新建项目
新建一个springboot项目springboot_es用于本次与ElasticSearch的整合，如下图
引入依赖
修改我们的pom.xml，加入spring-boot-starter-data-elasticsearch
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>编写配置文件
由于ElasticSearch从7.x版本开始淡化TransportClient甚至于在8.x版本中遗弃，所以spring data elasticsearch推荐我们使用rest客户端RestHingLevelClient（端口号使用9200）以及接口ElasticSearchRespositoy。
- RestHighLevelClient 更强大，更灵活，但是不能友好的操作对象
- ElasticSearchRepository 对象操作友好
首先我们编写配置文件如下
@Configuration
public class ElasticSearchRestClientConfig extends AbstractElasticsearchConfiguration{
@Override
@Bean
public RestHighLevelClient elasticsearchClient() {
final ClientConfiguration clientConfiguration = ClientConfiguration.builder()
.connectedTo("192.168.8.101:9200")
.build();
return RestClients.create(clientConfiguration).rest();
}
}springboot操作ES
RestHighLevelClient方式
有了上面的rest client，我们就可以在其他的地方注入该客户端对ElasticSearch进行操作。我们新建一个测试文件，使用客户端对ElasticSearch进行基本的操作
1.注入RestClient
@SpringBootTest
public class TestRestClient {
// 复杂查询使用：比如高亮查询
@Autowired
private RestHighLevelClient restHighLevelClient;
}2.插入一条文档
@Test
public void testAdd() throws IOException {
IndexRequest indexRequest = new IndexRequest("christy","user","11");
indexRequest.source("{\"name\":\"齐天大圣孙悟空\",\"age\":685,\"bir\":\"1685-01-01\",\"introduce\":\"花果山水帘洞美猴王齐天大圣孙悟空是也！\"," +
"\"address\":\"花果山\"}", XContentType.JSON);
IndexResponse indexResponse = restHighLevelClient.index(indexRequest, RequestOptions.DEFAULT);
System.out.println(indexResponse.status());
}
我们可以看到文档插入成功，我们去kibana中查询该条文档
完全没有问题的。
3.删除一条文档
@Test
public void deleteDoc() throws IOException {
// 我们把特朗普删除了
DeleteRequest deleteRequest = new DeleteRequest("christy","user","rYBNG3kBRz-Sn-2f3ViU");
DeleteResponse deleteResponse = restHighLevelClient.delete(deleteRequest, RequestOptions.DEFAULT);
System.out.println(deleteResponse.status());
}
}
4.更新一条文档
@Test
public void updateDoc() throws IOException {
UpdateRequest updateRequest = new UpdateRequest("christy","user","p4AtG3kBRz-Sn-2fMFjj");
updateRequest.doc("{\"name\":\"调皮捣蛋的hardy\"}",XContentType.JSON);
UpdateResponse updateResponse = restHighLevelClient.update(updateRequest, RequestOptions.DEFAULT);
System.out.println(updateResponse.status());
}
5.批量更新文档
@Test
public void bulkUpdate() throws IOException {
BulkRequest bulkRequest = new BulkRequest();
// 添加
IndexRequest indexRequest = new IndexRequest("christy","user","13");
indexRequest.source("{\"name\":\"天蓬元帅猪八戒\",\"age\":985,\"bir\":\"1685-01-01\",\"introduce\":\"天蓬元帅猪八戒因调戏嫦娥被贬下凡\",\"address\":\"高老庄\"}", XContentType.JSON);
bulkRequest.add(indexRequest);
// 删除
DeleteRequest deleteRequest01 = new DeleteRequest("christy","user","pYAtG3kBRz-Sn-2fMFjj");
DeleteRequest deleteRequest02 = new DeleteRequest("christy","user","uhTyGHkBExaVQsl4F9Lj");
DeleteRequest deleteRequest03 = new DeleteRequest("christy","user","C8zCGHkB5KgTrUTeLyE_");
bulkRequest.add(deleteRequest01);
bulkRequest.add(deleteRequest02);
bulkRequest.add(deleteRequest03);
// 修改
UpdateRequest updateRequest = new UpdateRequest("christy","user","10");
updateRequest.doc("{\"name\":\"炼石补天的女娲\"}",XContentType.JSON);
bulkRequest.add(updateRequest);
BulkResponse bulkResponse = restHighLevelClient.bulk(bulkRequest, RequestOptions.DEFAULT);
BulkItemResponse[] items = bulkResponse.getItems();
for (BulkItemResponse item : items) {
System.out.println(item.status());
}
}在kibana中查询结果
6.查询文档
@Test
public void testSearch() throws IOException {
//创建搜索对象
SearchRequest searchRequest = new SearchRequest("christy");
//搜索构建对象
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
searchSourceBuilder.query(QueryBuilders.matchAllQuery())//执行查询条件
.from(0)//起始条数
.size(10)//每页展示记录
.postFilter(QueryBuilders.matchAllQuery()) //过滤条件
.sort("age", SortOrder.DESC);//排序
//创建搜索请求
searchRequest.types("user").source(searchSourceBuilder);
SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
System.out.println("符合条件的文档总数: "+searchResponse.getHits().getTotalHits());
System.out.println("符合条件的文档最大得分: "+searchResponse.getHits().getMaxScore());
SearchHit[] hits = searchResponse.getHits().getHits();
for (SearchHit hit : hits) {
System.out.println(hit.getSourceAsMap());
}
}
ElasticSearchRepository方式
1.准备工作
ElasticSearchRepository方式主要通过注解和对接口实现的方式来实现ES的操作，我们在实体类上通过注解配置ES索引的映射关系后，当实现了ElasticSearchRepository接口的类第一次操作ES进行插入文档的时候，ES会自动生成所需要的一切。但是该种方式无法实现高亮查询，想要实现高亮查询只能使用RestHighLevelClient
开始之前我们需要熟悉一下接口方式为我们提供的注解，以及编写一些基础的类
1.清空ES数据
2.了解注解
@Document: 代表一个文档记录
indexName: 用来指定索引名称
type: 用来指定索引类型
@Id: 用来将对象中id和ES中_id映射
@Field: 用来指定ES中的字段对应Mapping
type: 用来指定ES中存储类型
analyzer: 用来指定使用哪种分词器
3.新建实体类
@Data
@Document(indexName = "christy",type = "user")
public class User {
@Id //用来将对象中id属性与文档中_id 一一对应
private String id;
// 用在属性上 代表mapping中一个属性 一个字段 type:属性 用来指定字段类型 analyzer:指定分词器
@Field(type = FieldType.Text,analyzer = "ik_max_word")
private String name;
@Field(type = FieldType.Integer)
private Integer age;
@Field(type = FieldType.Date)
@JsonFormat(pattern = "yyyy-MM-dd")
private Date bir;
@Field(type = FieldType.Text,analyzer = "ik_max_word")
private String content;
@Field(type = FieldType.Text,analyzer = "ik_max_word")
private String address;
}4.UserRepository
public interface
extends ElasticsearchRepository<User,String> {
}5.TestUserRepository
@SpringBootTest
public class TestUserRepository {
@Autowired
private UserRepository userRepository;
}2.保存文档
@Test
public void testSaveAndUpdate(){
User user = new User();
// id初识为空，此操作为新增
user.setId(UUID.randomUUID().toString());
user.setName("唐三藏");
user.setBir(new Date());
user.setIntroduce("西方世界如来佛祖大弟子金蝉子转世，十世修行的好人，得道高僧！");
user.setAddress("大唐白马寺");
userRepository.save(user);
}
3.修改文档
@Test
public void testSaveAndUpdate(){
User user = new User();
// 根据id修改信息
user.setId("1666eb47-0bbf-468b-ab45-07758c741461");
user.setName("唐三藏");
user.setBir(new Date());
user.setIntroduce("俗家姓陈,状元之后。西方世界如来佛祖大弟子金蝉子转世，十世修行的好人，得道高僧！");
user.setAddress("大唐白马寺");
userRepository.save(user);
}
4.删除文档
repository接口默认提供了4种删除方式，我们演示根据id进行删除
@Test
public void deleteDoc(){
userRepository.deleteById("1666eb47-0bbf-468b-ab45-07758c741461");
}
5.检索一条记录
@Test
public void testFindOne(){
Optional<User> optional = userRepository.findById("1666eb47-0bbf-468b-ab45-07758c741461");
System.out.println(optional.get());
}
6.查询所有
@Test
public void testFindAll(){
Iterable<User> all = userRepository.findAll();
all.forEach(user-> System.out.println(user));
}
7.排序
@Test
public void testFindAllSort(){
Iterable<User> all = userRepository.findAll(Sort.by(Sort.Order.asc("age")));
all.forEach(user-> System.out.println(user));
}
8.分页
@Test
public void testFindPage(){
//PageRequest.of 参数1: 当前页-1
Page<User> search = userRepository.search(QueryBuilders.matchAllQuery(), PageRequest.of(1, 1));
search.forEach(user-> System.out.println(user));
}
9.自定义查询
先给大家看一个表，是不是很晕_(¦3」∠)_
| Keyword | Sample | Elasticsearch Query String |
|---|---|---|
And |
findByNameAndPrice |
{"bool" : {"must" : [ {"field" : {"name" : "?"}}, {"field" : {"price" : "?"}} ]}} |
Or |
findByNameOrPrice |
{"bool" : {"should" : [ {"field" : {"name" : "?"}}, {"field" : {"price" : "?"}} ]}} |
Is |
findByName |
{"bool" : {"must" : {"field" : {"name" : "?"}}}} |
Not |
findByNameNot |
{"bool" : {"must_not" : {"field" : {"name" : "?"}}}} |
Between |
findByPriceBetween |
{"bool" : {"must" : {"range" : {"price" : {"from" : ?,"to" : ?,"include_lower" : true,"include_upper" : true}}}}} |
LessThanEqual |
findByPriceLessThan |
{"bool" : {"must" : {"range" : {"price" : {"from" : null,"to" : ?,"include_lower" : true,"include_upper" : true}}}}} |
GreaterThanEqual |
findByPriceGreaterThan |
{"bool" : {"must" : {"range" : {"price" : {"from" : ?,"to" : null,"include_lower" : true,"include_upper" : true}}}}} |
Before |
findByPriceBefore |
{"bool" : {"must" : {"range" : {"price" : {"from" : null,"to" : ?,"include_lower" : true,"include_upper" : true}}}}} |
After |
findByPriceAfter |
{"bool" : {"must" : {"range" : {"price" : {"from" : ?,"to" : null,"include_lower" : true,"include_upper" : true}}}}} |
Like |
findByNameLike |
{"bool" : {"must" : {"field" : {"name" : {"query" : "?*","analyze_wildcard" : true}}}}} |
StartingWith |
findByNameStartingWith |
{"bool" : {"must" : {"field" : {"name" : {"query" : "?*","analyze_wildcard" : true}}}}} |
EndingWith |
findByNameEndingWith |
{"bool" : {"must" : {"field" : {"name" : {"query" : "*?","analyze_wildcard" : true}}}}} |
Contains/Containing |
findByNameContaining |
{"bool" : {"must" : {"field" : {"name" : {"query" : "**?**","analyze_wildcard" : true}}}}} |
In |
findByNameIn(Collection<String>names) |
{"bool" : {"must" : {"bool" : {"should" : [ {"field" : {"name" : "?"}}, {"field" : {"name" : "?"}} ]}}}} |
NotIn |
findByNameNotIn(Collection<String>names) |
{"bool" : {"must_not" : {"bool" : {"should" : {"field" : {"name" : "?"}}}}}} |
Near |
findByStoreNear |
Not Supported Yet ! |
True |
findByAvailableTrue |
{"bool" : {"must" : {"field" : {"available" : true}}}} |
False |
findByAvailableFalse |
{"bool" : {"must" : {"field" : {"available" : false}}}} |
OrderBy |
findByAvailableTrueOrderByNameDesc |
{"sort" : [{ "name" : {"order" : "desc"} }],"bool" : {"must" : {"field" : {"available" : true}}}} |
这个表格看起来复杂，实际上也不简单，但是确实很牛逼。我们只要按照上面的定义在接口中定义相应的方法，无须写实现就可实现我们想要的功能
举个例子，上面有个findByName是下面这样定义的
假如我们现在有个需求需要按照名字查询用户，我们可以在UserRepository中定义一个方法，如下
// 根据姓名查询
List<User> findByName(String name);
系统提供的查询方法中findBy是一个固定写法，像上面我们定义的方法findByName，其中Name是我们实体类中的属性名，这个必须对应上。也就是说这个findByName不仅仅局限于name，还可以findByAddress、findByAge等等；
现在就拿findByName来讲，我们要查询名字叫唐三藏的用户
@Test
public void testFindByName(){
List<User> userList = userRepository.findByName("唐三藏");
userList.forEach(user-> System.out.println(user));
}
其实就是框架底层直接使用下面的命令帮我们实现的查询
GET /christy/user/_search
{
"query": {
"bool": {
"must": [
{
"term": {
"name":"?"
}
}
]
}
}
}
10.高亮查询
我们上面说了，ElasticSearchRepository实现不了高亮查询，想要实现高亮查询还是需要使用RestHighLevelClient方式。最后我们使用rest clientl实现一次高亮查询
@Test
public void testHighLightQuery() throws IOException, ParseException {
// 创建搜索请求
SearchRequest searchRequest = new SearchRequest("christy");
// 创建搜索对象
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
searchSourceBuilder.query(QueryBuilders.termQuery("introduce", "唐僧")) // 设置查询条件
.from(0) // 起始条数(当前页-1)*size的值
.size(10) // 每页展示条数
.sort("age", SortOrder.DESC) // 排序
.highlighter(new HighlightBuilder().field("*").requireFieldMatch(false).preTags("<span style='color:red;'>").postTags("</span>")); // 设置高亮
searchRequest.types("user").source(searchSourceBuilder);
SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
SearchHit[] hits = searchResponse.getHits().getHits();
List<User> userList = new ArrayList<>();
for (SearchHit hit : hits) {
Map<String, Object> sourceAsMap = hit.getSourceAsMap();
User user = new User();
user.setId(hit.getId());
user.setAge(Integer.parseInt(sourceAsMap.get("age").toString()));
user.setBir(new SimpleDateFormat("yyyy-MM-dd").parse(sourceAsMap.get("bir").toString()));
user.setIntroduce(sourceAsMap.get("introduce").toString());
user.setName(sourceAsMap.get("name").toString());
user.setAddress(sourceAsMap.get("address").toString());
Map<String, HighlightField> highlightFields = hit.getHighlightFields();
if(highlightFields.containsKey("name")){
user.setName(highlightFields.get("name").fragments()[0].toString());
}
if(highlightFields.containsKey("introduce")){
user.setIntroduce(highlightFields.get("introduce").fragments()[0].toString());
}
if(highlightFields.containsKey("address")){
user.setAddress(highlightFields.get("address").fragments()[0].toString());
}
userList.add(user);
}
userList.forEach(user -> System.out.println(user));
}
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