前言:Elasticsearch 是一個開源的搜索引擎,建立在一個全文搜索引擎庫 Apache Lucene? 基礎之上, Lucene 可以說是當下最先進、高性能、全功能的搜索引擎庫—?無論是開源還是私有,
但是 Lucene 僅僅只是一個庫,為了充分發揮其功能,你需要使用 Java 并將 Lucene 直接集成到應用程式中, 更糟糕的是,您可能需要獲得資訊檢索學位才能了解其作業原理,Lucene 非常 復雜,
Elasticsearch 也是使用 Java 撰寫的,它的內部使用 Lucene 做索引與搜索,但是它的目的是使全文檢索變得簡單, 通過隱藏 Lucene 的復雜性,取而代之的提供一套簡單一致的 RESTful API,
然而,Elasticsearch 不僅僅是 Lucene,并且也不僅僅只是一個全文搜索引擎, 它可以被下面這樣準確的形容:
一個分布式的實時檔案存盤,每個欄位 可以被索引與搜索
一個分布式實時分析搜索引擎
能勝任上百個服務節點的擴展,并支持 PB 級別的結構化或者非結構化資料
Elasticsearch 將所有的功能打包成一個單獨的服務,這樣你可以通程序式與它提供的簡單的 RESTful API 進行通信, 可以使用自己喜歡的編程語言充當 web 客戶端,甚至可以使用命令列(去充當這個客戶端),
就 Elasticsearch 而言,起步很簡單,對于初學者來說,它預設了一些適當的默認值,并隱藏了復雜的搜索理論知識, 它 開箱即用 ,只需最少的理解,你很快就能具有生產力,
中文檔案官網:https://www.elastic.co/guide/cn/elasticsearch/guide/current/routing-value.html
搭建使用:Elasticsearch的鏡像用7.9.0
docker pull elasticsearch:7.9.0
修改es單個節點 運行大小 啟動完成
docker run -e ES_JAVA_OPTS="-Xms256m -Xmx256m" -e "discovery.type=single-node" -d --name elasticsearch -p 9200:9200 -p 9300:9300 elasticsearch:7.9.0

訪問IP加埠

安裝 ik分詞器我也是用7.0.0版本的
下載地址https://github.com/medcl/elasticsearch-analysis-ik/releases

進入es的容器里
docker exec -it elasticsearch /bin/bash

在plugins目錄下創建ik檔案夾:
mkdir /usr/share/elasticsearch/plugins/ik
退出容器:exit
拷貝下載好的ik分詞器壓縮包到ik檔案夾中:
docker cp elasticsearch-analysis-ik-7.9.0.zip elasticsearch:/usr/share/elasticsearch/plugins/ik/
在進入容器
##進入容器
docker exec -it elasticsearch /bin/bash
##進入IK檔案夾下
cd plugins/ik/
解壓檔案
unzip elasticsearch-analysis-ik-7.9.0.zip

退出容器 重啟
docker restart elasticsearch
查看 ik 分詞器是否安裝成功 可已經進去es容器里的bin目錄下
elasticsearch-plugin list

接下來安裝Kibana 7 可視化工具
拉取鏡像
docker pull kibana:7.9.0
啟動容器
docker run -d -p 5601:5601 --name kibana --link elasticsearch:elasticsearch docker.io/kibana:7.9.0
ip加埠訪問

安裝之后測驗一下資料是否根據官方檔案提供的一個檔案 新增一條
6.0版本以后就沒有型別這個 只有索引 我先在是7.0但是他沒報錯
穿件索引 型別 資料 PUT 請求
PUT http://192.168.124.123:9200/user/test/1
修改 有就修改沒有就新增 PUT 請求
PUT http://192.168.124.123:9200/user/test/1
新增修改 如果不帶Id es自動生成一個字串Id 帶id修改 POST 請求
POST http://192.168.124.123:9200/user/test
洗掉 DELETE 請求
DELETE http://192.168.124.123:9200/user/test/1
查詢 GET 請求
GET http://192.168.124.123:9200/user/test/1

查看es索引


先說一下 查詢 條件
(1) 獲取所有資料 :索引/型別/_search
空搜索,它沒有任何查詢條件,查詢集群中的所有索引,默認回傳前10個檔案,
①took:整個搜索請求耗費了多少毫秒,
②timed_out:是否超時,false是沒有,默認無timeout
1)默認情況下,搜索請求不會超時,如果回應時間比完成結果更重要,你可以指定 timeout 為 10 或者 10ms(10毫秒),或者 1s(1秒):
2)GET /_search?timeout=10ms
3)timeout 不是停止執行查詢,它僅僅是告知正在協調的節點回傳到目前為止收集的結果并且關閉連接,
③_shards:分片數量
④hits.total:本次搜索,回傳了幾條結果
⑤hits.max_score:score的含義,就是document對于一個search的相關度的匹配分數,越相關,就越匹配,分數也高
⑥hits.hits:包含了匹配搜索的document的詳細資料,默認查詢前10條資料,按_score降序排序
⑦多索引,多型別
1)/_search:在所有的索引中搜索所有的型別
2)/gb/_search:在 gb 索引中搜索所有的型別
3)/gb,us/_search:在 gb 和 us 索引中搜索所有的檔案
4)/g*,u*/_search:在任何以 g 或者 u 開頭的索引中搜索所有的型別
5)/gb/user/_search:在 gb 索引中搜索 user 型別
6)/gb,us/user,tweet/_search:在 gb 和 us 索引中搜索 user 和 tweet 型別
7)/_all/user,tweet/_search:在所有的索引中搜索 user 和 tweet 型別
用Kibana 操作

range 查詢找出那些落在指定區間內的數字或者時間:
①gt大于
②gte大于等于
③lt小于
④lte小于等于
(2)term 查詢:
term 查詢被用于精確值匹配,這些精確值可能是數字、時間、布爾或者那些 not_analyzed 的字串
(3)terms 查詢:
terms 查詢和 term 查詢一樣,但它允許你指定多值進行匹配,如果這個欄位包含了指定值中的任何一個值,那么這個檔案滿足條件
(4)exists 查詢和 missing 查詢:
exists 查詢和 missing 查詢被用于查找那些指定欄位中有值或無值的檔案,
(5)bool查詢 : 用于多欄位組合查詢
①must:檔案 必須 匹配這些條件才能被包含進來,
②must_not:檔案 必須不 匹配這些條件才能被包含進來,
③should:如果滿足這些陳述句中的任意陳述句,將增加 _score ,否則,無任何影響,它們主要用于修正每個檔案的相關性得分,
④filter:必須 匹配,但它以不評分、過濾模式來進行,
##match_all:回傳所有檔案
GET _search
{
"query": {
"match_all": {
}
}
}
##查詢user索引中所有的資料
GET /user/_search
##查詢user索引下employee型別中所有的資料
GET /user/employee/_search
##term 查詢被用于精確值匹配,這些精確值可能是數字、時間
GET /user/employee/_search
{
"query": {
"term": {
"first_name":"張"
}
}
}
## 精確查詢
GET /user/employee/_search
{
"query": {
"match": {
"first_name":"張"
}
}
}
## match_phrase:短語匹配查詢 必須完全匹配才回傳
GET /user/employee/_search
{
"query": {
"match_phrase": {
"first_name":"張"
}
}
}
## multi_match:多欄位匹配查,query是要查詢的資料關鍵詞
##"fields": ["欄位","欄位"]
GET /user/employee/_search
{
"query": {
"multi_match": {
"query": "三",
"fields": ["first_name","last_name"]
}
}
}
當然還有聚合函式 這些按照es的 官方檔案就可以
使用ik 分詞器查詢
使用es自帶分詞器
##es自帶分詞器
POST _analyze
{
"analyzer":"standard","text":"我叫你呢不知道new"
}

IK分詞器,支持兩種演算法,分別為:
ik_smart :最少切分
ik_max_word :最細粒度切分
##es使用 IK分詞器
POST _analyze
{
"analyzer":"ik_smart","text":"我叫你呢不知道new"
}

搭建好在專案中使用
springboot
pom依賴
我安裝的es是7.9.0的所以依賴也是7.9.0 這 里注意springboot的版本必須2.2以上的版本
<!-- https://mvnrepository.com/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client -->
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.9.0</version>
</dependency>
pom檔案
注意 : <!--指定Springboot的es版本-->
<elasticsearch.version>7.9.0</elasticsearch.version>
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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>2.3.9.RELEASE</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<groupId>com.example</groupId>
<artifactId>demo</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>demo</name>
<description>Demo project for Spring Boot</description>
<properties>
<java.version>1.8</java.version>
<!--指定Springboot的es版本-->
<elasticsearch.version>7.9.0</elasticsearch.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>7.9.0</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
<exclusions>
<exclusion>
<groupId>org.junit.vintage</groupId>
<artifactId>junit-vintage-engine</artifactId>
</exclusion>
</exclusions>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
</plugin>
</plugins>
</build>
</project>
config檔案
package com.tang.cloud.config;
import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
@Configuration
public class EsConfig {
//設定項
public static final RequestOptions COMMON_OPTIONS;
static {
RequestOptions.Builder builder = RequestOptions.DEFAULT.toBuilder();
COMMON_OPTIONS = builder.build();
}
@Bean
public RestHighLevelClient restHighLevelClient() {
RestHighLevelClient restHighLevelClient = new RestHighLevelClient(
RestClient.builder(
new HttpHost("192.168.124.123", 9200, "http")
)
);
return restHighLevelClient;
}
}
對es 操作
注入RestHighLevelClient 對es操作
@Autowired
private RestHighLevelClient client;
新增索引
@Test
public void test() throws IOException {
//創建索引
IndexRequest indexRequest = new IndexRequest("user");
//設定id
indexRequest.id("1");
//創建user物件
User user=new User();
user.setName("張三");
user.setAge(18);
user.setSex("男");
String userJson = JSON.toJSONString(user );
//創建索引
indexRequest.source(userJson,XContentType.JSON);
IndexResponse index = client.index(indexRequest, EsConfig.COMMON_OPTIONS);
System.out.println(index);
System.out.println("ok");
}
查詢
GET /user/_search

查詢
按id查詢
//按 id查詢
@Test
public void test2() throws IOException {
//創建es檢索查詢條件
GetRequest getRequest = new GetRequest(
"user", //索引名稱
"1"); //檔案id
GetResponse getResponse = client.get(getRequest, RequestOptions.DEFAULT);
String s = JSON.toJSONString(getResponse.getSource());
System.out.println(s);
User user=JSON.parseObject(s,User.class);
System.out.println(user+"測驗結果");
}
查看索引是不是存在
//查看索引是不是存在 boolean型別
@Test
public void indexExists() throws IOException {
GetIndexRequest request = new GetIndexRequest("user");
boolean exists = client.indices().exists(request, RequestOptions.DEFAULT);
System.out.println(exists);
}
查詢檔案是是否存在
//查詢檔案是是否存在 回傳boolean型別
@Test
public void test3() throws IOException {
GetRequest getRequest = new GetRequest(
"user", //索引
"1"); //檔案id
getRequest.fetchSourceContext(new FetchSourceContext(false)); //禁用fetching _source.
getRequest.storedFields("_none_");
boolean exists = client.exists(getRequest, RequestOptions.DEFAULT);
System.out.println(exists);
}
query查詢
@Test
public void test1() throws IOException {
//創建檢索
SearchRequest searchRequest =new SearchRequest();
//要檢索的索引
searchRequest.indices("user");
//指定檢索條件
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
//使用 match 查詢
searchSourceBuilder.query(QueryBuilders.matchQuery("name","張"));
searchRequest.source(searchSourceBuilder);
//開始檢索
SearchResponse search = client.search(searchRequest, EsConfig.COMMON_OPTIONS);
//獲取檢索的資料
SearchHits hits = search.getHits();
//獲取物件
SearchHit[] hits1 = hits.getHits();
for (SearchHit s:hits1) {
String sourceAsString = s.getSourceAsString();
User user = JSON.parseObject(sourceAsString, User.class);
System.out.println(user);
}
}

封裝工具類
@Slf4j
@Component
public class RestHighLevelClientUtil {
@Qualifier("restHighLevelClient")
@Autowired
private RestHighLevelClient client;
@Autowired
private ObjectMapper mapper;
/**
* 創建索引
* @param indexName
* @param settings
* @param mapping
* @return
* @throws IOException
*/
public CreateIndexResponse createIndex(String indexName, String settings, String mapping) throws IOException {
CreateIndexRequest request = new CreateIndexRequest(indexName);
if (null != settings && !"".equals(settings)) {
request.settings(settings, XContentType.JSON);
}
if (null != mapping && !"".equals(mapping)) {
request.mapping(mapping, XContentType.JSON);
}
// 同步方式創建索引
return client.indices().create(request, RequestOptions.DEFAULT);
}
/**
* 洗掉索引
* @param indexNames
* @return
* @throws IOException
*/
public AcknowledgedResponse deleteIndex(String ... indexNames) throws IOException{
DeleteIndexRequest request = new DeleteIndexRequest(indexNames);
return client.indices().delete(request, RequestOptions.DEFAULT);
}
/**
* 判斷 index 是否存在
* @param indexName
* @return
* @throws IOException
*/
public boolean indexExists(String indexName) throws IOException {
GetIndexRequest request = new GetIndexRequest(indexName);
return client.indices().exists(request, RequestOptions.DEFAULT);
}
/**
* 簡單模糊匹配 默認分頁為 0,10
* @param field
* @param key
* @param page
* @param size
* @param indexNames
* @return
* @throws IOException
*/
public SearchResponse searchPage(String field, String key, int page, int size, String[] includeFields, String[] excludeFields, String ... indexNames) throws IOException{
SearchRequest request = new SearchRequest(indexNames);
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.query(new MatchQueryBuilder(field, key))
.from(page)
.size(size);
if (includeFields.length != 0 && excludeFields.length !=0) {
builder.fetchSource(includeFields, excludeFields);
}
request.source(builder);
return client.search(request, RequestOptions.DEFAULT);
}
/**
* 使用scroll查詢所有符合條件的不分頁
* @param field
* @param key
* @param includeFields
* @param excludeFields
* @param indexNames
* @return
* @throws IOException
*/
public List<SearchHit> searchAllByMatch(String field, String key, String[] includeFields, String[] excludeFields, String ... indexNames) throws IOException{
// 建立查詢請求
SearchRequest request = new SearchRequest(indexNames);
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.query(new MatchQueryBuilder(field, key));
if (includeFields.length != 0 && excludeFields.length !=0) {
builder.fetchSource(includeFields, excludeFields);
}
request.source(builder);
return this.searchAllByScroll(request);
}
public List<SearchHit> searchAllByWildcard(String field, String key, String[] includeFields, String[] excludeFields, String ... indexNames) throws IOException{
// 建立查詢請求
SearchRequest request = new SearchRequest(indexNames);
SearchSourceBuilder builder = new SearchSourceBuilder();
// builder.query(new MatchQueryBuilder(field, key));
builder.query(new WildcardQueryBuilder(field, "*" + key + "*"));
if (includeFields.length != 0 && excludeFields.length !=0) {
builder.fetchSource(includeFields, excludeFields);
}
request.source(builder);
return this.searchAllByScroll(request);
}
/**
* 針對查詢請求,通過 Scroll 查詢所有,不分頁
* @param request 查詢請求
* @return
*/
public List<SearchHit> searchAllByScroll(SearchRequest request) throws IOException {
request.scroll(TimeValue.timeValueSeconds(10));
List<SearchHit> searchHitList = new ArrayList<>();
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
String scrollId = response.getScrollId();
SearchHit[] searchHits = response.getHits().getHits();
while (searchHits != null && searchHits.length > 0) {
searchHitList.addAll(Arrays.asList(searchHits));
response = client.scroll(new SearchScrollRequest(scrollId).scroll(TimeValue.timeValueSeconds(10)), RequestOptions.DEFAULT);
scrollId = response.getScrollId();
searchHits = response.getHits().getHits();
}
ClearScrollRequest clearScrollRequest = new ClearScrollRequest();
clearScrollRequest.addScrollId(scrollId);
ClearScrollResponse clearScrollResponse = client.clearScroll(clearScrollRequest, RequestOptions.DEFAULT);
boolean succeeded = clearScrollResponse.isSucceeded();
System.out.println("clearScroll: " + succeeded);
return searchHitList;
}
public SearchResponse search(String field, String key, String rangeField, String from, String to,
String termField, String termVal, String ... indexNames) throws IOException{
SearchRequest request = new SearchRequest(indexNames);
SearchSourceBuilder builder = new SearchSourceBuilder();
BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();
boolQueryBuilder.must(new MatchQueryBuilder(field, key)).must(new RangeQueryBuilder(rangeField).from(from).to(to)).must(new TermQueryBuilder(termField, termVal));
builder.query(boolQueryBuilder);
request.source(builder);
log.info("[搜索陳述句為:{}]",request.source().toString());
return client.search(request, RequestOptions.DEFAULT);
}
/**
* term 查詢 精準匹配
* @param field
* @param key
* @param page
* @param size
* @param indexNames
* @return
* @throws IOException
*/
public SearchResponse termSearch(String field, String key, int page, int size, String ... indexNames) throws IOException{
SearchRequest request = new SearchRequest(indexNames);
SearchSourceBuilder builder = new SearchSourceBuilder();
builder.query(QueryBuilders.termsQuery(field, key))
.from(page)
.size(size);
request.source(builder);
return client.search(request, RequestOptions.DEFAULT);
}
/**
* 批量匯入
* @param indexName
* @param sourceList
* @return
* @throws IOException
*/
public BulkResponse importAll(String indexName, List<String> sourceList) throws IOException{
if (0 == sourceList.size()){
//todo 拋出例外 匯入資料為空
}
BulkRequest request = new BulkRequest();
for (String source : sourceList) {
request.add(new IndexRequest(indexName).source(source, XContentType.JSON));
}
return client.bulk(request, RequestOptions.DEFAULT);
}
/**
* 插入或者更新
* @param indexName
* @param jsonMap
* @return
* @throws IOException
*/
public IndexResponse insertOrUpdateOne(String indexName, String id, Map<String, Object> jsonMap) throws IOException {
IndexRequest request = new IndexRequest(indexName);
request.id(id);
request.source(jsonMap);
return client.index(request, RequestOptions.DEFAULT);
}
public DeleteResponse deleteDocumentById(String indexName, String id) throws IOException {
DeleteRequest request = new DeleteRequest(indexName, id);
return client.delete(request, RequestOptions.DEFAULT);
}
}
更多操作 查看官方 檔案
https://www.elastic.co/guide/en/elasticsearch/client/java-api/7.11/java-docs-get.html
倒排索引
https://lesliefish.blog.csdn.net/article/details/49648363?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-7.control&dist_request_id=a74b41a9-38fb-45f7-80f1-b4bb059093f8&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-7.control
轉載請註明出處,本文鏈接:https://www.uj5u.com/qita/262532.html
標籤:其他
