elasticsearch基本操作_Michael_lcf

irpas 02-07 1012

elasticsearch基本操作 1 elasticsearch单机安装2 kibana安装3 基本操作3.1 索引操作1) 创建索引2) 查看所有索引3) 查看单个索引4) 删除索引 3.2 文档操作1) 创建文档2)查看文档3)整体修改文档4) 局部修改文档（修改字段值）5) 删除文档6) 条件删除文档7) 删除全部文档 3.3 映射操作1)创建映射2) 查看映射3)同时创建索引映射关联 3.4 高级查询1) 查询所有文档2) 单字段匹配查询 match3) 多字段匹配查询 multi_match4) 关键字精确查询5) 多关键字精确查询6) 指定查询字段17) 指定查询字段28) 组合查询9) range范围查询10) 模糊查询11) 单字段排序12) 多字段排序13) 高亮查询14) 分页查询15) 聚合查询16) 桶聚合查询 4 我的测试

1 elasticsearch单机安装

解压

解压后的目录结构

启动elasticsearch bin/elasticsearch.bat 文件启动 ES 服务

注意： 9300 端口为 elasticsearch 集群间组件的通信端口； 9200 端口为浏览器访问的 http协议 restfull 端口；

浏览器访问：http://localhost:9200

2 kibana安装

1、解压缩下载的 zip 文件 https://artifacts.elastic.co/downloads/kibana/kibana-7.8.0-windows-x86_64.zip

2、修改 config/kibana.yml 文件

# 默认端口 server.port: 5601 # ES 服务器的地址 elasticsearch.hosts: ["http://localhost:9200"] # 索引名 kibana.index: ".kibana" # 支持中文 i18n.locale: "zh-CN"

3、 Windows 环境下执行 bin/kibana.bat 文件 4、通过浏览器访问 : http://localhost:5601

3 基本操作 3.1 索引操作 1) 创建索引 PUT：http://127.0.0.1:9200/shopping ============ { "acknowledged": true, "shards_acknowledged": true, "index": "shopping" }

不可重复创建索引；

2) 查看所有索引 GET：http://127.0.0.1:9200/_cat/indices?v =========== health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open student rfT7DVzyToSklO3j6FRRRg 1 1 0 0 208b 208b yellow open goods sriB1EQqS5iAvxziJOU9iQ 1 1 0 0 208b 208b 3) 查看单个索引 GET: http://127.0.0.1:9200/shopping ============ { "shopping" : { "aliases" : { }, "mappings" : { }, "settings" : { "index" : { "creation_date" : "1652014034548", "number_of_shards" : "1", "number_of_replicas" : "1", "uuid" : "_jzlUBikRAiqCrewEi4HJQ", "version" : { "created" : "7080099" }, "provided_name" : "shopping" } } } } 4) 删除索引 DELETE /shopping ============== { "acknowledged" : true } 3.2 文档操作 1) 创建文档

在Postman 中，向 ES 服务器发 POST 请求 http://127.0.0.1:9200/shopping/_doc 如果想要自定义唯一性标识，需要在创建时指定 http://127.0.0.1:9200/shopping/_doc/1 此处需要注意：如果增加数据时明确数据主键，那么请求方式也可以为PUT

# 指定 _id POST /shopping/_doc/1 # 默认 _id POST /shopping/_doc { "title":"小米手机", "category": "小米", "images":"http://·/xm.jpg", "price":3999.00 } ==================== { "_index" : "shopping", "_type" : "_doc", "_id" : "_eG3o4AB0LSa-GvBMnym", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 0, "_primary_term" : 1 } 2)查看文档

查看文档时，需要指明文档的唯一性标识，类似于 MySQL中数据的主键查询

GET /shopping/_doc/1 ============= { "_index" : "shopping", "_type" : "_doc", "_id" : "1", "_version" : 10, "_seq_no" : 10, "_primary_term" : 1, "found" : true, "_source" : { "title" : "小米手机", "category" : "小米", "images" : "http://·/xm.jpg", "price" : 3999.0 } } 3)整体修改文档

和新增文档一样，输入相同的 URL地址请求，如果请求体变化，会将原有的数据内容覆盖在 Postman中，向ES服务器发POST请求http://127.0.0.1:9200/shopping/_doc/1

POST /shopping/_doc/1 { "title":"VIVO手机", "category": "VIVO", "images":"http://·/vivo.jpg", "price":6999.00 } ============== { "_index" : "shopping", "_type" : "_doc", "_id" : "1", "_version" : 11, "result" : "updated", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 11, "_primary_term" : 1 } 4) 局部修改文档（修改字段值）

修改数据时，也可以只修改某一给条数据的局部信息

POST /shopping/_update/1 { "doc":{ "price":5999.00 } } ====================== { "_index" : "shopping", "_type" : "_doc", "_id" : "1", "_version" : 12, "result" : "updated", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 12, "_primary_term" : 1 } 5) 删除文档

删除一个文档不会立即从磁盘上移除，它只是被标记成已删除（逻辑删除）。

DELETE /shopping/_doc/1 ================= { "_index" : "shopping", "_type" : "_doc", "_id" : "1", "_version" : 22, "result" : "deleted", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 22, "_primary_term" : 1 } 6) 条件删除文档先添加数据 POST /shopping/_doc/11 { "title":"小米手机", "category": "小米", "images":"http://·/xm.jpg", "price":3999.00 } POST /shopping/_doc/12 { "title":"华为手机", "category": "华为", "images":"http://·/huawei.jpg", "price":6999.00 } POST /shopping/_doc/13 { "title":"VIVO手机", "category": "VIVO", "images":"http://·/vivo.jpg", "price":7999.00 } ================================== ================================== POST /shopping/_delete_by_query { "query":{ "match":{ "price":6999.00 } } } ===================== { "took" : 39, "timed_out" : false, "total" : 1, "deleted" : 1, "batches" : 1, "version_conflicts" : 0, "noops" : 0, "retries" : { "bulk" : 0, "search" : 0 }, "throttled_millis" : 0, "requests_per_second" : -1.0, "throttled_until_millis" : 0, "failures" : [ ] } 7) 删除全部文档 POST /shopping/_doc/_delete_by_query { "query": { "match_all": {} } } 3.3 映射操作 1)创建映射删除索引 DELETE /student 创建索引 PUT /student 创建文档 POST /student/_doc/11 { "name":"李太白", "sex": "男", "age":"22" } 创建映射 PUT /student/_mapping ===================== { "properties":{ "name":{ "type": "text", "index": true }, "sex":{ "type": "text", "index": false }, "age":{ "type": "long", "index": false } } }

字段名：任意填写；

type： ??String类型： ???? text：可分词； ???? keyword：不可分词，数据会作为完整字段进行匹配； ??Numerical类型： ???? 基本数据类型：long 、 integer 、 short 、 byte 、 double 、 float 、 half_float； ???? 浮点数的高精度类型：scaled_float； ??Date日期类型： ??Array数组类型： ??Object对象：

index：是否索引，默认为 true（即所有字段都会被索引）。 ??true：字段会被索引，则可以用来进行搜索 ??false：字段不会被索引，不能用来搜索

store：是否将数据进行独立存储，默认为 false 原始的文本会存储在_source 里面，默认情况下其他提取出来的字段都不是独立存储的，是从 _source 里面提取出来的。当然你也可以独立的存储某个字段，只要设置"store": true 即可，获取独立存储的字段要比从 _source 中解析快得多，但是也会占用更多的空间，所以要根据实际业务需求来设置。

analyzer：分词器，这里的 ik_max_word 即使用 ik 分词器； ?????1、ik_max_word：会对文本做最细力度的拆分，索引时面面俱到。 ?????2、ik_smart：会对文本做最粗粒度的拆分，搜索时精准匹配。 ??插入文档时，将text类型的字段做分词然后插入倒排索引： ?????"analyzer": "ik_max_word", ??在查询时，先对要查询的text类型的输入做分词，再去倒排索引搜索： ?????"search_analyzer": "ik_smart",

2) 查看映射 GET /student/_mapping ======================== { "student" : { "mappings" : { "properties" : { "age" : { "type" : "long", "index" : false }, "name" : { "type" : "text" }, "sex" : { "type" : "text", "index" : false } } } } } 3)同时创建索引映射关联 PUT /my_student { "settings":{}, "mappings":{ "properties":{ "name":{ "type": "keyword", "index": true }, nickname:{ "type": "text", "index": false } "sex":{ "type": "text", "index": false }, "age":{ "type": "long", "index": false } } } } 3.4 高级查询

Elasticsearch提供了基于 JSON 提供完整的查询 DSL 来定义查询；

// 创建测试数据： POST /student/_doc/1001 { "name":"李太白", "nickname":"青莲居士", "sex":"男", "age":33 } POST /student/_doc/1002 { "name":"李清照", "nickname":"易安居士", "sex":"女", "age":22 } POST /student/_doc/1003 { "name":"白居易", "nickname":"香山居士", "sex":"男", "age":28 } POST /student/_doc/1004 { "name":"李贺", "nickname":"诗鬼", "sex":"男", "age":25 } POST /student/_doc/1005 { "name":"DuFu", "nickname":"诗圣", "sex":"男", "age":36 } 1) 查询所有文档 GET /student/_search =================== { "took" : 857, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "11", "_score" : 1.0, "_source" : { "name" : "李太白", "sex" : "男", "age" : "22" } }, 。。。。。。。。。。。。 ] } } 2) 单字段匹配查询 match GET /student/_search { "query":{ "match":{ "name": "贺" } } } ========================= { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.5697745, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1004", "_score" : 1.5697745, "_source" : { "name" : "李贺", "nickname" : "诗鬼", "sex" : "男", "age" : 25 } } ] } } 3) 多字段匹配查询 multi_match GET /student/_search { "query":{ "multi_match":{ "query": "贺", "fields": ["name","nickname"] } } } ======================= { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.5697745, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1004", "_score" : 1.5697745, "_source" : { "name" : "李贺", "nickname" : "诗鬼", "sex" : "男", "age" : 25 } } ] } } 4) 关键字精确查询

term 查询，精确的关键词匹配查询，不对查询条件进行分词。在 Postman 中，向 ES 服务器发 GET 请求 http://127.0.0.1:9200/student/_search

GET /student/_search { "query":{ "term":{ "name": { "value": "DuFu" } } } } ======================= 试验没有查到数据，好奇怪？？？？？？？？？ { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 0, "relation" : "eq" }, "max_score" : null, "hits" : [ ] } } 5) 多关键字精确查询

terms 和 term 查询一样，但它允许你指定多值进行匹配。类似于mysql 的 in。

GET /student/_search { "query":{ "terms":{ "name": ["DuFu","李太白"] } } } ======================= 试验没有查到数据，好奇怪？？？？？？？？？ 6) 指定查询字段1

默认情况下，Elasticsearch 在搜索的结果中，会把文档中保存在 _source 的所有字段都返回。添加_source 的过滤，获取其中的部分字段。

GET /student/_search { "_source": ["name","nickname"], "query":{ "match":{ "name": "白居易" } } } ===================== { "took" : 450, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 4.4009094, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1003", "_score" : 4.4009094, "_source" : { "name" : "白居易", "nickname" : "香山居士" } }, { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : 1.2052968, "_source" : { "name" : "李太白", "nickname" : "青莲居士" } } ] } } 7) 指定查询字段2

includes ：来指定想要显示的字段； excludes ：来指定不想要显示的字段；

GET /student/_search { "_source":{ "excludes": ["name","nickname"] }, "query":{ "match":{ "name": "白居易" } } } ============================== { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 3.309578, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1003", "_score" : 3.309578, "_source" : { "sex" : "男", "age" : 28 } }, { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : 0.79423964, "_source" : { "sex" : "男", "age" : 33 } } ] } } 8) 组合查询

bool 把各种其它查询通过 must、 must_ not、 should 的方式进行组合

GET /student/_search { "query": { "bool": { "must": [ { "match": { "name":"白居易" } } ], "must_not": [ { "match": { "age":"28" } } ], "should": [ { "match": { "sex": "女" } } ] } } } ================== { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 0.79423964, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : 0.79423964, "_source" : { "name" : "李太白", "nickname" : "青莲居士", "sex" : "男", "age" : 33 } } ] } } 9) range范围查询 GET /student/_search { "query":{ "range":{ "age":{ "gte": 33, "lte": 36 } } } } =================== { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : 1.0, "_source" : { "name" : "李太白", "nickname" : "青莲居士", "sex" : "男", "age" : 33 } }, { "_index" : "student", "_type" : "_doc", "_id" : "1005", "_score" : 1.0, "_source" : { "name" : "DuFu", "nickname" : "诗圣", "sex" : "男", "age" : 36 } } ] } } 10) 模糊查询

返回包含与搜索字词相似的字词的文档。编辑距离是将一个术语转换为另一个术语所需的一个字符更改的次数。这些更改可以包括：

? 更改字符（ box -> fox ） ? 删除字符（ black -> lack ） ? 插入字符（ sic -> sick ） ? 转置两个相邻字符（ act -> cat ）

为了找到相似的术语， fuzzy 查询会在指定的编辑距离内创建一组搜索词的所有可能的变体或扩展。然后查询返回每个扩展的完全匹配。通过fuzziness 修改编辑距离。一般使用默认值 AUTO ，根据术语的长度生成编辑距离。

GET /student/_search { "query":{ "fuzzy":{ "name":{ "value": "白" } } } } ===================== { "took" : 0, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 0.79423964, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : 0.79423964, "_source" : { "name" : "李太白", "nickname" : "青莲居士", "sex" : "男", "age" : 33 } }, { "_index" : "student", "_type" : "_doc", "_id" : "1003", "_score" : 0.79423964, "_source" : { "name" : "白居易", "nickname" : "香山居士", "sex" : "男", "age" : 28 } } ] } } 11) 单字段排序

sort可以让我们按照不同的字段进行排序，并且通过 order 指定排序的方式。 desc 降序， asc升序。

GET /student/_search { "_source": { "excludes": [ "nickname", "sex" ] }, "query": { "match": { "name": "李太白" } }, "sort": [ { "age": { "order": "desc" } } ] } ============================== { "took" : 1, "timed_out" : false, "_shards" : {"total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : {"value" : 4, "relation" : "eq" }, "max_score" : null, "hits" : [ {"_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : null, "_source" : {"name" : "李太白", "age" : 33}, "sort" : [ 33 ] }, {"_index" : "student", "_type" : "_doc", "_id" : "1003", "_score" : null, "_source" : {"name" : "白居易", "age" : 28}, "sort" : [ 28 ] }, {"_index" : "student", "_type" : "_doc", "_id" : "1004", "_score" : null, "_source" : {"name" : "李贺", "age" : 25}, "sort" : [ 25 ] }, {"_index" : "student", "_type" : "_doc", "_id" : "1002", "_score" : null, "_source" : {"name" : "李清照", "age" : 22}, "sort" : [ 22 ] } ] } } 12) 多字段排序 GET /student/_search { "_source": { "excludes": [ "nickname", "sex" ] }, "query": { "match_all": { } }, "sort": [ { "age": { "order": "desc" } }, { "score": { "order": "asc" } } ] } ========================= 13) 高亮查询

在进行关键字搜索时，搜索出的内容中的关键字会显示不同的颜色，称之为高亮。在使用 match 查询的同时，加上一个 highlight 属性： ? pre_tags ：前置标签 ? post_tags ：后置标签 ? fields ：需要高亮的字段 ? title ：这里声明 title 字段需要高亮，后面可以为这个字段设置特有配置，也可以空

GET /student/_search { "query":{ "match":{ "name":"贺" } }, "highlight":{ "pre_tags": "<font color='red'>", "post_tags": "</font>", "fields":{ "name":{} } } } ====================== { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1, "relation" : "eq" }, "max_score" : 1.4877305, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1004", "_score" : 1.4877305, "_source" : { "name" : "李贺", "nickname" : "诗鬼", "sex" : "男", "age" : 25 }, "highlight" : { "name" : [ "李<font color='red'>贺</font>" ] } } ] } } 14) 分页查询

from：当前页的起始索引，默认从 0 开始。 f r o m = ( p a g e N u m ? 1 ) ? s i z e from = (pageNum-1) * size from=(pageNum?1)?size size：每页显示多少条

GET /student/_search { "from": 0, "size": 2, "_source": { "excludes": [ "nickname", "sex" ] }, "query": { "match_all": { } }, "sort": [ { "age": { "order": "desc" } } ] } ================ { "took" : 1, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 5, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "student", "_type" : "_doc", "_id" : "1005", "_score" : null, "_source" : { "name" : "DuFu", "age" : 36 }, "sort" : [ 36 ] }, { "_index" : "student", "_type" : "_doc", "_id" : "1001", "_score" : null, "_source" : { "name" : "李太白", "age" : 33 }, "sort" : [ 33 ] } ] } } 15) 聚合查询

对某个字段取最大值 max

GET /student/_search { "aggs":{ "max_age":{ "max":{"field":"age"} } }, "size":0 }

对某个字段取最小值 min

GET /student/_search { "aggs":{ "min_age":{ "min":{"field":"age"} } }, "size":0 }

对某个字段求和 sum

GET /student/_search { "aggs":{ "sum_age":{ "sum":{"field":"age"} } }, "size":0 }

对某个字段取平均值 avg

GET /student/_search { "aggs":{ "avg_age":{ "avg":{"field":"age"} } }, "size":0 }

对某个字段的值进行去重之后再取总数

GET /student/_search { "aggs":{ "distinct_age":{ "cardinality":{"field":"age"} } }, "size":0 }

State 聚合 stats聚合，对某个字段一次性返回 count max min avg 和 sum 五个指标。

GET /student/_search { "aggs":{ "stats_age":{ "stats":{"field":"age"} } }, "size":0 } 16) 桶聚合查询

桶聚和相当于sql 中的 group by 语句 ? terms 聚合，分组统计

GET /student/_search { "aggs":{ "age_group_by":{ "terms":{"field":"age"} } }, "size":0 }

在 terms 分组下再进行聚合

GET /student/_search { "aggs":{ "age_group_by":{ "terms":{"field":"age"} } }, "size":0 } 4 我的测试 GET /_cat/indices?v PUT /mydrug POST /mydrug/_mapping { "properties":{ "name":{ "type": "text", "index": true }, "jianpin":{ "type": "text", "index": true }, "quanpin":{ "type": "text", "index": true } } } GET /mydrug/_mapping POST /mydrug/_doc/1001 { "name":"北豆根", "jianpin":"bdg", "quanpin":"beidougen" } POST /mydrug/_doc/1002 { "name":"麸炒冬瓜子", "jianpin":"fcdgp", "quanpin":"fuchaodongguazi" } POST /mydrug/_doc/1003 { "name":"当归尾", "jianpin":"dgw", "quanpin":"dangguiwei" } POST /mydrug/_doc/1004 { "name":"山豆根", "jianpin":"sdg", "quanpin":"shandougen" } POST /mydrug/_doc/1005 { "name":"冬瓜皮", "jianpin":"dgp", "quanpin":"dongguapi" } POST /mydrug/_doc/1006 { "name":"当归", "jianpin":"dg", "quanpin":"danggui" } GET /mydrug/_search { "query":{ "match":{ "jianpin": "dg" } } } GET /mydrug/_search { "query":{ "match_phrase":{ "jianpin": "dg" } } } GET /mydrug/_search { "query":{ "match_phrase_prefix":{ "jianpin": "dg" } } } GET /mydrug/_search { "query":{ "match":{ "name": "当" } } } GET /mydrug/_search { "query":{ "match":{ "name": "皮" } } }