ElasticSearch聚合操作

基本语法

聚合的分类

后续示例数据

Metric Aggregation

Bucket Aggregation

ES聚合分析不精准原因分析

提高聚合精确度

ElasticSearch聚合操作

Elasticsearch除搜索以外，提供了针对ES 数据进行统计分析的功能。聚合(aggregations)可以让我们极其方便的实现对数据的统计、分析、运算。例如：

什么品牌的手机最受欢迎？

这些手机的平均价格、最高价格、最低价格？

这些手机每月的销售情况如何？

基本语法

聚合查询的语法结构与其他查询相似，通常包含以下部分：

查询条件：指定需要聚合的文档，可以使用标准的 Elasticsearch 查询语法，如 term、match、range 等等。

聚合函数：指定要执行的聚合操作，如 sum、avg、min、max、terms、date_histogram 等等。每个聚合命令都会生成一个聚合结果。

聚合嵌套：聚合命令可以嵌套，以便更细粒度地分析数据。

GET <index_name>/_search
{
  "aggs": {
    "<aggs_name>": { // 聚合名称需要自己定义
      "<agg_type>": {
        "field": "<field_name>"
      }
    }
  }
}

aggs_name：聚合函数的名称

agg_type：聚合种类，比如是桶聚合（terms）或者是指标聚合（avg、sum、min、max等）

field_name：字段名称或者叫域名。

聚合的分类

Metric Aggregation：—些数学运算，可以对文档字段进行统计分析，类比Mysql中的 min(), max(), sum() 操作。

SELECT MIN(price), MAX(price) FROM products
#Metric聚合的DSL类比实现：
{
    "aggs":{
        "avg_price":{
            "avg":{
                "field":"price"
            }
        }
    }
}

Bucket Aggregation：一些满足特定条件的文档的集合放置到一个桶里，每一个桶关联一个key，类比Mysql中的group by操作。

SELECT size COUNT(*) FROM products GROUP BY size
#bucket聚合的DSL类比实现：
{
 "aggs": {
    "by_size": {
      "terms": {
        "field": "size"
      }
  }
}

后续示例数据

DELETE /employees
#创建索引库
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 Aggregation

单值分析︰只输出一个分析结果（min, max, avg, sum等）

多值分析:输出多个分析结果（stats（统计）, extended stats等）

查询员工的最低最高和平均工资

#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"
      }
    }
  }
}

对salary进行统计

# 一个聚合，输出多值
POST /employees/_search
{
  "size": 0,
  "aggs": {
    "stats_salary": {
      "stats": {
        "field":"salary"
      }
    }
  }
}

cardinate对搜索结果去重

POST /employees/_search
{
  "size": 0,
  "aggs": {
    "cardinate": {
      "cardinality": {
        "field": "job.keyword"
      }
    }
  }
}

Bucket Aggregation

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提供的一些常见的 Bucket Aggregation。

Terms，需要字段支持filedata，如果是keyword 默认支持fielddata，如果是text需要在Mapping 中开启fielddata，会按照分词后的结果进行分桶。

数字类型支持Range / Data Range、Histogram（直方图） / Date Histogram。

支持嵌套: 也就在桶里再做分桶。

获取job的分类信息

# 对keword 进行聚合
GET /employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword"
      }
    }
  }
}

聚合可配置属性有：

field：指定聚合字段。

size：指定聚合结果数量。

order：指定聚合结果排序方式。

默认情况下，Bucket聚合会统计Bucket内的文档数量，记为_count，并且按照_count降序排序。我们可以指定order属性，自定义聚合的排序方式：

GET /employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword",
         "size": 10,
        "order": {
          "_count": "desc" 
        }
      }
    }
  }
}

限定聚合范围

#只对salary在10000元以上的文档聚合
GET /employees/_search
{
  "query": {
    "range": {
      "salary": {
        "gte": 10000 
      }
    }
  }, 
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword",
         "size": 10,
        "order": {
          "_count": "desc" 
        }
      }
    }
  }
}