一、ElasticSearch 簡介
1、簡介
ElasticSearch 是一個基於 Lucene 的搜索服務器。它提供了一個分佈式多員工能力的全文搜索引擎，基於 RESTful web 接口。Elasticsearch 是用 Java 語言開發的，並作為 Apache 許可條款下的開放源碼發佈，是一種流行的企業級搜索引擎。
ElasticSearch 用於雲計算中，能夠達到實時搜索，穩定，可靠，快速，安裝使用方便。
2、特性
- 分佈式的文檔存儲引擎
- 分佈式的搜索引擎和分析引擎
- 分佈式，支持PB級數據
3、使用場景
- 搜索領域：如百度、谷歌，全文檢索等。
- 門戶網站：訪問統計、文章點贊、留言評論等。
- 廣告推廣：記錄員工行為數據、消費趨勢、員工群體進行定製推廣等。
- 信息採集：記錄應用的埋點數據、訪問日誌數據等，方便大數據進行分析。
二、ElasticSearch 基礎概念
1、ElaticSearch 和 DB 的關係
在 Elasticsearch 中，文檔歸屬於一種類型 type，而這些類型存在於索引 index 中，我們可以列一些簡單的不同點，來類比傳統關係型數據庫：
- Relational DB -> Databases -> Tables -> Rows -> Columns
- Elasticsearch -> Indices -> Types -> Documents -> Fields
Elasticsearch 集群可以包含多個索引 indices，每一個索引可以包含多個類型 types，每一個類型包含多個文檔 documents，然後每個文檔包含多個字段 Fields。而在 DB 中可以有多個數據庫 Databases，每個庫中可以有多張表 Tables，沒個表中又包含多行Rows，每行包含多列Columns。
2、索引
索引基本概念（indices）：
索引是含義相同屬性的文檔集合，是 ElasticSearch 的一個邏輯存儲，可以理解為關係型數據庫中的數據庫，ElasticSearch 可以把索引數據存放到一臺服務器上，也可以 sharding 後存到多臺服務器上，每個索引有一個或多個分片，每個分片可以有多個副本。
索引類型（index_type）：
索引可以定義一個或多個類型，文檔必須屬於一個類型。在 ElasticSearch 中，一個索引對象可以存儲多個不同用途的對象，通過索引類型可以區分單個索引中的不同對象，可以理解為關係型數據庫中的表。每個索引類型可以有不同的結構，但是不同的索引類型不能為相同的屬性設置不同的類型。
3、文檔
文檔（document）：
文檔是可以被索引的基本數據單位。存儲在 ElasticSearch 中的主要實體叫文檔 document，可以理解為關係型數據庫中表的一行記錄。每個文檔由多個字段構成，ElasticSearch 是一個非結構化的數據庫，每個文檔可以有不同的字段，並且有一個唯一的標識符。
4、映射
映射（mapping）:
ElasticSearch 的 Mapping 非常類似於靜態語言中的數據類型：聲明一個變量為 int 類型的變量，以後這個變量都只能存儲 int 類型的數據。同樣的，一個 number 類型的 mapping 字段只能存儲 number 類型的數據。
同語言的數據類型相比，Mapping 還有一些其他的含義，Mapping 不僅告訴 ElasticSearch 一個 Field 中是什麼類型的值， 它還告訴 ElasticSearch 如何索引數據以及數據是否能被搜索到。
ElaticSearch 默認是動態創建索引和索引類型的 Mapping 的。這就相當於無需定義 Solr 中的 Schema，無需指定各個字段的索引規則就可以索引文件，很方便。但有時方便就代表著不靈活。比如，ElasticSearch 默認一個字段是要做分詞的，但我們有時要搜索匹配整個字段卻不行。如有統計工作要記錄每個城市出現的次數。對於 name 字段，若記錄 new york 文本，ElasticSearch 可能會把它拆分成 new 和 york 這兩個詞，分別計算這個兩個單詞的次數，而不是我們期望的 new york。
三、SpringBoot 項目引入 ElasticSearch 依賴
下面介紹下 SpringBoot 如何通過 elasticsearch-rest-high-level-client 工具操作 ElasticSearch，這裡需要說一下，為什麼沒有使用 Spring 家族封裝的 spring-data-elasticsearch。
主要原因是靈活性和更新速度，Spring 將 ElasticSearch 過度封裝，讓開發者很難跟 ES 的 DSL 查詢語句進行關聯。再者就是更新速度，ES 的更新速度是非常快，但是 spring-data-elasticsearch 更新速度比較緩慢。
由於上面兩點，所以選擇了官方推出的 Java 客戶端 elasticsearch-rest-high-level-client，它的代碼寫法跟 DSL 語句很相似，懂 ES 查詢的使用其上手很快。
1、Maven 引入相關依賴
- lombok 工具依賴。
- fastjson：用於將 JSON 轉換對象的依賴。
- spring-boot-starter-web的 Web 依賴。
- elasticsearch：ElasticSearch：依賴，需要和 ES 版本保持一致。
- elasticsearch-rest-high-level-client：用於操作 ES 的 Java 客戶端。
<code>
<project> xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelversion>4.0.0/<modelversion>
<parent>
<groupid>org.springframework.boot/<groupid>
<artifactid>spring-boot-starter-parent/<artifactid>
<version>2.2.4.RELEASE/<version>
<relativepath>
/<parent>
<groupid>club.mydlq/<groupid>
<artifactid>springboot-elasticsearch-example/<artifactid>
<version>0.0.1-SNAPSHOT/<version>
<name>springboot-elasticsearch-example/<name>
<description>Demo project for Spring Boot ElasticSearch/<description>
<properties>
<java.version>1.8/<java.version>
/<properties>
<dependencies>
<dependency>
<groupid>org.springframework.boot/<groupid>
<artifactid>spring-boot-starter-web/<artifactid>
/<dependency>
<dependency>
<groupid>org.projectlombok/<groupid>
<artifactid>lombok/<artifactid>
<optional>true/<optional>
/<dependency>
<dependency>
<groupid>com.alibaba/<groupid>
<artifactid>fastjson/<artifactid>
<version>1.2.61/<version>
/<dependency>
<dependency>
<groupid>org.elasticsearch.client/<groupid>
<artifactid>elasticsearch-rest-high-level-client/<artifactid>
<version>6.5.4/<version>
/<dependency>
<dependency>
<groupid>org.elasticsearch/<groupid>
<artifactid>elasticsearch/<artifactid>
<version>6.5.4/<version>
/<dependency>
/<dependencies>
<build>
<plugins>
<plugin>
<groupid>org.springframework.boot/<groupid>
<artifactid>spring-boot-maven-plugin/<artifactid>
/<plugin>
/<plugins>
/<build>
/<project>
/<code>
2、ElasticSearch 連接配置
(1)、application.yml 配置文件
為了方便更改連接 ES 的連接配置，所以我們將配置信息放置於 application.yaml 中：
<code>#base
server:
port: 8080
#spring
spring:
application:
name: springboot-elasticsearch-example
#elasticsearch
elasticsearch:
schema: http
address: 127.0.0.1:9200
connectTimeout: 5000
socketTimeout: 5000
connectionRequestTimeout: 5000
maxConnectNum: 100
maxConnectPerRoute: 100
/<code>
(2)、java 連接配置類
這裡需要寫一個 Java 配置類讀取 application 中的配置信息：
<code>
/**
* ElasticSearch 配置
*/
@Configuration
public class ElasticSearchConfig {
/** 協議 */
@Value("${elasticsearch.schema:http}")
private String schema;
/** 集群地址，如果有多個用“,”隔開 */
@Value("${elasticsearch.address}")
private String address;
/** 連接超時時間 */
@Value("${elasticsearch.connectTimeout:5000}")
private int connectTimeout;
/** Socket 連接超時時間 */
@Value("${elasticsearch.socketTimeout:10000}")
private int socketTimeout;
/** 獲取連接的超時時間 */
@Value("${elasticsearch.connectionRequestTimeout:5000}")
private int connectionRequestTimeout;
/** 最大連接數 */
@Value("${elasticsearch.maxConnectNum:100}")
private int maxConnectNum;
/** 最大路由連接數 */
@Value("${elasticsearch.maxConnectPerRoute:100}")
private int maxConnectPerRoute;
@Bean
public RestHighLevelClient restHighLevelClient() {
// 拆分地址
List<httphost> hostLists = new ArrayList<>();
String[] hostList = address.split(",");
for (String addr : hostList) {
String host = addr.split(":")[0];
String port = addr.split(":")[1];
hostLists.add(new HttpHost(host, Integer.parseInt(port), schema));
}
// 轉換成 HttpHost 數組
HttpHost[] httpHost = hostLists.toArray(new HttpHost[]{});
// 構建連接對象
RestClientBuilder builder = RestClient.builder(httpHost);
// 異步連接延時配置
builder.setRequestConfigCallback(requestConfigBuilder -> {
requestConfigBuilder.setConnectTimeout(connectTimeout);
requestConfigBuilder.setSocketTimeout(socketTimeout);
requestConfigBuilder.setConnectionRequestTimeout(connectionRequestTimeout);
return requestConfigBuilder;
});
// 異步連接數配置
builder.setHttpClientConfigCallback(httpClientBuilder -> {
httpClientBuilder.setMaxConnTotal(maxConnectNum);
httpClientBuilder.setMaxConnPerRoute(maxConnectPerRoute);
return httpClientBuilder;
});
return new RestHighLevelClient(builder);
}
}
/<httphost>/<code>
四、索引操作示例
這裡示例會指出通過 Kibana 工具操作與對應的 Java 代碼操作的兩個示例。
1、Restful 操作示例
創建索引
創建名為 mydlq-user 的索引與對應 Mapping。
<code>PUT /mydlq-user
{
"mappings": {
"doc": {
"dynamic": true,
"properties": {
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"address": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"remark": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"age": {
"type": "integer"
},
"salary": {
"type": "float"
},
"birthDate": {
"type": "date",
"format": "yyyy-MM-dd"
},
"createTime": {
"type": "date"
}
}
}
}
}
/<code>
刪除索引
刪除 mydlq-user 索引。
<code>DELETE /mydlq-user
/<code>
2、Java 代碼示例
<code>
@Slf4j
@Service
public class IndexService2 {
@Autowired
private RestHighLevelClient restHighLevelClient;
/**
* 創建索引
*/
public void createIndex() {
try {
// 創建 Mapping
XContentBuilder mapping = XContentFactory.jsonBuilder()
.startObject()
.field("dynamic", true)
.startObject("properties")
.startObject("name")
.field("type","text")
.startObject("fields")
.startObject("keyword")
.field("type","keyword")
.endObject()
.endObject()
.endObject()
.startObject("address")
.field("type","text")
.startObject("fields")
.startObject("keyword")
.field("type","keyword")
.endObject()
.endObject()
.endObject()
.startObject("remark")
.field("type","text")
.startObject("fields")
.startObject("keyword")
.field("type","keyword")
.endObject()
.endObject()
.endObject()
.startObject("age")
.field("type","integer")
.endObject()
.startObject("salary")
.field("type","float")
.endObject()
.startObject("birthDate")
.field("type","date")
.field("format", "yyyy-MM-dd")
.endObject()
.startObject("createTime")
.field("type","date")
.endObject()
.endObject()
.endObject();
// 創建索引配置信息，配置
Settings settings = Settings.builder()
.put("index.number_of_shards", 1)
.put("index.number_of_replicas", 0)
.build();
// 新建創建索引請求對象，然後設置索引類型（ES 7.0 將不存在索引類型）和 mapping 與 index 配置
CreateIndexRequest request = new CreateIndexRequest("mydlq-user", settings);
request.mapping("doc", mapping);
// RestHighLevelClient 執行創建索引
CreateIndexResponse createIndexResponse = restHighLevelClient.indices().create(request, RequestOptions.DEFAULT);
// 判斷是否創建成功
boolean isCreated = createIndexResponse.isAcknowledged();
log.info("是否創建成功：{}", isCreated);
} catch (IOException e) {
log.error("", e);
}
}
/**
* 刪除索引
*/
public void deleteIndex() {
try {
// 新建刪除索引請求對象
DeleteIndexRequest request = new DeleteIndexRequest("mydlq-user");
// 執行刪除索引
AcknowledgedResponse acknowledgedResponse = restHighLevelClient.indices().delete(request, RequestOptions.DEFAULT);
// 判斷是否刪除成功
boolean siDeleted = acknowledgedResponse.isAcknowledged();
log.info("是否刪除成功：{}", siDeleted);
} catch (IOException e) {
log.error("", e);
}
}
}
/<code>
五、文檔操作示例
1、Restful 操作示例
增加文檔信息
在索引 mydlq-user 中增加一條文檔信息。
<code>POST /mydlq-user/doc
{
"address": "北京市",
"age": 29,
"birthDate": "1990-01-10",
"createTime": 1579530727699,
"name": "張三",
"remark": "來自北京市的張先生",
"salary": 100
}
/<code>
獲取文檔信息
獲取 mydlq-user 的索引 id=1 的文檔信息。
<code>GET /mydlq-user/doc/1
/<code>
更新文檔信息
更新之前創建的 id=1 的文檔信息。
<code>PUT /mydlq-user/doc/1
{
"address": "北京市海淀區",
"age": 29,
"birthDate": "1990-01-10",
"createTime": 1579530727699,
"name": "張三",
"remark": "來自北京市的張先生",
"salary": 100
}
/<code>
刪除文檔信息
刪除之前創建的 id=1 的文檔信息。
<code>DELETE /mydlq-user/doc/1
/<code>
2、Java 代碼示例
<code>
@Slf4j
@Service
public class IndexService {
@Autowired
private RestHighLevelClient restHighLevelClient;
/**
* 增加文檔信息
*/
public void addDocument() {
try {
// 創建索引請求對象
IndexRequest indexRequest = new IndexRequest("mydlq-user", "doc", "1");
// 創建員工信息
UserInfo userInfo = new UserInfo();
userInfo.setName("張三");
userInfo.setAge(29);
userInfo.setSalary(100.00f);
userInfo.setAddress("北京市");
userInfo.setRemark("來自北京市的張先生");
userInfo.setCreateTime(new Date());
userInfo.setBirthDate("1990-01-10");
// 將對象轉換為 byte 數組
byte[] json = JSON.toJSONBytes(userInfo);
// 設置文檔內容
indexRequest.source(json, XContentType.JSON);
// 執行增加文檔
IndexResponse response = restHighLevelClient.index(indexRequest, RequestOptions.DEFAULT);
log.info("創建狀態：{}", response.status());
} catch (Exception e) {
log.error("", e);
}
}
/**
* 獲取文檔信息
*/
public void getDocument() {
try {
// 獲取請求對象
GetRequest getRequest = new GetRequest("mydlq-user", "doc", "1");
// 獲取文檔信息
GetResponse getResponse = restHighLevelClient.get(getRequest, RequestOptions.DEFAULT);
// 將 JSON 轉換成對象
if (getResponse.isExists()) {
UserInfo userInfo = JSON.parseObject(getResponse.getSourceAsBytes(), UserInfo.class);
log.info("員工信息：{}", userInfo);
}
} catch (IOException e) {
log.error("", e);
}
}
/**
* 更新文檔信息
*/
public void updateDocument() {
try {
// 創建索引請求對象
UpdateRequest updateRequest = new UpdateRequest("mydlq-user", "doc", "1");
// 設置員工更新信息
UserInfo userInfo = new UserInfo();
userInfo.setSalary(200.00f);
userInfo.setAddress("北京市海淀區");
// 將對象轉換為 byte 數組
byte[] json = JSON.toJSONBytes(userInfo);
// 設置更新文檔內容
updateRequest.doc(json, XContentType.JSON);
// 執行更新文檔
UpdateResponse response = restHighLevelClient.update(updateRequest, RequestOptions.DEFAULT);
log.info("創建狀態：{}", response.status());
} catch (Exception e) {
log.error("", e);
}
}
/**
* 刪除文檔信息
*/
public void deleteDocument() {
try {
// 創建刪除請求對象
DeleteRequest deleteRequest = new DeleteRequest("mydlq-user", "doc", "1");
// 執行刪除文檔
DeleteResponse response = restHighLevelClient.delete(deleteRequest, RequestOptions.DEFAULT);
log.info("刪除狀態：{}", response.status());
} catch (IOException e) {
log.error("", e);
}
}
}
/<code>
六、插入初始化數據
執行查詢示例前，先往索引中插入一批數據：
1、單條插入
POST mydlq-user/_doc
<code>{"name":"零零","address":"北京市豐臺區","remark":"低層員工","age":29,"salary":3000,"birthDate":"1990-11-11","createTime":"2019-11-11T08:18:00.000Z"}
/<code>2、批量插入
POST _bulk
<code>{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"劉一","address":"北京市豐臺區","remark":"低層員工","age":30,"salary":3000,"birthDate":"1989-11-11","createTime":"2019-03-15T08:18:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"陳二","address":"北京市昌平區","remark":"中層員工","age":27,"salary":7900,"birthDate":"1992-01-25","createTime":"2019-11-08T11:15:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"張三","address":"北京市房山區","remark":"中層員工","age":28,"salary":8800,"birthDate":"1991-10-05","createTime":"2019-07-22T13:22:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"李四","address":"北京市大興區","remark":"高層員工","age":26,"salary":9000,"birthDate":"1993-08-18","createTime":"2019-10-17T15:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"王五","address":"北京市密雲區","remark":"低層員工","age":31,"salary":4800,"birthDate":"1988-07-20","createTime":"2019-05-29T09:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"趙六","address":"北京市通州區","remark":"中層員工","age":32,"salary":6500,"birthDate":"1987-06-02","createTime":"2019-12-10T18:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"孫七","address":"北京市朝陽區","remark":"中層員工","age":33,"salary":7000,"birthDate":"1986-04-15","createTime":"2019-06-06T13:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"周八","address":"北京市西城區","remark":"低層員工","age":32,"salary":5000,"birthDate":"1987-09-26","createTime":"2019-01-26T14:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"吳九","address":"北京市海淀區","remark":"高層員工","age":30,"salary":11000,"birthDate":"1989-11-25","createTime":"2019-09-07T13:34:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"鄭十","address":"北京市東城區","remark":"低層員工","age":29,"salary":5000,"birthDate":"1990-12-25","createTime":"2019-03-06T12:08:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"蕭十一","address":"北京市平谷區","remark":"低層員工","age":29,"salary":3300,"birthDate":"1990-11-11","createTime":"2019-03-10T08:17:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"曹十二","address":"北京市懷柔區","remark":"中層員工","age":27,"salary":6800,"birthDate":"1992-01-25","createTime":"2019-12-03T11:09:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"吳十三","address":"北京市延慶區","remark":"中層員工","age":25,"salary":7000,"birthDate":"1994-10-05","createTime":"2019-07-27T14:22:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"馮十四","address":"北京市密雲區","remark":"低層員工","age":25,"salary":3000,"birthDate":"1994-08-18","createTime":"2019-04-22T15:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"蔣十五","address":"北京市通州區","remark":"低層員工","age":31,"salary":2800,"birthDate":"1988-07-20","createTime":"2019-06-13T10:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"苗十六","address":"北京市門頭溝區","remark":"高層員工","age":32,"salary":11500,"birthDate":"1987-06-02","createTime":"2019-11-11T18:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"魯十七","address":"北京市石景山區","remark":"高員工","age":33,"salary":9500,"birthDate":"1986-04-15","createTime":"2019-06-06T14:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"沈十八","address":"北京市朝陽區","remark":"中層員工","age":31,"salary":8300,"birthDate":"1988-09-26","createTime":"2019-09-25T14:00:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"呂十九","address":"北京市西城區","remark":"低層員工","age":31,"salary":4500,"birthDate":"1988-11-25","createTime":"2019-09-22T13:34:00.000Z"}
{"index":{"_index":"mydlq-user","_type":"doc"}}
{"name":"丁二十","address":"北京市東城區","remark":"低層員工","age":33,"salary":2100,"birthDate":"1986-12-25","createTime":"2019-03-07T12:08:00.000Z"}
/<code>3、查詢數據
插入完成後再查詢數據，查看之前插入的數據是否存在：
<code>GET mydlq-user/_search
/<code>
執行後得到下面記錄:
<code>{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 20,
"max_score": 1,
"hits": [
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "BeN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "劉一",
"address": "北京市豐臺區",
"remark": "低層員工",
"age": 30,
"salary": 3000,
"birthDate": "1989-11-11",
"createTime": "2019-03-15T08:18:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "BuN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "陳二",
"address": "北京市昌平區",
"remark": "中層員工",
"age": 27,
"salary": 7900,
"birthDate": "1992-01-25",
"createTime": "2019-11-08T11:15:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "B-N0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "張三",
"address": "北京市房山區",
"remark": "中層員工",
"age": 28,
"salary": 8800,
"birthDate": "1991-10-05",
"createTime": "2019-07-22T13:22:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "CON0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "李四",
"address": "北京市大興區",
"remark": "高層員工",
"age": 26,
"salary": 9000,
"birthDate": "1993-08-18",
"createTime": "2019-10-17T15:00:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "CeN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "王五",
"address": "北京市密雲區",
"remark": "低層員工",
"age": 31,
"salary": 4800,
"birthDate": "1988-07-20",
"createTime": "2019-05-29T09:00:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "CuN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "趙六",
"address": "北京市通州區",
"remark": "中層員工",
"age": 32,
"salary": 6500,
"birthDate": "1987-06-02",
"createTime": "2019-12-10T18:00:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "C-N0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "孫七",
"address": "北京市朝陽區",
"remark": "中層員工",
"age": 33,
"salary": 7000,
"birthDate": "1986-04-15",
"createTime": "2019-06-06T13:00:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "DON0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "周八",
"address": "北京市西城區",
"remark": "低層員工",
"age": 32,
"salary": 5000,
"birthDate": "1987-09-26",
"createTime": "2019-01-26T14:00:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "DeN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "吳九",
"address": "北京市海淀區",
"remark": "高層員工",
"age": 30,
"salary": 11000,
"birthDate": "1989-11-25",
"createTime": "2019-09-07T13:34:00.000Z"
}
},
{
"_index": "mydlq-user",
"_type": "_doc",
"_id": "DuN0BW8B7BNodGwRFTRj",
"_score": 1,
"_source": {
"name": "鄭十",
"address": "北京市東城區",
"remark": "低層員工",
"age": 29,
"salary": 5000,
"birthDate": "1990-12-25",
"createTime": "2019-03-06T12:08:00.000Z"
}
}
]
}
}
/<code>由於篇幅，請看下半部分
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