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  • 【Elasticsearch7.x】Elasticsearch 入门

    Elasticsearch 入门

    简介

    全文搜索属于最常见的需求，开源的 Elasticsearch 是目前全文搜索引擎的首选。它可以快速地存储、搜索和分析海量数据。它的底层是开源库 Lucene，但是 Lucene 不能直接使用，必须自己写代码去调用它的接口。而 Elastic 是 Lucene 的封装，提供了 Rest API，可以开箱即用。

    基本概念

    Index（索引）

    动词意思，添加数据，相当于 MySQL 中的 insert；

    名词意思，保存数据的地方，相当于 MySQL 中的 Database。

    Type（类型）

    在 Index（索引）中，可以定义一个或多个 Type（类型）。相当于 MySQL 中的 Table，它将每一种类型的数据放在一起。

    Document（文档）

    保存在某个 Index（索引）下，某种 Type（类型）的一个数据，就叫做 Document（文档），文档是 JSON 格式的，它相当于 MySQL 中的摸一个 Table 里面的内容。

    Elasticsearch基本概念

    倒排索引

    为什么 ES 搜索快？这是因为使用了倒排索引。

    通过分词，将整句拆分为单词。

    假设保存的记录为：

    • 红海行动
    • 探索红海行动
    • 红海特别行动
    • 红海记录片
    • 特工红海特别探索

    那么会得到倒排索引表为：

    词 记录
    红海 1,2,3,4,5
    行动 1,2,3
    探索 2,5
    特别 3,5
    纪录片 4,
    特工 5

    例如检索：红海特工行动，查出后计算相关性得分，3 号记录命中了 2 次，且 3 号本身才有 3 个单词，2/3，所以 3 号最匹配。

    例如检索：红海行动，1 号最匹配。

    去掉 Type 概念

    关系型数据库中两个数据表示是独立的，即使它们里面有相同名称的列也不影响使用，但 ES 中不是这样的。ES 是基于Lucene 开发的搜索引擎， ES 中不同 type 下名称相同的 filed 最终在 Lucene 中的处理方式是一样的。

    • 两个不同 type 下的两个 user_name，在 ES 同一个索引下其实被认为是同一个 filed，必须在两个不同的 type 中定义相同的 filed 映射。否则，不同 type 中的相同字段名称就会在处理中出现冲突的情况，导致 Lucene 处理效率下降。
    • 去掉 type 就是为了提高 ES 处理数据的效率。
    • Elasticsearch 7.x 中，URL 中的 type 参数为可选。比如，索引一个文档不再要求提供文档类型。
    • Elasticsearch 8.x 中，不再支持 URL 中的 type 参数。
    • 解决方法：将索引从多类型迁移到单类型，每种类型文档一个独立索引。

    Docker 安装 ES

    • 下载安装 elasticsearch（存储和检索）和 kibana（可视化检索）

      docker pull elasticsearch:7.8.0
docker pull kibana:7.8.0

    • 配置

      # 将 docker 里的目录挂载到 linux 的 /docker 目录中
# 修改 /docker 就可以改掉 docker 里的
mkdir -p /docker/elasticsearch7.8.0/config
mkdir -p /docker/elasticsearch7.8.0/data
mkdir -p /docker/elasticsearch7.8.0/plugins

# 让 es 可以被远程任何机器访问
echo "http.host: 0.0.0.0" >> /docker/elasticsearch7.8.0/config/elasticsearch.yml

# 修改文件权限
chmod -R 777 /docker/elasticsearch7.8.0/

    • 启动 elasticsearch

      # 查看可用内存
[root@10 /]# free -m
              total        used        free      shared  buff/cache   available
Mem:            990         616          72           1         302         232
Swap:          2047         393        1654

# 9200 是用户交互端口，9300 是集群心跳端口
# 第一个 -e，指定是单阶段运行
# 第二个 -e，指定占用的内存大小，生产时可以设置 32G
# 考虑到虚拟机情况，设置内存不超过 512m
docker run --name elasticsearch7.8.0 -p 9200:9200 -p 9300:9300 
-e "discovery.type=single-node" 
-e ES_JAVA_OPTS="-Xms64m -Xmx512m" 
-v /docker/elasticsearch7.8.0/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml 
-v /docker/elasticsearch7.8.0/data:/usr/share/elasticsearch/data 
-v /docker/elasticsearch7.8.0/plugins:/usr/share/elasticsearch/plugins 
-d elasticsearch:7.8.0

# 设置开机启动
docker update elasticsearch7.8.0 --restart=always

    • 测试 elasticsearch

      访问 http://192.168.56.56:9200/
返回 elasticsearch 版本信息
{
"name": "0f6d6c60bc96",
"cluster_name": "elasticsearch",
"cluster_uuid": "sDTdW7KnQayVrFC5ioijiQ",
"version": {
"number": "7.8.0",
"build_flavor": "default",
"build_type": "docker",
"build_hash": "757314695644ea9a1dc2fecd26d1a43856725e65",
"build_date": "2020-06-14T19:35:50.234439Z",
"build_snapshot": false,
"lucene_version": "8.5.1",
"minimum_wire_compatibility_version": "6.8.0",
"minimum_index_compatibility_version": "6.0.0-beta1"
},
"tagline": "You Know, for Search"
}

访问 http://192.168.56.56:9200/_cat/nodes
返回 elasticsearch 节点信息
127.0.0.1 60 93 6 0.04 0.19 0.18 dilmrt * 0f6d6c60bc96

    • 启动 kibana

      # kibana 指定了 ES 交互端口 9200
# 5601 为 kibana 主页端口
docker run --name kibana7.8.0 -e ELASTICSEARCH_HOSTS=http://192.168.56.56:9200 -p 5601:5601 -d kibana:7.8.0

# 设置开机启动
docker update kibana7.8.0 --restart=always

    • 测试 kibana

      访问 http://192.168.56.56:5601
返回可视化界面

    Docker 安装 Nginx

    • 启动一个 Nginx 实例，复制出配置文件

      # 不存在时会自动下载
docker run -p 80:80 --name nginx1.10 -d nginx:1.10
# 创建存放 nginx 的文件夹
mkdir docker/nginx1.10
# 把容器内的配置文件拷贝到当前目录
cd docker/
docker container cp nginx1.10:/etc/nginx .
# 暂停删除容器，修改文件名称为 conf，并移动到 nginx1.10 文件夹
docker stop nginx1.10
docker rm nginx1.10
mv nginx conf
mv conf nginx1.10/

    • 启动 Nginx

      docker run -p 80:80 --name nginx1.10 -v /docker/nginx1.10/html:/usr/share/nginx/html -v /docker/nginx1.10/logs:/var/log/nginx -v /docker/nginx1.10/conf:/etc/nginx -d nginx:1.10

# 设置开机启动
docker update nginx1.10 --restart=always

    • 测试 Nginx

      访问 http://192.168.56.56
返回界面

    初步检索

    检索信息

    • GET /_cat/nodes 查看所有节点

      # http://192.168.56.56:9200/_cat/nodes
127.0.0.1 64 93 2 0.00 0.03 0.10 dilmrt * 0f6d6c60bc96

# 0f6d6c60bc96 代表节点，* 代表主节点

    • GET /_cat/health 查看 es 健康状况

      # http://192.168.56.56:9200/_cat/health
1617779285 07:08:05 elasticsearch green 1 1 6 6 0 0 0 0 - 100.0%

# green 表示健康值正常

    • GET/_cat/master 查看主节点

      # http://192.168.56.56:9200/_cat/master
-fBJbk3HQxq4oxHVP5o8XQ 127.0.0.1 127.0.0.1 0f6d6c60bc96

# -fBJbk3HQxq4oxHVP5o8XQ 代表主节点唯一编号
# 127.0.0.1 代表虚拟机地址

    • GET/_cat/indices 查看所有索引，相当于 MySQL 中的 show databases;

      # http://192.168.56.56:9200/_cat/indices
green open .kibana-event-log-7.8.0-000001 NSvWWbd7SaqNmoJ6QmjIRg 1 0  1 0  5.3kb  5.3kb
green open .apm-custom-link               mn9tqI-0QnOkI5JAp1rCHw 1 0  0 0   208b   208b
green open .kibana_task_manager_1         k5bSwn03TA-Hpisuzf677A 1 0  5 2 74.2kb 74.2kb
green open .apm-agent-configuration       ZXRvqEdDSL2555OE8MyNSA 1 0  0 0   208b   208b
green open .kibana_1                      _yCppL1mQ1a0-v88yOXNTQ 1 0 13 1 72.4kb 72.4kb

    新增文档

    保存一个数据，保存在哪个索引的哪个类型下，相当于 MySQL 中的哪个数据库的哪张表下。指定用哪一个唯一标识。

    PUT customer/external/1 在 customer 索引下的 external 类型下保存 1 号数据：

    # postman 新增文档-PUT
# PUT http://192.168.56.56:9200/customer/external/1
# 创建数据成功后，显示 201 created 表示插入记录成功。
# 发送多次是更新操作
{
    "_index": "customer", # 表明该数据在哪个数据库下
    "_type": "external", # 表明该数据在哪个类型下
    "_id": "1", # 表明被保存数据的 id
    "_version": 1, # 被保存数据的版本
    "result": "created", # 创建了一条数据，如果重新 put 一条数据，则该状态会变为 updated，并且版本号也会发生变化
    "_shards": { # 分片
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 0, # 序列号
    "_primary_term": 1
}

    POST customer/external

    # postman 新增文档-POST
# POST http://192.168.56.56:9200/customer/external
# 发送多次是更新操作
{
    "_index": "customer",
    "_type": "external",
    "_id": "dBNCq3gBsa8QUaibccNi", # 不指定 ID，会自动的生成 id，并且类型是新增的
    "_version": 1,
    "result": "created",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 2,
    "_primary_term": 1
}

    POST customer/external/3

    # postman 新增文档-POST
# POST http://192.168.56.56:9200/customer/external/3
# 发送多次是更新操作
{
    "_index": "customer",
    "_type": "external",
    "_id": "3", # 指定 ID，会使用该 id，并且类型是新增的
    "_version": 1,
    "result": "created",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 5,
    "_primary_term": 1
}

    总结：

    • POST 新增。如果不指定 id，会自动生成 id。
      • 可以不指定 id，不指定 id 时永远为创建
      • 指定不存在的 id 时也为创建
      • 指定存在的 id 时为更新，并且 version 会根据内容变没变而指定版本号是否递增
    • PUT 新增或修改。PUT 必须指定 id。
      • 一般用来做修改操作，不指定 id 会报错
      • version 总是递增
    • _version 指版本号，起始值都为 1，每次对当前文档成功操作后都加 1
    • _seq_no 指序列号，在第一次为索引插入数据时为 0，每对索引内数据操作成功一次加 1， 并且文档会记录是第几次操作使它成为现在的情况的

    查询文档

    GET /customer/external/1

    # GET http://192.168.56.56:9200/customer/external/1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 2,
    "_seq_no": 1, # 并发控制字段，每次更新都会 +1，用来做乐观锁
    "_primary_term": 1, # 同上，主分片重新分配，如重启，就会变化
    "found": true,
    "_source": {
        "name": "parzulpan"
    }
}

    乐观锁用法：通过 if_seq_no=1&if_primary_term=1 参数，当序列号匹配的时候，才进行修改，否则不修改。

    • 将 name 更新为 parzulpan1

      # PUT http://192.168.56.56:9200/customer/external/1?if_seq_no=1&if_primary_term=1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 3,
    "result": "updated",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 8,
    "_primary_term": 1
}

# 再次查询
# GET http://192.168.56.56:9200/customer/external/1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 4,
    "_seq_no": 9,
    "_primary_term": 1,
    "found": true,
    "_source": {
        "name": "parzulpan1"
    }
}

    更新文档

    方式一：POST customer/external/1/_update

    # POST http://192.168.56.56:9200/customer/external/1/_update
{
    "doc": { # 注意要带上 doc
        "name": "parzulpanUpdate"
    }
}

# 返回
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 5,
    "result": "updated",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 10,
    "_primary_term": 1
}

# 如果再次执行更新，则不执行任何操作，版本号和序列号也不发生变化
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 5,
    "result": "noop", # 无操作
    "_shards": {
        "total": 0,
        "successful": 0,
        "failed": 0
    },
    "_seq_no": 10,
    "_primary_term": 1
}

    方式二：POST customer/external/1

    # POST http://192.168.56.56:9200/customer/external/1
{
    "name": "parzulpanUpdate"
}

# 返回
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 6,
    "result": "updated",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 11,
    "_primary_term": 1
}

# 如果再次执行更新，数据会更新成功，并且版本号和序列号会发生变化
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 7,
    "result": "updated",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 12,
    "_primary_term": 1
}

    方式三：PUT customer/external/1

    # PUT http://192.168.56.56:9200/customer/external/1/
{
    "name": "parzulpanUpdate"
}

# 返回
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 8,
    "result": "updated",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 13,
    "_primary_term": 1
}

# 如果再次执行更新，数据会更新成功，并且版本号和序列号会发生变化

    总结：

    • POST ，带 _update 时，如果数据相同，不会重新保存并且版本号和序列号不会发生变化
    • POST ，不带 _update 时，总是会重新保存并且版本号和序列号会发生变化
    • PUT，总是会重新保存并且版本号和序列号会发生变化
    • 使用场景：对于大并发更新，推荐不带 _update，而对于大并发查询且偶尔更新，推荐带 _update

    删除文档或索引

    注意，ES 并没有提供删除类型的操作，只提供了删除文档或者索引的操作。

    删除文档

    # 删除 id=1 的数据，删除后继续查询
# DELETE http://192.168.56.56:9200/customer/external/1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 10,
    "result": "deleted", # 已被删除
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 15,
    "_primary_term": 1
}

# 再次执行 DELETE http://192.168.56.56:9200/customer/external/1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 11,
    "result": "not_found", # 找不到
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 16,
    "_primary_term": 1
}

# GET http://192.168.56.56:9200/customer/external/1
{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "found": false # 找不到
}

    删除索引

    # 删除整个 customer 索引
# 删除前，GET http://192.168.56.56:9200/_cat/indices
green  open .kibana-event-log-7.8.0-000001 NSvWWbd7SaqNmoJ6QmjIRg 1 0  1 0  5.3kb  5.3kb
green  open .apm-custom-link               mn9tqI-0QnOkI5JAp1rCHw 1 0  0 0   208b   208b
green  open .kibana_task_manager_1         k5bSwn03TA-Hpisuzf677A 1 0  5 2 74.2kb 74.2kb
green  open .apm-agent-configuration       ZXRvqEdDSL2555OE8MyNSA 1 0  0 0   208b   208b
green  open .kibana_1                      _yCppL1mQ1a0-v88yOXNTQ 1 0 15 0 34.9kb 34.9kb
yellow open customer                       t6RCi8QZQoiEx-wxJQvlmw 1 1  5 0  4.6kb  4.6kb

# DELETE http://192.168.56.56:9200/customer
{
    "acknowledged": true
}

# 删除后，GET http://192.168.56.56:9200/_cat/indices
green open .kibana-event-log-7.8.0-000001 NSvWWbd7SaqNmoJ6QmjIRg 1 0  1 0  5.3kb  5.3kb
green open .apm-custom-link               mn9tqI-0QnOkI5JAp1rCHw 1 0  0 0   208b   208b
green open .kibana_task_manager_1         k5bSwn03TA-Hpisuzf677A 1 0  5 2 74.2kb 74.2kb
green open .apm-agent-configuration       ZXRvqEdDSL2555OE8MyNSA 1 0  0 0   208b   208b
green open .kibana_1                      _yCppL1mQ1a0-v88yOXNTQ 1 0 15 0 34.9kb 34.9kb

    批量操作-bulk

    这里的批量操作，指的是当发生某一条执行发生失败时，其他的数据仍然能够接着执行，也就是说彼此之间是独立的。

    bulk api 以此按顺序执行所有的 action（动作）。如果一个单个的动作因任何原因失败，它将继续处理它后面剩余的动作。当 bulk api 返回时，它将提供每个动作的状态（与发送的顺序相同），所以可以检查一个指定的动作是否失败了。

    注意，由于 bulk 不支持 json 或者 text 格式，所以不能在 postman 中测试，可以使用 kibana 的 DevTools。

    实例 1：执行多条数据

    # 在 kibana 的 DevTools 的控制台执行以下命令
POST /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"John Doe"}
{"index":{"_id":"2"}}
{"name":"John Doe"}

# 返回
#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
  "took" : 368, # 命令花费时间
  "errors" : false, # 没有发送任何错误
  "items" : [ # 每个数据的结果
    {
      "index" : { # 第一条数据
        "_index" : "customer",
        "_type" : "external",
        "_id" : "1",
        "_version" : 1,
        "result" : "created",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 0,
        "_primary_term" : 1,
        "status" : 201 # 新建完成
      }
    },
    {
      "index" : { # 第二条数据
        "_index" : "customer",
        "_type" : "external",
        "_id" : "2",
        "_version" : 1,
        "result" : "created",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 1,
        "_primary_term" : 1,
        "status" : 201
      }
    }
  ]
}

    实例 2：对于整个索引执行批量操作

    POST /_bulk
{"delete":{"_index":"website","_type":"blog","_id":"123"}}
{"create":{"_index":"website","_type":"blog","_id":"123"}}
{"title":"my first blog post"}
{"index":{"_index":"website","_type":"blog"}}
{"title":"my second blog post"}
{"update":{"_index":"website","_type":"blog","_id":"123"}}
{"doc":{"title":"my updated blog post"}}

# 返回
#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
  "took" : 450,
  "errors" : false,
  "items" : [
    {
      "delete" : { # 删除
        "_index" : "website",
        "_type" : "blog",
        "_id" : "123",
        "_version" : 1,
        "result" : "not_found",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 0,
        "_primary_term" : 1,
        "status" : 404
      }
    },
    {
      "create" : { # 创建
        "_index" : "website",
        "_type" : "blog",
        "_id" : "123",
        "_version" : 2,
        "result" : "created",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 1,
        "_primary_term" : 1,
        "status" : 201
      }
    },
    {
      "index" : { # 保存
        "_index" : "website",
        "_type" : "blog",
        "_id" : "nxPjrHgBsa8QUaibx-rD",
        "_version" : 1,
        "result" : "created",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 2,
        "_primary_term" : 1,
        "status" : 201
      }
    },
    {
      "update" : { # 更新
        "_index" : "website",
        "_type" : "blog",
        "_id" : "123",
        "_version" : 3,
        "result" : "updated",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 3,
        "_primary_term" : 1,
        "status" : 200
      }
    }
  ]
}

    样本测试数据

    一份顾客银行账户信息的虚构的 JSON 文档样本，文件地址

    格式为：

    {
	"account_number": 1,
	"balance": 39225,
	"firstname": "Amber",
	"lastname": "Duke",
	"age": 32,
	"gender": "M",
	"address": "880 Holmes Lane",
	"employer": "Pyrami",
	"email": "amberduke@pyrami.com",
	"city": "Brogan",
	"state": "IL"
}

    POST bank/account/_bulk
{"index":{"_id":"1"}}
{"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL"}
...

GET http://192.168.56.56:9200/_cat/indices
green  open .kibana-event-log-7.8.0-000001 NSvWWbd7SaqNmoJ6QmjIRg 1 0    1 0  5.3kb  5.3kb
yellow open website                        3rGabFSISrq8ZwdXxP331g 1 1    2 2  8.8kb  8.8kb
yellow open bank                           ZpN0_upESxqV84IVAgyvJw 1 1 1000 0  397kb  397kb
green  open .apm-custom-link               mn9tqI-0QnOkI5JAp1rCHw 1 0    0 0   208b   208b
green  open .kibana_task_manager_1         k5bSwn03TA-Hpisuzf677A 1 0    5 2 74.2kb 74.2kb
green  open .apm-agent-configuration       ZXRvqEdDSL2555OE8MyNSA 1 0    0 0   208b   208b
green  open .kibana_1                      _yCppL1mQ1a0-v88yOXNTQ 1 0   28 2 63.9kb 63.9kb
yellow open customer                       kYEsiy1iQWa2S_7JSsG9kQ 1 1    2 0  3.6kb  3.6kb

# 可以看到 bank 索引导入了 1000 条数据

    进阶检索

    SearchAPI

    ES 支持两种基本方式检索：

    • 通过 REST request uri 发送检索参数，即 uri + 检索参数

      GET http://192.168.56.56:9200/bank/_search?q=*&sort=account_number:asc
# q=* 表示查询所有
# sort 表示排序字段
# asc 表示升序

# 返回
{
    "took": 2, # 花费多少 ms 检索
    "timed_out": false, # 是否超时
    "_shards": { # 多少分片被搜索了，以及多少成功/失败的搜索分片
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1000, # 多少匹配文档被找到
            "relation": "eq"
        },
        "max_score": null, # 文档相关性最高得分
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "0",
                "_score": null, # 相关得分
                "_source": {
                    "account_number": 0,
                    "balance": 16623,
                    "firstname": "Bradshaw",
                    "lastname": "Mckenzie",
                    "age": 29,
                    "gender": "F",
                    "address": "244 Columbus Place",
                    "employer": "Euron",
                    "email": "bradshawmckenzie@euron.com",
                    "city": "Hobucken",
                    "state": "CO"
                },
                "sort": [ # 结果的排序 key（列），没有的话按照 score 排序
                    0
                ]
            },
            // ...
            {
                "_index": "bank",
                "_type": "account",
                "_id": "9",
                "_score": null,
                "_source": {
                    "account_number": 9,
                    "balance": 24776,
                    "firstname": "Opal",
                    "lastname": "Meadows",
                    "age": 39,
                    "gender": "M",
                    "address": "963 Neptune Avenue",
                    "employer": "Cedward",
                    "email": "opalmeadows@cedward.com",
                    "city": "Olney",
                    "state": "OH"
                },
                "sort": [
                    9
                ]
            }
        ]
    }
}

    • 通过 REST request body，即 uri + 请求体

      GET http://192.168.56.56:9200/bank/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "account_number": "asc" },
    { "balance":"desc"}
  ]
}

# 返回
{
    "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1000,
            "relation": "eq"
        },
        "max_score": null,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "0",
                "_score": null,
                "_source": {
                    "account_number": 0,
                    "balance": 16623,
                    "firstname": "Bradshaw",
                    "lastname": "Mckenzie",
                    "age": 29,
                    "gender": "F",
                    "address": "244 Columbus Place",
                    "employer": "Euron",
                    "email": "bradshawmckenzie@euron.com",
                    "city": "Hobucken",
                    "state": "CO"
                },
                "sort": [
                    0,
                    16623
                ]
            },
            // ...
            {
                "_index": "bank",
                "_type": "account",
                "_id": "9",
                "_score": null,
                "_source": {
                    "account_number": 9,
                    "balance": 24776,
                    "firstname": "Opal",
                    "lastname": "Meadows",
                    "age": 39,
                    "gender": "M",
                    "address": "963 Neptune Avenue",
                    "employer": "Cedward",
                    "email": "opalmeadows@cedward.com",
                    "city": "Olney",
                    "state": "OH"
                },
                "sort": [
                    9,
                    24776
                ]
            }
        ]
    }
}

    Query DSL

    ES 提供了一个可以执行查询的 json 风格 的 DSL（Domain specific language，领域特定语言 ），被称为 Query DSL。

    基本语法格式

    一个查询语句的典型结构：

    # 如果针对于某个字段，那么它的结构为：
{
  QUERY_NAME:{   # 使用的功能
     FIELD_NAME:{  #  功能参数
       ARGUMENT:VALUE,
       ARGUMENT:VALUE,...
      }   
   }
}

    查询示例：

    • query 定义如何查询，match_all 代表查询所有的索引
    • from 代表从第几条文档开始查询，size 代表查询文档个数，通常组合起来完成分页功能
    • sort 代表排序，多字段排序时，会在前序字段相等时后续字段内部排序，否则以前序为准
    GET http://192.168.56.56:9200/bank/_search
{
  "query": {  #  查询的字段
    "match_all": {}
  },
  "from": 0,  # 从第几条文档开始查
  "size": 5,
  "_source":["balance", "firstname"], # 要返回的字段
  "sort": [
    {
      "account_number": {  # 返回结果按哪个列排序
        "order": "desc"  # 降序
      }
    }
  ]
}

# 返回
{
    "took": 4,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1000,
            "relation": "eq"
        },
        "max_score": null,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "999",
                "_score": null,
                "_source": {
                    "firstname": "Dorothy",
                    "balance": 6087
                },
                "sort": [
                    999
                ]
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "998",
                "_score": null,
                "_source": {
                    "firstname": "Letha",
                    "balance": 16869
                },
                "sort": [
                    998
                ]
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "997",
                "_score": null,
                "_source": {
                    "firstname": "Combs",
                    "balance": 25311
                },
                "sort": [
                    997
                ]
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "996",
                "_score": null,
                "_source": {
                    "firstname": "Andrews",
                    "balance": 17541
                },
                "sort": [
                    996
                ]
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "995",
                "_score": null,
                "_source": {
                    "firstname": "Phelps",
                    "balance": 21153
                },
                "sort": [
                    995
                ]
            }
        ]
    }
}

    query/match 匹配查询

    如果是非字符串，会进行精确匹配。如果是字符串，会进行全文检索。

    1. 非字符串（基本类型），精确匹配

      GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "match": {
      "account_number": "20"
    }
  }
}

# 返回
{
    "took": 10,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1, # 得到一条记录
            "relation": "eq"
        },
        "max_score": 1.0, # 最大得分
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "20",
                "_score": 1.0,
                "_source": { # 文档信息
                    "account_number": 20,
                    "balance": 16418,
                    "firstname": "Elinor",
                    "lastname": "Ratliff",
                    "age": 36,
                    "gender": "M",
                    "address": "282 Kings Place",
                    "employer": "Scentric",
                    "email": "elinorratliff@scentric.com",
                    "city": "Ribera",
                    "state": "WA"
                }
            }
        ]
    }
}

    2. 字符串，全文检索，最终会按照评分进行排序，会对检索条件进行分词匹配。这是因为维护了一个倒排索引表。

      GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "match": {
      "address": "kings"
    }
  }
}

# 返回
{
    "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 2, # 得到两条记录
            "relation": "eq"
        },
        "max_score": 5.990829, # 最大得分
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "20",
                "_score": 5.990829, # 得分
                "_source": { # 文档信息
                    "account_number": 20,
                    "balance": 16418,
                    "firstname": "Elinor",
                    "lastname": "Ratliff",
                    "age": 36,
                    "gender": "M",
                    "address": "282 Kings Place",
                    "employer": "Scentric",
                    "email": "elinorratliff@scentric.com",
                    "city": "Ribera",
                    "state": "WA"
                }
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "722",
                "_score": 5.990829,
                "_source": {
                    "account_number": 722,
                    "balance": 27256,
                    "firstname": "Roberts",
                    "lastname": "Beasley",
                    "age": 34,
                    "gender": "F",
                    "address": "305 Kings Hwy",
                    "employer": "Quintity",
                    "email": "robertsbeasley@quintity.com",
                    "city": "Hayden",
                    "state": "PA"
                }
            }
        ]
    }
}

    query/match_phrase 不拆分匹配查询

    将需要匹配的值当成一整个单（不进行拆分）进行检索。

    • match_phrase 是做短语匹配，只要文本中包含匹配条件，就能匹配到。
    • 文本字段的匹配，使用 keyword，匹配的条件就是要显示字段的全部值，要进行精确匹配的。
    GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "match_phrase": {
      "address": "mill road" # 不要匹配只有 mill 或只有 road 的，要匹配 mill road 一整个子串
    }
  }
}

# 返回
{
    "took": 12,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 8.926605,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "970",
                "_score": 8.926605,
                "_source": {
                    "account_number": 970,
                    "balance": 19648,
                    "firstname": "Forbes",
                    "lastname": "Wallace",
                    "age": 28,
                    "gender": "M",
                    "address": "990 Mill Road", # Mill Road
                    "employer": "Pheast",
                    "email": "forbeswallace@pheast.com",
                    "city": "Lopezo",
                    "state": "AK"
                }
            }
        ]
    }
}

GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "match": {
      "address.keyword": "mill road" # 精准全部匹配
    }
  }
}

# 返回
{
    "took": 14,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 0,
            "relation": "eq"
        },
        "max_score": null,
        "hits": []
    }
}

GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "match": {
      "address.keyword": "990 Mill Road" # 精准全部匹配，而且区分大小写
    }
  }
}

# 返回
{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 6.5032897,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "970",
                "_score": 6.5032897,
                "_source": {
                    "account_number": 970,
                    "balance": 19648,
                    "firstname": "Forbes",
                    "lastname": "Wallace",
                    "age": 28,
                    "gender": "M",
                    "address": "990 Mill Road",
                    "employer": "Pheast",
                    "email": "forbeswallace@pheast.com",
                    "city": "Lopezo",
                    "state": "AK"
                }
            }
        ]
    }
}

    query/multi_match 多字段匹配查询

    state 或者 address 中包含 mill，并且在查询过程中，会对于查询条件进行分词。

    GET http://192.168.56.56:9200/bank/_search
{
  "query": {
    "multi_match": {  # 指定多个字段
      "query": "mill",
      "fields": [ # state 和 address 有 mill 子串，但不要求都有
        "state",
        "address"
      ]
    }
  }
}

# 返回
{
    "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 4,
            "relation": "eq"
        },
        "max_score": 5.4032025,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "970",
                "_score": 5.4032025,
                "_source": {
                    "account_number": 970,
                    "balance": 19648,
                    "firstname": "Forbes",
                    "lastname": "Wallace",
                    "age": 28,
                    "gender": "M",
                    "address": "990 Mill Road",
                    "employer": "Pheast",
                    "email": "forbeswallace@pheast.com",
                    "city": "Lopezo",
                    "state": "AK"
                }
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "136",
                "_score": 5.4032025,
                "_source": {
                    "account_number": 136,
                    "balance": 45801,
                    "firstname": "Winnie",
                    "lastname": "Holland",
                    "age": 38,
                    "gender": "M",
                    "address": "198 Mill Lane",
                    "employer": "Neteria",
                    "email": "winnieholland@neteria.com",
                    "city": "Urie",
                    "state": "IL"
                }
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "345",
                "_score": 5.4032025,
                "_source": {
                    "account_number": 345,
                    "balance": 9812,
                    "firstname": "Parker",
                    "lastname": "Hines",
                    "age": 38,
                    "gender": "M",
                    "address": "715 Mill Avenue",
                    "employer": "Baluba",
                    "email": "parkerhines@baluba.com",
                    "city": "Blackgum",
                    "state": "KY"
                }
            },
            {
                "_index": "bank",
                "_type": "account",
                "_id": "472",
                "_score": 5.4032025,
                "_source": {
                    "account_number": 472,
                    "balance": 25571,
                    "firstname": "Lee",
                    "lastname": "Long",
                    "age": 32,
                    "gender": "F",
                    "address": "288 Mill Street",
                    "employer": "Comverges",
                    "email": "leelong@comverges.com",
                    "city": "Movico",
                    "state": "MT"
                }
            }
        ]
    }
}

    query/bool/must 复合匹配查询

    复合语句必须合并，任何其他查询语句，包括符号语句。这也意味着，复合语句之间可以相互嵌套，可以表达非常复杂的逻辑。

    • must 必须匹配的条件
    • must_not 必须不匹配的条件
    • should 应该匹配的条件，满足最好，不满足也可以，满足了得分更高
    • 注意：should 列举的条件，如果到达会增加相关文档的评分，并不会改变查询的结果。如果 query 中有且只有 should 一种匹配规则，那么 should 的条件就会被作为默认匹配条件去改变查询结果。
    # 查询 gender=m，并且 address=mill 的数据
GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "bool": {
            "must": [
                {
                    "match": {
                        "address": "mill"
                    }
                },
                {
                    "match": {
                        "gender": "M"
                    }
                }
            ]
        }
    }
}

# 查询 gender=m，并且 address=mill，但是 age!=38 的数据
GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "bool": {
            "must": [
                {
                    "match": {
                        "gender": "M"
                    }
                },
                {
                    "match": {
                        "address": "mill"
                    }
                }
            ],
            "must_not": [
                {
                    "match": {
                        "age": "38"
                    }
                }
            ]
        }
    }
}

# 查询 gender=m，并且 address=mill，但是 age!=18，lastName 应该等于 Wallace 的数据
GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "bool": {
            "must": [
                {
                    "match": {
                        "gender": "M"
                    }
                },
                {
                    "match": {
                        "address": "mill"
                    }
                }
            ],
            "must_not": [
                {
                    "match": {
                        "age": "18"
                    }
                }
            ],
            "should": [
                {
                    "match": {
                        "lastname": "Wallace"
                    }
                }
            ]
        }
    }
}

    query/filter 查询结果过滤

    并不是所有的查询都需要产生分数，特别是哪些仅用于过滤的文档。为了不计算分数，ES 会自动检查场景并且优化查询的执行。must_not 也是一种 filter，所以也不会贡献得分。显然这样查询速度会更快。总结为：

    • must 贡献得分
    • should 贡献得分
    • must_not 不贡献得分
    • filter 不贡献得分
    # 查询所有匹配 address=mill 的文档，然后再根据 10000<=balance<=20000 进行过滤查询结果
GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "bool": {
            "must": [
                {
                    "match": {
                        "address": "mill"
                    }
                }
            ],
            "filter": {
                "range": {
                    "balance": {
                        "gte": "10000",
                        "lte": "20000"
                    }
                }
            }
        }
    }
}

# 返回
{
    "took": 5,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1,
            "relation": "eq"
        },
        "max_score": 5.4032025,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "970",
                "_score": 5.4032025,
                "_source": {
                    "account_number": 970,
                    "balance": 19648,
                    "firstname": "Forbes",
                    "lastname": "Wallace",
                    "age": 28,
                    "gender": "M",
                    "address": "990 Mill Road",
                    "employer": "Pheast",
                    "email": "forbeswallace@pheast.com",
                    "city": "Lopezo",
                    "state": "AK"
                }
            }
        ]
    }
}

# 单纯的过滤
GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "bool": {
            "filter": {
                "range": {
                    "balance": {
                        "gte": "10000",
                        "lte": "20000"
                    }
                }
            }
        }
    }
}

# 返回
{
    "took": 3,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 213,
            "relation": "eq"
        },
        "max_score": 0.0,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "20",
                "_score": 0.0, # 不得分
                "_source": {
                    "account_number": 20,
                    "balance": 16418,
                    "firstname": "Elinor",
                    "lastname": "Ratliff",
                    "age": 36,
                    "gender": "M",
                    "address": "282 Kings Place",
                    "employer": "Scentric",
                    "email": "elinorratliff@scentric.com",
                    "city": "Ribera",
                    "state": "WA"
                }
            },
            // ...
            {
                "_index": "bank",
                "_type": "account",
                "_id": "272",
                "_score": 0.0, # 不得分
                "_source": {
                    "account_number": 272,
                    "balance": 19253,
                    "firstname": "Lilly",
                    "lastname": "Morgan",
                    "age": 25,
                    "gender": "F",
                    "address": "689 Fleet Street",
                    "employer": "Biolive",
                    "email": "lillymorgan@biolive.com",
                    "city": "Sunbury",
                    "state": "OH"
                }
            }
        ]
    }
}

    query/term 非 text 字段匹配查询

    它和 query/match 一样，能匹配某个属性的值，但是 全文检索字段时用 match，其他非 text 字段时用 term。因为 ES 默认存储 text 值时用分词分析。

    aggs/aggName 聚合

    聚合提供了从数据中分组和提取数据的能力，最简单的聚合方法类似于 SQL 的 group by 和 聚合函数 等。

    在 ES 中，执行搜索返回 hits（命中结果），并且同时返回聚合结果。把已响应的所有命中结果分隔开的能力是非常实用的。可以执行查询和多个聚合，并且在一次使用中得到各自的返回结果，使用一次简洁和简化的 API 可以避免网络往返。

    聚合基本语法格式：

    "aggs":{ # 聚合
    "aggs_name":{ # 聚合的名字，方便展示在结果集中
        "AGG_TYPE":{} # 聚合的类型(avg,term,terms)
     }
}
# terms 看值的可能性分布，会合并锁查字段，给出计数即可
# avg   看值的分布平均

    搜索 address 中包含 mill 的所有人的年龄分布以及平均年龄，但不显示这些人的详情：

    GET http://192.168.56.56:9200/bank/_search
{
  "query": { # 查询出包含 mill 的
    "match": {
      "address": "Mill"
    }
  },
  "aggs": { # 基于查询聚合
    "ageAgg": {  # 第一个聚合，聚合的名字，可以随便起
      "terms": { # 看值的可能性分布
        "field": "age",
        "size": 10
      }
    },
    "ageAvg": {  # 第二个聚合
      "avg": { # 看 age 值的平均
        "field": "age"
      }
    },
    "balanceAvg": { # 第三个聚合
      "avg": { # 看 balance 的平均
        "field": "balance"
      }
    }
  },
  "size": 0  # 不看详情
}

# 返回
{
    "took": 11,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 4, # 命中 4 条记录
            "relation": "eq"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "ageAgg": { # ageAgg 聚合结果
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": 38, # age=38 有 2 条记录
                    "doc_count": 2
                },
                {
                    "key": 28,
                    "doc_count": 1
                },
                {
                    "key": 32,
                    "doc_count": 1
                }
            ]
        },
        "ageAvg": {
            "value": 34.0
        },
        "balanceAvg": {
            "value": 25208.0
        }
    }
}

    aggs/aggName/aggs/aggName 子聚合

    按照年龄聚合，求这些年龄段的这些人的平均薪资：

    GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "match_all": {}
    },
    "aggs": {
        "ageAgg": {
            "terms": { # 看值的可能性分布
                "field": "age",
                "size": 100
            },
            "aggs": { # 与 terms 并列
                "ageAvg": {
                    "avg": {
                        "field": "balance"
                    }
                }
            }
        }
    },
    "size": 0
}

# 返回
{
    "took": 60,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1000,
            "relation": "eq"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "ageAgg": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": 31,
                    "doc_count": 61,
                    "ageAvg": {
                        "value": 28312.918032786885
                    }
                },
                // ...
                {
                    "key": 29,
                    "doc_count": 35,
                    "ageAvg": {
                        "value": 29483.14285714286
                    }
                }
            ]
        }
    }
}

    查出所有年龄分布，并且这些年龄段中 M 的平均薪资和 F 的平均薪资以及这个年龄段的总体平均薪资：

    GET http://192.168.56.56:9200/bank/_search
{
    "query": {
        "match_all": {}
    },
    "aggs": {
        "ageAgg": {
            "terms": { # age 的分布
                "field": "age",
                "size": 100
            },
            "aggs": { # 子聚合
                "genderAgg": { #
                    "terms": { # gender 的分布
                        "field": "gender.keyword" # 使用 .keyword
                    },
                    "aggs": {
                        "balanceAvg": {
                            "avg": {
                                "field": "balance"
                            }
                        }
                    }
                },
                "ageBalanceAvg": { #
                    "avg": {
                        "field": "balance"
                    }
                }
            }
        }
    },
    "size": 0
}

# 返回
{
    "took": 82,
    "timed_out": false,
    "_shards": {
        "total": 1,
        "successful": 1,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": {
            "value": 1000,
            "relation": "eq"
        },
        "max_score": null,
        "hits": []
    },
    "aggregations": {
        "ageAgg": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
                {
                    "key": 31,
                    "doc_count": 61,
                    "genderAgg": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                            {
                                "key": "M",
                                "doc_count": 35,
                                "balanceAvg": {
                                    "value": 29565.628571428573
                                }
                            },
                            {
                                "key": "F",
                                "doc_count": 26,
                                "balanceAvg": {
                                    "value": 26626.576923076922
                                }
                            }
                        ]
                    },
                    "ageBalanceAvg": {
                        "value": 28312.918032786885
                    }
                },
                // ...
                {
                    "key": 29,
                    "doc_count": 35,
                    "genderAgg": {
                        "doc_count_error_upper_bound": 0,
                        "sum_other_doc_count": 0,
                        "buckets": [
                            {
                                "key": "M",
                                "doc_count": 23,
                                "balanceAvg": {
                                    "value": 29943.17391304348
                                }
                            },
                            {
                                "key": "F",
                                "doc_count": 12,
                                "balanceAvg": {
                                    "value": 28601.416666666668
                                }
                            }
                        ]
                    },
                    "ageBalanceAvg": {
                        "value": 29483.14285714286
                    }
                }
            ]
        }
    }
}

    nested 对象聚合

    参考：Elasticsearch 中使用 nested 类型的内嵌对象

    Mapping 字段映射

    映射是定义文档及其包含的字段的存储和索引方式的过程。每个文档都是字段的集合，每个字段都有自己的 数据类型。映射数据时，将创建一个映射定义，其中包含与文档相关的字段列表。

    字段类型

    核心类型：

    • 字符串
      • text 用于全文索引，搜索时会自动使用分词器进行分词再匹配
      • keyword 部分此，搜索时精确完整匹配
    • 数字类型
      • 整型：byte，short，integer，long
      • 浮点型：float, half_float, scaled_float，double
    • 日期类型
    • 布尔类型
    • 二进制类型

    复杂类型：

    • 数组类型
    • 对象类型
    • 嵌套类型

    地理类型：

    • 地理坐标
    • 地理图标

    详细可参考

    查看映射

    使用 mapping 来定义：

    • 哪些字符串属性应该被看做 全文本属性（full text fields）；
    • 哪些属性包含数字，日期或地理位置；
    • 文档中的所有属性是否都嫩被索引（all 配置）；
    • 日期的格式；
    • 自定义映射规则来执行动态添加属性；
    # 查看索引
GET /bank/_mapping
{
  "bank" : {
    "mappings" : {
      "properties" : {
        "account_number" : {
          "type" : "long" # long 类型
        },
        "address" : {
          "type" : "text", # text 类型，会进行全文检索，进行分词匹配
          "fields" : {
            "keyword" : {
              "type" : "keyword", # 精确匹配
              "ignore_above" : 256
            }
          }
        },
        "age" : {
          "type" : "long"
        },
        "balance" : {
          "type" : "long"
        },
        "city" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "email" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "employer" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "firstname" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "gender" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "lastname" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "state" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        }
      }
    }
  }
}

    创建映射

    # 创建映射
PUT /my_index
{
  "mappings": {
    "properties": {
      "age": {
        "type": "integer"
      },
      "email": {
        "type": "keyword"
      },
      "name": {
        "type": "text"
      }
    }
  }
}

# 输出
{
  "acknowledged" : true,
  "shards_acknowledged" : true,
  "index" : "my_index"
}

# 查看映射
GET /my_index

# 输出
{
  "my_index" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "age" : {
          "type" : "integer"
        },
        "email" : {
          "type" : "keyword"
        },
        "name" : {
          "type" : "text"
        }
      }
    },
    "settings" : {
      "index" : {
        "creation_date" : "1617960990447",
        "number_of_shards" : "1",
        "number_of_replicas" : "1",
        "uuid" : "KgYd5GOPR0uc5kEbUCeBDg",
        "version" : {
          "created" : "7080099"
        },
        "provided_name" : "my_index"
      }
    }
  }
}

# 添加新的字段映射
PUT /my_index/_mapping
{
  "properties": {
    "employee-id": {
      "type": "keyword",
      "index": false # 表示字段不能被检索
    }
  }
}

    更新映射

    对于已经存在的字段映射，我们不能更新，因为更改现有字段可能会使已经建立索引的数据无效。要更新必须创建新的索引，进行数据迁移。具体操作为：

    # 先创建新的索引，然后进行数据迁移

# 6.0 之后的写法
POST reindex
{
  "source":{
      "index":"old_index"
   },
  "dest":{
      "index":"new_index"
   }
}


# 老版本写法
POST reindex
{
  "source":{
      "index":"old_index",
      "type":"old_type"
   },
  "dest":{
      "index":"new_index"
   }
}

    案例： 原来 bank 索引的类型为 account，新版本没有类型了，所以我们把它去掉。

    GET /bank/_search
# 输出
{
  "took" : 19,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1000,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account", # 有类型
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 1,
          "balance" : 39225,
          "firstname" : "Amber",
          "lastname" : "Duke",
          "age" : 32,
          "gender" : "M",
          "address" : "880 Holmes Lane",
          "employer" : "Pyrami",
          "email" : "amberduke@pyrami.com",
          "city" : "Brogan",
          "state" : "IL"
        }
      },
      // ...
     ]
  }
}

# 先建立新的索引
PUT /newbank
{
  "mappings": {
    "properties": {
      "account_number": {
        "type": "long"
      },
      "address": {
        "type": "text"
      },
      "age": {
        "type": "integer"
      },
      "balance": {
        "type": "long"
      },
      "city": {
        "type": "keyword"
      },
      "email": {
        "type": "keyword"
      },
      "employer": {
        "type": "keyword"
      },
      "firstname": {
        "type": "text"
      },
      "gender": {
        "type": "keyword"
      },
      "lastname": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "state": {
        "type": "keyword"
      }
    }
  }
}

# 查看新的映射
GET /newbank/_mapping

# 返回
{
  "newbank" : {
    "mappings" : {
      "properties" : {
        "account_number" : {
          "type" : "long"
        },
        "address" : {
          "type" : "text"
        },
        "age" : {
          "type" : "integer" # 改为了 integer
        },
        "balance" : {
          "type" : "long"
        },
        "city" : {
          "type" : "keyword"
        },
        "email" : {
          "type" : "keyword"
        },
        "employer" : {
          "type" : "keyword"
        },
        "firstname" : {
          "type" : "text"
        },
        "gender" : {
          "type" : "keyword"
        },
        "lastname" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "state" : {
          "type" : "keyword"
        }
      }
    }
  }

# 进行迁移
POST _reindex
{
  "source": {
    "index": "bank",
    "type": "account"
  },
  "dest": {
    "index": "newbank"
  }
}
# 输出
#! Deprecation: [types removal] Specifying types in reindex requests is deprecated.
{
  "took" : 918,
  "timed_out" : false,
  "total" : 1000,
  "updated" : 0,
  "created" : 1000,
  "deleted" : 0,
  "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" : [ ]
}

# 查看 newbank
GET /newbank/_search
{
  "took" : 511,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1000,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "newbank",
        "_type" : "_doc", # 没有了类型
        "_id" : "1",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 1,
          "balance" : 39225,
          "firstname" : "Amber",
          "lastname" : "Duke",
          "age" : 32,
          "gender" : "M",
          "address" : "880 Holmes Lane",
          "employer" : "Pyrami",
          "email" : "amberduke@pyrami.com",
          "city" : "Brogan",
          "state" : "IL"
        }
      },
       // ...
     ]
  }
}

    分词

    一个 tokenizer（分词器）接收一个字符流，将之分割为独立的tokens（词元，通常是独立的单词），然后输出 tokens 流。

    例如：whitespace tokenizer 遇到空白字符时分割文本。它会将文本"Quick brown fox!"分割为[Quick,brown,fox!]。

    该 tokenizer（分词器）还负责记录各个 terms(词条) 的顺序或 position 位置（用于 phrase 短语和 word proximity 词近邻查询），以及 term（词条）所代表的原始 word（单词）的 start（起始）和 end（结束）的 character offsets（字符串偏移量）（用于高亮显示搜索的内容）。

    elasticsearch提供了很多内置的分词器（标准分词器），可以用来构建 custom analyzers（自定义分词器）。更多可参考

    标准分词器的使用：

    POST _analyze
{
  "analyzer": "standard",
  "text": "The 2 Brown-Foxes bone."
}
# 输出
{
  "tokens" : [
    {
      "token" : "the",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "brown",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "foxes",
      "start_offset" : 12,
      "end_offset" : 17,
      "type" : "<ALPHANUM>",
      "position" : 3
    },
    {
      "token" : "bone",
      "start_offset" : 18,
      "end_offset" : 22,
      "type" : "<ALPHANUM>",
      "position" : 4
    }
  ]
}

    所有的语言分词，默认使用的都是 “Standard Analyzer”，但是这些分词器针对于中文的分词，并不友好。为此需要安装中文的分词器。推荐使用 elasticsearch-analysis-ik。

    安装 ik 分词器

    1. 查看 ES 版本

      http://192.168.56.56:9200/
{
"name": "0f6d6c60bc96",
"cluster_name": "elasticsearch",
"cluster_uuid": "sDTdW7KnQayVrFC5ioijiQ",
"version": {
"number": "7.8.0", # 7.8.0
"build_flavor": "default",
"build_type": "docker",
"build_hash": "757314695644ea9a1dc2fecd26d1a43856725e65",
"build_date": "2020-06-14T19:35:50.234439Z",
"build_snapshot": false,
"lucene_version": "8.5.1",
"minimum_wire_compatibility_version": "6.8.0",
"minimum_index_compatibility_version": "6.0.0-beta1"
},
"tagline": "You Know, for Search"
}

    2. 由于使用 Docker 安装 ES 时，进行了路径映射，所以直接进入 ES 的 plugins 目录

      cd docker/elasticsearch7.8.0/plugins
# 安装 waget
yum install wget
# 安装 unzip
yum install unzip
# 下载 ik 压缩包
wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.8.0/elasticsearch-analysis-ik-7.8.0.zip
# 解压 ik
unzip elasticsearch-analysis-ik-7.8.0.zip -d ik
# 更改权限
chmod -R 777 ik
# 删除 ik 压缩包
rm -rf elasticsearch-analysis-ik-7.8.0.zip
# 重启 ES
docker restart elasticsearch7.8.0

    测试分词器

    # 使用默认分词器
GET _analyze
{
   "text":"我是中国人"
}
# 输出
{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "中",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "国",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "人",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    }
  ]
}

# 使用 ik
GET _analyze
{
  "analyzer": "ik_smart", 
   "text":"我是中国人"
}
# 输出
{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "中国人",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 2
    }
  ]
}

GET _analyze
{
   "analyzer": "ik_max_word", 
   "text":"我是中国人"
}
# 输出
{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "中国人",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "中国",
      "start_offset" : 2,
      "end_offset" : 4,
      "type" : "CN_WORD",
      "position" : 3
    },
    {
      "token" : "国人",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 4
    }
  ]
}

    自定义词库

    • 在 Nginx 的映射文件夹的 html 文件夹下创建 es 文件夹，用于保存 es相关数据

      mkdir es

    • 创建 fenci.txt 文件，将分词数据存放在此文件中

      cd es/
# 加入 高富帅 刘德华子 等自定义词
vi fenci.txt
访问 http://192.168.56.56/es/fenci.txt

    • 修改 plugins/ik/config 中的 IKAnalyzer.cfg.xml

      <?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
        <comment>IK Analyzer 扩展配置</comment>
        <!--用户可以在这里配置自己的扩展字典 -->
        <entry key="ext_dict"></entry>
        <!--用户可以在这里配置自己的扩展停止词字典-->
        <entry key="ext_stopwords"></entry>
        <!--用户可以在这里配置远程扩展字典 -->
        <entry key="remote_ext_dict">http://192.168.56.56/es/fenci.txt</entry>
        <!--用户可以在这里配置远程扩展停止词字典-->
        <!-- <entry key="remote_ext_stopwords">words_location</entry> -->
</properties>

# 重启 ES
docker restart elasticsearch7.8.0

    注意：更新完成后，ES 只会对于新增的数据用更新分词。历史数据是不会重新分词的。如果想要历史数据重新分词，需要执行 POST my_index/_update_by_query?conflicts=proceed

    测试：

    GET _analyze
{
  "analyzer": "ik_smart", 
   "text":"我是高富帅刘德华子"
}
# 输出
{
  "tokens" : [
    {
      "token" : "我",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "CN_CHAR",
      "position" : 0
    },
    {
      "token" : "是",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "CN_CHAR",
      "position" : 1
    },
    {
      "token" : "高富帅",
      "start_offset" : 2,
      "end_offset" : 5,
      "type" : "CN_WORD",
      "position" : 2
    },
    {
      "token" : "刘德华子",
      "start_offset" : 5,
      "end_offset" : 9,
      "type" : "CN_WORD",
      "position" : 3
    }
  ]
}

    ES REST CLIENT

    Java 操作 ES 有两种方式：

    • 通过 9300 端口，以 TCP 方式
      • 使用 spring-data-elasticsearch:transport-api.jar
      • springboot 版本不同，ransport-api.jar 不同，不能适配 ES 版本
      • 7.x 已经不建议使用，8 以后就要废弃
      • 具体可参考：Java API (deprecated)
    • 通过 9200 端口，以 HTTP 方式
      • jestClient: 非官方，更新慢
      • HttpClient、RestTemplate：模拟 HTTP 请求，ES 很多操作需要自己封装，麻烦
      • Elasticsearch-Rest-Client：官方 RestClient，封装了 ES 操作，API 层次分明，上手简单，推荐使用
      • Elasticsearch-Rest-Client 具体可参考：Java REST Client，并且使用 Java High Level REST Client，它与 Java Low Level REST Client 的区别类似于 MyBatis 和 JDBC。

    SpringBoot 整合 ES

    • 创建 SpringBoot 项目，选择 Web 依赖，但是不要选择 ES 依赖

    • 导入依赖

      <!-- ES Rest API-->
<dependency>
    <groupId>org.elasticsearch.client</groupId>
    <artifactId>elasticsearch-rest-high-level-client</artifactId>
    <version>7.8.0</version>
</dependency>

# 在 spring-boot-dependencies 中所依赖的ES版本位 6.8.5，要改掉
<properties>
    <java.version>1.8</java.version>
    <spring-cloud.version>Hoxton.SR8</spring-cloud.version>
    <elasticsearch.version>7.8.0</elasticsearch.version>
</properties>

    • 编写 Elasticsearch 配置类

      package cn.parzulpan.shopping.search.config;

import org.apache.http.HttpHost;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

/**
 * @author parzulpan
 * @version 1.0
 * @date 2021-04
 * @project shopping
 * @package cn.parzulpan.shopping.search.config
 * @desc Elasticsearch 配置类
 */

@Configuration
public class ShoppingElasticsearchConfig {
    // 请求测试项，比如 es 添加了安全访问规则，访问 es 需要添加一个安全头，就可以通过 requestOptions 设置
    // 官方建议把 requestOptions 创建成单实例
    public static final RequestOptions COMMON_OPTIONS;
    static {
        RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
        COMMON_OPTIONS = builder.build();
    }


    @Bean
    public RestHighLevelClient restHighLevelClient() {
        RestClientBuilder builder = null;
        // 可以指定多个 ES
        builder = RestClient.builder(new HttpHost("192.168.56.56", 9200, "http"));
        return new RestHighLevelClient(builder);
    }

}

    • 实例测试

      package cn.parzulpan.shopping.search;

import org.elasticsearch.client.RestHighLevelClient;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;

@SpringBootTest
class ShoppingSearchApplicationTests {

    @Autowired
    RestHighLevelClient client;

    @Test
    void contextLoads() {

    }

    @Test
    void testRestClient() {
        System.out.println(client);
    }

}

    • 保存数据

      @Data
class User {
    private String userName;
    private Integer age;
    private String gender;
}

/**
     * https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.x/java-rest-high-create-index.html
     * 保存方式分为同步和异步
     */
@Test
void indexData() throws IOException {
    // 设置索引
    IndexRequest users = new IndexRequest("users");
    users.id("1");

    //设置要保存的内容，指定数据和类型
    // 方式一
    //        users.source("userName", "zhang", "age", 18, "gender", "男");
    // 方式二
    User user = new User();
    user.setUserName("wang");
    user.setAge(20);
    user.setGender("女");
    Gson gson = new Gson();
    String userJson = gson.toJson(user);
    users.source(userJson, XContentType.JSON);

    // 执行创建索引和保存数据
    IndexResponse index = client.index(users, ShoppingElasticsearchConfig.COMMON_OPTIONS);

    System.out.println(index);
}

    • 获取数据

      /**
     * ES 获取数据
     * https://www.elastic.co/guide/en/elasticsearch/client/java-rest/7.x/java-rest-high-search.html
     * 搜索 address 中包含 mill 的所有人的年龄分布以及平均年龄
     * GET /bank/_search
     * {
     *   "query": { # 查询出包含 mill 的
     *     "match": {
     *       "address": "Mill"
     *     }
     *   },
     *   "aggs": { # 基于查询聚合
     *     "ageAgg": {  # 第一个聚合，聚合的名字，可以随便起
     *       "terms": { # 看值的可能性分布
     *         "field": "age",
     *         "size": 10
     *       }
     *     },
     *     "ageAvg": {  # 第二个聚合
     *       "avg": { # 看 age 值的平均
     *         "field": "age"
     *       }
     *     },
     *     "balanceAvg": { # 第三个聚合
     *       "avg": { # 看 balance 的平均
     *         "field": "balance"
     *       }
     *     }
     *   },
     *   "size": 0  # 不看详情
     * }
     */
@Test
void find() throws IOException {
    // 1. 创建检索请求
    SearchRequest searchRequest = new SearchRequest();
    searchRequest.indices("bank");
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    // 构造检索条件
    //        searchSourceBuilder.query();
    //        searchSourceBuilder.from();
    //        searchSourceBuilder.size();
    //        searchSourceBuilder.aggregation();
    searchSourceBuilder.query(QueryBuilders.matchQuery("address", "mill"));
    // 构建第一个聚合条件：看值的可能性分布
    TermsAggregationBuilder ageAgg = AggregationBuilders.terms("ageAgg").field("age").size(10);
    searchSourceBuilder.aggregation(ageAgg);
    // 构建第二个聚合条件：看 age 值的平均
    AvgAggregationBuilder ageAvg = AggregationBuilders.avg("ageAvg").field("age");
    searchSourceBuilder.aggregation(ageAvg);
    // 构建第三个聚合条件：看 balance 的平均
    AvgAggregationBuilder balanceAvg = AggregationBuilders.avg("balanceAvg").field("balance");
    searchSourceBuilder.aggregation(balanceAvg);
    // 不看详情
    //        searchSourceBuilder.size(0);

    System.out.println("searchSourceBuilder " + searchSourceBuilder.toString());
    searchRequest.source(searchSourceBuilder);

    // 2. 执行检索
    SearchResponse response = client.search(searchRequest, ShoppingElasticsearchConfig.COMMON_OPTIONS);

    // 3. 分析响应结果
    System.out.println("response " + response.toString());
    // 3.1 将响应结果转换为 Bean
    SearchHits hits = response.getHits();
    SearchHit[] hits1 = hits.getHits();
    Gson gson = new Gson();
    for (SearchHit hit: hits1) {
        System.out.println("id: " + hit.getId());
        System.out.println("index: " + hit.getIndex());
        String sourceAsString = hit.getSourceAsString();
        System.out.println("sourceAsString: " + sourceAsString);
        System.out.println("Account: " + gson.fromJson(sourceAsString, Account.class));
    }
    // 3.2 获取检索到的分析信息
    Aggregations aggregations = response.getAggregations();
    Terms ageAgg1 = aggregations.get("ageAgg");
    for (Terms.Bucket bucket : ageAgg1.getBuckets()) {
        System.out.println("ageAgg: " + bucket.getKeyAsString() + " => " + bucket.getDocCount());
    }
    Avg ageAvg1 = aggregations.get("ageAvg");
    System.out.println("ageAvg: " + ageAvg1.getValue());
    Avg balanceAvg1 = aggregations.get("balanceAvg");
    System.out.println("balanceAvg: " + balanceAvg1.getValue());
}

    总结和练习
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  • 原文地址：https://www.cnblogs.com/parzulpan/p/14639329.html
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