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起始标记->深入聚合分析（4讲）：「45 | Bucket & Metric聚合分析及嵌套聚合」
结尾标记->深入聚合分析（4讲）：「48 | 聚合分析的原理及精准度问题」

Bucket & Metric聚合分析及嵌套聚合

Bucket & Metric Aggregation

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

一个例子：工资统计系统

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

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
- 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

优化Terms 聚合的性能

www.elastic.co/quide/en/el…

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

CodeDemo

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"
              }
            }
          }
        }
      }
    }
  }
}

本节知识总结

对ES的聚合分析的语法做了深入讲解，还学习了Bucket 、Metric Aggregation通过例子对他进行深入的了解。

Pipeline聚合分析

Pipeline就是对聚合分析再做一次聚合分析

一个例子: Pipeline: min bucket

在员工数最多的工种里，找出平均工资最低的工种

结果和其他的聚合同级
min_bucket 求之前结果的最小值
通过 bucket_path 关键字指定路径

Pipeline

管道的概念: 支持对聚合分析的结果，再次进行聚合分析
Pipeline 的分析结果会输出到原结果中，根据位置的不同，分为两类
Sibling - 结果和现有分析结果同级
- Max，min，Avg & Sum Bucket
- Stats，Extended Status Bucket
- Percentiles Bucket
Parent - 结果内嵌到现有的聚合分析结果之中
- Derivative (求导)
- Cumultive Sum (累计求和)
- Moving Function(滑动窗口)

CodeDemo

DELETE employees
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}



# 平均工资最低的工作类型
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword",
        "size": 10
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        }
      }
    },
    "min_salary_by_job":{
      "min_bucket": {
        "buckets_path": "jobs>avg_salary"
      }
    }
  }
}


# 平均工资最高的工作类型
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword",
        "size": 10
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        }
      }
    },
    "max_salary_by_job":{
      "max_bucket": {
        "buckets_path": "jobs>avg_salary"
      }
    }
  }
}


# 平均工资的平均工资
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword",
        "size": 10
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        }
      }
    },
    "avg_salary_by_job":{
      "avg_bucket": {
        "buckets_path": "jobs>avg_salary"
      }
    }
  }
}


# 平均工资的统计分析
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword",
        "size": 10
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        }
      }
    },
    "stats_salary_by_job":{
      "stats_bucket": {
        "buckets_path": "jobs>avg_salary"
      }
    }
  }
}


# 平均工资的百分位数
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword",
        "size": 10
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        }
      }
    },
    "percentiles_salary_by_job":{
      "percentiles_bucket": {
        "buckets_path": "jobs>avg_salary"
      }
    }
  }
}



#按照年龄对平均工资求导
POST employees/_search
{
  "size": 0,
  "aggs": {
    "age": {
      "histogram": {
        "field": "age",
        "min_doc_count": 1,
        "interval": 1
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        },
        "derivative_avg_salary":{
          "derivative": {
            "buckets_path": "avg_salary"
          }
        }
      }
    }
  }
}


#Cumulative_sum
POST employees/_search
{
  "size": 0,
  "aggs": {
    "age": {
      "histogram": {
        "field": "age",
        "min_doc_count": 1,
        "interval": 1
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        },
        "cumulative_salary":{
          "cumulative_sum": {
            "buckets_path": "avg_salary"
          }
        }
      }
    }
  }
}

#Moving Function
POST employees/_search
{
  "size": 0,
  "aggs": {
    "age": {
      "histogram": {
        "field": "age",
        "min_doc_count": 1,
        "interval": 1
      },
      "aggs": {
        "avg_salary": {
          "avg": {
            "field": "salary"
          }
        },
        "moving_avg_salary":{
          "moving_fn": {
            "buckets_path": "avg_salary",
            "window":10,
            "script": "MovingFunctions.min(values)"
          }
        }
      }
    }
  }
}

本节知识总结

介绍了Pipeline Aggregation是对聚合分析再做一次聚合分析。通过阅读文档获取更多的知识。

作用范围与排序

聚合的作用范围

ES聚合分析的默认作用范围是 query 的查询结果集
同时ES还支持以下方式改变聚合的作用范围
- Filter
- PostFilter
- Globa

排序

指定 order，按照 count 和 key 进行排序
- 默认情况，按照 count 降序排序
- 指定 size，就能返回相应的桶

CodeDemo

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}



# Query
POST employees/_search
{
  "size": 0,
  "query": {
    "range": {
      "age": {
        "gte": 20
      }
    }
  },
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword"
        
      }
    }
  }
}


#Filter
POST employees/_search
{
  "size": 0,
  "aggs": {
    "older_person": {
      "filter":{
        "range":{
          "age":{
            "from":35
          }
        }
      },
      "aggs":{
         "jobs":{
           "terms": {
        "field":"job.keyword"
      }
      }
    }},
    "all_jobs": {
      "terms": {
        "field":"job.keyword"
        
      }
    }
  }
}



#Post field. 一条语句，找出所有的job类型。还能找到聚合后符合条件的结果
POST employees/_search
{
  "aggs": {
    "jobs": {
      "terms": {
        "field": "job.keyword"
      }
    }
  },
  "post_filter": {
    "match": {
      "job.keyword": "Dev Manager"
    }
  }
}


#global
POST employees/_search
{
  "size": 0,
  "query": {
    "range": {
      "age": {
        "gte": 40
      }
    }
  },
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword"
        
      }
    },
    
    "all":{
      "global":{},
      "aggs":{
        "salary_avg":{
          "avg":{
            "field":"salary"
          }
        }
      }
    }
  }
}


#排序 order
#count and key
POST employees/_search
{
  "size": 0,
  "query": {
    "range": {
      "age": {
        "gte": 20
      }
    }
  },
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword",
        "order":[
          {"_count":"asc"},
          {"_key":"desc"}
          ]
        
      }
    }
  }
}


#排序 order
#count and key
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword",
        "order":[  {
            "avg_salary":"desc"
          }]
        
        
      },
    "aggs": {
      "avg_salary": {
        "avg": {
          "field":"salary"
        }
      }
    }
    }
  }
}


#排序 order
#count and key
POST employees/_search
{
  "size": 0,
  "aggs": {
    "jobs": {
      "terms": {
        "field":"job.keyword",
        "order":[  {
            "stats_salary.min":"desc"
          }]
        
        
      },
    "aggs": {
      "stats_salary": {
        "stats": {
          "field":"salary"
        }
      }
    }
    }
  }
}

本节知识总结

学习了es聚合分析的作用范围，同时也学习了如何对聚合分析的结果做一个排序。

聚合分析的原理及精准度问题

分布式系统的近似统计算法

Min 聚合分析的执行流程

Terms Aggregation 的返回值

在 Terms Aggregation 的返回中有两个特殊的数值
- 被遗漏的doc_count_error_upper_bound : term 分桶，包含的文档，有可能的最大值
- sum_other_doc_count: 除了返回结果 bucket的 terms 以外，其他 terms 的文档总数 (总数-返回的总数)

Terms 聚合分析的执行流程

Terms 不正确的案例

如何解决 Terms 不准的问题: 提升 shard size 的参数

Terms 聚合分析不准的原因，数据分散在多个分片上， Coordinating Node 无法获取数据全貌
解决方案1:当数据量不大时，设置 PrimaryShard 为1;实现准确性
方案2:在分布式数据上，设置 shard size 参数，提高精确度
- 原理:每次从 Shard 上额外多获取数据，提升准确率

打开 show_term_doc_count_error

shard_size 设定

调整 shard size 大小，降低 doc_count_error_upper_bound 来提升准确度
- 增加整体计算量，提高了准确度，但会降低相应时间
Shard Size 默认大小设定
- shard size = size *1.5 +100
- www.elastic.co/guide/en/el…

CodeDemo

DELETE my_flights
PUT my_flights
{
  "settings": {
    "number_of_shards": 20
  },
  "mappings" : {
      "properties" : {
        "AvgTicketPrice" : {
          "type" : "float"
        },
        "Cancelled" : {
          "type" : "boolean"
        },
        "Carrier" : {
          "type" : "keyword"
        },
        "Dest" : {
          "type" : "keyword"
        },
        "DestAirportID" : {
          "type" : "keyword"
        },
        "DestCityName" : {
          "type" : "keyword"
        },
        "DestCountry" : {
          "type" : "keyword"
        },
        "DestLocation" : {
          "type" : "geo_point"
        },
        "DestRegion" : {
          "type" : "keyword"
        },
        "DestWeather" : {
          "type" : "keyword"
        },
        "DistanceKilometers" : {
          "type" : "float"
        },
        "DistanceMiles" : {
          "type" : "float"
        },
        "FlightDelay" : {
          "type" : "boolean"
        },
        "FlightDelayMin" : {
          "type" : "integer"
        },
        "FlightDelayType" : {
          "type" : "keyword"
        },
        "FlightNum" : {
          "type" : "keyword"
        },
        "FlightTimeHour" : {
          "type" : "keyword"
        },
        "FlightTimeMin" : {
          "type" : "float"
        },
        "Origin" : {
          "type" : "keyword"
        },
        "OriginAirportID" : {
          "type" : "keyword"
        },
        "OriginCityName" : {
          "type" : "keyword"
        },
        "OriginCountry" : {
          "type" : "keyword"
        },
        "OriginLocation" : {
          "type" : "geo_point"
        },
        "OriginRegion" : {
          "type" : "keyword"
        },
        "OriginWeather" : {
          "type" : "keyword"
        },
        "dayOfWeek" : {
          "type" : "integer"
        },
        "timestamp" : {
          "type" : "date"
        }
      }
    }
}


POST _reindex
{
  "source": {
    "index": "kibana_sample_data_flights"
  },
  "dest": {
    "index": "my_flights"
  }
}

GET kibana_sample_data_flights/_count
GET my_flights/_count

get kibana_sample_data_flights/_search


GET kibana_sample_data_flights/_search
{
  "size": 0,
  "aggs": {
    "weather": {
      "terms": {
        "field":"OriginWeather",
        "size":5,
        "show_term_doc_count_error":true
      }
    }
  }
}


GET my_flights/_search
{
  "size": 0,
  "aggs": {
    "weather": {
      "terms": {
        "field":"OriginWeather",
        "size":1,
        "shard_size":1,
        "show_term_doc_count_error":true
      }
    }
  }
}

本节知识总结

介绍了elasticsearch聚合分析精准度问题，当数据分散在不同的分片上时聚合分析的结果会出现不准确的情况，可以通过修改term查询中的shard size的方式去避免这样的情况发生，要注意到有可能对性能产生一定的影响。

此文章为4月Day3学习笔记，内容来源于极客时间《Elasticsearch 核心技术与实战》

Elasticsearch 学习笔记Day 19

Bucket & Metric聚合分析及嵌套聚合

Bucket & Metric Aggregation

Aggregation 的语法

一个例子：工资统计系统

Metric Aggregation

Metric 聚合的具体 Demo

Bucket

Terms Aggregation

优化Terms 聚合的性能

Range & Histogram 聚合

Bucket + Metric Aggregation

CodeDemo

相关阅读

本节知识总结

Pipeline聚合分析

一个例子: Pipeline: min bucket

Pipeline

CodeDemo

相关阅读

本节知识总结

作用范围与排序

聚合的作用范围

排序

CodeDemo

本节知识总结

聚合分析的原理及精准度问题

分布式系统的近似统计算法

Min 聚合分析的执行流程

Terms Aggregation 的返回值

Terms 聚合分析的执行流程

Terms 不正确的案例

如何解决 Terms 不准的问题: 提升 shard size 的参数

打开 show_term_doc_count_error

shard_size 设定

CodeDemo

本节知识总结