Sum aggregation
editSum aggregation
editA 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:
resp = client.search(
index="sales",
size="0",
query={
"constant_score": {
"filter": {
"match": {
"type": "hat"
}
}
}
},
aggs={
"hat_prices": {
"sum": {
"field": "price"
}
}
},
)
print(resp)
response = client.search(
index: 'sales',
size: 0,
body: {
query: {
constant_score: {
filter: {
match: {
type: 'hat'
}
}
}
},
aggregations: {
hat_prices: {
sum: {
field: 'price'
}
}
}
}
)
puts response
const response = await client.search({
index: "sales",
size: 0,
query: {
constant_score: {
filter: {
match: {
type: "hat",
},
},
},
},
aggs: {
hat_prices: {
sum: {
field: "price",
},
},
},
});
console.log(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.
Script
editIf you need to get the sum for something more complex than a single
field, run the aggregation on a runtime field.
resp = client.search(
index="sales",
size="0",
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"
}
}
}
},
aggs={
"hat_prices": {
"sum": {
"field": "price.weighted"
}
}
},
)
print(resp)
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
const response = await client.search({
index: "sales",
size: 0,
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",
},
},
},
},
aggs: {
hat_prices: {
sum: {
field: "price.weighted",
},
},
},
});
console.log(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 value
editThe 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.
resp = client.search(
index="sales",
size="0",
query={
"constant_score": {
"filter": {
"match": {
"type": "hat"
}
}
}
},
aggs={
"hat_prices": {
"sum": {
"field": "price",
"missing": 100
}
}
},
)
print(resp)
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
const response = await client.search({
index: "sales",
size: 0,
query: {
constant_score: {
filter: {
match: {
type: "hat",
},
},
},
},
aggs: {
hat_prices: {
sum: {
field: "price",
missing: 100,
},
},
},
});
console.log(response);
Histogram fields
editWhen 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:
resp = client.indices.create(
index="metrics_index",
mappings={
"properties": {
"latency_histo": {
"type": "histogram"
}
}
},
)
print(resp)
resp1 = client.index(
index="metrics_index",
id="1",
refresh=True,
document={
"network.name": "net-1",
"latency_histo": {
"values": [
0.1,
0.2,
0.3,
0.4,
0.5
],
"counts": [
3,
7,
23,
12,
6
]
}
},
)
print(resp1)
resp2 = client.index(
index="metrics_index",
id="2",
refresh=True,
document={
"network.name": "net-2",
"latency_histo": {
"values": [
0.1,
0.2,
0.3,
0.4,
0.5
],
"counts": [
8,
17,
8,
7,
6
]
}
},
)
print(resp2)
resp3 = client.search(
index="metrics_index",
size="0",
filter_path="aggregations",
aggs={
"total_latency": {
"sum": {
"field": "latency_histo"
}
}
},
)
print(resp3)
response = client.indices.create(
index: 'metrics_index',
body: {
mappings: {
properties: {
latency_histo: {
type: 'histogram'
}
}
}
}
)
puts response
response = client.index(
index: 'metrics_index',
id: 1,
refresh: true,
body: {
'network.name' => 'net-1',
latency_histo: {
values: [
0.1,
0.2,
0.3,
0.4,
0.5
],
counts: [
3,
7,
23,
12,
6
]
}
}
)
puts response
response = client.index(
index: 'metrics_index',
id: 2,
refresh: true,
body: {
'network.name' => 'net-2',
latency_histo: {
values: [
0.1,
0.2,
0.3,
0.4,
0.5
],
counts: [
8,
17,
8,
7,
6
]
}
}
)
puts response
response = client.search(
index: 'metrics_index',
size: 0,
filter_path: 'aggregations',
body: {
aggregations: {
total_latency: {
sum: {
field: 'latency_histo'
}
}
}
}
)
puts response
const response = await client.indices.create({
index: "metrics_index",
mappings: {
properties: {
latency_histo: {
type: "histogram",
},
},
},
});
console.log(response);
const response1 = await client.index({
index: "metrics_index",
id: 1,
refresh: "true",
document: {
"network.name": "net-1",
latency_histo: {
values: [0.1, 0.2, 0.3, 0.4, 0.5],
counts: [3, 7, 23, 12, 6],
},
},
});
console.log(response1);
const response2 = await client.index({
index: "metrics_index",
id: 2,
refresh: "true",
document: {
"network.name": "net-2",
latency_histo: {
values: [0.1, 0.2, 0.3, 0.4, 0.5],
counts: [8, 17, 8, 7, 6],
},
},
});
console.log(response2);
const response3 = await client.search({
index: "metrics_index",
size: 0,
filter_path: "aggregations",
aggs: {
total_latency: {
sum: {
field: "latency_histo",
},
},
},
});
console.log(response3);
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
}
}
}