Min aggregation
A single-value metrics aggregation that keeps track and returns the minimum value among numeric values extracted from the aggregated documents.
The min and max aggregation operate on the double representation of the data. As a consequence, the result may be approximate when running on longs whose absolute value is greater than 2^53.
Computing the min price value across all documents:
resp = client.search(
index="sales",
size="0",
aggs={
"min_price": {
"min": {
"field": "price"
}
}
},
)
print(resp) response = client.search(
index: 'sales',
size: 0,
body: {
aggregations: {
min_price: {
min: {
field: 'price'
}
}
}
}
)
puts response const response = await client.search({
index: "sales",
size: 0,
aggs: {
min_price: {
min: {
field: "price",
},
},
},
});
console.log(response); POST /sales/_search?size=0
{
"aggs": {
"min_price": { "min": { "field": "price" } }
}
} Response:
{
...
"aggregations": {
"min_price": {
"value": 10.0
}
}
} As can be seen, the name of the aggregation (min_price above) also serves as the key by which the aggregation result can be retrieved from the returned response.
Script
If you need to get the min of something more complex than a single field, run the aggregation on a runtime field.
resp = client.search(
index="sales",
size=0,
runtime_mappings={
"price.adjusted": {
"type": "double",
"script": "\n double price = doc['price'].value;\n if (doc['promoted'].value) {\n price *= 0.8;\n }\n emit(price);\n "
}
},
aggs={
"min_price": {
"min": {
"field": "price.adjusted"
}
}
},
)
print(resp) response = client.search(
index: 'sales',
body: {
size: 0,
runtime_mappings: {
'price.adjusted' => {
type: 'double',
script: "\n double price = doc['price'].value;\n if (doc['promoted'].value) {\n price *= 0.8;\n }\n emit(price);\n "
}
},
aggregations: {
min_price: {
min: {
field: 'price.adjusted'
}
}
}
}
)
puts response const response = await client.search({
index: "sales",
size: 0,
runtime_mappings: {
"price.adjusted": {
type: "double",
script:
"\n double price = doc['price'].value;\n if (doc['promoted'].value) {\n price *= 0.8;\n }\n emit(price);\n ",
},
},
aggs: {
min_price: {
min: {
field: "price.adjusted",
},
},
},
});
console.log(response); POST /sales/_search
{
"size": 0,
"runtime_mappings": {
"price.adjusted": {
"type": "double",
"script": """
double price = doc['price'].value;
if (doc['promoted'].value) {
price *= 0.8;
}
emit(price);
"""
}
},
"aggs": {
"min_price": {
"min": { "field": "price.adjusted" }
}
}
} Missing value
The missing parameter defines how documents that are missing a value should be treated. By default they will be ignored but it is also possible to treat them as if they had a value.
resp = client.search(
index="sales",
aggs={
"grade_min": {
"min": {
"field": "grade",
"missing": 10
}
}
},
)
print(resp) response = client.search(
index: 'sales',
body: {
aggregations: {
grade_min: {
min: {
field: 'grade',
missing: 10
}
}
}
}
)
puts response const response = await client.search({
index: "sales",
aggs: {
grade_min: {
min: {
field: "grade",
missing: 10,
},
},
},
});
console.log(response); POST /sales/_search
{
"aggs": {
"grade_min": {
"min": {
"field": "grade",
"missing": 10
}
}
}
} | Documents without a value in the |
Histogram fields
When min is computed on histogram fields, the result of the aggregation is the minimum of all elements in the values array. Note, that the counts array of the histogram is ignored.
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={
"min_latency": {
"min": {
"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: {
min_latency: {
min: {
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: {
min_latency: {
min: {
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" : {
"min_latency" : { "min" : { "field" : "latency_histo" } }
}
} The min aggregation will return the minimum value of all histogram fields:
{
"aggregations": {
"min_latency": {
"value": 0.1
}
}
}
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https://www.elastic.co/guide/en/elasticsearch/reference/8.17/search-aggregations-metrics-min-aggregation.html
