Lucene
在Java领域常用的搜索框架鼻祖Lucene，现在常用的Solr和ElasticSearch底层都是基于Lucene
倒排索引算法
索引和搜索，索引是现代搜索引擎的核心，建立索引的过程就是把源数据处理成非常方便查询的索引文件的过程。
luke
- 可以分词文件内容,这里需要注意版本。
ik-analyzer
分词器，Lucene对中文支持不友好，这里使用开源的分词器。
- 由于在maven仓库中找不到，所以需要手动编译。这里需要注意版本分支，比如我使用的是8.4所以要切换到8.4的版本。和lucene库版本统一。
mvn clean install -Dmaven.test.skip=true构建版本，如果要部署可以将jar部署到自己的私有仓库，或者直接已lib方式引入项目。
源码了解
Document 文档 是 Lucene 内部的数据结构，索引文档时，会按照一定规则去创建索引，生成倒排索引文件。
Field 直接为文档创建字段,它是搜索和索引的单位，也是字段的集合。
Term 这是搜索的单位。它由两个元素组成 key、value。
StandardAnalyzer 分词的目的是为了索引，索引的目的是为了搜索，就像查字典一样。ik-analyzer就是中文分词器。
演示代码
/**
* @author z201.coding@gmail.com
**/
@RestController
@Slf4j
public class AppController {
@Value(value = "${analyzer.path}")
private String path;
@RequestMapping(value = "add")
public Object add() {
ArticleVo articleEntity = new ArticleVo();
String test = RandomUtil.randomString(10);
articleEntity.setId(test);
articleEntity.setTitle(test);
articleEntity.setContent(test);
addOrUpIndex(articleEntity);
Map<String, Object> data = new HashMap<>();
data.put("code", "200");
data.put("data", articleEntity);
return data;
}
@RequestMapping(value = "list/{keyWord}")
public Object list(@PathVariable(required = false) String keyWord) {
if (StrUtil.isEmpty(keyWord)) {
keyWord = Strings.EMPTY;
}
List<ArticleVo> articleEntityList = fullTextSearch(keyWord, 1, 10);
Map<String, Object> data = new HashMap<>();
data.put("code", "200");
data.put("data", articleEntityList);
return data;
}
@RequestMapping(value = "search")
public Object search() {
List<ArticleVo> articleEntityList = fullTextSearch(null, 1, 10);
Map<String, Object> data = new HashMap<>();
data.put("code", "200");
data.put("data", articleEntityList);
return data;
}
@RequestMapping(value = "del/{id}")
public Object del(@PathVariable(required = false) String id) {
if (!StrUtil.isEmpty(id)) {
deleteIndex(id);
}
Map<String, Object> data = new HashMap<>();
data.put("code", "200");
data.put("data", id);
return data;
}
private void addOrUpIndex(ArticleVo entity) {
IndexWriter indexWriter = null;
IndexReader indexReader = null;
Directory directory = null;
Analyzer analyzer = null;
try {
//创建索引目录文件
File indexFile = new File(path);
File[] files = indexFile.listFiles();
// 1. 创建分词器,分析文档，对文档进行分词
analyzer = new IKAnalyzer();
// 2. 创建Directory对象,声明索引库的位置
directory = FSDirectory.open(Paths.get(path));
// 3. 创建IndexWriteConfig对象，写入索引需要的配置
IndexWriterConfig writerConfig = new IndexWriterConfig(analyzer);
// 4.创建IndexWriter写入对象
indexWriter = new IndexWriter(directory, writerConfig);
// 5.写入到索引库，通过IndexWriter添加文档对象document
Document doc = new Document();
//查询是否有该索引，没有添加，有则更新
TopDocs topDocs = null;
//判断索引目录文件是否存在文件，如果没有文件，则为首次添加，有文件，则查询id是否已经存在
if (files != null && files.length != 0) {
//创建查询对象
QueryParser queryParser = new QueryParser("id", analyzer);
Query query = queryParser.parse(String.valueOf(entity.getId()));
indexReader = DirectoryReader.open(directory);
IndexSearcher indexSearcher = new IndexSearcher(indexReader);
//查询获取命中条目
topDocs = indexSearcher.search(query, 1);
}
//StringField 不分词 直接建索引 存储
doc.add(new StringField("id", String.valueOf(entity.getId()), Field.Store.YES));
//TextField 分词 建索引 存储
doc.add(new TextField("title", entity.getTitle(), Field.Store.YES));
//TextField 分词 建索引 存储
doc.add(new TextField("content", entity.getContent(), Field.Store.YES));
//如果没有查询结果，添加
if (topDocs != null && topDocs.totalHits.value == 0) {
indexWriter.addDocument(doc);
//否则，更新
} else {
indexWriter.updateDocument(new Term("id", String.valueOf(entity.getId())), doc);
}
} catch (Exception e) {
log.error("添加索引库出错 {}", e.getMessage());
throw new RuntimeException("添加索引库出错：" + e.getMessage());
} finally {
if (indexWriter != null) {
try {
indexWriter.close();
} catch (IOException e) {
log.error("indexWriter.close {}", e.getMessage());
}
}
if (indexReader != null) {
try {
indexReader.close();
} catch (IOException e) {
log.error("indexReader.close {}", e.getMessage());
}
}
if (directory != null) {
try {
directory.close();
} catch (IOException e) {
log.error("directory.close {}", e.getMessage());
}
}
if (analyzer != null) {
analyzer.close();
}
}
}
/**
* @param keyWord 查询条件
* @param page 当前页
* @param limit 页大小
* @return
*/
public List<ArticleVo> fullTextSearch(String keyWord, Integer page, Integer limit) {
List<ArticleVo> searchList = new ArrayList<>(10);
File indexFile = new File(path);
File[] files = indexFile.listFiles();
//沒有索引文件，不然沒有查詢結果
if (files == null || files.length == 0) {
return searchList;
}
IndexReader indexReader = null;
Directory directory = null;
try (Analyzer analyzer = new IKAnalyzer()) {
directory = FSDirectory.open(Paths.get(path));
//多项查询条件
QueryParser queryParser = new MultiFieldQueryParser(new String[]{"title", "content"}, analyzer);
//单项
//QueryParser queryParser = new QueryParser("title", analyzer);
Query query = queryParser.parse(!StrUtil.isEmpty(keyWord) ? keyWord : "*:*");
indexReader = DirectoryReader.open(directory);
//索引查询对象
IndexSearcher indexSearcher = new IndexSearcher(indexReader);
// query 查询参数 , n - 指定返回排序以后的搜索结果的前n个
TopDocs topDocs = indexSearcher.search(query, 1);
// 计算记录起始数
int start = (page - 1) * limit;
//获取条数
int total = (int) topDocs.totalHits.value;
//获取结果集
ScoreDoc lastSd = null;
if (page > 1) {
int num = limit * (page - 1);
TopDocs tds = indexSearcher.search(query, start);
lastSd = tds.scoreDocs[num - 1];
}
log.info("total {}", total);
//通过最后一个元素去搜索下一页的元素 如果lastSd为null，查询第一页
// 利用Luncene提供的SearchAfter方法进行查询，它可以根据指定的最后一个元素查询接下来指定数目的元素，但这需要我们查询出前n个元素然后取最后一个元素传给SearchAfter方法。
TopDocs tds = indexSearcher.searchAfter(lastSd, query, limit);
QueryScorer queryScorer = new QueryScorer(query);
Highlighter highlighter = null;
if (StrUtil.isNotEmpty(keyWord)) {
//最佳摘要
SimpleSpanFragmenter simpleSpanFragmenter = new SimpleSpanFragmenter(queryScorer, 200);
//高亮前后标签
SimpleHTMLFormatter formatter = new SimpleHTMLFormatter("<b><font color='red'>", "</font></b>");
//高亮对象
highlighter = new Highlighter(formatter, queryScorer);
//设置高亮最佳摘要
highlighter.setTextFragmenter(simpleSpanFragmenter);
}
//遍历查询结果 把标题和内容替换为带高亮的最佳摘要
for (ScoreDoc sd : tds.scoreDocs) {
Document doc = indexSearcher.doc(sd.doc);
float score = sd.score;
ArticleVo articleEntity = new ArticleVo();
String id = doc.get("id");
articleEntity.setId(id);
articleEntity.setScore(score);
if (StrUtil.isNotEmpty(keyWord)) {
//获取标题的最佳摘要
String titleBestFragment = highlighter.getBestFragment(analyzer, "title", doc.get("title"));
//获取文章内容的最佳摘要
String contentBestFragment = highlighter.getBestFragment(analyzer, "content", doc.get("content"));
articleEntity.setTitle(titleBestFragment);
articleEntity.setContent(contentBestFragment);
} else {
articleEntity.setTitle(doc.get("title"));
articleEntity.setContent(doc.get("content"));
}
searchList.add(articleEntity);
}
return searchList;
} catch (Exception e) {
throw new RuntimeException("全文檢索出错：" + e.getMessage());
} finally {
if (indexReader != null) {
try {
indexReader.close();
} catch (IOException e) {
log.error("indexReader.close {}", e.getMessage());
}
}
if (directory != null) {
try {
directory.close();
} catch (IOException e) {
log.error("directory.close {}", e.getMessage());
}
}
}
}
private void deleteIndex(String id) {
//删除全文检索
IndexWriter indexWriter = null;
Directory directory = null;
try (Analyzer analyzer = new IKAnalyzer()) {
directory = FSDirectory.open(Paths.get(path));
IndexWriterConfig writerConfig = new IndexWriterConfig(analyzer);
indexWriter = new IndexWriter(directory, writerConfig);
//根据id字段进行删除
indexWriter.deleteDocuments(new Term("id", id));
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException("删除索引库出错：" + e.getMessage());
} finally {
if (indexWriter != null) {
try {
indexWriter.close();
} catch (IOException e) {
log.error("indexWriter.close {}", e.getMessage());
}
}
if (directory != null) {
try {
directory.close();
} catch (IOException e) {
log.error("directory.close {}", e.getMessage());
}
}
}
}
}