ES基础入门

ES

一、简介

1、Elastic Stack

ES技术栈：

• ElasticSearch：存数据+搜索；QL；
• Kibana：Web可视化平台，分析。
• LogStash：日志收集，Log4j:产生日志；log.info(xxx)
• 。。。。

使用场景：

• metrics：指标监控
• …

2、基本概念

• Index（索引）- 动词：保存（插入）- 名词：类似MySQL数据库，给数据
• Type（类型）- 已废弃，以前类似MySQL的表- 现在用索引对数据分类
• Document（文档）- 真正要保存的一个JSON数据- {name:"tcx"}

二、入门实战

{"name":"DESKTOP-1TSVGKG","cluster_name":"elasticsearch","cluster_uuid":"PFpgHGhaTZiajHD4xvRPkg","version":{"number":"7.14.0","build_flavor":"default","build_type":"zip","build_hash":"dd5a0a2acaa2045ff9624f3729fc8a6f40835aa1","build_date":"2021-07-29T20:49:32.864135063Z","build_snapshot":false,"lucene_version":"8.9.0","minimum_wire_compatibility_version":"6.8.0","minimum_index_compatibility_version":"6.0.0-beta1"},"tagline":"You Know, for Search"}

1、索引CRUD

1.创建索引

put http://localhost:9200/person

{"acknowledged":true, #响应结果
    "shards_acknowledged":true,  #分片
    "index":"person"   #索引名称
}

2.删除索引

deletehttp://localhost:9200/person

{"acknowledged":true}

3.修改索引

# 在修改时，需要先删掉索引
put http://localhost:9200/person

{"settings":{"index":{"number_of_shards":"3"}}}

4.查询索引

gethttp://localhost:9200/person

{"person":{"aliases":{},"mappings":{},"settings":{"index":{"routing":{"allocation":{"include":{"_tier_preference":"data_content"}}},"number_of_shards":"3","provided_name":"person","creation_date":"1664357188860","number_of_replicas":"1","uuid":"GOyDifOJTQi8yBzKHk5zNQ","version":{"created":"7140099"}}}}}

2、文档CRUD

1、保存-POST（非幂等性）

POST/person/_doc
{"id":"1","name":"田","age":"19"}

# 结果
{"_index":"person",//保存到的索引"_type":"_doc",//"_id":"BcKKg4MBcNCrjSmlWbAK",//刚才那条数据的唯一id"_version":1,//版本号"result":"created",//创建了数据"_shards":{//分片工作"total":2,"successful":1,"failed":0},"_seq_no":0,//乐观锁机制"_primary_term":1//乐观锁机制}

以上是：非幂等操作

• 幂等：同一个操作执行无数遍的结果都是一样的 - select- insert: 幂等
• 非幂等： - insert: 自增主键就是非幂等

2、保存-PUT（幂等性）

保存更新 二合一

PUT/person/_doc/1{"id":"1","name":"田","age":"19"}PUT/person/_doc/1{"id":"1","name":"田","age":"19","note":"做个小笔记"}

# 也可以任意扩展/缩减字段；（会覆盖）

3、POST-增量字段更新

POST person/_doc/1/_update
{"doc":{"name":"张三"}}

4、查询

# 查看所有
GET/person/_search

# 根据ID查询
GET/person/_doc/ID号

5、乐观锁

CAS

三、进阶检索

#  DSL 查询条件以Json
POST person/_search
{"query":{"match_all":{}}}

1、导入基本数据

POST/student/_doc/1001{"name":"李白","age":"18"}POST/student/_doc/1002{"name":"诗仙","age":"19"}POST/student/_doc/1003{"name":"杜甫","age":"20"}POST/student/_doc/1004{"name":"诗圣","age":"21"}POST/student/_doc/1005{"name":"诗圣 厉害","age":"21"}

2、查询所有文档

POST/student/_search
{"query":{"match_all":{}}}POST/student/_search
{"query":{"match_all":{}}}{"took":1,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":4,"relation":"eq"},"max_score":1.0,"hits":[{"_index":"student","_type":"_doc","_id":"1001","_score":1.0,"_source":{"name":"李白","age":"18"}},{"_index":"student","_type":"_doc","_id":"1002","_score":1.0,"_source":{"name":"诗仙","age":"19"}},{"_index":"student","_type":"_doc","_id":"1003","_score":1.0,"_source":{"name":"杜甫","age":"20"}},{"_index":"student","_type":"_doc","_id":"1004","_score":1.0,"_source":{"name":"诗圣","age":"21"}}]}}

3、match-全文索引精确查询

POST/student/_search
{"query":{"match":{"name.keyword":"诗圣"}}}

multi_match-多个字段查询

POST/student/_search
{"query":{"multi_match":{"query":"诗圣","fields":["name"]}}}

4、term-精确查询

POST/student/_search
{"query":{"term":{"name":{"value":"shixian"}}}}

5、terms-查询

# 查询age是20，18的
POST/student/_search
{"query":{"terms":{"age":["20","18"]}}}

6、多个字段查询

7、查询部分字段

POST/student/_search
{"query":{"match":{"name":"诗圣"}},"_source":["name"]}

8、区间查询

POST/student/_search
{"query":{"range":{"age":{"gte":18,"lte":20}}}}

9、复合查询

# must 

POST/student/_search
{"query":{"bool":{"must":[{"range":{"age":{"gte":18,"lte":20}}},{"match":{"name":"诗圣"}}]}}}

must和filter执行的结果是一样的，但是must会有分数，filter没有分数

10、过滤查询

POST/student/_search
{"query":{"bool":{"filter":[{"range":{"age":{"gte":18,"lte":20}}},{"match":{"name":"诗圣"}}]}}}

• 模糊匹配用match
• 精确查询用term
• 多条件组合是bool
• must,filter相配合
• should非强制要求

11、判断文档是否存在

#判断文档是否存在
HEAD/student/_doc/1001

12、exists查询

POST/student/_search
{"query":{"exists":{"field":"hello"}}}

# 找到有hello属性的文档
{"took":739,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":1,"relation":"eq"},"max_score":1.0,"hits":[{"_index":"student","_type":"_doc","_id":"1006","_score":1.0,"_source":{"name":"白居易","hello":"word"}}]}}

13、批量

#批量查询IDPOST student/_doc/_mget
{"ids":["1001","1002"]}

四、其他功能

1、排序

POST student/_search
{"query":{"match_all":{}},"sort":[{"age":{"order":"asc"}}]}

2、高亮

#高亮
GET student/_search
{"query":{"match":{"age":"21"}},"highlight":{"fields":{"age":{"pre_tags":"<span style='color:res'>","post_tags":"</span>"}}}}

3、_source-部分字段

POST/student/_search
{"query":{"match":{"name":"诗圣"}},"_source":["name"]}

4、分页

#分页

POST student/_search
{"query":{"match_all":{}},"from":0,"size":2}

聚合

五、映射与分词

1、Mapping

• ES里面保存是JSON
• 第一次保存了以后，每个索引的数据模型就确定好了，es在第一次保存一个json文档的时候就自动确定了。
• 在自动确定阶段，所有文本数据都会被确定为text,默认就可以全文检索，这个文本值在存储的时候分词，利用倒排索引存储
• Mappings第一次已经确定好的属性就不能修改了，但是未知的属性随便添加

自定义的数据类型(text,keyword)，一定告诉es，这个属性的精确类型

2、分词

• 分词发生在text字段
• 保存text类型的数据 - 把这个值先分词（英文：空格为分割 中文：词库 比较麻烦）- 存储利用倒排索引，会记录这些单词都在哪些文档出现
• 检索 - 搜索“我是中国人”- 把要搜索的”我是中国人“分词（利用词库对比） - 中国人： 看那些文档里有- 中国 看那些文档里有- 我 看那些文档里有- 是 看那些文档里有

GET/_analyze
{"analyzer":"standard","text":"Offer收割机"}

必须使用中文分词器

ik分词器

2.1安装ik分词器

下载当前对应版本；

没下载到对应版本的话，可以在ik分词器解压后，在

plugin-descriptor.properties

文件中，修改成对应的ES版本

GET/_analyze
{"analyzer":"ik_smart","text":"Offer收割机"}

PUT world
{"mappings":{"properties":{"name":{"type":"keyword"},"song":{"type":"text","analyzer":"ik_smart"}}}}

3 Nested嵌入式

PUT/test_nested
{"mappings":{"properties":{"user":{"type":"nested"}}}}

六、Springboot整合ES

6.1 引依赖

<!-- https://mvnrepository.com/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client --><dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-high-level-client</artifactId><version>7.14.0</version></dependency><dependency><groupId>org.elasticsearch</groupId><artifactId>elasticsearch</artifactId><version>7.14.0</version></dependency><dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-client</artifactId><version>7.14.0</version><exclusions><exclusion><artifactId>commons-logging</artifactId><groupId>commons-logging</groupId></exclusion></exclusions></dependency>

6.2 写配置文件

spring.elasticsearch.rest.uris[0]=http://localhost:9200

6.3 写测试代码

6.3.1 索引操作

@ResourceRestHighLevelClient restHighLevelClient;@TestpublicvoidcontextLoads(){System.out.println("restHighLevelClient = "+ restHighLevelClient);}@TestpublicvoidcreateIndex()throwsIOException{CreateIndexRequest request =newCreateIndexRequest("google");
        request.settings(Settings.builder().put("index.number_of_shards","1").put("index.number_of_replicas","1").build());CreateIndexResponse response = restHighLevelClient.indices().create(request,RequestOptions.DEFAULT);System.out.println("response = "+ response.isAcknowledged());}@TestpublicvoiddeleteIndex()throwsIOException{DeleteIndexRequest request =newDeleteIndexRequest("hello");AcknowledgedResponse delete = restHighLevelClient.indices().delete(request,RequestOptions.DEFAULT);boolean b = delete.isAcknowledged();System.out.println(b?"删除成功":"删除失败");}

6.3.2 待更新 ，嘻嘻

谢谢你的阅读

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