weighted' } } } } ) puts response

POST /sales/_search?size=0 { "runtime_mappings": { "price.weighted": { "type": "double", "script": """ double price = doc['price'].value; if (doc['promoted'].value) { price *= 0.8; } emit(price); """ } }, "query": { "constant_score": { "filter": { "match": { "type": "hat" } } } }, "aggs": { "hat_prices": { "sum": { "field": "price.weighted" } } } }

### Missing valueedit

The `missing`

parameter defines how documents that are missing a value should
be treated. By default documents missing the value will be ignored but it is
also possible to treat them as if they had a value. For example, this treats
all hat sales without a price as being `100`

.

response = client.search( index: 'sales', size: 0, body: { query: { constant_score: { filter: { match: { type: 'hat' } } } }, aggregations: { hat_prices: { sum: { field: 'price', missing: 100 } } } } ) puts response

### Histogram fieldsedit

When sum is computed on histogram fields, the result of the aggregation is the sum of all elements in the `values`

array multiplied by the number in the same position in the `counts`

array.

For example, for the following index that stores pre-aggregated histograms with latency metrics for different networks:

PUT metrics_index { "mappings": { "properties": { "latency_histo": { "type": "histogram" } } } } PUT metrics_index/_doc/1?refresh { "network.name" : "net-1", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], "counts" : [3, 7, 23, 12, 6] } } PUT metrics_index/_doc/2?refresh { "network.name" : "net-2", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], "counts" : [8, 17, 8, 7, 6] } } POST /metrics_index/_search?size=0&filter_path=aggregations { "aggs" : { "total_latency" : { "sum" : { "field" : "latency_histo" } } } }

For each histogram field, the `sum`

aggregation will add each number in the
`values`

array, multiplied by its associated count in the `counts`

array.

Eventually, it will add all values for all histograms and return the following result:

{ "aggregations": { "total_latency": { "value": 28.8 } } }