kibana7.x操作

elasticsearch7.x取消了type（类型的概念）对应数据库表的概念

一、添加一个索引

PUT 索引名 { "settings": { "number_of_shards": 1, "number_of_replicas": 0 } }

二、创建映射字段

analyzer：分词器 下载地址:https://github.com/medcl/elasticsearch-analysis-ik

PUT /索引名/_mapping { "properties": { "title":{ "type": "text", "analyzer": "ik_max_word" }, "images":{ "type": "keyword", "index": false }, "price":{ "type": "float" } } }

三、查看映射关系

GET /索引名/_mapping

四、新增数据

随机生成id

POST /索引库名/_doc { "title":"大米手机", "images":"http://image.leyou.com/12479122.jpg", "price":2899.00 }

自定义id

自定义id值不能重复,否则数据将会被覆盖

POST /索引库名/_doc/自定义id值 {"title":"超米手机","images":"http://image.leyou.com/12479122.jpg","price":3699.00,"Saleable":true }

五、修改数据

PUT /索引库/_doc/id值 { "title":"超大米手机", "images":"http://image.leyou.com/12479122.jpg", "price":3899.00, "stock": 100, "saleable":true }

六、删除数据

DELETE /索引库名/_doc/id值

七、查询

查询所有

GET /索引库名/_search { "query": { "match_all": {} } }

• 响应内容:

{"took" : 0,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 6,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "goods","_type" : "_doc","_id" : "1","_score" : 1.0,"_source" : {"title" : "小米手机","images" : "http://image.leyou.com/12479122.jpg","price" : 2699.0,"Saleable" : true}},{"_index" : "goods","_type" : "_doc","_id" : "mmHtSnEBVcsVh4Caiarl","_score" : 1.0,"_source" : {"title" : "大米手机","images" : "http://image.leyou.com/12479122.jpg","price" : 2899.0}},{"_index" : "goods","_type" : "_doc","_id" : "2","_score" : 1.0,"_source" : {"title" : "超米手机","images" : "http://image.leyou.com/12479122.jpg","price" : 3699.0,"Saleable" : true}},{"_index" : "goods","_type" : "_doc","_id" : "3","_score" : 1.0,"_source" : {"title" : "小米电视4A","images" : "http://image.leyou.com/12479122.jpg","price" : 4699.0,"Saleable" : true}},{"_index" : "goods","_type" : "_doc","_id" : "4","_score" : 1.0,"_source" : {"title" : "华为手机","subTitle" : "小米","images" : "http://image.leyou.com/12479122.jpg","price" : 4699.0}},{"_index" : "goods","_type" : "_doc","_id" : "5","_score" : 1.0,"_source" : {"title" : "oppo","subTitle" : "小米","images" : "http://image.leyou.com/12479122.jpg","price" : 4899.0}}]} }

• 字段解析:

• took：查询花费时间，单位是毫秒
• time_out：是否超时
• _shards：分片信息
• hits：搜索结果总览对象
  • total：搜索到的总条数
  • max_score：所有结果中文档得分的最高分
  • hits：搜索结果的文档对象数组，每个元素是一条搜索到的文档信息
    • _index：索引库
    • _type：文档类型
    • _id：文档id
    • _score：文档得分
    • _source：文档的源数据

匹配查询

GET /索引库名/_search{ "query": { "match": { "title": { "query": "小米手机电视","minimum_should_match": "60%" } } }}

多字段查询

• title,subTitle字段名

GET /索引库名/_search { "query": { "multi_match": { "query": "小米", "fields":["title","subTitle"] } }

}

词条查询:可分割的最小词条单位 title为字段名 [ “字段值” ]

GET /索引库名/_search {"query": {"terms": {"title": ["小米","手机"]}} }

多词条查询

GET /索引库名/_search { "query": { "terms": { "title": ["小米","手机"] } } }

结果过滤
excludes:不显示的字段 includes: 显示的字段

GET /索引库名/_search { "_source": { "excludes": "{images}" }, "query": { "terms": { "title": ["小米","手机"] } } }

布尔查询

标题一定有小米，或者价格为2699,4699
bool把各种其它查询通过must（与）、must_not（非）、should（或）的方式进行组合

GET /索引库名/_search { "query": { "bool": { "must": [ {"match": { "title": "小米" } } ], "should": [ {"terms": { "price": [ "2699", "2799" ] }} ] } } }

范围查询

价格大于等于2799 小于等于3899

GET /索引库名/_search { "query": { "range": { "price": { "gte": 2799, "lte": 3899 } } } }

模糊查询

标题为oppo 默认允许错误一个字母，最大为两个字母 正确标题 oppo

fuzziness:配置篇里

GET /索引库名/_search { "query": { "fuzzy": { "title": { "value": "oope", "fuzziness": 2 } } } }

过滤filter
不会影响查询的分数_score

GET /索引库名/_search { "query": { "bool": { "must": [ { "match": { "title": "小米" } } ], "filter": [ { "range": { "price": { "gte": 2699, "lte": 4999 } } } ] } } }

查询全部数据，如果数据很多，kibana默认只返回前10条数据

如果想一开始就设置的话，参考这篇文章
不然就在查询的时候，带上from和size这两个参数

## 查询所有 GET /poem/_search {"from" : 0, "size" : 50,"query": {"match_all": {}} }

参考文章

八、排序

GET /索引库名/_search { "query": { "bool": { "filter": [ { "range": { "price": { "gte": 2699, "lte": 4999 } } } ] } }, "sort": [ { "price": { "order": "desc" } }, { "_id":{ "order": "asc" } } ] }

九、聚合 aggregations

聚合可以让我们极其方便的实现对数据的统计、分析。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？

实现这些统计功能的比数据库的sql要方便的多，而且查询速度非常快，可以实现实时搜索效果。

基本概念

Elasticsearch中的聚合，包含多种类型，最常用的两种，一个叫桶，一个叫度量：

• 桶（bucket）

桶的作用，是按照某种方式对数据进行分组，每一组数据在ES中称为一个桶，例如我们根据国籍对人划分，可以得到中国桶、英国桶，日本桶……或者我们按照年龄段对人进行划分：0_10,1020,20_30,3040等。

Elasticsearch中提供的划分桶的方式有很多：

• Date Histogram Aggregation：根据日期阶梯分组，例如给定阶梯为周，会自动每周分为一组

• Histogram Aggregation：根据数值阶梯分组，与日期类似

• Terms Aggregation：根据词条内容分组，词条内容完全匹配的为一组

• Range Aggregation：数值和日期的范围分组，指定开始和结束，然后按段分组

• ……

bucket aggregations 只负责对数据进行分组，并不进行计算，因此往往bucket中往往会嵌套另一种聚合：metrics aggregations即度量

• 度量（metrics）

分组完成以后，我们一般会对组中的数据进行聚合运算，例如求平均值、最大、最小、求和等，这些在ES中称为度量

比较常用的一些度量聚合方式：

• Avg Aggregation：求平均值

• Max Aggregation：求最大值

• Min Aggregation：求最小值

• Percentiles Aggregation：求百分比

• Stats Aggregation：同时返回avg、max、min、sum、count等

• Sum Aggregation：求和

• Top hits Aggregation：求前几

• Value Count Aggregation：求总数

• ……

• 使用聚合先加入新的索引

PUT /cars { "settings": { "number_of_shards": 1, "number_of_replicas": 0 }, "mappings": { "properties": { "color": { "type": "keyword" }, "make": { "type": "keyword" } } } }

• 批量添加数据

POST /cars/_bulk { "index": {}} { "price" : 10000, "color" : "red", "make" : "honda", "sold" : "2014-10-28" } { "index": {}} { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } { "index": {}} { "price" : 30000, "color" : "green", "make" : "ford", "sold" : "2014-05-18" } { "index": {}} { "price" : 15000, "color" : "blue", "make" : "toyota", "sold" : "2014-07-02" } { "index": {}} { "price" : 12000, "color" : "green", "make" : "toyota", "sold" : "2014-08-19" } { "index": {}} { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } { "index": {}} { "price" : 80000, "color" : "red", "make" : "bmw", "sold" : "2014-01-01" } { "index": {}} { "price" : 25000, "color" : "blue", "make" : "ford", "sold" : "2014-02-12" }

• 聚合为桶

GET /cars/_search { "aggs": { "color": { "terms": { "field": "color" } } } }

• 桶内度量

GET /cars/_search { "size": 0, "aggs": { "color": { "terms": { "field": "color" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } } }

• 桶内嵌套桶

GET /cars/_search { "size": 0, "aggs": { "color": { "terms": { "field": "color" }, "aggs": { "avg_price": { "avg": { "field": "price" } }, "mark":{ "terms": { "field": "make" } } } } } }

• 阶梯分组
  对价格进行阶梯分组，最小数量为1才显示

GET /cars/_search { "size": 0, "aggs": { "price_histogram": { "histogram": { "field": "price", "interval": 5000, "min_doc_count": 1 } } } }

• 范围分组

GET /cars/_search { "size": 0, "aggs": { "price_range": { "range": { "field": "price", "ranges": [ { "from": 5000, "to": 15000 }, { "from": 15000, "to": 20000 }, { "from": 20000, "to": 25000 }, { "from": 25000, "to":35000 }, { "from": 35000, "to":40000 } ] } } } }

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