docker使用

来源:https://www.cnblogs.com/loongnuts/archive/2022/11/08/16870739.html
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安裝Docker Docker 分為 CE 和 EE 兩大版本。CE 即社區版（免費，支持周期 7 個月），EE 即企業版，強調安全，付費使用，支持周期 24 個月。 Docker CE 分為 stable test 和 nightly 三個更新頻道。 官方網站上有各種環境下的 安裝指南，這裡主要介 ...


安裝Docker

Docker 分為 CE 和 EE 兩大版本。CE 即社區版（免費，支持周期 7 個月），EE 即企業版，強調安全，付費使用，支持周期 24 個月。

Docker CE 分為 stable test 和 nightly 三個更新頻道。

官方網站上有各種環境下的 安裝指南，這裡主要介紹 Docker CE 在 CentOS上的安裝。

1.CentOS安裝Docker

Docker CE 支持 64 位版本 CentOS 7，並且要求內核版本不低於 3.10， CentOS 7 滿足最低內核的要求，所以我們在CentOS 7安裝Docker。

1.1.卸載（可選）

如果之前安裝過舊版本的Docker，可以使用下麵命令卸載：

yum remove docker \
                  docker-client \
                  docker-client-latest \
                  docker-common \
                  docker-latest \
                  docker-latest-logrotate \
                  docker-logrotate \
                  docker-selinux \
                  docker-engine-selinux \
                  docker-engine \
                  docker-ce

1.2.安裝docker

首先需要大家虛擬機聯網，安裝yum工具

yum install -y yum-utils \
           device-mapper-persistent-data \
           lvm2 --skip-broken

然後更新本地鏡像源：

# 設置docker鏡像源
yum-config-manager \
    --add-repo \
    https://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo
    
sed -i 's/download.docker.com/mirrors.aliyun.com\/docker-ce/g' /etc/yum.repos.d/docker-ce.repo

yum makecache fast

然後輸入命令：

yum install -y docker-ce

docker-ce為社區免費版本。稍等片刻，docker即可安裝成功。

1.3.啟動docker

Docker應用需要用到各種埠，逐一去修改防火牆設置。非常麻煩，因此建議大家直接關閉防火牆！

啟動docker前，一定要關閉防火牆後！！

啟動docker前，一定要關閉防火牆後！！

啟動docker前，一定要關閉防火牆後！！

# 關閉
systemctl stop firewalld
# 禁止開機啟動防火牆
systemctl disable firewalld

通過命令啟動docker：

systemctl start docker  # 啟動docker服務

systemctl enable docker

systemctl stop docker  # 停止docker服務

systemctl restart docker  # 重啟docker服務

然後輸入命令，可以查看docker版本：

docker -v

1.4.配置鏡像加速

docker官方鏡像倉庫網速較差，我們需要設置國內鏡像服務：

參考阿裡雲的鏡像加速文檔：https://cr.console.aliyun.com/cn-hangzhou/instances/mirrors

sudo mkdir -p /etc/docker
sudo tee /etc/docker/daemon.json <<-'EOF'
{
  "registry-mirrors": ["https://ja8iqg7n.mirror.aliyuncs.com"]
}
EOF
sudo systemctl daemon-reload
sudo systemctl restart docker

2.CentOS7安裝DockerCompose

2.1.下載

Linux下需要通過命令下載：

# 安裝
curl -L https://github.com/docker/compose/releases/download/1.23.1/docker-compose-`uname -s`-`uname -m` > /usr/local/bin/docker-compose

如果下載速度較慢，或者下載失敗，可以使用課前資料提供的docker-compose文件：

上傳到/usr/local/bin/目錄也可以。

2.2.修改文件許可權

修改文件許可權：

# 修改許可權
chmod +x /usr/local/bin/docker-compose

2.3.Base自動補全命令：

# 補全命令
curl -L https://raw.githubusercontent.com/docker/compose/1.29.1/contrib/completion/bash/docker-compose > /etc/bash_completion.d/docker-compose

如果這裡出現錯誤，需要修改自己的hosts文件：

echo "199.232.68.133 raw.githubusercontent.com" >> /etc/hosts

3.Docker鏡像倉庫

搭建鏡像倉庫可以基於Docker官方提供的DockerRegistry來實現。

官網地址：https://hub.docker.com/_/registry

3.1.簡化版鏡像倉庫

Docker官方的Docker Registry是一個基礎版本的Docker鏡像倉庫，具備倉庫管理的完整功能，但是沒有圖形化界面。

搭建方式比較簡單，命令如下：

docker run -d \
    --restart=always \
    --name registry	\
    -p 5000:5000 \
    -v registry-data:/var/lib/registry \
    registry

命令中掛載了一個數據捲registry-data到容器內的/var/lib/registry 目錄，這是私有鏡像庫存放數據的目錄。

訪問http://YourIp:5000/v2/_catalog 可以查看當前私有鏡像服務中包含的鏡像

3.2.帶有圖形化界面版本

使用DockerCompose部署帶有圖象界面的DockerRegistry，命令如下：

version: '3.0'
services:
  registry:
    image: registry
    volumes:
      - ./registry-data:/var/lib/registry
  ui:
    image: joxit/docker-registry-ui:static
    ports:
      - 8080:80
    environment:
      - REGISTRY_TITLE=傳智教育私有倉庫
      - REGISTRY_URL=http://registry:5000
    depends_on:
      - registry

3.3.配置Docker信任地址

我們的私服採用的是http協議，預設不被Docker信任，所以需要做一個配置：

# 打開要修改的文件
vi /etc/docker/daemon.json
# 添加內容：
"insecure-registries":["http://192.168.150.101:8080"]
# 重載入
systemctl daemon-reload
# 重啟docker
systemctl restart docker

1. ELASTICSEARCH

dokcer中安裝elastic search

（1）下載ealastic search和kibana

docker pull elasticsearch:7.12.1
docker pull kibana:7.12.1

（2）配置

mkdir -p /home/mydata/elasticsearch/config  創建目錄
mkdir -p /home/mydata/elasticsearch/data
echo "http.host: 0.0.0.0" >/home/mydata/elasticsearch/config/elasticsearch.yml

//將mydata/elasticsearch/文件夾中文件都可讀可寫
chmod -R 777 /mydata/elasticsearch/

（3）啟動Elastic search

docker run --name elasticsearch -p 9200:9200 -p 9300:9300 \
-e  "discovery.type=single-node" \
-e ES_JAVA_OPTS="-Xms64m -Xmx512m" \
-v /home/mydata/elasticsearch/config/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml \
-v /home/mydata/elasticsearch/data:/usr/share/elasticsearch/data \
-v  /home/mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
-d elasticsearch:7.12.1

設置開機啟動elasticsearch

docker update elasticsearch --restart=always

（4）啟動kibana：

docker run --name kibana -e ELASTICSEARCH_HOSTS=http://192.168.106.101:9200 -p 5601:5601 -d kibana:7.12.1

設置開機啟動kibana

docker update kibana  --restart=always

（5）測試

查看elasticsearch版本信息： http://192.168.6.128:9200/

{
    "name": "0adeb7852e00",
    "cluster_name": "elasticsearch",
    "cluster_uuid": "9gglpP0HTfyOTRAaSe2rIg",
    "version": {
        "number": "7.6.2",
        "build_flavor": "default",
        "build_type": "docker",
        "build_hash": "ef48eb35cf30adf4db14086e8aabd07ef6fb113f",
        "build_date": "2020-03-26T06:34:37.794943Z",
        "build_snapshot": false,
        "lucene_version": "8.4.0",
        "minimum_wire_compatibility_version": "6.8.0",
        "minimum_index_compatibility_version": "6.0.0-beta1"
    },
    "tagline": "You Know, for Search"
}

顯示elasticsearch 節點信息http://192.168.6.128:9200/_cat/nodes ，

127.0.0.1 76 95 1 0.26 1.40 1.22 dilm * 0adeb7852e00

訪問Kibana： http://192.168.6.128:5601/app/kibana

image-20200501192629304

2、初步檢索

1）_CAT

（1）GET/cat/nodes：查看所有節點

如：http://192.168.6.128:9200/_cat/nodes :

127.0.0.1 61 91 11 0.08 0.49 0.87 dilm * 0adeb7852e00

註：*表示集群中的主節點

（2）GET/cat/health：查看es健康狀況

如：http://192.168.6.128:9200/_cat/health

1588332616 11:30:16 elasticsearch green 1 1 3 3 0 0 0 0 - 100.0%

註：green表示健康值正常

（3）GET/_cat/master：查看主節點_信息

如： http://192.168.6.128:9200/_cat/master

vfpgxbusTC6-W3C2Np31EQ 127.0.0.1 127.0.0.1 0adeb7852e00

（4）GET/_cat/indicies：查看所有索引 ，等價於mysql資料庫的show databases;

如： http://192.168.6.128:9200/_cat/indices

green open .kibana_task_manager_1   KWLtjcKRRuaV9so_v15WYg 1 0 2 0 39.8kb 39.8kb
green open .apm-agent-configuration cuwCpJ5ER0OYsSgAJ7bVYA 1 0 0 0   283b   283b
green open .kibana_1                PqK_LdUYRpWMy4fK0tMSPw 1 0 7 0 31.2kb 31.2kb

2）索引一個文檔

保存一個數據，保存在哪個索引的哪個類型下，指定用那個唯一標識
PUT customer/external/1;在customer索引下的external類型下保存1號數據為

PUT customer/external/1


{
 "name":"John Doe"
}

PUT和POST都可以
POST新增。如果不指定id，會自動生成id。指定id就會修改這個數據，並新增版本號；
PUT可以新增也可以修改。PUT必須指定id；由於PUT需要指定id，我們一般用來做修改操作，不指定id會報錯。

創建數據成功後，顯示201 created表示插入記錄成功。

{
    "_index": "customer",
    "_type": "external",
    "_id": "1",
    "_version": 1,
    "result": "created",
    "_shards": {
        "total": 2,
        "successful": 1,
        "failed": 0
    },
    "_seq_no": 0,
    "_primary_term": 1
}

這些返回的JSON串的含義；這些帶有下劃線開頭的，稱為元數據，反映了當前的基本信息。

"_index": "customer" 表明該數據在哪個資料庫下；

"_type": "external" 表明該數據在哪個類型下；

"_id": "1" 表明被保存數據的id；

"_version": 1, 被保存數據的版本

"result": "created" 這裡是創建了一條數據，如果重新put一條數據，則該狀態會變為updated，並且版本號也會發生變化。

3）查看文檔

GET /customer/external/1

http://192.168.6.128:9200/customer/external/1

{
    "_index": "customer",//在哪個索引
    "_type": "external",//在哪個類型
    "_id": "1",//記錄id
    "_version": 3,//版本號
    "_seq_no": 6,//併發控制欄位，每次更新都會+1，用來做樂觀鎖
    "_primary_term": 1,//同上，主分片重新分配，如重啟，就會變化
    "found": true,
    "_source": {
        "name": "John Doe"
    }
}

通過“if_seq_no=1&if_primary_term=1 ”，當序列號匹配的時候，才進行修改，否則不修改。

實例：將id=1的數據更新為name=1，然後再次更新為name=2，起始_seq_no=6，_primary_term=1

（1）將name更新為1

http://192.168.6.128:9200/customer/external/1?if_seq_no=1&if_primary_term=1

（2）將name更新為2，更新過程中使用seq_no=6

http://192.168.6.128:9200/customer/external/1?if_seq_no=6&if_primary_term=1

出現更新錯誤。

（3）查詢新的數據

http://192.168.6.128:9200/customer/external/1

能夠看到_seq_no變為7。

（4）再次更新，更新成功

http://192.168.6.128:9200/customer/external/1?if_seq_no=7&if_primary_term=1

4）更新文檔

（1）POST更新文檔，帶有_update

http://192.168.6.128:9200/customer/external/1/_update

如果再次執行更新，則不執行任何操作，序列號也不發生變化

POST更新方式，會對比原來的數據，和原來的相同，則不執行任何操作（version和_seq_no）都不變。

（2）POST更新文檔，不帶_update

在更新過程中，重覆執行更新操作，數據也能夠更新成功，不會和原來的數據進行對比。

5）刪除文檔或索引

DELETE customer/external/1
DELETE customer

註：elasticsearch並沒有提供刪除類型的操作，只提供了刪除索引和文檔的操作。

實例：刪除id=1的數據，刪除後繼續查詢

實例：刪除整個costomer索引數據

刪除前，所有的索引

green  open .kibana_task_manager_1   KWLtjcKRRuaV9so_v15WYg 1 0 2 0 39.8kb 39.8kb
green  open .apm-agent-configuration cuwCpJ5ER0OYsSgAJ7bVYA 1 0 0 0   283b   283b
green  open .kibana_1                PqK_LdUYRpWMy4fK0tMSPw 1 0 7 0 31.2kb 31.2kb
yellow open customer                 nzDYCdnvQjSsapJrAIT8Zw 1 1 4 0  4.4kb  4.4kb

刪除“ customer ”索引

刪除後，所有的索引

green  open .kibana_task_manager_1   KWLtjcKRRuaV9so_v15WYg 1 0 2 0 39.8kb 39.8kb
green  open .apm-agent-configuration cuwCpJ5ER0OYsSgAJ7bVYA 1 0 0 0   283b   283b
green  open .kibana_1                PqK_LdUYRpWMy4fK0tMSPw 1 0 7 0 31.2kb 31.2kb

6）eleasticsearch的批量操作——bulk

語法格式：

{action:{metadata}}\n   //例如index保存記錄，update更新
{request body  }\n

{action:{metadata}}\n
{request body  }\n

這裡的批量操作，當發生某一條執行發生失敗時，其他的數據仍然能夠接著執行，也就是說彼此之間是獨立的。

bulk api以此按順序執行所有的action（動作）。如果一個單個的動作因任何原因失敗，它將繼續處理它後面剩餘的動作。當bulk api返回時，它將提供每個動作的狀態（與發送的順序相同），所以您可以檢查是否一個指定的動作是否失敗了。

實例1: 執行多條數據 (postman 報錯，在kibana中的Dev_tools中執行)

POST /customer/external/_bulk
{"index":{"_id":"1"}}
{"name":"John Doe"}
{"index":{"_id":"2"}}
{"name":"John Doe"}

執行結果

#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
  "took" : 491,
  "errors" : false,
  "items" : [
    {
      "index" : {
        "_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
      }
    },
    {
      "index" : {
        "_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
      }
    }
  ]
}

實例2：對於整個索引執行批量操作

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"}}

運行結果：

#! Deprecation: [types removal] Specifying types in bulk requests is deprecated.
{
  "took" : 608,
  "errors" : false,
  "items" : [
    {
      "delete" : {
        "_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" : {
        "_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" : {
        "_index" : "website",
        "_type" : "blog",
        "_id" : "MCOs0HEBHYK_MJXUyYIz",
        "_version" : 1,
        "result" : "created",
        "_shards" : {
          "total" : 2,
          "successful" : 1,
          "failed" : 0
        },
        "_seq_no" : 2,
        "_primary_term" : 1,
        "status" : 201
      }
    },
    {
      "update" : {
        "_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
      }
    }
  ]
}

7）樣本測試數據

準備了一份顧客銀行賬戶信息的虛構的JSON文檔樣本。每個文檔都有下列的schema（模式）。

{
	"account_number": 1,
	"balance": 39225,
	"firstname": "Amber",
	"lastname": "Duke",
	"age": 32,
	"gender": "M",
	"address": "880 Holmes Lane",
	"employer": "Pyrami",
	"email": "[email protected]",
	"city": "Brogan",
	"state": "IL"
}

https://github.com/elastic/elasticsearch/blob/master/docs/src/test/resources/accounts.json?raw=true ，導入測試數據，

POST bank/account/_bulk

3、檢索

1）search Api

ES支持兩種基本方式檢索；

  • 通過REST request uri 發送搜索參數 （uri +檢索參數）；
  • 通過REST request body 來發送它們（uri+請求體）；

uri+請求體進行檢索

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

HTTP客戶端工具（），get請求不能夠攜帶請求體，

GET bank/_search?q=*&sort=account_number:asc
//q=* 查詢所有，sort=account_number:asc 按照account_number進行asc升序排列sort

返回結果：

{
  "took" : 235,
  "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" : "[email protected]",
          "city" : "Hobucken",
          "state" : "CO"
        },
        "sort" : [
          0
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "1",
        "_score" : null,
        "_source" : {
          "account_number" : 1,
          "balance" : 39225,
          "firstname" : "Amber",
          "lastname" : "Duke",
          "age" : 32,
          "gender" : "M",
          "address" : "880 Holmes Lane",
          "employer" : "Pyrami",
          "email" : "[email protected]",
          "city" : "Brogan",
          "state" : "IL"
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "2",
        "_score" : null,
        "_source" : {
          "account_number" : 2,
          "balance" : 28838,
          "firstname" : "Roberta",
          "lastname" : "Bender",
          "age" : 22,
          "gender" : "F",
          "address" : "560 Kingsway Place",
          "employer" : "Chillium",
          "email" : "[email protected]",
          "city" : "Bennett",
          "state" : "LA"
        },
        "sort" : [
          2
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "3",
        "_score" : null,
        "_source" : {
          "account_number" : 3,
          "balance" : 44947,
          "firstname" : "Levine",
          "lastname" : "Burks",
          "age" : 26,
          "gender" : "F",
          "address" : "328 Wilson Avenue",
          "employer" : "Amtap",
          "email" : "[email protected]",
          "city" : "Cochranville",
          "state" : "HI"
        },
        "sort" : [
          3
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "4",
        "_score" : null,
        "_source" : {
          "account_number" : 4,
          "balance" : 27658,
          "firstname" : "Rodriquez",
          "lastname" : "Flores",
          "age" : 31,
          "gender" : "F",
          "address" : "986 Wyckoff Avenue",
          "employer" : "Tourmania",
          "email" : "[email protected]",
          "city" : "Eastvale",
          "state" : "HI"
        },
        "sort" : [
          4
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "5",
        "_score" : null,
        "_source" : {
          "account_number" : 5,
          "balance" : 29342,
          "firstname" : "Leola",
          "lastname" : "Stewart",
          "age" : 30,
          "gender" : "F",
          "address" : "311 Elm Place",
          "employer" : "Diginetic",
          "email" : "[email protected]",
          "city" : "Fairview",
          "state" : "NJ"
        },
        "sort" : [
          5
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "6",
        "_score" : null,
        "_source" : {
          "account_number" : 6,
          "balance" : 5686,
          "firstname" : "Hattie",
          "lastname" : "Bond",
          "age" : 36,
          "gender" : "M",
          "address" : "671 Bristol Street",
          "employer" : "Netagy",
          "email" : "[email protected]",
          "city" : "Dante",
          "state" : "TN"
        },
        "sort" : [
          6
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "7",
        "_score" : null,
        "_source" : {
          "account_number" : 7,
          "balance" : 39121,
          "firstname" : "Levy",
          "lastname" : "Richard",
          "age" : 22,
          "gender" : "M",
          "address" : "820 Logan Street",
          "employer" : "Teraprene",
          "email" : "[email protected]",
          "city" : "Shrewsbury",
          "state" : "MO"
        },
        "sort" : [
          7
        ]
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "8",
        "_score" : null,
        "_source" : {
          "account_number" : 8,
          "balance" : 48868,
          "firstname" : "Jan",
          "lastname" : "Burns",
          "age" : 35,
          "gender" : "M",
          "address" : "699 Visitation Place",
          "employer" : "Glasstep",
          "email" : "[email protected]",
          "city" : "Wakulla",
          "state" : "AZ"
        },
        "sort" : [
          8
        ]
      },
      {
        "_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" : "[email protected]",
          "city" : "Olney",
          "state" : "OH"
        },
        "sort" : [
          9
        ]
      }
    ]
  }
}

（1）只有9條數據，這是因為存在分頁查詢；

（2）詳細的欄位信息，參照： https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started-search.html

The response also provides the following information about the search request:

  • took – how long it took Elasticsearch to run the query, in milliseconds
  • timed_out – whether or not the search request timed out
  • _shards – how many shards were searched and a breakdown of how many shards succeeded, failed, or were skipped.
  • max_score – the score of the most relevant document found
  • hits.total.value - how many matching documents were found
  • hits.sort - the document’s sort position (when not sorting by relevance score)
  • hits._score - the document’s relevance score (not applicable when using match_all)

2）Query DSL

（1）基本語法格式

Elasticsearch提供了一個可以執行查詢的Json風格的DSL。這個被稱為Query DSL，該查詢語言非常全面。

一個查詢語句的典型結構

QUERY_NAME:{
   ARGUMENT:VALUE,
   ARGUMENT:VALUE,...
}

如果針對於某個欄位，那麼它的結構如下：

{
  QUERY_NAME:{
     FIELD_NAME:{
       ARGUMENT:VALUE,
       ARGUMENT:VALUE,...
      }   
   }
}

GET bank/_search
{
  "query": {
    "match_all": {}
  },
  "from": 0,
  "size": 5,
  "sort": [
    {
      "account_number": {
        "order": "desc"
      }
    }
  ]
}
//match_al查詢所有，從第0個數據拿5個數據

query定義如何查詢；

  • match_all查詢類型【代表查詢所有的所有】，es中可以在query中組合非常多的查詢類型完成複雜查詢；
  • 除了query參數之外，我們可也傳遞其他的參數以改變查詢結果，如sort，size；
  • from+size限定，完成分頁功能；
  • sort排序，多欄位排序，會在前序欄位相等時後續欄位內部排序，否則以前序為準；
（2）返回部分欄位
GET bank/_search
{
  "query": {
    "match_all": {}
  },
  "from": 0,
  "size": 5,
  "sort": [
    {
      "account_number": {
        "order": "desc"
      }
    }
  ],
  "_source": ["balance","firstname"]
  
}

查詢結果：

{
  "took" : 18,
  "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
        ]
      }
    ]
  }
}
（3）match匹配查詢
  • 基本類型（非字元串），"account_number": 20 可加可不加“ ” 不加就是精確匹配
GET bank/_search
{
  "query": {
    "match": {
      "account_number": "20"
    }
  }
}

match返回account_number=20的數據。

查詢結果：

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "20",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "[email protected]",
          "city" : "Ribera",
          "state" : "WA"
        }
      }
    ]
  }
}
  • 字元串，全文檢索“ ” 模糊查詢
GET bank/_search
{
  "query": {
    "match": {
      "address": "kings"
    }
  }
}

全文檢索，最終會按照評分進行排序，會對檢索條件進行分詞匹配。

查詢結果：

{
  "took" : 30,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 5.990829,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "20",
        "_score" : 5.990829,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "[email protected]",
          "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" : "[email protected]",
          "city" : "Hayden",
          "state" : "PA"
        }
      }
    ]
  }
}
（4） match_phrase [短句匹配]

將需要匹配的值當成一整個單詞（不分詞）進行檢索

GET bank/_search
{
  "query": {
    "match_phrase": {
      "address": "mill road"
    }
  }
}

查處address中包含mill_road的所有記錄，並給出相關性得分

查看結果：

{
  "took" : 32,
  "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",
          "employer" : "Pheast",
          "email" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      }
    ]
  }
}

match_phrase和match的區別，觀察如下實例：

match_phrase是做短語匹配

match是分詞匹配，例如990 Mill匹配含有990或者Mill的結果

GET bank/_search
{
  "query": {
    "match_phrase": {
      "address": "990 Mill"
    }
  }
}

查詢結果：

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 10.806405,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "970",
        "_score" : 10.806405,
        "_source" : {
          "account_number" : 970,
          "balance" : 19648,
          "firstname" : "Forbes",
          "lastname" : "Wallace",
          "age" : 28,
          "gender" : "M",
          "address" : "990 Mill Road",
          "employer" : "Pheast",
          "email" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      }
    ]
  }
}

使用match的keyword

GET bank/_search
{
  "query": {
    "match": {
      "address.keyword": "990 Mill"
    }
  }
}

查詢結果，一條也未匹配到

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

修改匹配條件為“990 Mill Road”

GET bank/_search
{
  "query": {
    "match": {
      "address.keyword": "990 Mill Road"
    }
  }
}

查詢出一條數據

{
  "took" : 1,
  "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" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      }
    ]
  }
}

文本欄位的匹配，使用keyword，匹配的條件就是要顯示欄位的全部值，要進行精確匹配的。

match_phrase是做短語匹配，只要文本中包含匹配條件既包含這個短語，就能匹配到。

（5）multi_math【多欄位匹配】
GET bank/_search
{
  "query": {
    "multi_match": {
      "query": "mill",
      "fields": [
        "state",
        "address"
      ]
    }
  }
}

state或者address中包含mill，並且在查詢過程中，會對於查詢條件進行分詞。

查詢結果：

{
  "took" : 28,
  "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" : "[email protected]",
          "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" : "[email protected]",
          "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" : "[email protected]",
          "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" : "[email protected]",
          "city" : "Movico",
          "state" : "MT"
        }
      }
    ]
  }
}
（6）bool用來做複合查詢

複合語句可以合併，任何其他查詢語句，包括符合語句。這也就意味著，複合語句之間
可以互相嵌套，可以表達非常複雜的邏輯。

must：必須達到must所列舉的所有條件

GET bank/_search
{
   "query":{
        "bool":{
             "must":[
              {"match":{"address":"mill"}},
              {"match":{"gender":"M"}}
             ]
         }
    }
}

must_not，必須不匹配must_not所列舉的所有條件。

should，應該滿足should所列舉的條件。

實例：查詢gender=m，並且address=mill的數據

GET bank/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "gender": "M"
          }
        },
        {
          "match": {
            "address": "mill"
          }
        }
      ]
    }
  }
}

查詢結果：

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 6.0824604,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "970",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 970,
          "balance" : 19648,
          "firstname" : "Forbes",
          "lastname" : "Wallace",
          "age" : 28,
          "gender" : "M",
          "address" : "990 Mill Road",
          "employer" : "Pheast",
          "email" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "136",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 136,
          "balance" : 45801,
          "firstname" : "Winnie",
          "lastname" : "Holland",
          "age" : 38,
          "gender" : "M",
          "address" : "198 Mill Lane",
          "employer" : "Neteria",
          "email" : "[email protected]",
          "city" : "Urie",
          "state" : "IL"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "345",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 345,
          "balance" : 9812,
          "firstname" : "Parker",
          "lastname" : "Hines",
          "age" : 38,
          "gender" : "M",
          "address" : "715 Mill Avenue",
          "employer" : "Baluba",
          "email" : "[email protected]",
          "city" : "Blackgum",
          "state" : "KY"
        }
      }
    ]
  }
}

must_not：必須不是指定的情況

實例：查詢gender=m，並且address=mill的數據，但是age不等於38的

GET bank/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "gender": "M"
          }
        },
        {
          "match": {
            "address": "mill"
          }
        }
      ],
      "must_not": [
        {
          "match": {
            "age": "38"
          }
        }
      ]
    }
  }

查詢結果：

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 6.0824604,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "970",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 970,
          "balance" : 19648,
          "firstname" : "Forbes",
          "lastname" : "Wallace",
          "age" : 28,
          "gender" : "M",
          "address" : "990 Mill Road",
          "employer" : "Pheast",
          "email" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      }
    ]
  }
}

should：應該達到should列舉的條件，如果到達會增加相關文檔的評分，並不會改變查詢的結果。如果query中只有should且只有一種匹配規則，那麼should的條件就會被作為預設匹配條件二區改變查詢結果。

實例：匹配lastName應該等於Wallace的數據

GET bank/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "gender": "M"
          }
        },
        {
          "match": {
            "address": "mill"
          }
        }
      ],
      "must_not": [
        {
          "match": {
            "age": "18"
          }
        }
      ],
      "should": [
        {
          "match": {
            "lastname": "Wallace"
          }
        }
      ]
    }
  }
}

查詢結果：

{
  "took" : 5,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 12.585751,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "970",
        "_score" : 12.585751,
        "_source" : {
          "account_number" : 970,
          "balance" : 19648,
          "firstname" : "Forbes",
          "lastname" : "Wallace",
          "age" : 28,
          "gender" : "M",
          "address" : "990 Mill Road",
          "employer" : "Pheast",
          "email" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "136",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 136,
          "balance" : 45801,
          "firstname" : "Winnie",
          "lastname" : "Holland",
          "age" : 38,
          "gender" : "M",
          "address" : "198 Mill Lane",
          "employer" : "Neteria",
          "email" : "[email protected]",
          "city" : "Urie",
          "state" : "IL"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "345",
        "_score" : 6.0824604,
        "_source" : {
          "account_number" : 345,
          "balance" : 9812,
          "firstname" : "Parker",
          "lastname" : "Hines",
          "age" : 38,
          "gender" : "M",
          "address" : "715 Mill Avenue",
          "employer" : "Baluba",
          "email" : "[email protected]",
          "city" : "Blackgum",
          "state" : "KY"
        }
      }
    ]
  }
}

能夠看到相關度越高，得分也越高。

（7）Filter【結果過濾】

並不是所有的查詢都需要產生分數，特別是哪些僅用於filtering過濾的文檔。為了不計算分數，elasticsearch會自動檢查場景並且優化查詢的執行。

GET bank/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "address": "mill"
          }
        }
      ],
      "filter": {
        "range": {
          "balance": {
            "gte": "10000",
            "lte": "20000"
          }
        }
      }
    }
  }
}

這裡先是查詢所有匹配address=mill的文檔，然後再根據10000<=balance<=20000進行過濾查詢結果

查詢結果：

{
  "took" : 2,
  "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" : "[email protected]",
          "city" : "Lopezo",
          "state" : "AK"
        }
      }
    ]
  }
}

Each must, should, and must_not element in a Boolean query is referred to as a query clause. How well a document meets the criteria in each must or should clause contributes to the document’s relevance score. The higher the score, the better the document matches your search criteria. By default, Elasticsearch returns documents ranked by these relevance scores.

在boolean查詢中，must, should 和must_not 元素都被稱為查詢子句 。 文檔是否符合每個“must”或“should”子句中的標準，決定了文檔的“相關性得分”。 得分越高，文檔越符合您的搜索條件。 預設情況下，Elasticsearch返回根據這些相關性得分排序的文檔。

The criteria in a must_not clause is treated as a filter. It affects whether or not the document is included in the results, but does not contribute to how documents are scored. You can also explicitly specify arbitrary filters to include or exclude documents based on structured data.

“must_not”子句中的條件被視為“過濾器”。 它影響文檔是否包含在結果中， 但不影響文檔的評分方式。 還可以顯式地指定任意過濾器來包含或排除基於結構化數據的文檔。

filter在使用過程中，並不會計算相關性得分_score：

GET bank/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "address": "mill"
          }
        }
      ],
      "filter": {
        "range": {
          "balance": {
            "gte": "10000",
            "lte": "20000"
          }
        }
      }
    }
  }
}
//gte:>=  lte:<=

查詢結果：

{
  "took" : 1,
  "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,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "[email protected]",
          "city" : "Ribera",
          "state" : "WA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "37",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 37,
          "balance" : 18612,
          "firstname" : "Mcgee",
          "lastname" : "Mooney",
          "age" : 39,
          "gender" : "M",
          "address" : "826 Fillmore Place",
          "employer" : "Reversus",
          "email" : "[email protected]",
          "city" : "Tooleville",
          "state" : "OK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "51",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 51,
          "balance" : 14097,
          "firstname" : "Burton",
          "lastname" : "Meyers",
          "age" : 31,
          "gender" : "F",
          "address" : "334 River Street",
          "employer" : "Bezal",
          "email" : "[email protected]",
          "city" : "Jacksonburg",
          "state" : "MO"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "56",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 56,
          "balance" : 14992,
          "firstname" : "Josie",
          "lastname" : "Nelson",
          "age" : 32,
          "gender" : "M",
          "address" : "857 Tabor Court",
          "employer" : "Emtrac",
          "email" : "[email protected]",
          "city" : "Sunnyside",
          "state" : "UT"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "121",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 121,
          "balance" : 19594,
          "firstname" : "Acevedo",
          "lastname" : "Dorsey",
          "age" : 32,
          "gender" : "M",
          "address" : "479 Nova Court",
          "employer" : "Netropic",
          "email" : "[email protected]",
          "city" : "Islandia",
          "state" : "CT"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "176",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 176,
          "balance" : 18607,
          "firstname" : "Kemp",
          "lastname" : "Walters",
          "age" : 28,
          "gender" : "F",
          "address" : "906 Howard Avenue",
          "employer" : "Eyewax",
          "email" : "[email protected]",
          "city" : "Why",
          "state" : "KY"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "183",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 183,
          "balance" : 14223,
          "firstname" : "Hudson",
          "lastname" : "English",
          "age" : 26,
          "gender" : "F",
          "address" : "823 Herkimer Place",
          "employer" : "Xinware",
          "email" : "[email protected]",
          "city" : "Robbins",
          "state" : "ND"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "222",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 222,
          "balance" : 14764,
          "firstname" : "Rachelle",
          "lastname" : "Rice",
          "age" : 36,
          "gender" : "M",
          "address" : "333 Narrows Avenue",
          "employer" : "Enaut",
          "email" : "[email protected]",
          "city" : "Wright",
          "state" : "AZ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "227",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 227,
          "balance" : 19780,
          "firstname" : "Coleman",
          "lastname" : "Berg",
          "age" : 22,
          "gender" : "M",
          "address" : "776 Little Street",
          "employer" : "Exoteric",
          "email" : "[email protected]",
          "city" : "Eagleville",
          "state" : "WV"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "272",
        "_score" : 0.0,
        "_source" : {
          "account_number" : 272,
          "balance" : 19253,
          "firstname" : "Lilly",
          "lastname" : "Morgan",
          "age" : 25,
          "gender" : "F",
          "address" : "689 Fleet Street",
          "employer" : "Biolive",
          "email" : "[email protected]",
          "city" : "Sunbury",
          "state" : "OH"
        }
      }
    ]
  }
}

能看到所有文檔的 "_score" : 0.0。

（8）term

和match一樣。匹配某個屬性的值。全文檢索欄位用match，其他非text欄位匹配用term。

Avoid using the term query for text fields.

避免對文本欄位使用“term”查詢

By default, Elasticsearch changes the values of text fields as part of analysis. This can make finding exact matches for text field values difficult.

預設情況下，Elasticsearch作為analysis的一部分更改' text '欄位的值。這使得為“text”欄位值尋找精確匹配變得困難。

To search text field values, use the match.

要搜索“text”欄位值，請使用匹配。

https://www.elastic.co/guide/en/elasticsearch/reference/7.6/query-dsl-term-query.html

使用term匹配查詢

GET bank/_search
{
  "query": {
    "term": {
      "age": "28"
    }
  }
}

如果是text則查不到：

GET bank/_search
{
  "query": {
    "term": {
      "gender" : "F"
    }
  }
}


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

一條也沒有匹配到

而更換為match匹配時，能夠匹配到32個文檔

image-20200502120921830

也就是說，全文檢索欄位用match，其他非text欄位匹配用term。

（9）Aggregation（執行聚合）

聚合提供了從數據中分組和提取數據的能力。最簡單的聚合方法大致等於SQL Group by和SQL聚合函數。在elasticsearch中，執行搜索返回this（命中結果），並且同時返回聚合結果，把以響應中的所有hits（命中結果）分隔開的能力。這是非常強大且有效的，你可以執行查詢和多個聚合，並且在一次使用中得到各自的（任何一個的）返回結果，使用一次簡潔和簡化的API啦避免網路往返。

"size":0

size:0不顯示搜索數據
aggs：執行聚合。聚合語法如下：

"aggs":{
    "aggs_name這次聚合的名字，方便展示在結果集中":{
        "AGG_TYPE聚合的類型(avg,term,terms)":{}
     }
}，

搜索address中包含mill的所有人的年齡分佈以及平均年齡，但不顯示這些人的詳情

GET bank/_search
{
  "query": {
    "match": {
      "address": "Mill"
    }
  },
  "aggs": {
    "ageAgg": {
      "terms": {
        "field": "age",
        "size": 10
      }
    },
    "ageAvg": {
      "avg": {
        "field": "age"
      }
    },
    "balanceAvg": {
      "avg": {
        "field": "balance"
      }
    }
  },
  "size": 0
}
//ageAgg:聚合名字  terms：聚合類型  "field": "age":按照age欄位聚合  size:10：取出前十種age
//avg：平均值聚合類型
//不顯示這些人的詳情，只看聚合結果

查詢結果：

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 4,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "ageAgg" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : 38,
          "doc_count" : 2
        },
        {
          "key" : 28,
          "doc_count" : 1
        },
        {
          "key" : 32,
          "doc_count" : 1
        }
      ]
    },
    "ageAvg" : {
      "value" : 34.0
    },
    "balanceAvg" : {
      "value" : 25208.0
    }
  }
}

複雜：
按照年齡聚合，並且求這些年齡段的這些人的平均薪資

GET bank/_search
{
  "query": {
    "match_all": {}
  },
  "aggs": {
    "ageAgg": {
      "terms": {
        "field": "age",
        "size": 100
      },
      "aggs": {
        "ageAvg": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  },
  "size": 0
}

輸出結果：

{
  "took" : 49,
  "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" : 39,
          "doc_count" : 60,
          "ageAvg" : {
            "value" : 25269.583333333332
          }
        },
        {
          "key" : 26,
          "doc_count" : 59,
          "ageAvg" : {
            "value" : 23194.813559322032
          }
        },
        {
          "key" : 32,
          "doc_count" : 52,
          "ageAvg" : {
            "value" : 23951.346153846152
          }
        },
        {
          "key" : 35,
          "doc_count" : 52,
          "ageAvg" : {
            "value" : 22136.69230769231
          }
        },
        {
          "key" : 36,
          "doc_count" : 52,
          "ageAvg" : {
            "value" : 22174.71153846154
          }
        },
        {
          "key" : 22,
          "doc_count" : 51,
          "ageAvg" : {
            "value" : 24731.07843137255
          }
        },
        {
          "key" : 28,
          "doc_count" : 51,
          "ageAvg" : {
            "value" : 28273.882352941175
          }
        },
        {
          "key" : 33,
          "doc_count" : 50,
          "ageAvg" : {
            "value" : 25093.94
          }
        },
        {
          "key" : 34,
          "doc_count" : 49,
          "ageAvg" : {
            "value" : 26809.95918367347
          }
        },
        {
          "key" : 30,
          "doc_count" : 47,
          "ageAvg" : {
            "value" : 22841.106382978724
          }
        },
        {
          "key" : 21,
          "doc_count" : 46,
          "ageAvg" : {
            "value" : 26981.434782608696
          }
        },
        {
          "key" : 40,
          "doc_count" : 45,
          "ageAvg" : {
            "value" : 27183.17777777778
          }
        },
        {
          "key" : 20,
          "doc_count" : 44,
          "ageAvg" : {
            "value" : 27741.227272727272
          }
        },
        {
          "key" : 23,
          "doc_count" : 42,
          "ageAvg" : {
            "value" : 27314.214285714286
          }
        },
        {
          "key" : 24,
          "doc_count" : 42,
          "ageAvg" : {
            "value" : 28519.04761904762
          }
        },
        {
          "key" : 25,
          "doc_count" : 42,
          "ageAvg" : {
            "v
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