Elasticsearch常用代码
目录
链接
SpringBoot整合ElasticSearch（控制度查询，父子关系建立）
elasticsearch 基础 —— Query String
Elasticsearch——使用Java API实现ES中的索引、映射、文档操作
ElasticSearch系列（六）springboot中使用QueryBuilders、NativeSearchQuery实现复杂查询
BoolQueryBuilder
must的性能要低一些，为什么？因为他要进行打分评估，也就是说要进行_score，而filter则不会。
TermQueryBuilder： 词条查询是ElasticSearch的一个简单查询。它仅匹配在给定字段中含有该词条的文档，而且是确切的、未经分析的词条。term查询会查找我们设定的准确值。term查询本身很简单，它接受一个字段名和我们希望查找的值。
TermsQueryBuilder： 词条查询（Term Query）允许匹配单个未经分析的词条，多词条查询（Terms Query）可以用来匹配多个这样的词条。只要指定字段包含任一我们给定的词条，就可以查询到该文档。
MatchQueryBuilder： 与TermQueryBuilder类似。
踩坑：使用Term(s)QueryBuilder查询内容需要转换成全小写，而MatchQueryBuilder不需要转换。 原因：Term(s)QueryBuilder输入的词条不会经过处理；而MatchQueryBuilder输入的词条 会被es解析并进行分词，在此过程中就已经转换成全小写。（es在存储字段时，已做解析、分词和小写处理。看见的是大写的字段内容，实际在es里已经被当作小写进行处理）
/**
* 拼装查询条件
* @param query
* @return
*/
public static BoolQueryBuilder buildEsParam(QueryGatewayTradeEsVo query) {
BoolQueryBuilder queryBuilder = QueryBuilders.boolQuery();
//filter 效率比 must高的多
if (StringUtils.isNotBlank(query.getRouterDatabaseNo())) {
queryBuilder.filter(QueryBuilders.termQuery("routerDatabaseNo", query.getRouterDatabaseNo()));
}
if (StringUtils.isNotBlank(query.getTradeNo())) {
queryBuilder.filter(QueryBuilders.termQuery("tradeNo", query.getTradeNo()));
}
if (null != query.getSystemId()) {
queryBuilder.filter(QueryBuilders.termQuery("systemId", query.getSystemId()));
}
if (null != query.getRefundType()) {
queryBuilder.filter(QueryBuilders.termQuery("refundType", query.getRefundType()));
}
if (null != query.getRefundStatus()) {
queryBuilder.filter(QueryBuilders.termQuery("refundStatus", query.getRefundStatus()));
}
//时间段 一定要有头有尾 不然会出现慢查询
if (null != query.getCreateTime() && null != query.getUpdateTime()) {
queryBuilder.filter(QueryBuilders.rangeQuery("createTime").from( query.getCreateTime()).to(query.getUpdateTime()));
}
return queryBuilder;
}
/**
* 模糊查找标题含有 “战”的,内容中必须没有“星球”的
*
*/
@Test
public void searchMutil1(){
SearchRequestBuilder srb = client.prepareSearch("firm").setTypes("dongzuo");
QueryBuilder qb1 = QueryBuilders.matchPhraseQuery("title", "战");
QueryBuilder qb2 = QueryBuilders.matchQuery("content","星球");
SearchResponse response = srb.setQuery(
QueryBuilders.boolQuery().must(qb1).mustNot(qb2)
).execute().actionGet();
SearchHits hits = response.getHits();
for(SearchHit hit : hits){
System.out.println(hit.getSourceAsString());
}
}
/**
* should 的使用 ， 提高得分
* 标题含有战的
* 得分：1.含有星球的得分 2. 出版日期在指定区间范围的得分
*/
@Test
public void shouldSearch(){
SearchRequestBuilder srb = client.prepareSearch("firm").setTypes("dongzuo");
MatchPhraseQueryBuilder mqb = QueryBuilders.matchPhraseQuery("title", "战");
MatchPhraseQueryBuilder mqb2 = QueryBuilders.matchPhraseQuery("content", "星球");
RangeQueryBuilder range = QueryBuilders.rangeQuery("publishDate").gt("2018-01-01");
SearchResponse response = srb.setQuery(
QueryBuilders.boolQuery().must(mqb).should(mqb2).should(range)
).execute().actionGet();
SearchHits hits = response.getHits();
for(SearchHit hit:hits){
System.out.println(hit.getScore() + ":" +hit.getSourceAsString());
}
}
/**
* 标题含有战的
* 过滤: 票价必须小于40的
*
*/
@Test
public void filterSearch(){
SearchRequestBuilder srb = client.prepareSearch("firm").setTypes("dongzuo");
MatchPhraseQueryBuilder q1 = QueryBuilders.matchPhraseQuery("title", "战");
RangeQueryBuilder priceRange = QueryBuilders.rangeQuery("price").lt(40);
SearchResponse response = srb.setQuery(
QueryBuilders.boolQuery().must(q1).filter(priceRange)
).execute().actionGet();
SearchHits hits = response.getHits();
for(SearchHit hit: hits){
System.out.println(hit.getSourceAsString());
}
}sort
https://blog.csdn.net/weixin_26800111/article/details/112084457
public Map<String, Object> findByQuestion(String content, int page) {
Map<String, Object> results = new HashMap<>();
MatchQueryBuilder matchQueryBuilder = new MatchQueryBuilder("contents", content);
matchQueryBuilder.analyzer("ik");
TermQueryBuilder termQueryType = new TermQueryBuilder("type", "question");
Page<BookPage> questionQa = bookPageRepository.search(new NativeSearchQueryBuilder()
.withQuery(new BoolQueryBuilder().filter(matchQueryBuilder).filter(termQueryType))
.withPageable(PageRequest.of(0, 10))
.withSort(new ScoreSortBuilder().order(SortOrder.DESC))
.withSort(new ScriptSortBuilder(new Script("'qyer'==doc['source'].value?0:('mafengwo'==doc['source'].value?1:2)"), ScriptSortBuilder.ScriptSortType.NUMBER).order(SortOrder.ASC))
.withSort(new FieldSortBuilder("view_cnt").order(SortOrder.DESC))
.withSort(new FieldSortBuilder("view_cnt").order(SortOrder.DESC))
.build());
System.err.println("----------------------------------------");
System.err.println("总问题数:" + questionQa.getTotalElements());
System.err.println("总页数:" + questionQa.getTotalPages());
System.err.println("----------------------------------------");
Map<String, Object> map = new HashMap<>();
List<Map<String, Object>> listmap = new ArrayList<>();
// for (BookPage qa : questionQa.getContent()) {
// map.put("question", qa);
// Page<BookPage> answerQas = bookPageRepository.search(new NativeSearchQueryBuilder()
// .withQuery(new AndQueryBuilder().add(new TermQueryBuilder("qid", qa.getQid())).add(new TermQueryBuilder("type", "answer")))
// .withSort(new FieldSortBuilder("view_cnt").order(SortOrder.DESC))
// .build());
// List<BookPage> qas = new ArrayList<BookPage>();
// for (BookPage answerQa : answerQas.getContent()) {
// qas.add(answerQa);
// }
// map.put("answer", qas);
// listmap.add(map);
// }
results.put("results", listmap);
return results;
}
public void testSort () {
String scoreScript = "if (doc['type'].value == 3) {" + " return 100;" + "} else {" + " return 1;" + "}";
FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[]{new FunctionScoreQueryBuilder.FilterFunctionBuilder(QueryBuilders.matchAllQuery(), ScoreFunctionBuilders.scriptFunction(new Script(scoreScript)))};
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
boolQuery.must(QueryBuilders.termQuery("userId", 1));
FunctionScoreQueryBuilder functionScoreQueryBuilder = QueryBuilders.functionScoreQuery(boolQuery, filterFunctionBuilders);
searchSourceBuilder.query(functionScoreQueryBuilder).sort("_score", SortOrder.DESC).sort("heat", SortOrder.DESC);
SearchRequest searchRequest = new SearchRequest(elasticSearchIndexConfig.getArticleSearchIndexName());
searchRequest.types(EsConstant.DEFAULT_TYPE);
searchRequest.source(searchSourceBuilder);
List searchResults = kittyRestHighLevelClient.search(searchRequest, ArticleDocument.class);
searchResults.forEach(doc -> {
System.out.println(doc.getId() + "\t" + doc.getType() + "\t" + doc.getHeat());
});
}query_string
query_string:支持通配符*、AND、OR、必须包含+ 不包含-
GET _search
{
"query":{
"query_string": {
"default_field": "message",
"query": "*Exception AND -*exception=null"
}
}
}
您可以使用query_string query字段的变体在keyword中执行相同的操作。
{
"query": {
"query_string": {
"query": "some value",
"default_field": "some_field.value.keyword"
}
}
}bool
{
"bool": {
"must": [
{
"multi_match": {
"query": "?0",
"type": "most_fields",
"fields": [
"title",
"content"
]
}
},
{
"match": {
"catory": "?1"
}
}
]
}
}Elasticsearch中的一些概念整理
https://blog.csdn.net/u013041642/article/details/93799337
文档：
Elasticsearch是面向文档(document oriented)的，这意味着它可以存储整个对象或文档(document)。然而它不仅仅是存储，还会索引(index，注意这里是动词的意思)每个文档的内容使之可以被搜索。在Elasticsearch中，你可以对文档（而非成行成列的数据）进行索引、搜索、排序、过滤。这种理解数据的方式与以往完全不同，这也是Elasticsearch能够执行复杂的全文搜索的原因之一。
索引：
在Elasticsearch中存储数据的行为就叫做索引(indexing, 动词)，不过在索引之前，我们需要明确数据应该存储在哪里，同样，存储在索引(indices, 名词)中。
「索引」含义的区分
你可能已经注意到索引(index)这个词在Elasticsearch中有着不同的含义，所以有必要在此做一下区分:
索引（名词） 如上文所述，一个索引(index)就像是传统关系数据库中的数据库，它是相关文档存储的地方，index的复数是indices 或indexes。 索引（动词） 「索引一个文档」表示把一个文档存储到索引（名词）里，以便它可以被检索或者查询。这很像SQL中的INSERT关键字，差别是，如果文档已经存在，新的文档将覆盖旧的文档。 倒排索引 传统数据库为特定列增加一个索引，例如B-Tree索引来加速检索。Elasticsearch和Lucene使用一种叫做倒排索引(inverted index)的数据结构来达到相同目的。 类型 文档归属于一种类型(type),而这些类型存在于索引(index)中，我们可以画一些简单的对比图来类比传统关系型数据库：
Relational DB -> Databases -> Tables -> Rows -> Columns Elasticsearch -> Indices -> Types -> Documents -> Fields
Elasticsearch集群可以包含多个索引(indices)（数据库），每一个索引可以包含多个类型(types)（表），每一个类型包含多个文档(documents)（行），然后每个文档包含多个字段(Fields)（列）。
分析
分析(analysis)是这样一个过程： 首先， 标记化一个文本块为适用于倒排索引单独的词(term) 然后标准化这些词为标准形式， 提高它们的“可搜索性”或“查全率”
要快速检索的字段，分析以后，生成倒排索引。检索的时候，输入的字符串也会经过同样规则的分析，这样可以提高“可搜索性”
倒排索引
Elasticsearch使用一种叫做倒排索引(inverted index)的结构来做快速的全文搜索。 倒排索引由在文档中出现的唯一的单词列表， 以及对于每个单词在文档中的位置组成。
spring data elasticsearch
三、代码实现
1. 实体注解
注：Spring Data通过注解来声明字段的映射属性，有下面的三个注解：
@Document：作用在类，标记实体类为文档对象，一般有两个属性
indexName：对应索引库名称
type：对应在索引库中的类型
shards：分片数量，默认5
replicas：副本数量，默认1
@Id：作用在成员变量，标记一个字段作为id主键
@Fie
ld：作用在成员变量，标记为文档的字段，并指定字段映射属性：
type：字段类型，是枚举：FieldType，可以是text、long、short、date、integer、object等
text：存储数据时候，会自动分词，并生成索引
keyword：存储数据时候，不会分词建立索引
Numerical：数值类型，分两类
基本数据类型：long、interger、short、byte、double、float、half_float
浮点数的高精度类型：scaled_float。
需要指定一个精度因子，比如10或100。elasticsearch会把真实值乘以这个因子后存储，取出时再还原。
Date：日期类型。elasticsearch可以对日期格式化为字符串存储，但是建议我们存储为毫秒值，存储为long，节省空间
index：是否索引，布尔类型，默认是true
store：是否存储，布尔类型，默认是false
analyzer：分词器名称，这里的ik_max_word即使用ik分词器2. 创建实体
此处的实体为提供搜索用的实体，应与业务实体区分开，如果有相同实体，为避免混淆应写两份。正常情况下全局搜索接口可单独部署服务器。 实体的配置方式有多种，除了下方每个字段使用注解外，spring boot还为我们提供了@Setting及@Mapping注解方式。如对字段设置要求较高的建议使用后者，后者可在配置文件处写原生DDL语句，虽然麻烦些，但是较为清晰。
常用代码
实现全局高亮分页搜索
springboot整合Elasticsearch - 实现全局高亮分页搜索
elasticsearch基本操作之--使用QueryBuilders进行查询
spring-data-elasticsearch 中QueryBuilders的用法
NativeSearchQueryBuilder
public Page<ProductDoc> search(String keyword, Boolean isSelfRun, Pageable pageable) {
BoolQueryBuilder queryBuilder = QueryBuilders.boolQuery();
if (StringUtils.isNotEmpty(keyword)) {
queryBuilder.should(QueryBuilders.matchQuery(ProductDoc._name, keyword).boost(3)); // 给name字段更高的权重
queryBuilder.should(QueryBuilders.matchQuery(ProductDoc._description, keyword)); // description 默认权重 1
queryBuilder.minimumNumberShouldMatch(1); // 至少一个should条件满足
}
if (isSelfRun!=null && isSelfRun) {
queryBuilder.must(QueryBuilders.matchQuery(ProductDoc._isSelfRun, Boolean.TRUE)); // 精准值条件查询
}
SearchQuery searchQuery = new NativeSearchQueryBuilder().withQuery(queryBuilder)
.withPageable(pageable).build();
LOGGER.info("\n search(): searchContent [" + keyword + "] \n DSL = \n " + searchQuery.getQuery().toString());
return productDocRespository.search(searchQuery);
}基于注解
https://www.pomit.cn/p/1959424408619521#105
public interface QuestionElasticsearchRepository extends ElasticsearchRepository<FQuestionElasticssearch, Long> {
Page<FQuestionElasticssearch> findByCatory(String catory, Pageable pageable);
@Query("{ \"bool\":{ \"must\":[ { \"multi_match\": { \"query\": \"?0\", \"type\": \"most_fields\", \"fields\": [ \"title\", \"content\" ] } }, { \"match\": { \"catory\": \"?1\" } } ] } } ")
Page<FQuestionElasticssearch> searchByKeyWordsAndCatory(String keyword, String catory, Pageable pageable);
}springBoot+elasticSearch 使用function_score自定义评分
@Override
public SearchDto improveSearch(SearchDto searchDto) {
String text = searchDto.getTerm();
String type = searchDto.getType();
HighSearchParam searchParam = searchDto.getSearchParam();
// 搜索请求对象
SearchRequest searchRequest = new SearchRequest(BwbdType.ES_INDEX);
// 指定类型
searchRequest.types(BwbdType.ES_TYPE);
// 搜索源构建对象
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 搜索方式
// 首先构造多关键字查询条件
BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
if (StringUtils.isNotEmpty(text)) {
text = QueryParser.escape(text); // 主要就是这一句把特殊字符都转义,那么lucene就可以识别
MultiMatchQueryBuilder matchQueryBuilder = QueryBuilders
.multiMatchQuery(text, BwbdType.PROPERTY_NUMBERS
, BwbdType.PROPERTY_TITLES, BwbdType.PROPERTY_CONTENTS)
.field(BwbdType.PROPERTY_NUMBERS, 1f)
.field(BwbdType.PROPERTY_TITLES, 0.1f)
.field(BwbdType.PROPERTY_CONTENTS, 0.01f)
.minimumShouldMatch(BwbdType.MATCH_LEVEL_THREE);
// 添加条件到布尔查询
boolQueryBuilder.must(matchQueryBuilder);
} else {
if (null == searchDto.getSearchParam() && StringUtils.isEmpty(type)) {
searchDto.setType(BwbdType.DATA_TYPE_FG);
}
}
// // 通过布尔查询来构造过滤查询
// boolQueryBuilder.filter(QueryBuilders.matchQuery("economics","L"));
if (StringUtils.isNotEmpty(type)) {
boolQueryBuilder.filter(QueryBuilders
.matchQuery(BwbdType.PROPERTY_DATA_TYPE, type));
}
addFilterProperties(text,searchParam, boolQueryBuilder, searchSourceBuilder);
FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = buildFilterFunctionBuilders();
FunctionScoreQueryBuilder query = QueryBuilders.functionScoreQuery(boolQueryBuilder,filterFunctionBuilders)
.boostMode(CombineFunction.SUM)
.scoreMode(FunctionScoreQuery.ScoreMode.SUM);
// 将查询条件封装给查询对象
searchSourceBuilder.query(query);
if (searchDto.getSize() > 20) {
searchDto.setSize(20);
}
searchSourceBuilder.size(searchDto.getSize());
searchSourceBuilder.from(searchDto.getPage() - 1);
// ***********************
// 高亮查询
HighlightBuilder highlightBuilder = new HighlightBuilder();
highlightBuilder.preTags(CommonConstraint.LIGHT_TAG_START); // 高亮前缀
highlightBuilder.postTags(CommonConstraint.LIGHT_TAG_END); // 高亮后缀
List<HighlightBuilder.Field> fields = highlightBuilder.fields();
fields.add(new HighlightBuilder
.Field(BwbdType.PROPERTY_NUMBERS)); // 高亮字段
fields.add(new HighlightBuilder
.Field(BwbdType.PROPERTY_TITLES)); // 高亮字段
fields.add(new HighlightBuilder
.Field(BwbdType.PROPERTY_CONTENTS).fragmentSize(100000)); // 高亮字段
// 添加高亮查询条件到搜索源
searchSourceBuilder.highlighter(highlightBuilder);
// ***********************
// // 设置源字段过虑,第一个参数结果集包括哪些字段，第二个参数表示结果集不包括哪些字段
// searchSourceBuilder.fetchSource(new String[]{"name","studymodel","price","timestamp"},new String[]{});
// 向搜索请求对象中设置搜索源
searchRequest.source(searchSourceBuilder);
// 执行搜索,向ES发起http请求
SearchResponse searchResponse = null;
try (RestHighLevelClient client = new RestHighLevelClient(restClientBuilder)) {
searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
obtainFgType(searchDto, searchResponse);
} catch (IOException e) {
e.printStackTrace();
}
return searchDto;
}
private FunctionScoreQueryBuilder.FilterFunctionBuilder[] buildFilterFunctionBuilders() {
FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[3];
// 时间相关
ScoreFunctionBuilder<?> dateFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("contentDate")
.missing(946656000000d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(0.1f);
FunctionScoreQueryBuilder.FilterFunctionBuilder date = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dateFieldValueScoreFunction);
filterFunctionBuilders[0] = date;
// 类型相关
ScoreFunctionBuilder<?> dataTypeFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("dataTypeRelation")
.missing(10d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(1f);
FunctionScoreQueryBuilder.FilterFunctionBuilder dataType = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dataTypeFieldValueScoreFunction);
filterFunctionBuilders[1] = dataType;
// 来源相关
ScoreFunctionBuilder<?> originFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("originTypeRelation")
.missing(10d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(1f);
FunctionScoreQueryBuilder.FilterFunctionBuilder origin = new FunctionScoreQueryBuilder.FilterFunctionBuilder(originFieldValueScoreFunction);
filterFunctionBuilders[2] = origin;
return filterFunctionBuilders;
}
private void addFilterProperties(String text, HighSearchParam searchParam, BoolQueryBuilder boolQueryBuilder, SearchSourceBuilder searchSourceBuilder) {
if (null != searchParam) {
if (StringUtils.isNotEmpty(searchParam.getYearStr())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery(BwbdType.PROPERTY_YEARS, searchParam.getYearStr()));
}
if (StringUtils.isNotEmpty(searchParam.getReasonName())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("reasonName", searchParam.getReasonName()));
}
if (StringUtils.isNotEmpty(searchParam.getCaseType())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("caseType", searchParam.getCaseType()));
}
if (StringUtils.isNotEmpty(searchParam.getTrialRoundText())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("trialRoundText", searchParam.getTrialRoundText()));
}
if (StringUtils.isNotEmpty(searchParam.getJudgementType())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("judgementType", searchParam.getJudgementType()));
}
if (StringUtils.isNotEmpty(searchParam.getAreaCode())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("areaId", searchParam.getAreaCode()));
}
if (StringUtils.isNotEmpty(searchParam.getIndustry())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("economics", searchParam.getIndustry()));
}
if (StringUtils.isNotEmpty(searchParam.getTaxType())) {
boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("stypes", searchParam.getTaxType()));
}
// 排序
// 根据 years 降序排列
if (BwbdType.ORDER_TYPE_DATE.equals(searchParam.getOrderType())) {
searchSourceBuilder.sort(new FieldSortBuilder("contentDate").order(SortOrder.DESC));
}
}
// 如果没有检索内容 默认时间排序
if (StringUtils.isEmpty(text)) {
searchSourceBuilder.sort(new FieldSortBuilder("contentDate").order(SortOrder.DESC));
}
// 根据分数 _score 降序排列 (默认行为)
// searchSourceBuilder.sort(new ScoreSortBuilder().order(SortOrder.DESC));
}
private void obtainFgType(AbstractTxjDto searchDto, SearchResponse searchResponse) {
// 搜索结果
SearchHits hits = searchResponse.getHits();
// 匹配到的总记录数
long totalHits = hits.getTotalHits();
searchDto.setTotal(totalHits);
// 得到匹配度高的文档
SearchHit[] searchHits = hits.getHits();
List<BwbdType> bwbdTypes = new ArrayList<>();
for (SearchHit hit : searchHits) {
String content = hit.getSourceAsString();//使用ES的java接口将实体类对应的内容转换为json字符串
BwbdType bwbdType = JSONObject.parseObject(content, BwbdType.class); //生成pojo对象
// 获取高亮查询的内容。如果存在，则替换原来的name
Map<String, HighlightField> highlightFields = hit.getHighlightFields();
if (highlightFields != null) {
HighlightField nameField = highlightFields.get(bwbdType.PROPERTY_NUMBERS);
if (nameField != null) {
Text[] fragments = nameField.getFragments();
StringBuffer stringBuffer = new StringBuffer();
for (Text str : fragments) {
stringBuffer.append(str.string());
}
String numbers = stringBuffer.toString();
bwbdType.setNumbers(numbers);
}
HighlightField titlesField = highlightFields.get(bwbdType.PROPERTY_TITLES);
if (titlesField != null) {
Text[] fragments = titlesField.getFragments();
StringBuffer stringBuffer = new StringBuffer();
for (Text str : fragments) {
stringBuffer.append(str.string());
}
String titles = stringBuffer.toString();
bwbdType.setTitles(titles);
}
HighlightField contentsField = highlightFields.get(bwbdType.PROPERTY_CONTENTS);
if (contentsField != null) {
Text[] fragments = contentsField.getFragments();
StringBuffer stringBuffer = new StringBuffer();
for (Text str : fragments) {
stringBuffer.append(str.string());
}
bwbdType.setContents(stringBuffer.toString());
}
// 处理内容
handleResult(bwbdType);
}
bwbdTypes.add(bwbdType);
}
searchDto.setRows(bwbdTypes);
}spring data
| Keyword | Sample | Elasticsearch Query String |
|---|---|---|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "?", "fields" : [ "name" ] } },
{ "query_string" : { "query" : "?", "fields" : [ "price" ] } }
]
}
|
|
|
"bool" : {
"should" : [
{ "query_string" : { "query" : "?", "fields" : [ "name" ] } },
{ "query_string" : { "query" : "?", "fields" : [ "price" ] } }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "?", "fields" : [ "name" ] } }
]
}
|
|
|
"bool" : {
"must_not" : [
{ "query_string" : { "query" : "?", "fields" : [ "name" ] } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : ?, "to" : ?, "include_lower" : true, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : null, "to" : ?, "include_lower" : true, "include_upper" : false } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : null, "to" : ?, "include_lower" : true, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : ?, "to" : null, "include_lower" : false, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : ?, "to" : null, "include_lower" : true, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : null, "to" : ?, "include_lower" : true, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{"range" : {"price" : {"from" : ?, "to" : null, "include_lower" : true, "include_upper" : true } } }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "?*", "fields" : [ "name" ] }, "analyze_wildcard": true }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "?*", "fields" : [ "name" ] }, "analyze_wildcard": true }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "*?", "fields" : [ "name" ] }, "analyze_wildcard": true }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "*?*", "fields" : [ "name" ] }, "analyze_wildcard": true }
]
}
|
|
|
"bool" : {
"must" : [
{"bool" : {"must" : [
{"terms" : {"name" : ["?","?"]}}
]
}
}
]
}
|
|
|
"bool" : {
"must" : [
{"bool" : {"must_not" : [
{"terms" : {"name" : ["?","?"]}}
]
}
}
]
}
|
|
|
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "true", "fields" : [ "available" ] } }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "false", "fields" : [ "available" ] } }
]
}
|
|
|
"bool" : {
"must" : [
{ "query_string" : { "query" : "true", "fields" : [ "available" ] } }
]
}
}, "sort":[{"name":{"order":"desc"}}]
|
| Callback | Method | Description | Order |
|---|---|---|---|
Reactive/BeforeConvertCallback |
|
Invoked before a domain object is converted to |
|
Reactive/AfterConvertCallback |
|
Invoked after a domain object is converted from |
|
Reactive/AuditingEntityCallback |
|
Marks an auditable entity created or modified |
100 |
Reactive/AfterSaveCallback |
|
Invoked after a domain object is saved. |
|
elasticsearch中的Suggest用法
elasticsearch中的Suggest用法 Elasticsearch搜索Suggest功能优化 elasticsearch拼音插件安装以及（IK+pinyin使用）
Elasticsearch学习笔记5: suggest实现搜索补全
Spring Data Elasticsearch 调用分词器和搜索建议接口 Elasticsearch自动补全-ngram
settings
创建特殊字符的映射关系 https://www.cnblogs.com/clonen/p/6674888.html
touch /alidata/dockerdata/elasticsearch/config/char_filter.txt
[Elasticsearch Index Template（索引模板)]
PUT _template/zy
{
"index_patterns":"zy_*",
"settings":{
"index.number_of_replicas":"1",
"index.number_of_shards":"5",
"index.translog.flush_threshold_size":"512mb",
"index.translog.sync_interval":"60s",
"index.codec":"best_compression",
"analysis":{
"filter":{
"edge_ngram_filter":{
"type":"edge_ngram",
"min_gram":1,
"max_gram":50
},
"pinyin_simple_filter":{
"type":"pinyin",
"keep_first_letter":true,
"keep_separate_first_letter":true,
"keep_full_pinyin":false,
"keep_original":false,
"limit_first_letter_length":50,
"lowercase":true
},
"pinyin_full_filter":{
"type":"pinyin",
"keep_first_letter":false,
"keep_separate_first_letter":false,
"keep_full_pinyin":true,
"none_chinese_pinyin_tokenize":true,
"keep_original":false,
"limit_first_letter_length":50,
"lowercase":true
},
"t2s_convert":{
"type":"stconvert",
"delimiter":",",
"convert_type":"t2s"
}
},
"char_filter":{
"charconvert":{
"type":"mapping",
"mappings_path":"char_filter.txt"
},
"tsconvert":{
"type":"stconvert",
"convert_type":"t2s"
}
},
"analyzer":{
"ngramIndexAnalyzer":{
"type":"custom",
"tokenizer":"keyword",
"filter":[
"edge_ngram_filter",
"lowercase"
],
"char_filter":[
"charconvert",
"tsconvert"
]
},
"ngramSearchAnalyzer":{
"type":"custom",
"tokenizer":"keyword",
"filter":[
"lowercase"
],
"char_filter":[
"charconvert",
"tsconvert"
]
},
"ikIndexAnalyzer":{
"type":"custom",
"tokenizer":"ik_max_word",
"char_filter":[
"charconvert",
"tsconvert"
]
},
"ikSearchAnalyzer":{
"type":"custom",
"tokenizer":"ik_smart",
"char_filter":[
"charconvert",
"tsconvert"
]
},
"pinyiSimpleIndexAnalyzer":{
"tokenizer":"keyword",
"filter":[
"pinyin_simple_filter",
"edge_ngram_filter",
"lowercase"
]
},
"pinyiSimpleSearchAnalyzer":{
"tokenizer":"keyword",
"filter":[
"pinyin_simple_filter",
"lowercase"
]
},
"pinyiFullIndexAnalyzer":{
"tokenizer":"keyword",
"filter":[
"pinyin_full_filter",
"lowercase"
]
},
"pinyiFullSearchAnalyzer":{
"tokenizer":"keyword",
"filter":[
"pinyin_full_filter",
"lowercase"
]
},
"tsconvert":{
"tokenizer":"tsconvert"
}
},
"tokenizer":{
"tsconvert":{
"type":"stconvert",
"delimiter":"#",
"keep_both":false,
"convert_type":"t2s"
}
}
}
}
}mapping
@Document(type = "book", _timestamp = true, _ttl = @TTL(enabled = true, _default = "5m"))
public class Book {
/*ID,只能是Long或者String类型*/
@Id
private Long id;
/*数值类型*/
@Field(type = FieldType.Double, ignoreMalformed = true)
private Double price;
/*数值类型*/
@Field(type = FieldType.Integer)
private Integer pageCount;
/*未分词String型*/
@Field(type = FieldType.String, index = FieldIndex.not_analyzed)
private String isnNo;
/*bool型*/
@Field(type = FieldType.Boolean, nullValue = "false")
private Boolean isValid;
/*日期类型*/
@Field(type = FieldType.Date, format = DateFormat.basic_time_no_millis)
private Date publishDate;
/*分词String类型,并设置fielddata加载限制(当然也可不设置用默认)*/
@Field(
type = FieldType.String,
index = FieldIndex.analyzed,
analyzer = "ik_max_word",
searchAnalyzer = "ik_smart",
termVector = TermVector.with_positions_offsets,
fielddata = @Fielddata(
format = FielddataFormat.paged_bytes,
frequency = @FielddataFrequencyFilter(
enable = true,
min = 0.001,
max = 1.2,
minSegmentSize = 500
),
loading = FielddataLoading.eager_global_ordinals
)
)
private String author;
/*multi field 类型(用于多字段搜索)*/
@MultiField(
mainField = @Field(type = FieldType.String, index = FieldIndex.analyzed, analyzer = "ik_max_word", searchAnalyzer = "ik_smart"),
otherFields = {
@MultiNestedField(dotSuffix = "pinyin", nestedField = @Field(
type = FieldType.String,
index = FieldIndex.analyzed,
analyzer = "lc_index",
searchAnalyzer = "lc_search")
),
@MultiNestedField(dotSuffix = "english", nestedField = @Field(
type = FieldType.String,
index = FieldIndex.analyzed,
analyzer = "standard")
)
}
)
private String title;
/*Completion Context Suggester配置(如果不配置CompletionContext则是Completion Suggester)*/
@CompletionField(analyzer = "ik", payloads = true, context = {
@CompletionContext(name = "bookType", type = CompletionContextType.category, defaultVal = {"algorithm"}),
@CompletionContext(name = "bookColor", type = CompletionContextType.category, defaultVal = {"red"})
})
private String suggestContextField;
/*二进制类型*/
@Field(type = FieldType.Binary)
private byte[] pdf;
/*内嵌类型*/
@NestedObject(clazz = SalesArea.class)
private SalesArea salesArea;
}排序
public Map<String, Object> findByQuestion(String content,int page){
Map<String, Object> results=new HashMap<>();
MatchQueryBuilder matchQueryBuilder=new MatchQueryBuilder("contents", content);
matchQueryBuilder.analyzer("ik");
TermQueryBuilder termQueryType=new TermQueryBuilder("type", "question");
Page<QA> questionQa=qaRepository.search(new NativeSearchQueryBuilder()
.withQuery(new AndQueryBuilder().add(matchQueryBuilder).add(termQueryType))
.withPageable(new PageRequest(page, 10))
.withSort(new ScoreSortBuilder().order(SortOrder.DESC))
.withSort(new ScriptSortBuilder("'qyer'==doc['source'].value?0:('mafengwo'==doc['source'].value?1:2)","number").order(SortOrder.ASC))
.withSort(new FieldSortBuilder("view_cnt").order(SortOrder.DESC))
.build());
System.err.println("----------------------------------------");
System.err.println("总问题数:"+questionQa.getTotalElements());
System.err.println("总页数:"+questionQa.getTotalPages());
System.err.println("----------------------------------------");
Map<String, Object> map=new HashMap<>();
List<Map<String, Object>> listmap=new ArrayList<>();
for(QA qa:questionQa.getContent()){
map.put("question",qa);
Page<QA> answerQas=qaRepository.search(new NativeSearchQueryBuilder()
.withQuery(new AndQueryBuilder().add(new TermQueryBuilder("qid", qa.getQid())).add(new TermQueryBuilder("type", "answer")))
.withSort(new FieldSortBuilder("view_cnt").order(SortOrder.DESC))
.build());
List<QA> qas=new ArrayList<QA>();
for(QA answerQa:answerQas.getContent()){
qas.add(answerQa);
}
map.put("answer", qas);
listmap.add(map);
}
results.put("results", listmap);
return results;
}
————————————————
版权声明：本文为CSDN博主「张小竟」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/zhanglu1236789/article/details/56678443
{
"from": 0,
"size": "100",
"min_score": 1,
"query": {
"function_score": {
"filter": {
"bool": {
"should": {
"multi_match": {
"query": "bmw",
"fields": [
"name",
"host"
]
}
}
}
},
"functions": [
{
"filter": {
"match": {
"name": {
"query": " bmw",
"minimum_should_match": "100%"
}
}
},
"weight": "4"
},
{
"filter": {
"match": {
"host": "bmw"
}
},
"weight": "10"
},
{
"script_score": {
"script": "width=doc['rank'].value;if(width==0){return -4};return 0"
}
}
],
"score_mode": "sum"
}
},
"sort": [
{
"_score": {
"order": "desc"
}
}
]
}
备注
function_score 自定义 score 得分使用 函数
min_score 最小score 的值, 显示大于当前score 的值
score_mode score 得分计算方式 ;
script_score script 的作用 ，如果 rank 字段的值等于 0 该记录的score 得分 = score得分 - 4; 比如 原来 得分 10 现在的得分是6
sort 按照 score 得分进行排序
————————————————
版权声明：本文为CSDN博主「记忆的残缺」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u014017121/article/details/69951202问题解决
Rejecting mapping update to [zhongyao] as the final mapping would have more than 1 type: [_doc, zhongyao]
已存在所有,所以手工设置索引时,需要createIndex = false
No mapping found for [id] in order to sort on
去掉order
