安装拼音分词器
查看ES安装与使用下面的拼音分词器安装
自定义分词器
elasticsearch中分词器（analyzer）的组成包含三部分：
- 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
测试
PUT /test2
{
"mappings": {
"properties": {
"title": {
"type": "completion"
}
}
}
}
POST /test2/_doc
{
"title": ["Sony", "WH-1000XM4"]
}
POST /test2/_doc
{
"title": ["SK-II", "PITERA"]
}
POST /test2/_doc
{
"title": ["Nintendo", "switch" ]
}
GET /test2/_search
{
"suggest": {
"titleSuggest": {
"text": "s", //查询的关键字
"completion": {
"field": "title", //补全查询的字段
"skip_duplicates": true, //跳过重复数据
"size": 10 //获取前10条结果
}
}
}
}
自定义
结合ik_smart自定义分词器的语法如下：
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，这里是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": {
"name": {
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "ik_smart"
}
}
}
}
RestClient使用
修改es的mapping映射
DELETE /hotel
# 酒店数据索引库
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",
"copy_to": "all"
},
"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"
}
}
}
}
实现
改造实体类
@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;
// 排序时的 距离值
private Object distance;
private Boolean isAD;
/**
* 新增自动补全字段
*
* @param hotel
*/
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();
if (this.business.contains("/")) {
String[] arr = this.business.split("/");
this.suggestion = new ArrayList<>();
this.suggestion.add(this.brand);
Collections.addAll(this.suggestion, arr);
} else {
this.suggestion = Arrays.asList(this.brand, this.business);
}
}
}
批量导入
/**
* 批量添加数据
*/
@Test
void testBulkRequest() throws IOException {
List<Hotel> hotels = hotelService.list();
BulkRequest bulkRequest = new BulkRequest();
for (Hotel hotel : hotels) {
HotelDoc hotelDoc = new HotelDoc(hotel);
bulkRequest.add(new IndexRequest("hotel")
.source(JSONUtil.toJsonStr(hotelDoc),XContentType.JSON));
}
client.bulk(bulkRequest,RequestOptions.DEFAULT);
}
查询与响应
@Test
public void testSuggest() throws IOException {
SearchRequest request = new SearchRequest("hotel");
request.source().suggest(new SuggestBuilder().addSuggestion("suggestions",
SuggestBuilders.completionSuggestion("suggestion")
.prefix("sh")
.skipDuplicates(true)
.size(10)
));
SearchResponse response = client.search(request, RequestOptions.DEFAULT);
Suggest suggest = response.getSuggest();
CompletionSuggestion suggestions = suggest.getSuggestion("suggestions");
List<CompletionSuggestion.Entry.Option> options = suggestions.getOptions();
for (CompletionSuggestion.Entry.Option option : options) {
String text = option.getText().toString();
System.out.println(text);
}
}
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