Bucket & Metric Aggregation
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-
Metric - ⼀些系列的统计⽅法
-
Bucket - ⼀组满⾜条件的⽂档
Aggregation 的语法
- Aggregation 属于Search的一部分。一般情况下，建议将其 Size 指定为0
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⼀个例⼦：⼯资统计信息
# 多个 Metric 聚合，找到最低最高和平均工资
POST employees/_search
{
"size": 0,
"aggs": {
"max_salary": {
"max": {
"field": "salary"
}
},
"min_salary": {
"min": {
"field": "salary"
}
},
"avg_salary": {
"avg": {
"field": "salary"
}
}
}
}
res:
{
"took" : 64,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 20,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"max_salary" : {
"value" : 50000.0
},
"avg_salary" : {
"value" : 24700.0
},
"min_salary" : {
"value" : 9000.0
}
}
}
Metric Aggregation
-
单值分析：只输出⼀个分析结果
-
min, max, avg, sum
-
Cardinality （类似 distinct Count）
-
-
多值分析：输出多个分析结果
-
stats, extended stats
-
percentile, percentile rank
-
top hits （排在前⾯的示例）
-
Metric 聚合的具体 Demo
-
查看最低⼯资
-
查看最⾼⼯资
-
⼀个聚合输出多个值
-
⼀次查询包含多个聚合
- 同时查看最低，最⾼和平均⼯资
Bucket
image.png
-
按照⼀定的规则，将⽂档分配到不同的 桶中，从⽽达到分类的⽬的。ES 提供的 ⼀些常⻅的 Bucket Aggregation
-
Terms
-
数字类型
-
Range / Data Range
-
Histogram / Date Histogram
-
⽀持嵌套：也就在桶⾥再做分桶
-
Terms Aggregation
-
字段需要打开 fielddata，才能进⾏ Terms Aggregation
-
Keyword 默认⽀持 doc_values
-
Text 需要在 Mapping 中 enable。会按照分词后的结果进⾏分
-
-
Demo
-
对 job 和 job.keyword 进⾏聚合
-
对性别进⾏ Terms 聚合
-
指定 bucket size
-
Cardinality
- 类似 SQL 中的 Distinct
POST employees/_search
{
"size": 0,
"aggs": {
"cardinate": {
"cardinality": {
"field": "job.keyword"
}
}
}
}
res:
{
"took" : 133,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 20,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"cardinate" : {
"value" : 7
}
}
}
Bucket Size & Top Hits Demo
-
应⽤场景：当获取分桶后，桶内最匹配的顶部⽂档列表
-
Size：按年龄分桶，找出指定数据量的分桶信息
-
Top Hits：查看各个⼯种中，年纪最⼤的 3 名员⼯
优化 Terms 聚合的性能
image.png
Range & Histogram 聚合
-
按照数字的范围，进⾏分桶
-
在 Range Aggregation 中，可以⾃定义 Key
-
Demo：
-
按照⼯资的 Range 分桶
-
按照⼯资的间隔（Histogram）分桶
-
Bucket + Metric Aggregation
-
Bucket 聚合分析允许通过添加⼦聚合分析来进⼀步分析，⼦聚合分析可以是
-
Bucket
-
Metric
-
-
Demo
-
按照⼯作类型进⾏分桶，并统计⼯资信息
-
先按照⼯作类型分桶，然后按性别分桶，并统计⼯资信息
-
本节知识点
-
聚合分析的具体语法
- ⼀个聚合查询中可以包含多个聚合； 每个 Bucket 聚合可以包含⼦聚合
-
Metrix
- 单值输出 & 多值输出
-
Bucket
- Terms & 数字范围
demos
DELETE /employees
PUT /employees/
{
"mappings" : {
"properties" : {
"age" : {
"type" : "integer"
},
"gender" : {
"type" : "keyword"
},
"job" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 50
}
}
},
"name" : {
"type" : "keyword"
},
"salary" : {
"type" : "integer"
}
}
}
}
PUT /employees/_bulk
{ "index" : { "_id" : "1" } }
{ "name" : "Emma","age":32,"job":"Product Manager","gender":"female","salary":35000 }
{ "index" : { "_id" : "2" } }
{ "name" : "Underwood","age":41,"job":"Dev Manager","gender":"male","salary": 50000}
{ "index" : { "_id" : "3" } }
{ "name" : "Tran","age":25,"job":"Web Designer","gender":"male","salary":18000 }
{ "index" : { "_id" : "4" } }
{ "name" : "Rivera","age":26,"job":"Web Designer","gender":"female","salary": 22000}
{ "index" : { "_id" : "5" } }
{ "name" : "Rose","age":25,"job":"QA","gender":"female","salary":18000 }
{ "index" : { "_id" : "6" } }
{ "name" : "Lucy","age":31,"job":"QA","gender":"female","salary": 25000}
{ "index" : { "_id" : "7" } }
{ "name" : "Byrd","age":27,"job":"QA","gender":"male","salary":20000 }
{ "index" : { "_id" : "8" } }
{ "name" : "Foster","age":27,"job":"Java Programmer","gender":"male","salary": 20000}
{ "index" : { "_id" : "9" } }
{ "name" : "Gregory","age":32,"job":"Java Programmer","gender":"male","salary":22000 }
{ "index" : { "_id" : "10" } }
{ "name" : "Bryant","age":20,"job":"Java Programmer","gender":"male","salary": 9000}
{ "index" : { "_id" : "11" } }
{ "name" : "Jenny","age":36,"job":"Java Programmer","gender":"female","salary":38000 }
{ "index" : { "_id" : "12" } }
{ "name" : "Mcdonald","age":31,"job":"Java Programmer","gender":"male","salary": 32000}
{ "index" : { "_id" : "13" } }
{ "name" : "Jonthna","age":30,"job":"Java Programmer","gender":"female","salary":30000 }
{ "index" : { "_id" : "14" } }
{ "name" : "Marshall","age":32,"job":"Javascript Programmer","gender":"male","salary": 25000}
{ "index" : { "_id" : "15" } }
{ "name" : "King","age":33,"job":"Java Programmer","gender":"male","salary":28000 }
{ "index" : { "_id" : "16" } }
{ "name" : "Mccarthy","age":21,"job":"Javascript Programmer","gender":"male","salary": 16000}
{ "index" : { "_id" : "17" } }
{ "name" : "Goodwin","age":25,"job":"Javascript Programmer","gender":"male","salary": 16000}
{ "index" : { "_id" : "18" } }
{ "name" : "Catherine","age":29,"job":"Javascript Programmer","gender":"female","salary": 20000}
{ "index" : { "_id" : "19" } }
{ "name" : "Boone","age":30,"job":"DBA","gender":"male","salary": 30000}
{ "index" : { "_id" : "20" } }
{ "name" : "Kathy","age":29,"job":"DBA","gender":"female","salary": 20000}
# Metric 聚合，找到最低的工资
POST employees/_search
{
"size": 0,
"aggs": {
"min_salary": {
"min": {
"field":"salary"
}
}
}
}
# Metric 聚合，找到最高的工资
POST employees/_search
{
"size": 0,
"aggs": {
"max_salary": {
"max": {
"field":"salary"
}
}
}
}
# 多个 Metric 聚合，找到最低最高和平均工资
POST employees/_search
{
"size": 0,
"aggs": {
"max_salary": {
"max": {
"field": "salary"
}
},
"min_salary": {
"min": {
"field": "salary"
}
},
"avg_salary": {
"avg": {
"field": "salary"
}
}
}
}
# 一个聚合，输出多值
POST employees/_search
{
"size": 0,
"aggs": {
"stats_salary": {
"stats": {
"field":"salary"
}
}
}
}
# 对keword 进行聚合
POST employees/_search
{
"size": 0,
"aggs": {
"jobs": {
"terms": {
"field":"job.keyword"
}
}
}
}
# 对 Text 字段进行 terms 聚合查询，失败
POST employees/_search
{
"size": 0,
"aggs": {
"jobs": {
"terms": {
"field":"job"
}
}
}
}
# 对 Text 字段打开 fielddata，支持terms aggregation
PUT employees/_mapping
{
"properties" : {
"job":{
"type": "text",
"fielddata": true
}
}
}
# 对 Text 字段进行 terms 分词。分词后的terms
POST employees/_search
{
"size": 0,
"aggs": {
"jobs": {
"terms": {
"field":"job"
}
}
}
}
POST employees/_search
{
"size": 0,
"aggs": {
"jobs": {
"terms": {
"field":"job.keyword"
}
}
}
}
# 对job.keyword 和 job 进行 terms 聚合，分桶的总数并不一样
POST employees/_search
{
"size": 0,
"aggs": {
"cardinate": {
"cardinality": {
"field": "job"
}
}
}
}
# 对 性别的 keyword 进行聚合
POST employees/_search
{
"size": 0,
"aggs": {
"gender": {
"terms": {
"field":"gender"
}
}
}
}
#指定 bucket 的 size
POST employees/_search
{
"size": 0,
"aggs": {
"ages_5": {
"terms": {
"field":"age",
"size":3
}
}
}
}
# 指定size，不同工种中，年纪最大的3个员工的具体信息
POST employees/_search
{
"size": 0,
"aggs": {
"jobs": {
"terms": {
"field":"job.keyword"
},
"aggs":{
"old_employee":{
"top_hits":{
"size":3,
"sort":[
{
"age":{
"order":"desc"
}
}
]
}
}
}
}
}
}
#Salary Ranges 分桶，可以自己定义 key
POST employees/_search
{
"size": 0,
"aggs": {
"salary_range": {
"range": {
"field":"salary",
"ranges":[
{
"to":10000
},
{
"from":10000,
"to":20000
},
{
"key":">20000",
"from":20000
}
]
}
}
}
}
#Salary Histogram,工资0到10万，以 5000一个区间进行分桶
POST employees/_search
{
"size": 0,
"aggs": {
"salary_histrogram": {
"histogram": {
"field":"salary",
"interval":5000,
"extended_bounds":{
"min":0,
"max":100000
}
}
}
}
}
# 嵌套聚合1，按照工作类型分桶，并统计工资信息
POST employees/_search
{
"size": 0,
"aggs": {
"Job_salary_stats": {
"terms": {
"field": "job.keyword"
},
"aggs": {
"salary": {
"stats": {
"field": "salary"
}
}
}
}
}
}
# 多次嵌套。根据工作类型分桶，然后按照性别分桶，计算工资的统计信息
POST employees/_search
{
"size": 0,
"aggs": {
"Job_gender_stats": {
"terms": {
"field": "job.keyword"
},
"aggs": {
"gender_stats": {
"terms": {
"field": "gender"
},
"aggs": {
"salary_stats": {
"stats": {
"field": "salary"
}
}
}
}
}
}
}
}
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