elasticsearch5.5.0基础场景使用，以及大数据搜索及统计技能

系统管理员 2023-02-28 05:45 32阅读 0赞

# 一、基础信息查看 #

kibana中可直接执行

0、[入门简介][Link 1]

1、[查看某个字段数据的分词结果][Link 2]

GET /$\{index\}/$\{type\}/$\{id\}/\_termvectors?fields=$\{fields\_name\}

示例：http://127.0.0.1:9200/portal/product/22/\_termvectors?fields=label

2、[查看ES下有哪些索引][ES]

GET /\_cat/indices?v

[标题各字段含义][Link 3]、[集群yellow和red状态][yellow_red]

示例：http://127.0.0.1:9200/\_cat/indices

3、查看索引数据结构（类似show create table xxx）

GET /$\{index\}/$\{type\}/\_mapping

示例：http://127.0.0.1:9200/INDEX/TYPE/\_mapping

4、 [match, match\_phrase, term区别][match_ match_phrase_ term]

5、[Elasticsearch bool过滤查询][Elasticsearch bool]（sql关键词：and、or、not）

[range过滤][range]（如sql大于小于）；[文章2][2]

6、[检索和过滤的区别 (query 和 filter)][_query _ filter]

7、[ElasticSearch的认识：搜索、过滤、排序][ElasticSearch]（内容全）

ElasticSearch中常见的聚合分析函数有`terms（分组函数）`、`avg（平均数）`、`range（区间分组）`、`max（求最大值）`、`min（求最小值）`、`cardinality（获取唯一值的数量）`、`value_count(获取值的数量，不去重，可以得出多少个值参与了聚合)`

`8、ElasticSearch - 聚合 aggs`

`   elasticsearch 之聚合分组计数、平均数、最大值、script、最小值、总和`

`9、对聚合后的结果进行统计 ①sum bucket `

`   示例1`

[Elasticsearch多字段分组聚合, 并对分组聚合的count进行筛选][Elasticsearch_ _count]

`10、`[ElasticSearch基础操作及ElasticsearchTemplate API][ElasticSearch_ElasticsearchTemplate API]（推荐、全面）

`11、各种统计用java代码写出来`

`  ElasticSearch6.X版本Java Api中文详解（十二）之Aggregations解析`

`12、基于search template实现模板查询（推荐）`

`   search template官网文档`

`   search template动态条件控制（类似if条件，文中：5、conditional）`

二、实际场景运用

1、右模糊匹配(同sql：**like** 'xxx%')

GET /test/bill/_search
    {
      "query": {
        "prefix": {
          "orderId": { //字段名称
            "value": "3192733310040"
          }
        }
      }
    }

2、[分组统计][Link 4]（sql关键词：**group by；order by**）

传送门：

①、[通过 Elasticsearch 实现聚合检索 (分组统计)][Elasticsearch _]

②、[sql的having用法][sql_having]

说明：数据库耗时：81秒，ES耗时：134毫秒（吐槽……这尼玛效率恐怖）；

sql实现方式：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hnZzkyMw_size_16_color_FFFFFF_t_70][]

ES实现方式：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hnZzkyMw_size_16_color_FFFFFF_t_70 1][]

3、筛选20200715<=reDay<=20200721,并按reDay升序排列，计算newOrderNum、paidOrderNum、refundedOrderNum总和

着重说明：①根据当前aggs分组字段排序：order取"**\_term**"（补充：**\_count**:按doc\_count总数排序），

其他排序可取嵌套aggs里字段，如例子中：newOrderNum、paidOrderNum、refundedOrderNum

GET /revenue/day/_search
    {
        "size":0,
        "query": {
            "bool": {
                "filter": [
                    { "range": { "reDay": { "gte": 20200515 } } },
                    { "range": { "reDay": { "lte": 20200721 } } }
                ]
            }
        },
        "aggs": {
          "group_by_day": {
            "terms": {
              "field": "reDay",
              "size": 100,
              "order": {
                "_term": "asc"
              }
            },
            "aggs": {          
              "newOrderNum": {
                "sum": {
                  "field": "newOrderNum"
                }
              },
              "paidOrderNum": {
                "sum": {
                  "field": "paidOrderNum"
                }
              },
              "refundedOrderNum": {
                "sum": {
                  "field": "refundedOrderNum"
                }
              }
            }
          }
        }
    }

[Link 1]: https://www.cnblogs.com/weipt0106/p/13138371.html
[Link 2]: https://blog.csdn.net/zl18310999566/article/details/79027897?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase
[ES]: https://elasticsearch.cn/question/240
[Link 3]: https://elasticsearch.cn/question/3701
[yellow_red]: https://blog.csdn.net/weixin_34023863/article/details/92926499?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param
[match_ match_phrase_ term]: https://www.cnblogs.com/buxizhizhoum/p/9874703.html
[Elasticsearch bool]: https://blog.csdn.net/aa1215018028/article/details/87930642
[range]: https://www.cnblogs.com/ghj1976/p/5293250.html
[2]: http://elasticsearch.cn/question/9810
[_query _ filter]: https://www.cnblogs.com/shoufeng/p/11278046.html
[ElasticSearch]: https://www.cnblogs.com/progor/p/11557869.html
[Elasticsearch_ _count]: https://blog.csdn.net/qq_28988969/article/details/84337405?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase
[ElasticSearch_ElasticsearchTemplate API]: https://blog.csdn.net/mj86534210/article/details/79910909?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param
[Link 4]: https://blog.csdn.net/Qensq/article/details/103081923?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase
[Elasticsearch _]: https://www.cnblogs.com/shoufeng/p/11290669.html
[sql_having]: http://elasticsearch.cn/question/4214
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