值计数聚合
编辑值计数聚合编辑
一种 单值 指标聚合，用于计算从聚合文档中提取的值的数量。这些值可以从文档中的特定字段中提取，也可以由提供的脚本生成。通常，此聚合器将与其他单值聚合器结合使用。例如，在计算 avg 时，您可能会对计算平均值的数值数量感兴趣。
value_count 不会对值进行去重，因此即使字段中有重复值，每个值也会被单独计数。
resp = client.search(
index="sales",
size="0",
body={"aggs": {"types_count": {"value_count": {"field": "type"}}}},
)
print(resp)
response = client.search(
index: 'sales',
size: 0,
body: {
aggregations: {
types_count: {
value_count: {
field: 'type'
}
}
}
}
)
puts response
res, err := es.Search(
es.Search.WithIndex("sales"),
es.Search.WithBody(strings.NewReader(`{
"aggs": {
"types_count": {
"value_count": {
"field": "type"
}
}
}
}`)),
es.Search.WithSize(0),
es.Search.WithPretty(),
)
fmt.Println(res, err)
POST /sales/_search?size=0
{
"aggs" : {
"types_count" : { "value_count" : { "field" : "type" } }
}
}
响应
{
...
"aggregations": {
"types_count": {
"value": 7
}
}
}
聚合的名称（上面的 types_count）也用作从返回的响应中检索聚合结果的键。
脚本编辑
如果您需要计算比单个字段中的值更复杂的内容，则应在 运行时字段 上运行聚合。
resp = client.search(
index="sales",
body={
"size": 0,
"runtime_mappings": {
"tags": {
"type": "keyword",
"script": "\n emit(doc['type'].value);\n if (doc['promoted'].value) {\n emit('hot');\n }\n ",
}
},
"aggs": {"tags_count": {"value_count": {"field": "tags"}}},
},
)
print(resp)
response = client.search(
index: 'sales',
body: {
size: 0,
runtime_mappings: {
tags: {
type: 'keyword',
script: "\n emit(doc['type'].value);\n if (doc['promoted'].value) {\n emit('hot');\n }\n "
}
},
aggregations: {
tags_count: {
value_count: {
field: 'tags'
}
}
}
}
)
puts response
POST /sales/_search
{
"size": 0,
"runtime_mappings": {
"tags": {
"type": "keyword",
"script": """
emit(doc['type'].value);
if (doc['promoted'].value) {
emit('hot');
}
"""
}
},
"aggs": {
"tags_count": {
"value_count": {
"field": "tags"
}
}
}
}
直方图字段编辑
当在 直方图字段 上计算 value_count 聚合时，聚合的结果是直方图的 counts 数组中所有数字的总和。
例如，对于以下存储不同网络的预聚合直方图（包含延迟指标）的索引
resp = client.index(
index="metrics_index",
id="1",
body={
"network.name": "net-1",
"latency_histo": {
"values": [0.1, 0.2, 0.3, 0.4, 0.5],
"counts": [3, 7, 23, 12, 6],
},
},
)
print(resp)
resp = client.index(
index="metrics_index",
id="2",
body={
"network.name": "net-2",
"latency_histo": {
"values": [0.1, 0.2, 0.3, 0.4, 0.5],
"counts": [8, 17, 8, 7, 6],
},
},
)
print(resp)
resp = client.search(
index="metrics_index",
size="0",
body={
"aggs": {
"total_requests": {"value_count": {"field": "latency_histo"}}
}
},
)
print(resp)
response = client.index(
index: 'metrics_index',
id: 1,
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,
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,
body: {
aggregations: {
total_requests: {
value_count: {
field: 'latency_histo'
}
}
}
}
)
puts response
{
res, err := es.Index(
"metrics_index",
strings.NewReader(`{
"network.name": "net-1",
"latency_histo": {
"values": [
0.1,
0.2,
0.3,
0.4,
0.5
],
"counts": [
3,
7,
23,
12,
6
]
}
}`),
es.Index.WithDocumentID("1"),
es.Index.WithPretty(),
)
fmt.Println(res, err)
}
{
res, err := es.Index(
"metrics_index",
strings.NewReader(`{
"network.name": "net-2",
"latency_histo": {
"values": [
0.1,
0.2,
0.3,
0.4,
0.5
],
"counts": [
8,
17,
8,
7,
6
]
}
}`),
es.Index.WithDocumentID("2"),
es.Index.WithPretty(),
)
fmt.Println(res, err)
}
{
res, err := es.Search(
es.Search.WithIndex("metrics_index"),
es.Search.WithBody(strings.NewReader(`{
"aggs": {
"total_requests": {
"value_count": {
"field": "latency_histo"
}
}
}
}`)),
es.Search.WithSize(0),
es.Search.WithPretty(),
)
fmt.Println(res, err)
}
PUT metrics_index/_doc/1
{
"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
{
"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
{
"aggs": {
"total_requests": {
"value_count": { "field": "latency_histo" }
}
}
}
对于每个直方图字段，value_count 聚合将对 counts 数组 <1> 中的所有数字求和。最终，它将添加所有直方图的所有值，并返回以下结果
{
...
"aggregations": {
"total_requests": {
"value": 97
}
}
}