[[search-aggregations-metrics-sum-aggregation]] === Sum Aggregation 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 <> fields in the documents, or be generated by a provided script. Assuming the data consists of documents representing sales records we can sum the sale price of all hats with: [source,console] -------------------------------------------------- POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "hat_prices" : { "sum" : { "field" : "price" } } } } -------------------------------------------------- // TEST[setup:sales] Resulting in: [source,console-result] -------------------------------------------------- { ... "aggregations" : { "hat_prices" : { "value" : 450.0 } } } -------------------------------------------------- // TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,"hits": $body.hits,/] 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. ==== Script We could also use a script to fetch the sales price: [source,console] -------------------------------------------------- POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "hat_prices" : { "sum" : { "script" : { "source": "doc.price.value" } } } } } -------------------------------------------------- // TEST[setup:sales] This will interpret the `script` parameter as an `inline` script with the `painless` script language and no script parameters. To use a stored script use the following syntax: [source,console] -------------------------------------------------- POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "hat_prices" : { "sum" : { "script" : { "id": "my_script", "params" : { "field" : "price" } } } } } } -------------------------------------------------- // TEST[setup:sales,stored_example_script] ===== Value Script It is also possible to access the field value from the script using `_value`. For example, this will sum the square of the prices for all hats: [source,console] -------------------------------------------------- POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "square_hats" : { "sum" : { "field" : "price", "script" : { "source": "_value * _value" } } } } } -------------------------------------------------- // TEST[setup:sales] ==== Missing value 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`. [source,console] -------------------------------------------------- POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "hat_prices" : { "sum" : { "field" : "price", "missing": 100 <1> } } } } -------------------------------------------------- // TEST[setup:sales] [[search-aggregations-metrics-sum-aggregation-histogram-fields]] ==== Histogram fields When the sums are computed on <>, 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, if we have the following index that stores pre-aggregated histograms with latency metrics for different networks: [source,console] -------------------------------------------------- PUT metrics_index/_doc/1 { "network.name" : "net-1", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], <1> "counts" : [3, 7, 23, 12, 6] <2> } } PUT metrics_index/_doc/2 { "network.name" : "net-2", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], <1> "counts" : [8, 17, 8, 7, 6] <2> } } POST /metrics_index/_search?size=0 { "aggs" : { "total_latency" : { "sum" : { "field" : "latency_histo" } } } } -------------------------------------------------- For each histogram field the sum aggregation will multiply each number in the `values` array <1> multiplied with its associated count in the `counts` array <2>. Eventually, it will add all values for all histograms and return the following result: [source,console-result] -------------------------------------------------- { ... "aggregations" : { "total_latency" : { "value" : 28.8 } } } -------------------------------------------------- // TESTRESPONSE[skip:test not setup]