使用kibana操作elasticsearch7.x 教程
由于elasticsearch7.x取消了type（类型的概念）对应数据库表的概念
kibana的配置以及安装地址:https://www.cnblogs.com/TJ21/p/12642219.html
添加一个索引
PUT 索引名 { "settings": { "number_of_shards": 1, "number_of_replicas": 0 } }
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" } } }
查看映射关系
GET /索引名/_mapping
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 }
修改数据,
将上面自定义id的请求方式修改
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 }
删除数据
DELETE /索引库名/_doc/id值
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 } } ] } }
View Code
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
View Code
# 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"] } } }
#1.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"] } } }
#2.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 } } } ] } } }
#排序
GET /索引库名/_search { "query": { "bool": { "filter": [ { "range": { "price": { "gte": 2699, "lte": 4999 } } } ] } }, "sort": [ { "price": { "order": "desc" } }, { "_id":{ "order": "asc" } } ] }
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？
4.1 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：0~10,10~20,20~30,30~40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 } ] } } } }