ES 24 - 如何通过Elasticsearch进行聚合检索 (分组统计)

目录

• 1 普通聚合分析
  • 1.1 直接聚合统计
  • 1.2 先检索, 再聚合
  • 1.3 扩展: fielddata和keyword的聚合比较
• 2 嵌套聚合
  • 2.1 先分组, 再聚合统计
  • 2.2 先分组, 再统计, 最后排序
  • 2.3 先分组, 组内再分组, 然后统计、排序
• 版权声明

1 普通聚合分析

1.1 直接聚合统计

(1) 计算每个tag下的文档数量, 请求语法:

GET book_shop/it_book/_search
{
    "size": 0,    			// 不显示命中(hits)的所有文档信息
    "aggs": {
        "group_by_tags": {	// 聚合结果的名称, 需要自定义(复制时请去掉此注释)
            "terms": {
                "field": "tags"
            }
        }
    }
}

(2) 发生错误:

说明: 索引book_shop的mapping映射是ES自动创建的, 它把tag解析成了text类型, 在发起对tag的聚合请求后, 将抛出如下错误:

{
    "error": {
        "root_cause": [
            {
                "type": "illegal_argument_exception",
                "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [tags] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
            }
        ],
        "type": "search_phase_execution_exception",
        "reason": "all shards failed",
        "phase": "query",
        "grouped": true,
        "failed_shards": [......]
    },
    "status": 400
}

(3) 错误分析:

错误信息: Set fielddata=true on [xxxx] ......

错误分析: 默认情况下, Elasticsearch 对 text 类型的字段(field)禁用了 fielddata;

text 类型的字段在创建索引时会进行分词处理, 而聚合操作必须基于字段的原始值进行分析;

所以如果要对 text 类型的字段进行聚合操作, 就需要存储其原始值 —— 创建mapping时指定fielddata=true, 以便通过反转倒排索引(即正排索引)将索引数据加载至内存中.

(4) 解决方案一: 对text类型的字段开启fielddata属性:

• 将要分组统计的text field(即tags)的fielddata设置为true:

  PUT book_shop/_mapping/it_book
  {
      "properties": {
          "tags": {
              "type": "text",
              "fielddata": true
          }
      }
  }
  

• 可参考官方文档进行设置:
  
  https://www.elastic.co/guide/en/elasticsearch/reference/6.6/fielddata.html. 成功后的结果如下:

  {
    "acknowledged": true
  }
  

• 再次统计, 得到的结果如下:

  {
      "took": 153,
      "timed_out": false,
      "_shards": {
          "total": 5,
          "successful": 5,
          "skipped": 0,
          "failed": 0
      },
      "hits": {
          "total": 4,
          "max_score": 0.0,
          "hits": []
      },
      "aggregations": {
          "group_by_tags": {
              "doc_count_error_upper_bound": 0,
              "sum_other_doc_count": 6,
              "buckets": [
                  {
                      "key": "java",
                      "doc_count": 3
                  },
                  {
                      "key": "程",
                      "doc_count": 2
                  },
                  ......
              ]
          }
      }
  }
  

(5) 解决方法二: 使用内置keyword字段:

• 开启fielddata将占用大量的内存.

• Elasticsearch 5.x 版本开始支持通过text的内置字段keyword作精确查询、聚合分析:

  GET shop/it_book/_search
  {
      "size": 0,
      "aggs": {
          "group_by_tags": {
              "terms": {
                  "field": "tags.keyword"	// 使用text类型的内置keyword字段
      	    }
      	}
      }
  }
  

1.2 先检索, 再聚合

(1) 统计name中含有“jvm”的图书中每个tag的文档数量, 请求语法:

GET book_shop/it_book/_search
{
    "query": {
        "match": { "name": "jvm" }
    },
    "aggs": {
        "group_by_tags": {  // 聚合结果的名称, 需要自定义. 下面使用内置的keyword字段:
            "terms": { "field": "tags.keyword" }
        }
    }
}

(2) 响应结果:

{
  "took" : 7,
  "timed_out" : false,
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : 1,
    "max_score" : 0.64072424,
    "hits" : [
      {
        "_index" : "book_shop",
        "_type" : "it_book",
        "_id" : "2",
        "_score" : 0.64072424,
        "_source" : {
          "name" : "深入理解Java虚拟机：JVM高级特性与最佳实践",
          "author" : "周志明",
          "category" : "编程语言",
          "desc" : "Java图书领域公认的经典著作",
          "price" : 79.0,
          "date" : "2013-10-01",
          "publisher" : "机械工业出版社",
          "tags" : [
            "Java",
            "虚拟机",
            "最佳实践"
          ]
        }
      }
    ]
  },
  "aggregations" : {
    "group_by_tags" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "Java",
          "doc_count" : 1
        },
        {
          "key" : "最佳实践",
          "doc_count" : 1
        },
        {
          "key" : "虚拟机",
          "doc_count" : 1
        }
      ]
    }
  }
}

1.3 扩展: fielddata和keyword的聚合比较

• 为某个 text 类型的字段开启fielddata字段后, 聚合分析操作会对这个字段的所有分词分别进行聚合, 获得的结果大多数情况下并不符合我们的需求.

• 使用keyword内置字段, 不会对相关的分词进行聚合, 结果可能更有用.

—— 推荐使用text类型字段的内置keyword进行聚合操作.

2 嵌套聚合

2.1 先分组, 再聚合统计

(1) 先按tags分组, 再计算每个tag下图书的平均价格, 请求语法:

GET book_shop/it_book/_search
{
    "size": 0,
    "aggs": {
        "group_by_tags": {
            "terms": { "field": "tags.keyword" },
            "aggs": {
                "avg_price": {
                    "avg": { "field": "price" }
                }
            }
        }
    }
}

(2) 响应结果:

  "hits" : {
    "total" : 3,
    "max_score" : 0.0,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_tags" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "Java",
          "doc_count" : 3,
          "avg_price" : {
            "value" : 102.33333333333333
          }
        },
        {
          "key" : "编程语言",
          "doc_count" : 2,
          "avg_price" : {
            "value" : 114.0
          }
        },
        ......
      ]
    }
  }

2.2 先分组, 再统计, 最后排序

(1) 计算每个tag下图书的平均价格, 再按平均价格降序排序, 查询语法:

GET book_shop/it_book/_search
{
    "size": 0,
    "aggs": {
        "all_tags": {
            "terms": {
                "field": "tags.keyword",
                "order": { "avg_price": "desc" } // 根据下述统计的结果排序
            },
            "aggs": {
                "avg_price": {
                    "avg": { "field": "price" }
                }
            }
        }
    }
}

(2) 响应结果:

与#2.1节内容相似, 区别在于按照价格排序显示了.

2.3 先分组, 组内再分组, 然后统计、排序

(1) 先按价格区间分组, 组内再按tags分组, 计算每个tags组的平均价格, 查询语法:

GET book_shop/it_book/_search
{
    "size": 0,
    "aggs": {
        "group_by_price": {
            "range": {
                "field": "price",
                "ranges": [
                    { "from": 00,  "to": 100 },
                    { "from": 100, "to": 150 }
                ]
            },
            "aggs": {
                "group_by_tags": {
                    "terms": { "field": "tags.keyword" },
                    "aggs": {
                        "avg_price": {
                            "avg": { "field": "price" }
                        }
                    }
                }
            }
        }
    }
}

(2) 响应结果:

  "hits" : {
    "total" : 3,
    "max_score" : 0.0,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_price" : {
      "buckets" : [
        {
          "key" : "0.0-100.0",    // 区间0.0-100.0
          "from" : 0.0,
          "to" : 100.0,
          "doc_count" : 1,        // 共查找到了3条文档
          "group_by_tags" : {     // 对tags分组聚合
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "Java",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 79.0
                }
              },
              ......
            ]
          }
        },
        {
          "key" : "100.0-150.0",
          "from" : 100.0,
          "to" : 150.0,
          "doc_count" : 2,
          "group_by_tags" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "Java",
                "doc_count" : 2,
                "avg_price" : {
                  "value" : 114.0
                }
              },
              ......
              }
            ]
          }
        }
      ]
    }
  }

版权声明

作者: 马瘦风(https://healchow.com)

出处: 博客园 马瘦风的博客(https://www.cnblogs.com/shoufeng)

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