Elasticsearch 系列
内容 链接 Elasticsearch 基础操作 https://blog.yexca.net/archives/226 Elasticsearch 查询操作 https://blog.yexca.net/archives/227 RestClient 基础操作 https://blog.yexca.net/archives/228 RestClient 查询操作 https://blog.yexca.net/archives/229 Elasticsearch 数据聚合 https://blog.yexca.net/archives/231 Elasticsearch 自动补全 本文 Elasticsearch 数据同步 https://blog.yexca.net/archives/234 Elasticsearch 集群 https://blog.yexca.net/archives/235
当用户在搜索框输入字符时，应提示与该字符有关的搜索项，根据输入的字母提供完整词条功能，就是自动补全
拼音分词
要实现根据字母做补全，就必须对文档按照拼音分词
项目地址： https://github.com/medcl/elasticsearch-analysis-pinyin
安装方式同 IK 分词器，以下为在线安装方式，首先进入容器
docker exec -it es /bin/bash
执行命令
./bin/elasticsearch-plugin install https://github.com/infinilabs/analysis-pinyin/releases/download/v7.12.1/elasticsearch-analysis-pinyin-7.12.1.zip
然后退出重启
# 退出
exit
# 重启
docker restart es
测试
# 测试拼音分词
POST /_analyze
{
"text": "世界第一可爱",
"analyzer": "pinyin"
}
自定义分词器
默认的拼音分词器会将每个汉字单独分为拼音，而我们希望的是每个词条形成一组拼音，需要对拼音分词器做个性化定制，形成自定义分词器
es 中分词器 (analyzer) 的组成分为三部分：
- character filters：在 tokenizer 之前对文本进行处理。例如删除字符、替换字符
- tokenizer：将文本按照一定的规则切割成词条 (term)。例如 keyword，就是不分词；还有 ik_smart
- tokenizer filter：将 tokenizer 输出的词条做进一步处理。例如大小写转换、同义词处理、拼音处理等
声明自定义分词器的语法如下
# 自定义分词器
PUT /test
{
"settings": {
"analysis": {
"analyzer": { // 自定义分词器
"my_analyzer": { // 分词器名称
"tokenizer": "ik_max_word",
"filter": "py"
}
},
"filter": { // 自定义tokenizer filter
"py": { // 过滤器名称
"type": "pinyin", // 过滤器类型
// 配置项在Github有说明
"keep_full_piny": false,
"keep_joined_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"remove_duplicated_term": true,
"none_chinese_pinyin_tokenize": false
}
}
}
},
"mappings": {
"properties": {
"name": {
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "ik_smart"
}
}
}
}
测试
# 测试自定义分词器
POST /test/_analyze
{
"text": "世界第一可爱",
"analyzer": "my_analyzer"
}
自动补全查询
es 提供了 Completion Suggester 查询来实现自动补全功能。这个查询会匹配以用户输入内容开头的词条并返回。为了提高补全查询的效率，对于文档中字段的类型有一些约束：
- 参与补全查询的字段必须是 completion 类型
- 字段的内容一般是用来补全的多个词条形成的数组
创建测试索引库
PUT /test
{
"mappings": {
"properties": {
"title": {
"type": "completion"
}
}
}
}
插入测试数据
# 示例数据
POST /test/_doc
{
"title": ["Sony", "WH-1000XM3"]
}
POST /test/_doc
{
"title": ["SK-II", "PITERA"]
}
POST /test/_doc
{
"title": ["Nintendo", "switch"]
}
查询
# 自动补全查询
GET /test/_search
{
"suggest": {
"title_suggest": {
"text": "s", // 关键字
"completion": {
"field": "title", // 自动补全查询的字段
"skip_duplicates": true, // 跳过重复
"size": 10 // 获取前10条数据
}
}
}
}
自动补全 Java
上述 DSL 的 Java 请求
@Test
public void testAutoIn(){
SearchRequest request = new SearchRequest("hotel");
// 请求参数
request.source()
.suggest(new SuggestBuilder().addSuggestion(
"title_suggest", // 查询名称
SuggestBuilders
.completionSuggestion("title") // 自动补全查询的字段
.prefix("s") // 关键字
.skipDuplicates(true) // 跳过重复
.size(10) // 获取前10条数据
));
// 发送请求
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
}
响应处理：
@Test
public void testAutoIn(){
SearchRequest request = new SearchRequest("hotel");
// 请求参数
request.source()
.suggest(new SuggestBuilder().addSuggestion(
"mySuggestion",
SuggestBuilders
.completionSuggestion("suggestion")
.prefix("h")
.skipDuplicates(true)
.size(10)
));
// 发送请求
try {
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
// 处理响应
Suggest suggest = response.getSuggest();
// 根据名称获取补全结果
CompletionSuggestion mySuggestion = suggest.getSuggestion("mySuggestion");
// 获取options并遍历
for (CompletionSuggestion.Entry.Option option : mySuggestion.getOptions()) {
String text = option.getText().string();
System.out.println(text);
}
} catch (IOException e) {
throw new RuntimeException(e);
}
}
酒店搜索自动补全
之前的 hotel 索引库没设置拼音分词器，但索引库无法修改，所以需要删除重建
# 删除重建
DELETE /hotel
PUT /hotel
{
"settings": {
"analysis": {
"analyzer": {
"text_analyzer": {
"tokenizer": "ik_max_word",
"filter": "py"
},
"completion_analyzer": {
"tokenizer": "keyword",
"filter": "py"
}
},
"filter": {
"py": {
"type": "pinyin",
"keep_full_pinyin": false,
"keep_joined_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"remove_duplicated_term": true,
"none_chinese_pinyin_tokenize": false
}
}
}
},
"mappings": {
"properties": {
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "text_analyzer",
"search_analyzer": "ik_smart",
"copy_to": "all"
},
"address": {
"type": "keyword",
"index": false
},
"price": {
"type": "integer"
},
"score": {
"type": "integer"
},
"brand": {
"type": "keyword",
"copy_to": "all"
},
"city": {
"type": "keyword"
},
"starName": {
"type": "keyword"
},
"business": {
"type": "keyword",
"copy_to": "all"
},
"location": {
"type": "geo_point"
},
"pic": {
"type": "keyword",
"index": false
},
"all": {
"type": "text",
"analyzer": "text_analyzer",
"search_analyzer": "ik_smart"
},
"suggestion": {
"type": "completion",
"analyzer": "completion_analyzer"
}
}
}
}
修改 HotelDoc 实体类，添加 suggestion 字段
@Data
@NoArgsConstructor
public class HotelDoc {
private Long id;
private String name;
private String address;
private Integer price;
private Integer score;
private String brand;
private String city;
private String starName;
private String business;
private String location;
private String pic;
private Object distance;
// 广告
private Boolean isAD;
// 自动补全
private List<String> suggestion;
public HotelDoc(Hotel hotel) {
this.id = hotel.getId();
this.name = hotel.getName();
this.address = hotel.getAddress();
this.price = hotel.getPrice();
this.score = hotel.getScore();
this.brand = hotel.getBrand();
this.city = hotel.getCity();
this.starName = hotel.getStarName();
this.business = hotel.getBusiness();
this.location = hotel.getLatitude() + ", " + hotel.getLongitude();
this.pic = hotel.getPic();
// 组装suggestion
if(this.business.contains("/")){
// bussiness有多个值，需要切割
String[] arr = this.business.split("/");
// 添加元素
this.suggestion = new ArrayList<>();
this.suggestion.add(this.brand);
Collections.addAll(this.suggestion, arr);
}else {
this.suggestion = Arrays.asList(this.brand, this.business);
}
}
}
重新导入数据
@Test
public void testBulk() throws IOException {
// 批量查询数据
List<Hotel> hotelList = hotelService.list();
// 创建bulk请求
BulkRequest request = new BulkRequest();
// 添加批量请求
for (Hotel hotel : hotelList) {
// 转换文档类型
HotelDoc hotelDoc = new HotelDoc(hotel);
// 创建新增文档request对象
request.add(new IndexRequest("hotel")
.id(hotelDoc.getId().toString())
.source(JSON.toJSONString(hotelDoc), XContentType.JSON)
);
}
// 发起bulk请求
client.bulk(request, RequestOptions.DEFAULT);
}
查询测试
GET /hotel/_search
{
"query": {
"match_all": {}
}
}
可以看到查询结果 suggestion 字段，然后编写业务代码
Controller
@GetMapping("/suggestion")
public List<String> getSuggestion(@RequestParam("key") String prefix){
return hotelService.getSuggestion(prefix);
}
Service
public List<String> getSuggestion(String prefix) {
SearchRequest request = new SearchRequest("hotel");
request.source().suggest(
new SuggestBuilder().addSuggestion(
"mySuggestion",
SuggestBuilders
.completionSuggestion("suggestion")
.prefix(prefix)
.size(10)
.skipDuplicates(true)
)
);
try {
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
Suggest suggestions = response.getSuggest();
CompletionSuggestion mySuggestion = suggestions.getSuggestion("mySuggestion");
List<CompletionSuggestion.Entry.Option> options = mySuggestion.getOptions();
ArrayList<String> list = new ArrayList<>(options.size());
for (CompletionSuggestion.Entry.Option option : options) {
String text = option.getText().string();
list.add(text);
}
return list;
} catch (IOException e) {
throw new RuntimeException(e);
}
}